From 6401d54232712dc31bf362b1c17ca0f79df3a399 Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Sat, 25 May 2019 09:49:03 +0100
Subject: [PATCH 01/39] add seq2seq model; add seq2seq test

---
 .../text_generation/tutorial_generate_text.py |    3 +
 examples/text_generation/vocab.txt            | 9799 +++++++++++++++++
 tensorlayer/models/seq2seq.py                 |  131 +
 tests/models/test_auto_naming.py              |   21 +-
 tests/models/test_seq2seq_model.py            |   99 +
 5 files changed, 10033 insertions(+), 20 deletions(-)
 create mode 100644 examples/text_generation/vocab.txt
 create mode 100644 tensorlayer/models/seq2seq.py
 create mode 100644 tests/models/test_seq2seq_model.py

diff --git a/examples/text_generation/tutorial_generate_text.py b/examples/text_generation/tutorial_generate_text.py
index 50c320632..03f25cdf5 100644
--- a/examples/text_generation/tutorial_generate_text.py
+++ b/examples/text_generation/tutorial_generate_text.py
@@ -266,9 +266,12 @@ def main_lstm_generate_text():
         # reset all states at the begining of every epoch
         lstm_state = None
         for step, (x, y) in enumerate(tl.iterate.ptb_iterator(train_data, batch_size, sequence_length)):
+            print(">>>>>", y)
             with tf.GradientTape() as tape:
+
                 ## compute outputs
                 logits, lstm_state = net(x, initial_state=lstm_state)
+                print(">>>>logits" , logits)
                 ## compute loss and update model
                 cost = tl.cost.cross_entropy(logits, tf.reshape(y, [-1]), name='train_loss')
 
diff --git a/examples/text_generation/vocab.txt b/examples/text_generation/vocab.txt
new file mode 100644
index 000000000..9bb13b916
--- /dev/null
+++ b/examples/text_generation/vocab.txt
@@ -0,0 +1,9799 @@
+<PAD> 0
+. 10273
+, 8203
+the 7039
+to 4891
+and 4573
+i 3631
+of 3415
+a 3272
+that 2596
+we 2401
+in 2346
+it 2191
+' 2167
+have 2025
+not 1983
+is 1816
+s 1796
+are 1623
+they 1453
+for 1327
+our 1317
+you 1314
+be 978
+with 960
+will 954
+people 948
+on 880
+he 821
+but 813
+this 802
+was 779
+as 705
+what 633
+all 630
+“ 625
+me 615
+my 599
+who 597
+can 595
+do 591
+so 589
+” 588
+about 522
+if 519
+their 518
+at 509
+country 509
+? 508
+has 505
+don 498
+going 495
+get 486
+by 485
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+america 462
+when 461
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+or 439
+know 435
+them 434
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+out 390
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+obama 369
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+up 344
+m 344
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+$ 322
+like 321
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+his 310
+way 299
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+right 282
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+say 265
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+china 254
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+arts 1
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+exercise 1
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+tutoring 1
+paced 1
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+detainee 1
+packaged 1
+carrot 1
+sticks 1
+celery 1
+bar 1
+beverage 1
+bars 1
+communication 1
+ease 1
+availability 1
+postage 1
+correspondence 1
+libraries 1
+penal 1
+wear 1
+frequency 1
+searches 1
+recreation 1
+accommodations—paid 1
+taxpayer—to 1
+insanity 1
+opposing 1
+minors 1
+anchors 1
+defy 1
+become—and 1
+expediency 1
+irresponsible 1
+tarnishing 1
+saddled 1
+bowed 1
+mobsters 1
+screeching 1
+depressing 1
+56 1
+saddened 1
+humiliated 1
+disrespected 1
+disappointment 1
+line—and 1
+implemented 1
+reined 1
+easily—we 1
+guts—and 1
+countries—many 1
+freefall 1
+ditch 1
+utopian 1
+transforming 1
+times—someone 1
+inherit 1
+great—we 1
+fate 1
+rests 1
+dared 1
+belt 1
+wasteland 1
+americans—more 1
+country—now 1
+shuttered 1
+highs 1
+trashed 1
+afterword 1
+katherine 1
+publisher 1
+invitations 1
+arrived 1
+operatives 1
+celebrities—you 1
+paparazzi 1
+sincerely 1
+festivities 1
+comedian 1
+meyers 1
+sounded 1
+marbles 1
+frowning 1
+blonde 1
+supermodel 1
+andy 1
+roddick 1
+tennis 1
+hilarious 1
+roasted 1
+anna 1
+wintour 1
+metropolitan 1
+museum 1
+thanked 1
+classy 1
+tapped 1
+breath 1
+fiving 1
+stellar 1
+brutal 1
+ridiculed 1
+immensely 1
+wannabe 1
+ride 1
+coattails 1
+compensate 1
+rave 1
+lunatic 1
+television—at 1
+pawlenty 1
+scarborough 1
+brzezinski 1
+vibrant 1
+alluded 1
+alluding 1
+irritating 1
+viewing 1
+kravis 1
+cerberus 1
+apollo 1
+it—i 1
+furthest 1
+transformed 1
+jerk 1
+taste 1
+estate—he 1
+guest 1
+shortly 1
+imploded 1
+antics 1
+nude 1
+photos 1
+pleasant 1
+nicer 1
+studio 1
+participate 1
+raving 1
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+moron 1
+prophetic 1
+reason—personality 1
+fright 1
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+abc 1
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+matt 1
+re 1
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+filling 1
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+cultures 1
+williams 1
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+smash 1
+karl 1
+decided—without 1
+guess—to 1
+torpedo 1
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+booted 1
+imus 1
+jackson 1
+sharpton 1
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+outstanding 1
+rebut 1
+creator 1
+baier 1
+gretchen 1
+carlson 1
+doocy 1
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+voicing 1
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+unforced 1
+error 1
+phil 1
+ruffin 1
+mobbed 1
+catered 1
+foul 1
+phenomenally 1
+curser 1
+overrated 1
+remorse 1
+harnessing 1
+negativity 1
+people—a 1
+cynical 1
+law—called 1
+time—that 1
+prevents 1
+it—because 1
+distinctly 1
+friday 1
+blaring 1
+monday 1
+schedules 1
+‘donald 1
+hourly 1
+primetime 1
+precise 1
+reiterating 1
+smart—the 1
+all—but 1
+compliment 1
+predictive 1
+instructions 1
+sometime 1
+submittal 1
+miserable 1
+petty 1
+jealous 1
+wannabes 1
+fabricate 1
+transparency 1
+embroiled 1
+divorce 1
+charlottesville 1
+liquid 1
+price—cash 1
+race—most 1
+palin 1
+bedlam 1
+swarming 1
+stir 1
+parlor 1
+bachmann 1
+bee 1
+stole 1
+thunder 1
+protector 1
+georges 1
+personable 1
+forceful 1
+someplace 1
+severely 1
+inclined 1
+flip 1
+flopping 1
+magnetic 1
+personality 1
+singer 1
+swarmed 1
+badmouthing 1
+bloodsuckers 1
+leech 1
+distinct 1
+governorship 1
+resume 1
+money—and 1
+rumors 1
+back—it 1
+polite 1
+continuously 1
+barricades—and 1
+disturbance 1
+disruption 1
+maligns 1
+ridicules 1
+mocks 1
+patriots 1
+747 1
+decimate 1
+sincere 1
+fisker 1
+sweetheart 1
+connected 1
+hammer 1
+bailing 1
+bankers 1
+cahoots 1
+sparking 1
+innovator 1
+apple—he 1
+ceos 1
+isaacson 1
+biography 1
+messed 1
+micromanage 1
+innovators 1
+dreamers 1
+competitions 1
+prizes 1
+manned 1
+spacecraft 1
+invent 1
+unchained 1
+regnery 1
+publishing 1
+wynton 1
+schweizer 1
+marji 1
+ross 1
+carneal 1
+crocker 1
+apparent 1
+kacey 1
+thuy 1
+colayco 1
\ No newline at end of file
diff --git a/tensorlayer/models/seq2seq.py b/tensorlayer/models/seq2seq.py
new file mode 100644
index 000000000..7b81709b5
--- /dev/null
+++ b/tensorlayer/models/seq2seq.py
@@ -0,0 +1,131 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+import tensorflow as tf
+import tensorlayer as tl
+import numpy as np
+from tensorlayer.models import Model
+from tensorlayer.layers import Dense, Dropout, Input
+from tensorlayer.layers.core import Layer
+
+
+class Seq2seq(Model):
+    def __init__(
+            self,
+            decoder_seq_length,
+            cell_enc,
+            cell_dec,
+            n_units=256,
+            n_layer=3,
+            embedding_layer=None,
+            is_train=True,
+            name="seq2seq_"
+    ):
+        super(Seq2seq, self).__init__(name=name)
+        self.embedding_layer = embedding_layer
+        self.vocabulary_size = embedding_layer.vocabulary_size
+        self.embedding_size = embedding_layer.embedding_size
+        self.n_layer = n_layer
+        self.enc_layers = []
+        self.dec_layers = []
+        for i in range(n_layer):
+            if (i == 0):
+                self.enc_layers.append(tl.layers.RNN(cell=cell_enc(units=n_units), in_channels=self.embedding_size, return_last_state=True))
+            else:
+                self.enc_layers.append(tl.layers.RNN(cell=cell_enc(units=n_units), in_channels=n_units, return_last_state=True))
+
+        for i in range(n_layer):
+            if (i == 0):
+                self.dec_layers.append(tl.layers.RNN(cell=cell_dec(units=n_units), in_channels=self.embedding_size, return_last_state=True))
+            else:
+                self.dec_layers.append(tl.layers.RNN(cell=cell_dec(units=n_units), in_channels=n_units, return_last_state=True))
+
+
+
+        self.reshape_layer = tl.layers.Reshape([-1, n_units])
+        self.dense_layer = tl.layers.Dense(n_units=self.vocabulary_size, in_channels=n_units)
+        self.reshape_layer_after = tl.layers.Reshape([-1, decoder_seq_length, self.vocabulary_size])
+        self.reshape_layer_individual_sequence = tl.layers.Reshape([-1, 1, self.vocabulary_size])
+
+    def inference(self, encoding, seq_length, start_token, top_n):
+
+        feed_output = self.embedding_layer(encoding)
+
+        state = [None for i in range(self.n_layer)]
+
+        for i in range(self.n_layer):
+            feed_output, state[i] = self.enc_layers[i](feed_output, return_state=True)
+
+        batch_size = len(encoding) 
+        decoding = [[start_token] for i in range(batch_size)]
+        feed_output = self.embedding_layer(decoding)
+
+        for i in range(self.n_layer):
+            feed_output, state[i] = self.dec_layers[i](feed_output, initial_state=state[i], return_state=True)
+        
+        feed_output = self.reshape_layer(feed_output)
+        feed_output = self.dense_layer(feed_output)
+        feed_output = self.reshape_layer_individual_sequence(feed_output)
+
+        if (top_n is not None):
+            idx = np.argpartition(feed_output[0][0], -top_n)[-top_n:]
+            probs = [feed_output[0][0][i] for i in idx]
+            probs = probs / np.sum(probs)
+            feed_output = np.random.choice(idx, p=probs)
+            feed_output = tf.convert_to_tensor([[feed_output]])
+        else:
+            feed_output = tf.argmax(feed_output, -1)
+        final_output = feed_output
+        for i in range(seq_length - 1):
+            feed_output = self.embedding_layer(feed_output)
+            for i in range(self.n_layer):
+                feed_output, state[i] = self.dec_layers[i](feed_output, initial_state=state[i], return_state=True)
+            feed_output = self.reshape_layer(feed_output)
+            feed_output = self.dense_layer(feed_output)
+            feed_output = self.reshape_layer_individual_sequence(feed_output)
+
+            if (top_n is not None):
+                idx = np.argpartition(feed_output[0][0], -top_n)[-top_n:]
+                probs = [feed_output[0][0][i] for i in idx]
+                probs = probs / np.sum(probs)
+                feed_output = np.random.choice(idx, p=probs)
+                feed_output = [[feed_output]]
+            else:
+                feed_output = tf.argmax(feed_output, -1)
+            final_output = tf.concat([final_output, feed_output], 1)
+
+        return final_output, state
+
+    def forward(self,
+                inputs,
+                seq_length=20,
+                start_token=None,
+                return_state=False,
+                top_n = None):
+
+        state = [None for i in range(self.n_layer)]
+        if (self.is_train):
+            encoding = inputs[0]
+            enc_output = self.embedding_layer(encoding)
+
+
+            for i in range(self.n_layer):
+                enc_output, state[i] = self.enc_layers[i](enc_output, return_state=True)
+
+            decoding = inputs[1]
+            dec_output = self.embedding_layer(decoding)
+
+            for i in range(self.n_layer):
+                dec_output, state[i] = self.dec_layers[i](dec_output, initial_state=state[i], return_state=True)
+
+            dec_output = self.reshape_layer(dec_output)
+            denser_output = self.dense_layer(dec_output)
+            output = self.reshape_layer_after(denser_output)
+        else:
+            encoding = inputs
+            output, state = self.inference(encoding, seq_length, start_token, top_n)
+
+        if (return_state):
+            return output, state
+        else:
+            return output
diff --git a/tests/models/test_auto_naming.py b/tests/models/test_auto_naming.py
index fb8f03720..81cb23436 100644
--- a/tests/models/test_auto_naming.py
+++ b/tests/models/test_auto_naming.py
@@ -14,26 +14,7 @@
 from tests.utils import CustomTestCase
 
 
-def basic_static_model(name=None, conv1_name="conv1", conv2_name="conv2"):
-    ni = Input((None, 24, 24, 3))
-    nn = Conv2d(16, (5, 5), (1, 1), padding='SAME', act=tf.nn.relu, name=conv1_name)(ni)
-    nn = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool1')(nn)
-
-    nn = Conv2d(16, (5, 5), (1, 1), padding='SAME', act=tf.nn.relu, name=conv2_name)(nn)
-    nn = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool2')(nn)
-
-    M = Model(inputs=ni, outputs=nn, name=name)
-    return M
-
-
-def nested_static_model(name=None, inner_model_name=None):
-    ni = Input((None, 24, 24, 3))
-    nn = ModelLayer(basic_static_model(inner_model_name))(ni)
-    M = Model(inputs=ni, outputs=nn, name=name)
-    return M
-
-
-class basic_dynamic_model(Model):
+class seq2seq(Model):
 
     def __init__(self, name=None, conv1_name="conv1", conv2_name="conv2"):
         super(basic_dynamic_model, self).__init__(name=name)
diff --git a/tests/models/test_seq2seq_model.py b/tests/models/test_seq2seq_model.py
new file mode 100644
index 000000000..cf186633c
--- /dev/null
+++ b/tests/models/test_seq2seq_model.py
@@ -0,0 +1,99 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import os
+import unittest
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+import numpy as np
+import tensorflow as tf
+import tensorlayer as tl
+from tqdm import tqdm
+from sklearn.utils import shuffle
+from tensorlayer.models.seq2seq import Seq2seq
+from tensorlayer.models.seq2seq import Seq2seq
+from tests.utils import CustomTestCase
+from tensorlayer.cost import cross_entropy_seq
+
+
+class Model_SEQ2SEQ_Test(CustomTestCase):
+
+    @classmethod
+    def setUpClass(cls):
+
+        cls.batch_size = 16
+
+        cls.vocab_size = 20
+        cls.embedding_size = 32
+        cls.dec_seq_length = 5
+        cls.trainX = np.random.randint(20, size=(50, 6))
+        cls.trainY = np.random.randint(20, size=(50, cls.dec_seq_length+1))
+        cls.trainY[:,0] = 0 # start_token == 0
+
+            # Parameters
+        cls.src_len = len(cls.trainX)
+        cls.tgt_len = len(cls.trainY)
+
+        assert cls.src_len == cls.tgt_len
+    
+
+        cls.num_epochs=100
+        cls.n_step = cls.src_len//cls.batch_size
+
+
+    @classmethod
+    def tearDownClass(cls):
+        pass
+
+    def test_basic_simpleSeq2Seq(self):
+        model_ = Seq2seq(
+            decoder_seq_length = 5,
+            cell_enc=tf.keras.layers.GRUCell,
+            cell_dec=tf.keras.layers.GRUCell,
+            n_layer=3,
+            n_units=128,
+            embedding_layer=tl.layers.Embedding(vocabulary_size=self.vocab_size, embedding_size=self.embedding_size),
+            )
+        
+        optimizer = tf.optimizers.Adam(learning_rate=0.001)
+
+        
+        for epoch in range(self.num_epochs):
+            model_.train()
+            trainX, trainY = shuffle(self.trainX, self.trainY)
+            total_loss, n_iter = 0, 0
+            for X, Y in tqdm(tl.iterate.minibatches(inputs=trainX, targets=trainY, batch_size=self.batch_size, shuffle=False), 
+                            total=self.n_step, desc='Epoch[{}/{}]'.format(epoch + 1, self.num_epochs), leave=False):
+
+                dec_seq = Y[:,:-1]
+                target_seq = Y[:,1:] 
+
+                with tf.GradientTape() as tape:
+                    ## compute outputs
+                    output = model_(inputs = [X, dec_seq])
+
+                    output = tf.reshape(output, [-1, self.vocab_size])
+
+                    loss = cross_entropy_seq(logits=output, target_seqs=target_seq)
+
+                    grad = tape.gradient(loss, model_.all_weights)
+                    optimizer.apply_gradients(zip(grad, model_.all_weights))
+                
+                total_loss += loss
+                n_iter += 1
+            
+
+            model_.eval()
+            test_sample = trainX[0,:].tolist()
+
+            top_n = 1
+            for i in range(top_n):
+                prediction = model_([test_sample], seq_length = self.dec_seq_length, start_token = 0, top_n = top_n)
+                print("Prediction: >>>>>  ", prediction[0], "\n Target: >>>>>  ", trainY[0,1:], "\n\n")
+
+            # printing average loss after every epoch
+            print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1, self.num_epochs, total_loss / n_iter))
+
+if __name__ == '__main__':
+    unittest.main()

From 1123fec1c5de1fc8a02bf4350b4bde580c92cc21 Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Sat, 25 May 2019 09:53:32 +0100
Subject: [PATCH 02/39] Revert "add seq2seq model; add seq2seq test"

This reverts commit 6401d54232712dc31bf362b1c17ca0f79df3a399.
---
 .../text_generation/tutorial_generate_text.py |    3 -
 examples/text_generation/vocab.txt            | 9799 -----------------
 tensorlayer/models/seq2seq.py                 |  131 -
 tests/models/test_auto_naming.py              |   21 +-
 tests/models/test_seq2seq_model.py            |   99 -
 5 files changed, 20 insertions(+), 10033 deletions(-)
 delete mode 100644 examples/text_generation/vocab.txt
 delete mode 100644 tensorlayer/models/seq2seq.py
 delete mode 100644 tests/models/test_seq2seq_model.py

diff --git a/examples/text_generation/tutorial_generate_text.py b/examples/text_generation/tutorial_generate_text.py
index 03f25cdf5..50c320632 100644
--- a/examples/text_generation/tutorial_generate_text.py
+++ b/examples/text_generation/tutorial_generate_text.py
@@ -266,12 +266,9 @@ def main_lstm_generate_text():
         # reset all states at the begining of every epoch
         lstm_state = None
         for step, (x, y) in enumerate(tl.iterate.ptb_iterator(train_data, batch_size, sequence_length)):
-            print(">>>>>", y)
             with tf.GradientTape() as tape:
-
                 ## compute outputs
                 logits, lstm_state = net(x, initial_state=lstm_state)
-                print(">>>>logits" , logits)
                 ## compute loss and update model
                 cost = tl.cost.cross_entropy(logits, tf.reshape(y, [-1]), name='train_loss')
 
diff --git a/examples/text_generation/vocab.txt b/examples/text_generation/vocab.txt
deleted file mode 100644
index 9bb13b916..000000000
--- a/examples/text_generation/vocab.txt
+++ /dev/null
@@ -1,9799 +0,0 @@
-<PAD> 0
-. 10273
-, 8203
-the 7039
-to 4891
-and 4573
-i 3631
-of 3415
-a 3272
-that 2596
-we 2401
-in 2346
-it 2191
-' 2167
-have 2025
-not 1983
-is 1816
-s 1796
-are 1623
-they 1453
-for 1327
-our 1317
-you 1314
-be 978
-with 960
-will 954
-people 948
-on 880
-he 821
-but 813
-this 802
-was 779
-as 705
-what 633
-all 630
-“ 625
-me 615
-my 599
-who 597
-can 595
-do 591
-so 589
-” 588
-about 522
-if 519
-their 518
-at 509
-country 509
-? 508
-has 505
-don 498
-going 495
-get 486
-by 485
-one 464
-america 462
-when 461
-very 458
-would 445
-or 439
-know 435
-them 434
-more 430
-from 424
-great 420
-no 405
-there 390
-out 390
-make 377
-an 374
-president 370
-obama 369
-many 353
-need 350
-just 348
-than 346
-because 344
-up 344
-m 344
-been 335
-how 324
-$ 322
-like 321
-now 318
-had 313
-his 310
-way 299
-want 295
-world 289
-think 289
-time 285
-jobs 285
-said 283
-right 282
-us 274
-say 265
-american 260
-these 257
-: 257
-should 254
-china 254
-even 253
-were 248
-take 247
-other 242
-again 241
-back 237
-over 234
-only 231
-am 228
-years 227
-government 223
-which 222
-new 221
-well 217
-money 216
-every 215
-into 208
-tax 205
-look 204
-much 197
-some 195
-him 191
-those 190
-good 190
-never 189
-most 187
-work 186
-percent 184
-first 183
-lot 181
-deal 180
-trump 179
-states 178
-let 178
-here 177
-made 172
-then 171
-business 169
-go 167
-— 166
-also 163
-better 162
-americans 157
-why 157
-oil 155
-million 155
-care 155
-where 154
-could 154
-got 152
-done 151
-big 150
-united 149
-come 149
-did 149
-its 148
-tell 148
-your 148
-down 147
-military 146
-believe 146
-billion 144
-really 143
-! 142
-any 140
-doing 139
-being 138
-ever 137
-support 136
-thing 134
-fact 133
-best 133
-iran 131
-put 130
-off 130
-things 129
-illegal 128
-see 128
-pay 127
-immigration 126
-before 126
-must 125
-year 122
-doesn 121
-too 121
-job 120
-something 120
-( 120
-) 120
-problem 119
-she 118
-trade 115
-dollars 113
-000 112
-politicians 112
-two 111
-state 110
-real 110
-system 108
-day 108
-countries 107
-own 106
-didn 106
-bad 104
-bring 104
-wall 104
-plan 102
-after 101
-win 101
-give 100
-far 100
-economic 100
-security 99
-long 99
-love 98
-nothing 98
-policy 98
-companies 98
-through 97
-important 96
-keep 96
-economy 95
-tremendous 94
-person 94
-respect 94
-border 93
-called 92
-life 92
-wrong 92
-talking 92
-number 91
-1 91
-always 91
-tough 90
-hard 90
-energy 90
-making 90
-help 89
-against 88
-create 88
-taxes 87
-won 86
-talk 85
-around 85
-health 85
-national 84
-foreign 84
-chinese 84
-understand 83
-washington 83
-guy 83
-nobody 82
-build 82
-hillary 81
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-magnetic 1
-personality 1
-singer 1
-swarmed 1
-badmouthing 1
-bloodsuckers 1
-leech 1
-distinct 1
-governorship 1
-resume 1
-money—and 1
-rumors 1
-back—it 1
-polite 1
-continuously 1
-barricades—and 1
-disturbance 1
-disruption 1
-maligns 1
-ridicules 1
-mocks 1
-patriots 1
-747 1
-decimate 1
-sincere 1
-fisker 1
-sweetheart 1
-connected 1
-hammer 1
-bailing 1
-bankers 1
-cahoots 1
-sparking 1
-innovator 1
-apple—he 1
-ceos 1
-isaacson 1
-biography 1
-messed 1
-micromanage 1
-innovators 1
-dreamers 1
-competitions 1
-prizes 1
-manned 1
-spacecraft 1
-invent 1
-unchained 1
-regnery 1
-publishing 1
-wynton 1
-schweizer 1
-marji 1
-ross 1
-carneal 1
-crocker 1
-apparent 1
-kacey 1
-thuy 1
-colayco 1
\ No newline at end of file
diff --git a/tensorlayer/models/seq2seq.py b/tensorlayer/models/seq2seq.py
deleted file mode 100644
index 7b81709b5..000000000
--- a/tensorlayer/models/seq2seq.py
+++ /dev/null
@@ -1,131 +0,0 @@
-#! /usr/bin/python
-# -*- coding: utf-8 -*-
-
-import tensorflow as tf
-import tensorlayer as tl
-import numpy as np
-from tensorlayer.models import Model
-from tensorlayer.layers import Dense, Dropout, Input
-from tensorlayer.layers.core import Layer
-
-
-class Seq2seq(Model):
-    def __init__(
-            self,
-            decoder_seq_length,
-            cell_enc,
-            cell_dec,
-            n_units=256,
-            n_layer=3,
-            embedding_layer=None,
-            is_train=True,
-            name="seq2seq_"
-    ):
-        super(Seq2seq, self).__init__(name=name)
-        self.embedding_layer = embedding_layer
-        self.vocabulary_size = embedding_layer.vocabulary_size
-        self.embedding_size = embedding_layer.embedding_size
-        self.n_layer = n_layer
-        self.enc_layers = []
-        self.dec_layers = []
-        for i in range(n_layer):
-            if (i == 0):
-                self.enc_layers.append(tl.layers.RNN(cell=cell_enc(units=n_units), in_channels=self.embedding_size, return_last_state=True))
-            else:
-                self.enc_layers.append(tl.layers.RNN(cell=cell_enc(units=n_units), in_channels=n_units, return_last_state=True))
-
-        for i in range(n_layer):
-            if (i == 0):
-                self.dec_layers.append(tl.layers.RNN(cell=cell_dec(units=n_units), in_channels=self.embedding_size, return_last_state=True))
-            else:
-                self.dec_layers.append(tl.layers.RNN(cell=cell_dec(units=n_units), in_channels=n_units, return_last_state=True))
-
-
-
-        self.reshape_layer = tl.layers.Reshape([-1, n_units])
-        self.dense_layer = tl.layers.Dense(n_units=self.vocabulary_size, in_channels=n_units)
-        self.reshape_layer_after = tl.layers.Reshape([-1, decoder_seq_length, self.vocabulary_size])
-        self.reshape_layer_individual_sequence = tl.layers.Reshape([-1, 1, self.vocabulary_size])
-
-    def inference(self, encoding, seq_length, start_token, top_n):
-
-        feed_output = self.embedding_layer(encoding)
-
-        state = [None for i in range(self.n_layer)]
-
-        for i in range(self.n_layer):
-            feed_output, state[i] = self.enc_layers[i](feed_output, return_state=True)
-
-        batch_size = len(encoding) 
-        decoding = [[start_token] for i in range(batch_size)]
-        feed_output = self.embedding_layer(decoding)
-
-        for i in range(self.n_layer):
-            feed_output, state[i] = self.dec_layers[i](feed_output, initial_state=state[i], return_state=True)
-        
-        feed_output = self.reshape_layer(feed_output)
-        feed_output = self.dense_layer(feed_output)
-        feed_output = self.reshape_layer_individual_sequence(feed_output)
-
-        if (top_n is not None):
-            idx = np.argpartition(feed_output[0][0], -top_n)[-top_n:]
-            probs = [feed_output[0][0][i] for i in idx]
-            probs = probs / np.sum(probs)
-            feed_output = np.random.choice(idx, p=probs)
-            feed_output = tf.convert_to_tensor([[feed_output]])
-        else:
-            feed_output = tf.argmax(feed_output, -1)
-        final_output = feed_output
-        for i in range(seq_length - 1):
-            feed_output = self.embedding_layer(feed_output)
-            for i in range(self.n_layer):
-                feed_output, state[i] = self.dec_layers[i](feed_output, initial_state=state[i], return_state=True)
-            feed_output = self.reshape_layer(feed_output)
-            feed_output = self.dense_layer(feed_output)
-            feed_output = self.reshape_layer_individual_sequence(feed_output)
-
-            if (top_n is not None):
-                idx = np.argpartition(feed_output[0][0], -top_n)[-top_n:]
-                probs = [feed_output[0][0][i] for i in idx]
-                probs = probs / np.sum(probs)
-                feed_output = np.random.choice(idx, p=probs)
-                feed_output = [[feed_output]]
-            else:
-                feed_output = tf.argmax(feed_output, -1)
-            final_output = tf.concat([final_output, feed_output], 1)
-
-        return final_output, state
-
-    def forward(self,
-                inputs,
-                seq_length=20,
-                start_token=None,
-                return_state=False,
-                top_n = None):
-
-        state = [None for i in range(self.n_layer)]
-        if (self.is_train):
-            encoding = inputs[0]
-            enc_output = self.embedding_layer(encoding)
-
-
-            for i in range(self.n_layer):
-                enc_output, state[i] = self.enc_layers[i](enc_output, return_state=True)
-
-            decoding = inputs[1]
-            dec_output = self.embedding_layer(decoding)
-
-            for i in range(self.n_layer):
-                dec_output, state[i] = self.dec_layers[i](dec_output, initial_state=state[i], return_state=True)
-
-            dec_output = self.reshape_layer(dec_output)
-            denser_output = self.dense_layer(dec_output)
-            output = self.reshape_layer_after(denser_output)
-        else:
-            encoding = inputs
-            output, state = self.inference(encoding, seq_length, start_token, top_n)
-
-        if (return_state):
-            return output, state
-        else:
-            return output
diff --git a/tests/models/test_auto_naming.py b/tests/models/test_auto_naming.py
index 81cb23436..fb8f03720 100644
--- a/tests/models/test_auto_naming.py
+++ b/tests/models/test_auto_naming.py
@@ -14,7 +14,26 @@
 from tests.utils import CustomTestCase
 
 
-class seq2seq(Model):
+def basic_static_model(name=None, conv1_name="conv1", conv2_name="conv2"):
+    ni = Input((None, 24, 24, 3))
+    nn = Conv2d(16, (5, 5), (1, 1), padding='SAME', act=tf.nn.relu, name=conv1_name)(ni)
+    nn = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool1')(nn)
+
+    nn = Conv2d(16, (5, 5), (1, 1), padding='SAME', act=tf.nn.relu, name=conv2_name)(nn)
+    nn = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool2')(nn)
+
+    M = Model(inputs=ni, outputs=nn, name=name)
+    return M
+
+
+def nested_static_model(name=None, inner_model_name=None):
+    ni = Input((None, 24, 24, 3))
+    nn = ModelLayer(basic_static_model(inner_model_name))(ni)
+    M = Model(inputs=ni, outputs=nn, name=name)
+    return M
+
+
+class basic_dynamic_model(Model):
 
     def __init__(self, name=None, conv1_name="conv1", conv2_name="conv2"):
         super(basic_dynamic_model, self).__init__(name=name)
diff --git a/tests/models/test_seq2seq_model.py b/tests/models/test_seq2seq_model.py
deleted file mode 100644
index cf186633c..000000000
--- a/tests/models/test_seq2seq_model.py
+++ /dev/null
@@ -1,99 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-import os
-import unittest
-
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
-import numpy as np
-import tensorflow as tf
-import tensorlayer as tl
-from tqdm import tqdm
-from sklearn.utils import shuffle
-from tensorlayer.models.seq2seq import Seq2seq
-from tensorlayer.models.seq2seq import Seq2seq
-from tests.utils import CustomTestCase
-from tensorlayer.cost import cross_entropy_seq
-
-
-class Model_SEQ2SEQ_Test(CustomTestCase):
-
-    @classmethod
-    def setUpClass(cls):
-
-        cls.batch_size = 16
-
-        cls.vocab_size = 20
-        cls.embedding_size = 32
-        cls.dec_seq_length = 5
-        cls.trainX = np.random.randint(20, size=(50, 6))
-        cls.trainY = np.random.randint(20, size=(50, cls.dec_seq_length+1))
-        cls.trainY[:,0] = 0 # start_token == 0
-
-            # Parameters
-        cls.src_len = len(cls.trainX)
-        cls.tgt_len = len(cls.trainY)
-
-        assert cls.src_len == cls.tgt_len
-    
-
-        cls.num_epochs=100
-        cls.n_step = cls.src_len//cls.batch_size
-
-
-    @classmethod
-    def tearDownClass(cls):
-        pass
-
-    def test_basic_simpleSeq2Seq(self):
-        model_ = Seq2seq(
-            decoder_seq_length = 5,
-            cell_enc=tf.keras.layers.GRUCell,
-            cell_dec=tf.keras.layers.GRUCell,
-            n_layer=3,
-            n_units=128,
-            embedding_layer=tl.layers.Embedding(vocabulary_size=self.vocab_size, embedding_size=self.embedding_size),
-            )
-        
-        optimizer = tf.optimizers.Adam(learning_rate=0.001)
-
-        
-        for epoch in range(self.num_epochs):
-            model_.train()
-            trainX, trainY = shuffle(self.trainX, self.trainY)
-            total_loss, n_iter = 0, 0
-            for X, Y in tqdm(tl.iterate.minibatches(inputs=trainX, targets=trainY, batch_size=self.batch_size, shuffle=False), 
-                            total=self.n_step, desc='Epoch[{}/{}]'.format(epoch + 1, self.num_epochs), leave=False):
-
-                dec_seq = Y[:,:-1]
-                target_seq = Y[:,1:] 
-
-                with tf.GradientTape() as tape:
-                    ## compute outputs
-                    output = model_(inputs = [X, dec_seq])
-
-                    output = tf.reshape(output, [-1, self.vocab_size])
-
-                    loss = cross_entropy_seq(logits=output, target_seqs=target_seq)
-
-                    grad = tape.gradient(loss, model_.all_weights)
-                    optimizer.apply_gradients(zip(grad, model_.all_weights))
-                
-                total_loss += loss
-                n_iter += 1
-            
-
-            model_.eval()
-            test_sample = trainX[0,:].tolist()
-
-            top_n = 1
-            for i in range(top_n):
-                prediction = model_([test_sample], seq_length = self.dec_seq_length, start_token = 0, top_n = top_n)
-                print("Prediction: >>>>>  ", prediction[0], "\n Target: >>>>>  ", trainY[0,1:], "\n\n")
-
-            # printing average loss after every epoch
-            print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1, self.num_epochs, total_loss / n_iter))
-
-if __name__ == '__main__':
-    unittest.main()

From 07c91f8d8186c81e6e2866e6397250e4e9280475 Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Sat, 25 May 2019 09:55:37 +0100
Subject: [PATCH 03/39] UNDO last commit

---
 tensorlayer/models/seq2seq.py      | 131 +++++++++++++++++++++++++++++
 tests/models/test_seq2seq_model.py |  99 ++++++++++++++++++++++
 2 files changed, 230 insertions(+)
 create mode 100644 tensorlayer/models/seq2seq.py
 create mode 100644 tests/models/test_seq2seq_model.py

diff --git a/tensorlayer/models/seq2seq.py b/tensorlayer/models/seq2seq.py
new file mode 100644
index 000000000..7b81709b5
--- /dev/null
+++ b/tensorlayer/models/seq2seq.py
@@ -0,0 +1,131 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+import tensorflow as tf
+import tensorlayer as tl
+import numpy as np
+from tensorlayer.models import Model
+from tensorlayer.layers import Dense, Dropout, Input
+from tensorlayer.layers.core import Layer
+
+
+class Seq2seq(Model):
+    def __init__(
+            self,
+            decoder_seq_length,
+            cell_enc,
+            cell_dec,
+            n_units=256,
+            n_layer=3,
+            embedding_layer=None,
+            is_train=True,
+            name="seq2seq_"
+    ):
+        super(Seq2seq, self).__init__(name=name)
+        self.embedding_layer = embedding_layer
+        self.vocabulary_size = embedding_layer.vocabulary_size
+        self.embedding_size = embedding_layer.embedding_size
+        self.n_layer = n_layer
+        self.enc_layers = []
+        self.dec_layers = []
+        for i in range(n_layer):
+            if (i == 0):
+                self.enc_layers.append(tl.layers.RNN(cell=cell_enc(units=n_units), in_channels=self.embedding_size, return_last_state=True))
+            else:
+                self.enc_layers.append(tl.layers.RNN(cell=cell_enc(units=n_units), in_channels=n_units, return_last_state=True))
+
+        for i in range(n_layer):
+            if (i == 0):
+                self.dec_layers.append(tl.layers.RNN(cell=cell_dec(units=n_units), in_channels=self.embedding_size, return_last_state=True))
+            else:
+                self.dec_layers.append(tl.layers.RNN(cell=cell_dec(units=n_units), in_channels=n_units, return_last_state=True))
+
+
+
+        self.reshape_layer = tl.layers.Reshape([-1, n_units])
+        self.dense_layer = tl.layers.Dense(n_units=self.vocabulary_size, in_channels=n_units)
+        self.reshape_layer_after = tl.layers.Reshape([-1, decoder_seq_length, self.vocabulary_size])
+        self.reshape_layer_individual_sequence = tl.layers.Reshape([-1, 1, self.vocabulary_size])
+
+    def inference(self, encoding, seq_length, start_token, top_n):
+
+        feed_output = self.embedding_layer(encoding)
+
+        state = [None for i in range(self.n_layer)]
+
+        for i in range(self.n_layer):
+            feed_output, state[i] = self.enc_layers[i](feed_output, return_state=True)
+
+        batch_size = len(encoding) 
+        decoding = [[start_token] for i in range(batch_size)]
+        feed_output = self.embedding_layer(decoding)
+
+        for i in range(self.n_layer):
+            feed_output, state[i] = self.dec_layers[i](feed_output, initial_state=state[i], return_state=True)
+        
+        feed_output = self.reshape_layer(feed_output)
+        feed_output = self.dense_layer(feed_output)
+        feed_output = self.reshape_layer_individual_sequence(feed_output)
+
+        if (top_n is not None):
+            idx = np.argpartition(feed_output[0][0], -top_n)[-top_n:]
+            probs = [feed_output[0][0][i] for i in idx]
+            probs = probs / np.sum(probs)
+            feed_output = np.random.choice(idx, p=probs)
+            feed_output = tf.convert_to_tensor([[feed_output]])
+        else:
+            feed_output = tf.argmax(feed_output, -1)
+        final_output = feed_output
+        for i in range(seq_length - 1):
+            feed_output = self.embedding_layer(feed_output)
+            for i in range(self.n_layer):
+                feed_output, state[i] = self.dec_layers[i](feed_output, initial_state=state[i], return_state=True)
+            feed_output = self.reshape_layer(feed_output)
+            feed_output = self.dense_layer(feed_output)
+            feed_output = self.reshape_layer_individual_sequence(feed_output)
+
+            if (top_n is not None):
+                idx = np.argpartition(feed_output[0][0], -top_n)[-top_n:]
+                probs = [feed_output[0][0][i] for i in idx]
+                probs = probs / np.sum(probs)
+                feed_output = np.random.choice(idx, p=probs)
+                feed_output = [[feed_output]]
+            else:
+                feed_output = tf.argmax(feed_output, -1)
+            final_output = tf.concat([final_output, feed_output], 1)
+
+        return final_output, state
+
+    def forward(self,
+                inputs,
+                seq_length=20,
+                start_token=None,
+                return_state=False,
+                top_n = None):
+
+        state = [None for i in range(self.n_layer)]
+        if (self.is_train):
+            encoding = inputs[0]
+            enc_output = self.embedding_layer(encoding)
+
+
+            for i in range(self.n_layer):
+                enc_output, state[i] = self.enc_layers[i](enc_output, return_state=True)
+
+            decoding = inputs[1]
+            dec_output = self.embedding_layer(decoding)
+
+            for i in range(self.n_layer):
+                dec_output, state[i] = self.dec_layers[i](dec_output, initial_state=state[i], return_state=True)
+
+            dec_output = self.reshape_layer(dec_output)
+            denser_output = self.dense_layer(dec_output)
+            output = self.reshape_layer_after(denser_output)
+        else:
+            encoding = inputs
+            output, state = self.inference(encoding, seq_length, start_token, top_n)
+
+        if (return_state):
+            return output, state
+        else:
+            return output
diff --git a/tests/models/test_seq2seq_model.py b/tests/models/test_seq2seq_model.py
new file mode 100644
index 000000000..cf186633c
--- /dev/null
+++ b/tests/models/test_seq2seq_model.py
@@ -0,0 +1,99 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import os
+import unittest
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+import numpy as np
+import tensorflow as tf
+import tensorlayer as tl
+from tqdm import tqdm
+from sklearn.utils import shuffle
+from tensorlayer.models.seq2seq import Seq2seq
+from tensorlayer.models.seq2seq import Seq2seq
+from tests.utils import CustomTestCase
+from tensorlayer.cost import cross_entropy_seq
+
+
+class Model_SEQ2SEQ_Test(CustomTestCase):
+
+    @classmethod
+    def setUpClass(cls):
+
+        cls.batch_size = 16
+
+        cls.vocab_size = 20
+        cls.embedding_size = 32
+        cls.dec_seq_length = 5
+        cls.trainX = np.random.randint(20, size=(50, 6))
+        cls.trainY = np.random.randint(20, size=(50, cls.dec_seq_length+1))
+        cls.trainY[:,0] = 0 # start_token == 0
+
+            # Parameters
+        cls.src_len = len(cls.trainX)
+        cls.tgt_len = len(cls.trainY)
+
+        assert cls.src_len == cls.tgt_len
+    
+
+        cls.num_epochs=100
+        cls.n_step = cls.src_len//cls.batch_size
+
+
+    @classmethod
+    def tearDownClass(cls):
+        pass
+
+    def test_basic_simpleSeq2Seq(self):
+        model_ = Seq2seq(
+            decoder_seq_length = 5,
+            cell_enc=tf.keras.layers.GRUCell,
+            cell_dec=tf.keras.layers.GRUCell,
+            n_layer=3,
+            n_units=128,
+            embedding_layer=tl.layers.Embedding(vocabulary_size=self.vocab_size, embedding_size=self.embedding_size),
+            )
+        
+        optimizer = tf.optimizers.Adam(learning_rate=0.001)
+
+        
+        for epoch in range(self.num_epochs):
+            model_.train()
+            trainX, trainY = shuffle(self.trainX, self.trainY)
+            total_loss, n_iter = 0, 0
+            for X, Y in tqdm(tl.iterate.minibatches(inputs=trainX, targets=trainY, batch_size=self.batch_size, shuffle=False), 
+                            total=self.n_step, desc='Epoch[{}/{}]'.format(epoch + 1, self.num_epochs), leave=False):
+
+                dec_seq = Y[:,:-1]
+                target_seq = Y[:,1:] 
+
+                with tf.GradientTape() as tape:
+                    ## compute outputs
+                    output = model_(inputs = [X, dec_seq])
+
+                    output = tf.reshape(output, [-1, self.vocab_size])
+
+                    loss = cross_entropy_seq(logits=output, target_seqs=target_seq)
+
+                    grad = tape.gradient(loss, model_.all_weights)
+                    optimizer.apply_gradients(zip(grad, model_.all_weights))
+                
+                total_loss += loss
+                n_iter += 1
+            
+
+            model_.eval()
+            test_sample = trainX[0,:].tolist()
+
+            top_n = 1
+            for i in range(top_n):
+                prediction = model_([test_sample], seq_length = self.dec_seq_length, start_token = 0, top_n = top_n)
+                print("Prediction: >>>>>  ", prediction[0], "\n Target: >>>>>  ", trainY[0,1:], "\n\n")
+
+            # printing average loss after every epoch
+            print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1, self.num_epochs, total_loss / n_iter))
+
+if __name__ == '__main__':
+    unittest.main()

From e1ab432c40ab0114a9acb26ef4d9aaba92619580 Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Sat, 25 May 2019 10:10:02 +0100
Subject: [PATCH 04/39] Print list instead of tensor

---
 tests/models/test_seq2seq_model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/models/test_seq2seq_model.py b/tests/models/test_seq2seq_model.py
index cf186633c..87ac8c86b 100644
--- a/tests/models/test_seq2seq_model.py
+++ b/tests/models/test_seq2seq_model.py
@@ -90,7 +90,7 @@ def test_basic_simpleSeq2Seq(self):
             top_n = 1
             for i in range(top_n):
                 prediction = model_([test_sample], seq_length = self.dec_seq_length, start_token = 0, top_n = top_n)
-                print("Prediction: >>>>>  ", prediction[0], "\n Target: >>>>>  ", trainY[0,1:], "\n\n")
+                print("Prediction: >>>>>  ", prediction[0].numpy(), "\n Target: >>>>>  ", trainY[0,1:], "\n\n")
 
             # printing average loss after every epoch
             print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1, self.num_epochs, total_loss / n_iter))

From ae87ed7047b64506b5fbcf6914ebe87fe94d8780 Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Fri, 31 May 2019 09:46:32 +0100
Subject: [PATCH 05/39] Add comments

---
 tensorlayer/models/seq2seq.py | 31 +++++++++++++++++++++++++++++--
 1 file changed, 29 insertions(+), 2 deletions(-)

diff --git a/tensorlayer/models/seq2seq.py b/tensorlayer/models/seq2seq.py
index 7b81709b5..287bc6950 100644
--- a/tensorlayer/models/seq2seq.py
+++ b/tensorlayer/models/seq2seq.py
@@ -10,6 +10,34 @@
 
 
 class Seq2seq(Model):
+
+    """vanilla stacked layer Seq2Seq model.
+
+    Parameters
+    ----------
+    decoder_seq_length: int
+        The length of your target sequence
+    cell_enc : str, tf.function
+        The RNN function cell for your encoder stack, i.e. tf.keras.layers.GRUCell
+    cell_dec : str, tf.function
+        The RNN function cell for your decoder stack, i.e. tf.keras.layers.GRUCell
+    n_layer : int
+        The number of your RNN layers for both encoder and decoder block
+    embbedding_layer : tl.function
+        The embedding layer function, i.e. tl.layers.Embedding(vocabulary_size=voc_size, embedding_size=emb_dim)
+    is_train : bool
+        True if train mode, False if Inference mode
+    name : str
+        The model name
+    
+    Examples
+    ---------
+    Classify stacked-layer Seq2Seq model, see `chatbot <https://github.com/tensorlayer/seq2seq-chatbot>`__
+
+    Returns
+    -------
+        static stacked-layer Seq2Seq model.
+    """
     def __init__(
             self,
             decoder_seq_length,
@@ -40,8 +68,6 @@ def __init__(
             else:
                 self.dec_layers.append(tl.layers.RNN(cell=cell_dec(units=n_units), in_channels=n_units, return_last_state=True))
 
-
-
         self.reshape_layer = tl.layers.Reshape([-1, n_units])
         self.dense_layer = tl.layers.Dense(n_units=self.vocabulary_size, in_channels=n_units)
         self.reshape_layer_after = tl.layers.Reshape([-1, decoder_seq_length, self.vocabulary_size])
@@ -75,6 +101,7 @@ def inference(self, encoding, seq_length, start_token, top_n):
             feed_output = tf.convert_to_tensor([[feed_output]])
         else:
             feed_output = tf.argmax(feed_output, -1)
+
         final_output = feed_output
         for i in range(seq_length - 1):
             feed_output = self.embedding_layer(feed_output)

From b83ca22c8fcbd4ddb0511e9d2cf69117b8040c0e Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Fri, 31 May 2019 11:04:22 +0100
Subject: [PATCH 06/39] ADD batch testing at Inference

---
 tests/models/test_seq2seq_model.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/tests/models/test_seq2seq_model.py b/tests/models/test_seq2seq_model.py
index 87ac8c86b..8568cd715 100644
--- a/tests/models/test_seq2seq_model.py
+++ b/tests/models/test_seq2seq_model.py
@@ -12,7 +12,6 @@
 from tqdm import tqdm
 from sklearn.utils import shuffle
 from tensorlayer.models.seq2seq import Seq2seq
-from tensorlayer.models.seq2seq import Seq2seq
 from tests.utils import CustomTestCase
 from tensorlayer.cost import cross_entropy_seq
 
@@ -85,12 +84,12 @@ def test_basic_simpleSeq2Seq(self):
             
 
             model_.eval()
-            test_sample = trainX[0,:].tolist()
+            test_sample = trainX[0:2,:].tolist()
 
             top_n = 1
             for i in range(top_n):
-                prediction = model_([test_sample], seq_length = self.dec_seq_length, start_token = 0, top_n = top_n)
-                print("Prediction: >>>>>  ", prediction[0].numpy(), "\n Target: >>>>>  ", trainY[0,1:], "\n\n")
+                prediction = model_([test_sample], seq_length = self.dec_seq_length, start_token = 0, top_n = 1)
+                print("Prediction: >>>>>  ", prediction, "\n Target: >>>>>  ", trainY[0:2,1:], "\n\n")
 
             # printing average loss after every epoch
             print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1, self.num_epochs, total_loss / n_iter))

From 3c8225f5fb86183851dfdf001b7beb5576388651 Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Fri, 31 May 2019 11:05:18 +0100
Subject: [PATCH 07/39] ADD batch testing at Inference

---
 tensorlayer/models/seq2seq.py | 38 ++++++++++++++++-------------------
 1 file changed, 17 insertions(+), 21 deletions(-)

diff --git a/tensorlayer/models/seq2seq.py b/tensorlayer/models/seq2seq.py
index 287bc6950..520275e14 100644
--- a/tensorlayer/models/seq2seq.py
+++ b/tensorlayer/models/seq2seq.py
@@ -75,34 +75,24 @@ def __init__(
 
     def inference(self, encoding, seq_length, start_token, top_n):
 
-        feed_output = self.embedding_layer(encoding)
-
+        feed_output = self.embedding_layer(encoding[0])
         state = [None for i in range(self.n_layer)]
 
         for i in range(self.n_layer):
             feed_output, state[i] = self.enc_layers[i](feed_output, return_state=True)
-
-        batch_size = len(encoding) 
+        batch_size = len(encoding[0].numpy()) 
         decoding = [[start_token] for i in range(batch_size)]
         feed_output = self.embedding_layer(decoding)
-
         for i in range(self.n_layer):
             feed_output, state[i] = self.dec_layers[i](feed_output, initial_state=state[i], return_state=True)
         
         feed_output = self.reshape_layer(feed_output)
         feed_output = self.dense_layer(feed_output)
         feed_output = self.reshape_layer_individual_sequence(feed_output)
-
-        if (top_n is not None):
-            idx = np.argpartition(feed_output[0][0], -top_n)[-top_n:]
-            probs = [feed_output[0][0][i] for i in idx]
-            probs = probs / np.sum(probs)
-            feed_output = np.random.choice(idx, p=probs)
-            feed_output = tf.convert_to_tensor([[feed_output]])
-        else:
-            feed_output = tf.argmax(feed_output, -1)
-
+        feed_output = tf.argmax(feed_output, -1)
+        # [B, 1]
         final_output = feed_output
+
         for i in range(seq_length - 1):
             feed_output = self.embedding_layer(feed_output)
             for i in range(self.n_layer):
@@ -110,13 +100,19 @@ def inference(self, encoding, seq_length, start_token, top_n):
             feed_output = self.reshape_layer(feed_output)
             feed_output = self.dense_layer(feed_output)
             feed_output = self.reshape_layer_individual_sequence(feed_output)
-
+            ori_feed_output = feed_output
             if (top_n is not None):
-                idx = np.argpartition(feed_output[0][0], -top_n)[-top_n:]
-                probs = [feed_output[0][0][i] for i in idx]
-                probs = probs / np.sum(probs)
-                feed_output = np.random.choice(idx, p=probs)
-                feed_output = [[feed_output]]
+                for k in range(batch_size):
+                    idx = np.argpartition(ori_feed_output[k][0], -top_n)[-top_n:]
+                    probs = [ori_feed_output[k][0][i] for i in idx]
+                    probs = probs / np.sum(probs)
+                    feed_output = np.random.choice(idx, p=probs)
+                    feed_output = tf.convert_to_tensor([[feed_output]], dtype=tf.int64)
+                    if (k == 0):
+                        final_output_temp = feed_output
+                    else:
+                        final_output_temp = tf.concat([final_output_temp, feed_output], 0)
+                feed_output = final_output_temp
             else:
                 feed_output = tf.argmax(feed_output, -1)
             final_output = tf.concat([final_output, feed_output], 1)

From 50feb74ee5e03b2c8f787b8c9ace4c70573053d8 Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Fri, 31 May 2019 14:11:24 +0100
Subject: [PATCH 08/39] FIX typo and ADD some comments

---
 tensorlayer/models/seq2seq.py | 27 ++++++++++++++++++---------
 1 file changed, 18 insertions(+), 9 deletions(-)

diff --git a/tensorlayer/models/seq2seq.py b/tensorlayer/models/seq2seq.py
index 520275e14..c3835e438 100644
--- a/tensorlayer/models/seq2seq.py
+++ b/tensorlayer/models/seq2seq.py
@@ -18,15 +18,13 @@ class Seq2seq(Model):
     decoder_seq_length: int
         The length of your target sequence
     cell_enc : str, tf.function
-        The RNN function cell for your encoder stack, i.e. tf.keras.layers.GRUCell
+        The RNN function cell for your encoder stack, e.g tf.keras.layers.GRUCell
     cell_dec : str, tf.function
-        The RNN function cell for your decoder stack, i.e. tf.keras.layers.GRUCell
+        The RNN function cell for your decoder stack, e.g. tf.keras.layers.GRUCell
     n_layer : int
         The number of your RNN layers for both encoder and decoder block
-    embbedding_layer : tl.function
-        The embedding layer function, i.e. tl.layers.Embedding(vocabulary_size=voc_size, embedding_size=emb_dim)
-    is_train : bool
-        True if train mode, False if Inference mode
+    embedding_layer : tl.Layer
+        A embedding layer, e.g. tl.layers.Embedding(vocabulary_size=voc_size, embedding_size=emb_dim)
     name : str
         The model name
     
@@ -46,8 +44,7 @@ def __init__(
             n_units=256,
             n_layer=3,
             embedding_layer=None,
-            is_train=True,
-            name="seq2seq_"
+            name=None
     ):
         super(Seq2seq, self).__init__(name=name)
         self.embedding_layer = embedding_layer
@@ -74,7 +71,19 @@ def __init__(
         self.reshape_layer_individual_sequence = tl.layers.Reshape([-1, 1, self.vocabulary_size])
 
     def inference(self, encoding, seq_length, start_token, top_n):
-
+        """Inference mode"""
+        """
+        Parameters
+        ----------
+        encoding : input tensor
+            The source sequences 
+        seq_length : int
+            The expected length of your predicted sequence.
+        start_token : int
+            <SOS> : The token of "start of sequence"
+        top_n : int
+            Random search algorithm based on the top top_n words sorted by the probablity. 
+        """
         feed_output = self.embedding_layer(encoding[0])
         state = [None for i in range(self.n_layer)]
 

From e07645649406d5d3b3ba1a15842cf47ae9e7712b Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Fri, 31 May 2019 16:23:19 +0100
Subject: [PATCH 09/39] FIX the Travis CI build

---
 conf.py                            | 52 ++++++++++++++++++++++++++++++
 index.rst                          | 20 ++++++++++++
 make.bat                           | 35 ++++++++++++++++++++
 tensorlayer/models/seq2seq.py      | 45 ++++++++++++--------------
 tests/models/test_seq2seq_model.py | 40 +++++++++++------------
 5 files changed, 147 insertions(+), 45 deletions(-)
 create mode 100644 conf.py
 create mode 100644 index.rst
 create mode 100644 make.bat

diff --git a/conf.py b/conf.py
new file mode 100644
index 000000000..7bb931009
--- /dev/null
+++ b/conf.py
@@ -0,0 +1,52 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# http://www.sphinx-doc.org/en/master/config
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+# import os
+# import sys
+# sys.path.insert(0, os.path.abspath('.'))
+
+
+# -- Project information -----------------------------------------------------
+
+project = 'tensorlayer'
+copyright = '2019, lingjun liu'
+author = 'lingjun liu'
+
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = 'alabaster'
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
diff --git a/index.rst b/index.rst
new file mode 100644
index 000000000..dfbb70be4
--- /dev/null
+++ b/index.rst
@@ -0,0 +1,20 @@
+.. tensorlayer documentation master file, created by
+   sphinx-quickstart on Sat May 25 10:14:56 2019.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to tensorlayer's documentation!
+=======================================
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
diff --git a/make.bat b/make.bat
new file mode 100644
index 000000000..27f573b87
--- /dev/null
+++ b/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
+
+:end
+popd
diff --git a/tensorlayer/models/seq2seq.py b/tensorlayer/models/seq2seq.py
index c3835e438..ca6931463 100644
--- a/tensorlayer/models/seq2seq.py
+++ b/tensorlayer/models/seq2seq.py
@@ -10,7 +10,6 @@
 
 
 class Seq2seq(Model):
-
     """vanilla stacked layer Seq2Seq model.
 
     Parameters
@@ -36,16 +35,8 @@ class Seq2seq(Model):
     -------
         static stacked-layer Seq2Seq model.
     """
-    def __init__(
-            self,
-            decoder_seq_length,
-            cell_enc,
-            cell_dec,
-            n_units=256,
-            n_layer=3,
-            embedding_layer=None,
-            name=None
-    ):
+
+    def __init__(self, decoder_seq_length, cell_enc, cell_dec, n_units=256, n_layer=3, embedding_layer=None, name=None):
         super(Seq2seq, self).__init__(name=name)
         self.embedding_layer = embedding_layer
         self.vocabulary_size = embedding_layer.vocabulary_size
@@ -55,15 +46,27 @@ def __init__(
         self.dec_layers = []
         for i in range(n_layer):
             if (i == 0):
-                self.enc_layers.append(tl.layers.RNN(cell=cell_enc(units=n_units), in_channels=self.embedding_size, return_last_state=True))
+                self.enc_layers.append(
+                    tl.layers.RNN(
+                        cell=cell_enc(units=n_units), in_channels=self.embedding_size, return_last_state=True
+                    )
+                )
             else:
-                self.enc_layers.append(tl.layers.RNN(cell=cell_enc(units=n_units), in_channels=n_units, return_last_state=True))
+                self.enc_layers.append(
+                    tl.layers.RNN(cell=cell_enc(units=n_units), in_channels=n_units, return_last_state=True)
+                )
 
         for i in range(n_layer):
             if (i == 0):
-                self.dec_layers.append(tl.layers.RNN(cell=cell_dec(units=n_units), in_channels=self.embedding_size, return_last_state=True))
+                self.dec_layers.append(
+                    tl.layers.RNN(
+                        cell=cell_dec(units=n_units), in_channels=self.embedding_size, return_last_state=True
+                    )
+                )
             else:
-                self.dec_layers.append(tl.layers.RNN(cell=cell_dec(units=n_units), in_channels=n_units, return_last_state=True))
+                self.dec_layers.append(
+                    tl.layers.RNN(cell=cell_dec(units=n_units), in_channels=n_units, return_last_state=True)
+                )
 
         self.reshape_layer = tl.layers.Reshape([-1, n_units])
         self.dense_layer = tl.layers.Dense(n_units=self.vocabulary_size, in_channels=n_units)
@@ -89,12 +92,12 @@ def inference(self, encoding, seq_length, start_token, top_n):
 
         for i in range(self.n_layer):
             feed_output, state[i] = self.enc_layers[i](feed_output, return_state=True)
-        batch_size = len(encoding[0].numpy()) 
+        batch_size = len(encoding[0].numpy())
         decoding = [[start_token] for i in range(batch_size)]
         feed_output = self.embedding_layer(decoding)
         for i in range(self.n_layer):
             feed_output, state[i] = self.dec_layers[i](feed_output, initial_state=state[i], return_state=True)
-        
+
         feed_output = self.reshape_layer(feed_output)
         feed_output = self.dense_layer(feed_output)
         feed_output = self.reshape_layer_individual_sequence(feed_output)
@@ -128,19 +131,13 @@ def inference(self, encoding, seq_length, start_token, top_n):
 
         return final_output, state
 
-    def forward(self,
-                inputs,
-                seq_length=20,
-                start_token=None,
-                return_state=False,
-                top_n = None):
+    def forward(self, inputs, seq_length=20, start_token=None, return_state=False, top_n=None):
 
         state = [None for i in range(self.n_layer)]
         if (self.is_train):
             encoding = inputs[0]
             enc_output = self.embedding_layer(encoding)
 
-
             for i in range(self.n_layer):
                 enc_output, state[i] = self.enc_layers[i](enc_output, return_state=True)
 
diff --git a/tests/models/test_seq2seq_model.py b/tests/models/test_seq2seq_model.py
index 8568cd715..d77aa47ba 100644
--- a/tests/models/test_seq2seq_model.py
+++ b/tests/models/test_seq2seq_model.py
@@ -27,19 +27,17 @@ def setUpClass(cls):
         cls.embedding_size = 32
         cls.dec_seq_length = 5
         cls.trainX = np.random.randint(20, size=(50, 6))
-        cls.trainY = np.random.randint(20, size=(50, cls.dec_seq_length+1))
-        cls.trainY[:,0] = 0 # start_token == 0
+        cls.trainY = np.random.randint(20, size=(50, cls.dec_seq_length + 1))
+        cls.trainY[:, 0] = 0  # start_token == 0
 
-            # Parameters
+        # Parameters
         cls.src_len = len(cls.trainX)
         cls.tgt_len = len(cls.trainY)
 
         assert cls.src_len == cls.tgt_len
-    
-
-        cls.num_epochs=100
-        cls.n_step = cls.src_len//cls.batch_size
 
+        cls.num_epochs = 100
+        cls.n_step = cls.src_len // cls.batch_size
 
     @classmethod
     def tearDownClass(cls):
@@ -47,30 +45,30 @@ def tearDownClass(cls):
 
     def test_basic_simpleSeq2Seq(self):
         model_ = Seq2seq(
-            decoder_seq_length = 5,
+            decoder_seq_length=5,
             cell_enc=tf.keras.layers.GRUCell,
             cell_dec=tf.keras.layers.GRUCell,
             n_layer=3,
             n_units=128,
             embedding_layer=tl.layers.Embedding(vocabulary_size=self.vocab_size, embedding_size=self.embedding_size),
-            )
-        
+        )
+
         optimizer = tf.optimizers.Adam(learning_rate=0.001)
 
-        
         for epoch in range(self.num_epochs):
             model_.train()
             trainX, trainY = shuffle(self.trainX, self.trainY)
             total_loss, n_iter = 0, 0
-            for X, Y in tqdm(tl.iterate.minibatches(inputs=trainX, targets=trainY, batch_size=self.batch_size, shuffle=False), 
-                            total=self.n_step, desc='Epoch[{}/{}]'.format(epoch + 1, self.num_epochs), leave=False):
+            for X, Y in tqdm(tl.iterate.minibatches(inputs=trainX, targets=trainY, batch_size=self.batch_size,
+                                                    shuffle=False), total=self.n_step,
+                             desc='Epoch[{}/{}]'.format(epoch + 1, self.num_epochs), leave=False):
 
-                dec_seq = Y[:,:-1]
-                target_seq = Y[:,1:] 
+                dec_seq = Y[:, :-1]
+                target_seq = Y[:, 1:]
 
                 with tf.GradientTape() as tape:
                     ## compute outputs
-                    output = model_(inputs = [X, dec_seq])
+                    output = model_(inputs=[X, dec_seq])
 
                     output = tf.reshape(output, [-1, self.vocab_size])
 
@@ -78,21 +76,21 @@ def test_basic_simpleSeq2Seq(self):
 
                     grad = tape.gradient(loss, model_.all_weights)
                     optimizer.apply_gradients(zip(grad, model_.all_weights))
-                
+
                 total_loss += loss
                 n_iter += 1
-            
 
             model_.eval()
-            test_sample = trainX[0:2,:].tolist()
+            test_sample = trainX[0:2, :].tolist()
 
             top_n = 1
             for i in range(top_n):
-                prediction = model_([test_sample], seq_length = self.dec_seq_length, start_token = 0, top_n = 1)
-                print("Prediction: >>>>>  ", prediction, "\n Target: >>>>>  ", trainY[0:2,1:], "\n\n")
+                prediction = model_([test_sample], seq_length=self.dec_seq_length, start_token=0, top_n=1)
+                print("Prediction: >>>>>  ", prediction, "\n Target: >>>>>  ", trainY[0:2, 1:], "\n\n")
 
             # printing average loss after every epoch
             print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1, self.num_epochs, total_loss / n_iter))
 
+
 if __name__ == '__main__':
     unittest.main()

From 5608328baac05a5044978293d3311a74894b2399 Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Wed, 5 Jun 2019 18:52:38 +0100
Subject: [PATCH 10/39] ADD attention-based seq2seq model

---
 tensorlayer/models/seq2seq_with_attention.py | 181 +++++++++++++++++++
 tests/models/test_seq2seq_with_attention.py  |  91 ++++++++++
 2 files changed, 272 insertions(+)
 create mode 100644 tensorlayer/models/seq2seq_with_attention.py
 create mode 100644 tests/models/test_seq2seq_with_attention.py

diff --git a/tensorlayer/models/seq2seq_with_attention.py b/tensorlayer/models/seq2seq_with_attention.py
new file mode 100644
index 000000000..1115da030
--- /dev/null
+++ b/tensorlayer/models/seq2seq_with_attention.py
@@ -0,0 +1,181 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+import tensorflow as tf
+import tensorlayer as tl
+import numpy as np
+from tensorlayer.models import Model
+from tensorlayer.layers import Dense, Dropout, Input
+from tensorlayer.layers.core import Layer
+
+
+
+
+
+class Encoder(Layer):
+    def __init__(self, hidden_size, cell, embedding_layer, name=None):
+        super(Encoder, self).__init__(name)
+        self.cell = cell(hidden_size)
+        self.hidden_size = hidden_size
+        self.embedding_layer = embedding_layer
+        self.build((None, None, self.embedding_layer.embedding_size))
+        self._built = True
+        
+    def build(self, inputs_shape):
+        self.cell.build(input_shape=tuple(inputs_shape))
+        self._built = True
+        if self._trainable_weights is None:
+            self._trainable_weights = list()
+        
+        for var in self.cell.trainable_variables:
+            self._trainable_weights.append(var)
+
+    def forward(self, src_seq, initial_state=None):
+        
+        states = initial_state if initial_state is not None else self.cell.get_initial_state(src_seq)
+        encoding_hidden_states = list()
+        total_steps = src_seq.get_shape().as_list()[1]
+        for time_step in range(total_steps):
+            if not isinstance(states, list):
+                states = [states]
+            output, states = self.cell.call(src_seq[:,time_step,:], states, training=self.is_train)
+            encoding_hidden_states.append(states[0])
+        return output, encoding_hidden_states, states[0]
+        
+
+
+
+
+class Decoder_Attention(Layer):
+    def __init__(self, hidden_size, cell, embedding_layer, method, name = None):
+        super(Decoder_Attention, self).__init__(name)
+        self.cell = cell(hidden_size)
+        self.hidden_size = hidden_size
+        self.embedding_layer = embedding_layer
+        self.method = method
+        self.build((None, hidden_size+self.embedding_layer.embedding_size))
+        self._built = True
+        
+        
+    def build(self, inputs_shape):
+        self.cell.build(input_shape=tuple(inputs_shape))
+        self._built = True
+        if self.method is "concat":
+            self.W = self._get_weights("W", shape=(2*self.hidden_size, self.hidden_size))
+            self.V = self._get_weights("V", shape=(self.hidden_size, 1))
+        elif self.method is "general":
+            self.W = self._get_weights("W", shape=(self.hidden_size, self.hidden_size))
+        if self._trainable_weights is None:
+            self._trainable_weights = list()
+        
+        for var in self.cell.trainable_variables:
+            self._trainable_weights.append(var)
+
+    def score(self, encoding_hidden, hidden, method):
+        # encoding = [B, T, H]
+        # hidden = [B, H]
+        # combined = [B,T,2H]
+        if method is "concat":
+            # hidden = [B,H]->[B,1,H]->[B,T,H]
+            hidden = tf.expand_dims(hidden, 1)
+            hidden = tf.tile(hidden, [1, encoding_hidden.shape[1],1])
+            # combined = [B,T,2H]
+            combined = tf.concat([hidden, encoding_hidden], 2)
+            combined = tf.cast(combined, tf.float32)
+            score = tf.tensordot(combined, self.W, axes=[[2], [0]]) # score = [B,T,H]
+            score = tf.nn.tanh(score) # score = [B,T,H]
+            score = tf.tensordot(self.V, score, axes=[[0], [2]]) # score = [1,B,T]
+            score = tf.squeeze(score, axis=0) # score = [B,T]
+            
+        elif method is "dot":
+            # hidden = [B,H]->[B,H,1]
+            hidden = tf.expand_dims(hidden, 2)
+            score = tf.matmul(encoding_hidden, hidden)
+            score = tf.squeeze(score, axis=2)
+        elif method is "general":
+            # hidden = [B,H]->[B,H,1]
+            score = tf.matmul(hidden, self.W)
+            score = tf.expand_dims(score, 2)
+            score = tf.matmul(encoding_hidden, score)
+            score = tf.squeeze(score, axis=2)
+            
+        score = tf.nn.softmax(score, axis=-1) # score = [B,T]
+        return score
+
+    def forward(self, dec_seq, enc_hiddens, last_hidden, method, return_last_state=False):
+        # dec_seq = [B, T_, V], enc_hiddens = [B, T, H], last_hidden = [B, H]
+        total_steps = dec_seq.get_shape().as_list()[1]
+        states = last_hidden
+        cell_outputs = list()
+        for time_step in range(total_steps):
+            attention_weights = self.score(enc_hiddens, last_hidden, method) 
+            attention_weights = tf.expand_dims(attention_weights, 1) #[B, 1, T]
+            context = tf.matmul(attention_weights, enc_hiddens) #[B, 1, H]
+            context = tf.squeeze(context, 1) #[B, H]
+            inputs = tf.concat([dec_seq[:,time_step,:], context], 1)
+            if not isinstance(states, list):
+                states = [states]
+            cell_output, states = self.cell.call(inputs, states, training=self.is_train)
+            cell_outputs.append(cell_output)
+            last_hidden = states[0]
+
+        cell_outputs = tf.convert_to_tensor(cell_outputs)
+        cell_outputs = tf.transpose(cell_outputs, perm=[1,0,2])
+        if (return_last_state):
+            return cell_outputs, last_hidden
+        return cell_outputs
+
+
+
+
+
+
+class Seq2seq_Attention(Model):
+    def __init__(self, hidden_size, embedding_layer, cell, method, name=None):
+        super(Seq2seq_Attention, self).__init__(name)
+        self.enc_layer = Encoder(hidden_size, cell, embedding_layer)
+        self.dec_layer = Decoder_Attention(hidden_size, cell, embedding_layer, method=method)
+        self.embedding_layer = embedding_layer
+        self.dense_layer = tl.layers.Dense(n_units=self.embedding_layer.vocabulary_size, in_channels=hidden_size)
+        self.method = method
+
+
+    def inference(self, src_seq, encoding_hidden_states, last_hidden_states, seq_length, sos):
+        batch_size = src_seq.shape[0]
+        decoding = [[sos] for i in range(batch_size)]
+        dec_output = self.embedding_layer(decoding)
+        outputs = [[0] for i in range(batch_size)]
+        for step in range(seq_length):
+            dec_output, last_hidden_states = self.dec_layer(dec_output, encoding_hidden_states, last_hidden_states, method=self.method, return_last_state=True)
+            dec_output = tf.reshape(dec_output, [-1, dec_output.shape[-1]])
+            dec_output = self.dense_layer(dec_output)
+            dec_output = tf.reshape(dec_output, [batch_size, -1, dec_output.shape[-1]])
+            dec_output = tf.argmax(dec_output, -1)
+            outputs = tf.concat([outputs, dec_output], 1)
+            dec_output = self.embedding_layer(dec_output)
+            
+        return outputs[:,1:]
+
+    
+    def forward(self, inputs, seq_length=20, sos=None):
+        src_seq = inputs[0]
+        src_seq = self.embedding_layer(src_seq)
+        enc_output, encoding_hidden_states, last_hidden_states = self.enc_layer(src_seq)
+        encoding_hidden_states = tf.convert_to_tensor(encoding_hidden_states)
+        encoding_hidden_states = tf.transpose(encoding_hidden_states, perm=[1,0,2])
+        last_hidden_states = tf.convert_to_tensor(last_hidden_states)
+
+        if (self.is_train):
+            dec_seq = inputs[1]
+            dec_seq = self.embedding_layer(dec_seq)
+            dec_output = self.dec_layer(dec_seq, encoding_hidden_states, last_hidden_states, method=self.method)
+            batch_size = dec_output.shape[0]
+            dec_output = tf.reshape(dec_output, [-1, dec_output.shape[-1]])
+            dec_output = self.dense_layer(dec_output)
+            dec_output = tf.reshape(dec_output, [batch_size, -1, dec_output.shape[-1]])
+        else:
+            dec_output = self.inference(src_seq, encoding_hidden_states, last_hidden_states, seq_length, sos)
+
+        return dec_output
+
+
diff --git a/tests/models/test_seq2seq_with_attention.py b/tests/models/test_seq2seq_with_attention.py
new file mode 100644
index 000000000..ea981d8e8
--- /dev/null
+++ b/tests/models/test_seq2seq_with_attention.py
@@ -0,0 +1,91 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import os
+import unittest
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+import numpy as np
+import tensorflow as tf
+import tensorlayer as tl
+from tqdm import tqdm
+from sklearn.utils import shuffle
+from tensorlayer.models.seq2seq_with_attention import Seq2seq_Attention
+from tests.utils import CustomTestCase
+from tensorlayer.cost import cross_entropy_seq
+
+
+class Model_SEQ2SEQ_WITH_ATTENTION_Test(CustomTestCase):
+
+    @classmethod
+    def setUpClass(cls):
+
+        cls.batch_size = 16
+
+        cls.vocab_size = 20
+        cls.embedding_size = 32
+        cls.dec_seq_length = 5
+        cls.trainX = np.random.randint(20, size=(50, 6))
+        cls.trainY = np.random.randint(20, size=(50, cls.dec_seq_length + 1))
+        cls.trainY[:, 0] = 0  # start_token == 0
+
+        # Parameters
+        cls.src_len = len(cls.trainX)
+        cls.tgt_len = len(cls.trainY)
+
+        assert cls.src_len == cls.tgt_len
+
+        cls.num_epochs = 500
+        cls.n_step = cls.src_len // cls.batch_size
+
+    @classmethod
+    def tearDownClass(cls):
+        pass
+
+    def test_basic_simpleSeq2Seq(self):
+
+        model_ = Seq2seq_Attention(
+            hidden_size=128,
+            cell = tf.keras.layers.SimpleRNNCell,
+            embedding_layer=tl.layers.Embedding(vocabulary_size=self.vocab_size, embedding_size=self.embedding_size),
+            method = 'dot')
+        optimizer = tf.optimizers.Adam(learning_rate=0.001)
+
+        for epoch in range(self.num_epochs):
+            model_.train()
+            trainX, trainY = shuffle(self.trainX, self.trainY)
+            total_loss, n_iter = 0, 0
+            for X, Y in tqdm(tl.iterate.minibatches(inputs=trainX, targets=trainY, batch_size=self.batch_size,
+                                                    shuffle=False), total=self.n_step,
+                             desc='Epoch[{}/{}]'.format(epoch + 1, self.num_epochs), leave=False):
+                dec_seq = Y[:, :-1]
+                target_seq = Y[:, 1:]
+
+                with tf.GradientTape() as tape:
+                    ## compute outputs
+                    output = model_(inputs=[X, dec_seq])
+                    # print(output)
+                    output = tf.reshape(output, [-1, self.vocab_size])
+
+                    loss = cross_entropy_seq(logits=output, target_seqs=target_seq)
+                    grad = tape.gradient(loss, model_.trainable_weights)
+                    optimizer.apply_gradients(zip(grad, model_.trainable_weights))
+
+                total_loss += loss
+                n_iter += 1
+
+            model_.eval()
+            test_sample = trainX[0:2, :].tolist()
+
+            top_n = 1
+            for i in range(top_n):
+                prediction = model_([test_sample], seq_length=self.dec_seq_length, sos=0)
+                print("Prediction: >>>>>  ", prediction, "\n Target: >>>>>  ", trainY[0:2, 1:], "\n\n")
+
+            # printing average loss after every epoch
+            print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1, self.num_epochs, total_loss / n_iter))
+
+
+if __name__ == '__main__':
+    unittest.main()

From ac177a96ffe2af6c103d8cabcb2b06804386f2b6 Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Wed, 5 Jun 2019 19:01:55 +0100
Subject: [PATCH 11/39] ADD comments

---
 tensorlayer/models/seq2seq_with_attention.py | 36 ++++++++++++++++++++
 1 file changed, 36 insertions(+)

diff --git a/tensorlayer/models/seq2seq_with_attention.py b/tensorlayer/models/seq2seq_with_attention.py
index 1115da030..b875d81f4 100644
--- a/tensorlayer/models/seq2seq_with_attention.py
+++ b/tensorlayer/models/seq2seq_with_attention.py
@@ -131,6 +131,26 @@ def forward(self, dec_seq, enc_hiddens, last_hidden, method, return_last_state=F
 
 
 class Seq2seq_Attention(Model):
+    """Attention-based Seq2Seq model.
+
+    Parameters
+    ----------
+    hidden_size: int
+        The hidden size of both encoder and decoder RNN cells
+    cell : str, tf.function
+        The RNN function cell for your encoder and decoder stack, e.g. tf.keras.layers.GRUCell
+    embedding_layer : tl.Layer
+        A embedding layer, e.g. tl.layers.Embedding(vocabulary_size=voc_size, embedding_size=emb_dim)
+    mothod : str
+        The three alternatives to calculate the attention scores, e.g. "dot", "general" and "concat"
+    name : str
+        The model name
+    
+
+    Returns
+    -------
+        static single layer attention-based Seq2Seq model.
+    """
     def __init__(self, hidden_size, embedding_layer, cell, method, name=None):
         super(Seq2seq_Attention, self).__init__(name)
         self.enc_layer = Encoder(hidden_size, cell, embedding_layer)
@@ -141,6 +161,22 @@ def __init__(self, hidden_size, embedding_layer, cell, method, name=None):
 
 
     def inference(self, src_seq, encoding_hidden_states, last_hidden_states, seq_length, sos):
+        """Inference mode"""
+        """
+        Parameters
+        ----------
+        src_seq : input tensor
+            The source sequences 
+        encoding_hidden_states : a list of tensor
+            The list of encoder's hidden states at each time step
+        last_hidden_states: tensor
+            The last hidden_state from encoder
+        seq_length : int
+            The expected length of your predicted sequence.
+        sos : int
+            <SOS> : The token of "start of sequence"
+        """
+        
         batch_size = src_seq.shape[0]
         decoding = [[sos] for i in range(batch_size)]
         dec_output = self.embedding_layer(decoding)

From ae34f967cc3b9ca65585e0d8ead99376337960e5 Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Sat, 25 May 2019 09:49:03 +0100
Subject: [PATCH 12/39] add seq2seq model; add seq2seq test

---
 .../text_generation/tutorial_generate_text.py |    3 +
 examples/text_generation/vocab.txt            | 9799 +++++++++++++++++
 tests/models/test_auto_naming.py              |   21 +-
 tests/models/test_seq2seq_model.py            |    1 -
 4 files changed, 9803 insertions(+), 21 deletions(-)
 create mode 100644 examples/text_generation/vocab.txt

diff --git a/examples/text_generation/tutorial_generate_text.py b/examples/text_generation/tutorial_generate_text.py
index d157b1ed5..e48b2b45d 100644
--- a/examples/text_generation/tutorial_generate_text.py
+++ b/examples/text_generation/tutorial_generate_text.py
@@ -266,9 +266,12 @@ def main_lstm_generate_text():
         # reset all states at the begining of every epoch
         lstm_state = None
         for step, (x, y) in enumerate(tl.iterate.ptb_iterator(train_data, batch_size, sequence_length)):
+            print(">>>>>", y)
             with tf.GradientTape() as tape:
+
                 ## compute outputs
                 logits, lstm_state = net(x, initial_state=lstm_state)
+                print(">>>>logits" , logits)
                 ## compute loss and update model
                 cost = tl.cost.cross_entropy(logits, tf.reshape(y, [-1]), name='train_loss')
 
diff --git a/examples/text_generation/vocab.txt b/examples/text_generation/vocab.txt
new file mode 100644
index 000000000..9bb13b916
--- /dev/null
+++ b/examples/text_generation/vocab.txt
@@ -0,0 +1,9799 @@
+<PAD> 0
+. 10273
+, 8203
+the 7039
+to 4891
+and 4573
+i 3631
+of 3415
+a 3272
+that 2596
+we 2401
+in 2346
+it 2191
+' 2167
+have 2025
+not 1983
+is 1816
+s 1796
+are 1623
+they 1453
+for 1327
+our 1317
+you 1314
+be 978
+with 960
+will 954
+people 948
+on 880
+he 821
+but 813
+this 802
+was 779
+as 705
+what 633
+all 630
+“ 625
+me 615
+my 599
+who 597
+can 595
+do 591
+so 589
+” 588
+about 522
+if 519
+their 518
+at 509
+country 509
+? 508
+has 505
+don 498
+going 495
+get 486
+by 485
+one 464
+america 462
+when 461
+very 458
+would 445
+or 439
+know 435
+them 434
+more 430
+from 424
+great 420
+no 405
+there 390
+out 390
+make 377
+an 374
+president 370
+obama 369
+many 353
+need 350
+just 348
+than 346
+because 344
+up 344
+m 344
+been 335
+how 324
+$ 322
+like 321
+now 318
+had 313
+his 310
+way 299
+want 295
+world 289
+think 289
+time 285
+jobs 285
+said 283
+right 282
+us 274
+say 265
+american 260
+these 257
+: 257
+should 254
+china 254
+even 253
+were 248
+take 247
+other 242
+again 241
+back 237
+over 234
+only 231
+am 228
+years 227
+government 223
+which 222
+new 221
+well 217
+money 216
+every 215
+into 208
+tax 205
+look 204
+much 197
+some 195
+him 191
+those 190
+good 190
+never 189
+most 187
+work 186
+percent 184
+first 183
+lot 181
+deal 180
+trump 179
+states 178
+let 178
+here 177
+made 172
+then 171
+business 169
+go 167
+— 166
+also 163
+better 162
+americans 157
+why 157
+oil 155
+million 155
+care 155
+where 154
+could 154
+got 152
+done 151
+big 150
+united 149
+come 149
+did 149
+its 148
+tell 148
+your 148
+down 147
+military 146
+believe 146
+billion 144
+really 143
+! 142
+any 140
+doing 139
+being 138
+ever 137
+support 136
+thing 134
+fact 133
+best 133
+iran 131
+put 130
+off 130
+things 129
+illegal 128
+see 128
+pay 127
+immigration 126
+before 126
+must 125
+year 122
+doesn 121
+too 121
+job 120
+something 120
+( 120
+) 120
+problem 119
+she 118
+trade 115
+dollars 113
+000 112
+politicians 112
+two 111
+state 110
+real 110
+system 108
+day 108
+countries 107
+own 106
+didn 106
+bad 104
+bring 104
+wall 104
+plan 102
+after 101
+win 101
+give 100
+far 100
+economic 100
+security 99
+long 99
+love 98
+nothing 98
+policy 98
+companies 98
+through 97
+important 96
+keep 96
+economy 95
+tremendous 94
+person 94
+respect 94
+border 93
+called 92
+life 92
+wrong 92
+talking 92
+number 91
+1 91
+always 91
+tough 90
+hard 90
+energy 90
+making 90
+help 89
+against 88
+create 88
+taxes 87
+won 86
+talk 85
+around 85
+health 85
+national 84
+foreign 84
+chinese 84
+understand 83
+washington 83
+guy 83
+nobody 82
+build 82
+hillary 81
+clinton 81
+– 81
+under 80
+thousands 80
+another 80
+middle 80
+deals 80
+saying 79
+same 79
+israel 79
+obamacare 79
+wouldn 78
+needs 78
+getting 78
+businesses 78
+such 77
+went 77
+everybody 77
+times 75
+next 75
+else 75
+stop 75
+while 75
+york 75
+use 74
+place 74
+second 74
+federal 74
+used 74
+problems 74
+away 73
+laws 72
+become 72
+last 72
+building 72
+millions 72
+three 72
+republican 72
+biggest 72
+her 71
+nation 71
+trillion 71
+maybe 71
+already 70
+question 70
+seen 69
+change 69
+isis 69
+law 69
+ago 69
+workers 69
+leaders 69
+iraq 69
+strong 69
+proud 69
+house 69
+today 68
+debt 68
+mexico 68
+built 68
+nuclear 68
+almost 68
+spending 68
+does 67
+both 67
+run 67
+little 67
+children 66
+means 66
+welfare 66
+end 65
+example 65
+company 65
+actually 65
+anything 65
+show 65
+smart 64
+special 64
+media 64
+knows 64
+2 63
+bill 63
+since 62
+still 62
+total 62
+mean 62
+taking 62
+came 61
+war 61
+5 61
+u 61
+part 61
+working 61
+reason 60
+political 60
+friends 60
+; 60
+may 60
+totally 59
+yet 59
+billions 59
+10 59
+high 59
+told 59
+office 59
+city 59
+social 59
+kind 58
+public 58
+anyone 58
+sure 58
+home 58
+probably 58
+different 58
+citizens 57
+asked 57
+kids 57
+costs 57
+interests 56
+along 56
+start 56
+price 56
+line 55
+administration 55
+immigrants 55
+leadership 55
+east 55
+cost 55
+budget 55
+able 55
+continue 54
+women 54
+spent 54
+congress 54
+less 54
+disaster 54
+isn 54
+polls 54
+wants 53
+without 53
+everything 53
+small 53
+nice 53
+family 52
+makes 52
+campaign 52
+instead 52
+sense 51
+whether 51
+massive 51
+waste 51
+course 51
+absolutely 51
+together 50
+find 50
+five 50
+point 50
+ted 50
+leader 50
+florida 49
+four 49
+programs 49
+having 49
+donald 49
+everyone 49
+program 49
+currency 49
+either 49
+top 49
+single 48
+act 48
+coming 48
+fight 48
+hundreds 48
+excuse 48
+insurance 48
+free 47
+power 47
+until 47
+thought 47
+rich 47
+financial 47
+honor 47
+took 47
+call 47
+radical 46
+few 46
+future 46
+worse 46
+schools 46
+case 46
+incredible 46
+someone 46
+successful 46
+stand 46
+taxpayers 46
+thank 45
+lives 45
+name 45
+running 45
+protect 45
+across 45
+once 45
+party 45
+wealth 45
+opec 45
+anybody 45
+15 45
+including 44
+created 44
+gave 44
+trying 44
+huge 44
+beautiful 44
+white 44
+man 44
+enough 44
+threat 43
+wanted 43
+cannot 43
+says 43
+others 43
+education 43
+south 43
+gets 43
+started 43
+ok 43
+control 42
+each 42
+idea 42
+greatest 42
+serious 42
+agree 42
+history 41
+wonderful 41
+speak 41
+fair 41
+reagan 41
+record 41
+income 41
+borders 40
+whole 40
+happen 40
+save 40
+during 40
+gas 40
+20 40
+clear 40
+story 40
+benefits 40
+exactly 40
+worst 39
+allies 39
+large 39
+common 39
+happened 39
+100 39
+major 39
+feel 39
+action 39
+veterans 39
+news 39
+wasn 39
+ask 38
+given 38
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\ No newline at end of file
diff --git a/tests/models/test_auto_naming.py b/tests/models/test_auto_naming.py
index fb8f03720..81cb23436 100644
--- a/tests/models/test_auto_naming.py
+++ b/tests/models/test_auto_naming.py
@@ -14,26 +14,7 @@
 from tests.utils import CustomTestCase
 
 
-def basic_static_model(name=None, conv1_name="conv1", conv2_name="conv2"):
-    ni = Input((None, 24, 24, 3))
-    nn = Conv2d(16, (5, 5), (1, 1), padding='SAME', act=tf.nn.relu, name=conv1_name)(ni)
-    nn = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool1')(nn)
-
-    nn = Conv2d(16, (5, 5), (1, 1), padding='SAME', act=tf.nn.relu, name=conv2_name)(nn)
-    nn = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool2')(nn)
-
-    M = Model(inputs=ni, outputs=nn, name=name)
-    return M
-
-
-def nested_static_model(name=None, inner_model_name=None):
-    ni = Input((None, 24, 24, 3))
-    nn = ModelLayer(basic_static_model(inner_model_name))(ni)
-    M = Model(inputs=ni, outputs=nn, name=name)
-    return M
-
-
-class basic_dynamic_model(Model):
+class seq2seq(Model):
 
     def __init__(self, name=None, conv1_name="conv1", conv2_name="conv2"):
         super(basic_dynamic_model, self).__init__(name=name)
diff --git a/tests/models/test_seq2seq_model.py b/tests/models/test_seq2seq_model.py
index d77aa47ba..e3afe2ed6 100644
--- a/tests/models/test_seq2seq_model.py
+++ b/tests/models/test_seq2seq_model.py
@@ -91,6 +91,5 @@ def test_basic_simpleSeq2Seq(self):
             # printing average loss after every epoch
             print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1, self.num_epochs, total_loss / n_iter))
 
-
 if __name__ == '__main__':
     unittest.main()

From d43b7855e4423b5e294643dc5bb5820759931bfd Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Thu, 6 Jun 2019 15:44:03 +0100
Subject: [PATCH 13/39] rebase

---
 .../text_generation/tutorial_generate_text.py |    3 -
 examples/text_generation/vocab.txt            | 9799 -----------------
 tests/models/test_auto_naming.py              |   21 +-
 3 files changed, 20 insertions(+), 9803 deletions(-)
 delete mode 100644 examples/text_generation/vocab.txt

diff --git a/examples/text_generation/tutorial_generate_text.py b/examples/text_generation/tutorial_generate_text.py
index e48b2b45d..d157b1ed5 100644
--- a/examples/text_generation/tutorial_generate_text.py
+++ b/examples/text_generation/tutorial_generate_text.py
@@ -266,12 +266,9 @@ def main_lstm_generate_text():
         # reset all states at the begining of every epoch
         lstm_state = None
         for step, (x, y) in enumerate(tl.iterate.ptb_iterator(train_data, batch_size, sequence_length)):
-            print(">>>>>", y)
             with tf.GradientTape() as tape:
-
                 ## compute outputs
                 logits, lstm_state = net(x, initial_state=lstm_state)
-                print(">>>>logits" , logits)
                 ## compute loss and update model
                 cost = tl.cost.cross_entropy(logits, tf.reshape(y, [-1]), name='train_loss')
 
diff --git a/examples/text_generation/vocab.txt b/examples/text_generation/vocab.txt
deleted file mode 100644
index 9bb13b916..000000000
--- a/examples/text_generation/vocab.txt
+++ /dev/null
@@ -1,9799 +0,0 @@
-<PAD> 0
-. 10273
-, 8203
-the 7039
-to 4891
-and 4573
-i 3631
-of 3415
-a 3272
-that 2596
-we 2401
-in 2346
-it 2191
-' 2167
-have 2025
-not 1983
-is 1816
-s 1796
-are 1623
-they 1453
-for 1327
-our 1317
-you 1314
-be 978
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-will 954
-people 948
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-fate 1
-rests 1
-dared 1
-belt 1
-wasteland 1
-americans—more 1
-country—now 1
-shuttered 1
-highs 1
-trashed 1
-afterword 1
-katherine 1
-publisher 1
-invitations 1
-arrived 1
-operatives 1
-celebrities—you 1
-paparazzi 1
-sincerely 1
-festivities 1
-comedian 1
-meyers 1
-sounded 1
-marbles 1
-frowning 1
-blonde 1
-supermodel 1
-andy 1
-roddick 1
-tennis 1
-hilarious 1
-roasted 1
-anna 1
-wintour 1
-metropolitan 1
-museum 1
-thanked 1
-classy 1
-tapped 1
-breath 1
-fiving 1
-stellar 1
-brutal 1
-ridiculed 1
-immensely 1
-wannabe 1
-ride 1
-coattails 1
-compensate 1
-rave 1
-lunatic 1
-television—at 1
-pawlenty 1
-scarborough 1
-brzezinski 1
-vibrant 1
-alluded 1
-alluding 1
-irritating 1
-viewing 1
-kravis 1
-cerberus 1
-apollo 1
-it—i 1
-furthest 1
-transformed 1
-jerk 1
-taste 1
-estate—he 1
-guest 1
-shortly 1
-imploded 1
-antics 1
-nude 1
-photos 1
-pleasant 1
-nicer 1
-studio 1
-participate 1
-raving 1
-meaner 1
-ideally 1
-snide 1
-rambled 1
-moron 1
-prophetic 1
-reason—personality 1
-fright 1
-reasons—they 1
-twelfth 1
-debut 1
-bowl 1
-smashed 1
-asleep 1
-pretends 1
-russert 1
-abc 1
-wright 1
-will—in 1
-lightweights 1
-goof 1
-spiritedness 1
-lackluster 1
-offends 1
-matt 1
-re 1
-gregory 1
-filling 1
-shoes 1
-fair—and 1
-cultures 1
-williams 1
-show—and 1
-smash 1
-karl 1
-decided—without 1
-guess—to 1
-torpedo 1
-way—not 1
-stephanopoulos 1
-fans 1
-overprotective 1
-first—i 1
-sprang 1
-screaming 1
-protective 1
-guarded 1
-gloves 1
-authentic 1
-irritates 1
-segment 1
-mocking 1
-booted 1
-imus 1
-jackson 1
-sharpton 1
-journalistic 1
-job—at 1
-disappointing 1
-aisle 1
-charles 1
-watters 1
-greta 1
-outstanding 1
-rebut 1
-creator 1
-baier 1
-gretchen 1
-carlson 1
-doocy 1
-kilmeade 1
-handsome 1
-me—it 1
-was— 1
-sensation 1
-hotter 1
-music 1
-celebrities 1
-singers 1
-personalities 1
-shouting 1
-keen 1
-leno—it 1
-lame 1
-duck 1
-conan 1
-were—he 1
-collide 1
-nastier 1
-leno—he 1
-defaulted 1
-figuring 1
-smelled 1
-raged 1
-haircut 1
-actuality 1
-lawyer 1
-participating 1
-now—and 1
-billion+ 1
-transaction 1
-investigative 1
-examination 1
-fishy 1
-enterprises 1
-survivor 1
-voicing 1
-years—that 1
-unforced 1
-error 1
-phil 1
-ruffin 1
-mobbed 1
-catered 1
-foul 1
-phenomenally 1
-curser 1
-overrated 1
-remorse 1
-harnessing 1
-negativity 1
-people—a 1
-cynical 1
-law—called 1
-time—that 1
-prevents 1
-it—because 1
-distinctly 1
-friday 1
-blaring 1
-monday 1
-schedules 1
-‘donald 1
-hourly 1
-primetime 1
-precise 1
-reiterating 1
-smart—the 1
-all—but 1
-compliment 1
-predictive 1
-instructions 1
-sometime 1
-submittal 1
-miserable 1
-petty 1
-jealous 1
-wannabes 1
-fabricate 1
-transparency 1
-embroiled 1
-divorce 1
-charlottesville 1
-liquid 1
-price—cash 1
-race—most 1
-palin 1
-bedlam 1
-swarming 1
-stir 1
-parlor 1
-bachmann 1
-bee 1
-stole 1
-thunder 1
-protector 1
-georges 1
-personable 1
-forceful 1
-someplace 1
-severely 1
-inclined 1
-flip 1
-flopping 1
-magnetic 1
-personality 1
-singer 1
-swarmed 1
-badmouthing 1
-bloodsuckers 1
-leech 1
-distinct 1
-governorship 1
-resume 1
-money—and 1
-rumors 1
-back—it 1
-polite 1
-continuously 1
-barricades—and 1
-disturbance 1
-disruption 1
-maligns 1
-ridicules 1
-mocks 1
-patriots 1
-747 1
-decimate 1
-sincere 1
-fisker 1
-sweetheart 1
-connected 1
-hammer 1
-bailing 1
-bankers 1
-cahoots 1
-sparking 1
-innovator 1
-apple—he 1
-ceos 1
-isaacson 1
-biography 1
-messed 1
-micromanage 1
-innovators 1
-dreamers 1
-competitions 1
-prizes 1
-manned 1
-spacecraft 1
-invent 1
-unchained 1
-regnery 1
-publishing 1
-wynton 1
-schweizer 1
-marji 1
-ross 1
-carneal 1
-crocker 1
-apparent 1
-kacey 1
-thuy 1
-colayco 1
\ No newline at end of file
diff --git a/tests/models/test_auto_naming.py b/tests/models/test_auto_naming.py
index 81cb23436..fb8f03720 100644
--- a/tests/models/test_auto_naming.py
+++ b/tests/models/test_auto_naming.py
@@ -14,7 +14,26 @@
 from tests.utils import CustomTestCase
 
 
-class seq2seq(Model):
+def basic_static_model(name=None, conv1_name="conv1", conv2_name="conv2"):
+    ni = Input((None, 24, 24, 3))
+    nn = Conv2d(16, (5, 5), (1, 1), padding='SAME', act=tf.nn.relu, name=conv1_name)(ni)
+    nn = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool1')(nn)
+
+    nn = Conv2d(16, (5, 5), (1, 1), padding='SAME', act=tf.nn.relu, name=conv2_name)(nn)
+    nn = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool2')(nn)
+
+    M = Model(inputs=ni, outputs=nn, name=name)
+    return M
+
+
+def nested_static_model(name=None, inner_model_name=None):
+    ni = Input((None, 24, 24, 3))
+    nn = ModelLayer(basic_static_model(inner_model_name))(ni)
+    M = Model(inputs=ni, outputs=nn, name=name)
+    return M
+
+
+class basic_dynamic_model(Model):
 
     def __init__(self, name=None, conv1_name="conv1", conv2_name="conv2"):
         super(basic_dynamic_model, self).__init__(name=name)

From 2a3ddb182b563b0e6204de3c942c15f3bf4993d0 Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Fri, 31 May 2019 16:23:19 +0100
Subject: [PATCH 14/39] FIX the Travis CI build

---
 conf.py                            | 52 ++++++++++++++++++++++++++++++
 index.rst                          | 20 ++++++++++++
 make.bat                           | 35 ++++++++++++++++++++
 tests/models/test_seq2seq_model.py |  1 +
 4 files changed, 108 insertions(+)
 create mode 100644 conf.py
 create mode 100644 index.rst
 create mode 100644 make.bat

diff --git a/conf.py b/conf.py
new file mode 100644
index 000000000..7bb931009
--- /dev/null
+++ b/conf.py
@@ -0,0 +1,52 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# http://www.sphinx-doc.org/en/master/config
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+# import os
+# import sys
+# sys.path.insert(0, os.path.abspath('.'))
+
+
+# -- Project information -----------------------------------------------------
+
+project = 'tensorlayer'
+copyright = '2019, lingjun liu'
+author = 'lingjun liu'
+
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = 'alabaster'
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
diff --git a/index.rst b/index.rst
new file mode 100644
index 000000000..dfbb70be4
--- /dev/null
+++ b/index.rst
@@ -0,0 +1,20 @@
+.. tensorlayer documentation master file, created by
+   sphinx-quickstart on Sat May 25 10:14:56 2019.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to tensorlayer's documentation!
+=======================================
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
diff --git a/make.bat b/make.bat
new file mode 100644
index 000000000..27f573b87
--- /dev/null
+++ b/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
+
+:end
+popd
diff --git a/tests/models/test_seq2seq_model.py b/tests/models/test_seq2seq_model.py
index e3afe2ed6..d77aa47ba 100644
--- a/tests/models/test_seq2seq_model.py
+++ b/tests/models/test_seq2seq_model.py
@@ -91,5 +91,6 @@ def test_basic_simpleSeq2Seq(self):
             # printing average loss after every epoch
             print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1, self.num_epochs, total_loss / n_iter))
 
+
 if __name__ == '__main__':
     unittest.main()

From bb5675e611cfaf6ded6d8c5be4041796254c4ee4 Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Wed, 5 Jun 2019 18:52:38 +0100
Subject: [PATCH 15/39] ADD attention-based seq2seq model

---
 tensorlayer/models/seq2seq_with_attention.py | 181 +++++++++++++++++++
 tests/models/test_seq2seq_with_attention.py  |  91 ++++++++++
 2 files changed, 272 insertions(+)
 create mode 100644 tensorlayer/models/seq2seq_with_attention.py
 create mode 100644 tests/models/test_seq2seq_with_attention.py

diff --git a/tensorlayer/models/seq2seq_with_attention.py b/tensorlayer/models/seq2seq_with_attention.py
new file mode 100644
index 000000000..1115da030
--- /dev/null
+++ b/tensorlayer/models/seq2seq_with_attention.py
@@ -0,0 +1,181 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+import tensorflow as tf
+import tensorlayer as tl
+import numpy as np
+from tensorlayer.models import Model
+from tensorlayer.layers import Dense, Dropout, Input
+from tensorlayer.layers.core import Layer
+
+
+
+
+
+class Encoder(Layer):
+    def __init__(self, hidden_size, cell, embedding_layer, name=None):
+        super(Encoder, self).__init__(name)
+        self.cell = cell(hidden_size)
+        self.hidden_size = hidden_size
+        self.embedding_layer = embedding_layer
+        self.build((None, None, self.embedding_layer.embedding_size))
+        self._built = True
+        
+    def build(self, inputs_shape):
+        self.cell.build(input_shape=tuple(inputs_shape))
+        self._built = True
+        if self._trainable_weights is None:
+            self._trainable_weights = list()
+        
+        for var in self.cell.trainable_variables:
+            self._trainable_weights.append(var)
+
+    def forward(self, src_seq, initial_state=None):
+        
+        states = initial_state if initial_state is not None else self.cell.get_initial_state(src_seq)
+        encoding_hidden_states = list()
+        total_steps = src_seq.get_shape().as_list()[1]
+        for time_step in range(total_steps):
+            if not isinstance(states, list):
+                states = [states]
+            output, states = self.cell.call(src_seq[:,time_step,:], states, training=self.is_train)
+            encoding_hidden_states.append(states[0])
+        return output, encoding_hidden_states, states[0]
+        
+
+
+
+
+class Decoder_Attention(Layer):
+    def __init__(self, hidden_size, cell, embedding_layer, method, name = None):
+        super(Decoder_Attention, self).__init__(name)
+        self.cell = cell(hidden_size)
+        self.hidden_size = hidden_size
+        self.embedding_layer = embedding_layer
+        self.method = method
+        self.build((None, hidden_size+self.embedding_layer.embedding_size))
+        self._built = True
+        
+        
+    def build(self, inputs_shape):
+        self.cell.build(input_shape=tuple(inputs_shape))
+        self._built = True
+        if self.method is "concat":
+            self.W = self._get_weights("W", shape=(2*self.hidden_size, self.hidden_size))
+            self.V = self._get_weights("V", shape=(self.hidden_size, 1))
+        elif self.method is "general":
+            self.W = self._get_weights("W", shape=(self.hidden_size, self.hidden_size))
+        if self._trainable_weights is None:
+            self._trainable_weights = list()
+        
+        for var in self.cell.trainable_variables:
+            self._trainable_weights.append(var)
+
+    def score(self, encoding_hidden, hidden, method):
+        # encoding = [B, T, H]
+        # hidden = [B, H]
+        # combined = [B,T,2H]
+        if method is "concat":
+            # hidden = [B,H]->[B,1,H]->[B,T,H]
+            hidden = tf.expand_dims(hidden, 1)
+            hidden = tf.tile(hidden, [1, encoding_hidden.shape[1],1])
+            # combined = [B,T,2H]
+            combined = tf.concat([hidden, encoding_hidden], 2)
+            combined = tf.cast(combined, tf.float32)
+            score = tf.tensordot(combined, self.W, axes=[[2], [0]]) # score = [B,T,H]
+            score = tf.nn.tanh(score) # score = [B,T,H]
+            score = tf.tensordot(self.V, score, axes=[[0], [2]]) # score = [1,B,T]
+            score = tf.squeeze(score, axis=0) # score = [B,T]
+            
+        elif method is "dot":
+            # hidden = [B,H]->[B,H,1]
+            hidden = tf.expand_dims(hidden, 2)
+            score = tf.matmul(encoding_hidden, hidden)
+            score = tf.squeeze(score, axis=2)
+        elif method is "general":
+            # hidden = [B,H]->[B,H,1]
+            score = tf.matmul(hidden, self.W)
+            score = tf.expand_dims(score, 2)
+            score = tf.matmul(encoding_hidden, score)
+            score = tf.squeeze(score, axis=2)
+            
+        score = tf.nn.softmax(score, axis=-1) # score = [B,T]
+        return score
+
+    def forward(self, dec_seq, enc_hiddens, last_hidden, method, return_last_state=False):
+        # dec_seq = [B, T_, V], enc_hiddens = [B, T, H], last_hidden = [B, H]
+        total_steps = dec_seq.get_shape().as_list()[1]
+        states = last_hidden
+        cell_outputs = list()
+        for time_step in range(total_steps):
+            attention_weights = self.score(enc_hiddens, last_hidden, method) 
+            attention_weights = tf.expand_dims(attention_weights, 1) #[B, 1, T]
+            context = tf.matmul(attention_weights, enc_hiddens) #[B, 1, H]
+            context = tf.squeeze(context, 1) #[B, H]
+            inputs = tf.concat([dec_seq[:,time_step,:], context], 1)
+            if not isinstance(states, list):
+                states = [states]
+            cell_output, states = self.cell.call(inputs, states, training=self.is_train)
+            cell_outputs.append(cell_output)
+            last_hidden = states[0]
+
+        cell_outputs = tf.convert_to_tensor(cell_outputs)
+        cell_outputs = tf.transpose(cell_outputs, perm=[1,0,2])
+        if (return_last_state):
+            return cell_outputs, last_hidden
+        return cell_outputs
+
+
+
+
+
+
+class Seq2seq_Attention(Model):
+    def __init__(self, hidden_size, embedding_layer, cell, method, name=None):
+        super(Seq2seq_Attention, self).__init__(name)
+        self.enc_layer = Encoder(hidden_size, cell, embedding_layer)
+        self.dec_layer = Decoder_Attention(hidden_size, cell, embedding_layer, method=method)
+        self.embedding_layer = embedding_layer
+        self.dense_layer = tl.layers.Dense(n_units=self.embedding_layer.vocabulary_size, in_channels=hidden_size)
+        self.method = method
+
+
+    def inference(self, src_seq, encoding_hidden_states, last_hidden_states, seq_length, sos):
+        batch_size = src_seq.shape[0]
+        decoding = [[sos] for i in range(batch_size)]
+        dec_output = self.embedding_layer(decoding)
+        outputs = [[0] for i in range(batch_size)]
+        for step in range(seq_length):
+            dec_output, last_hidden_states = self.dec_layer(dec_output, encoding_hidden_states, last_hidden_states, method=self.method, return_last_state=True)
+            dec_output = tf.reshape(dec_output, [-1, dec_output.shape[-1]])
+            dec_output = self.dense_layer(dec_output)
+            dec_output = tf.reshape(dec_output, [batch_size, -1, dec_output.shape[-1]])
+            dec_output = tf.argmax(dec_output, -1)
+            outputs = tf.concat([outputs, dec_output], 1)
+            dec_output = self.embedding_layer(dec_output)
+            
+        return outputs[:,1:]
+
+    
+    def forward(self, inputs, seq_length=20, sos=None):
+        src_seq = inputs[0]
+        src_seq = self.embedding_layer(src_seq)
+        enc_output, encoding_hidden_states, last_hidden_states = self.enc_layer(src_seq)
+        encoding_hidden_states = tf.convert_to_tensor(encoding_hidden_states)
+        encoding_hidden_states = tf.transpose(encoding_hidden_states, perm=[1,0,2])
+        last_hidden_states = tf.convert_to_tensor(last_hidden_states)
+
+        if (self.is_train):
+            dec_seq = inputs[1]
+            dec_seq = self.embedding_layer(dec_seq)
+            dec_output = self.dec_layer(dec_seq, encoding_hidden_states, last_hidden_states, method=self.method)
+            batch_size = dec_output.shape[0]
+            dec_output = tf.reshape(dec_output, [-1, dec_output.shape[-1]])
+            dec_output = self.dense_layer(dec_output)
+            dec_output = tf.reshape(dec_output, [batch_size, -1, dec_output.shape[-1]])
+        else:
+            dec_output = self.inference(src_seq, encoding_hidden_states, last_hidden_states, seq_length, sos)
+
+        return dec_output
+
+
diff --git a/tests/models/test_seq2seq_with_attention.py b/tests/models/test_seq2seq_with_attention.py
new file mode 100644
index 000000000..ea981d8e8
--- /dev/null
+++ b/tests/models/test_seq2seq_with_attention.py
@@ -0,0 +1,91 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import os
+import unittest
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+import numpy as np
+import tensorflow as tf
+import tensorlayer as tl
+from tqdm import tqdm
+from sklearn.utils import shuffle
+from tensorlayer.models.seq2seq_with_attention import Seq2seq_Attention
+from tests.utils import CustomTestCase
+from tensorlayer.cost import cross_entropy_seq
+
+
+class Model_SEQ2SEQ_WITH_ATTENTION_Test(CustomTestCase):
+
+    @classmethod
+    def setUpClass(cls):
+
+        cls.batch_size = 16
+
+        cls.vocab_size = 20
+        cls.embedding_size = 32
+        cls.dec_seq_length = 5
+        cls.trainX = np.random.randint(20, size=(50, 6))
+        cls.trainY = np.random.randint(20, size=(50, cls.dec_seq_length + 1))
+        cls.trainY[:, 0] = 0  # start_token == 0
+
+        # Parameters
+        cls.src_len = len(cls.trainX)
+        cls.tgt_len = len(cls.trainY)
+
+        assert cls.src_len == cls.tgt_len
+
+        cls.num_epochs = 500
+        cls.n_step = cls.src_len // cls.batch_size
+
+    @classmethod
+    def tearDownClass(cls):
+        pass
+
+    def test_basic_simpleSeq2Seq(self):
+
+        model_ = Seq2seq_Attention(
+            hidden_size=128,
+            cell = tf.keras.layers.SimpleRNNCell,
+            embedding_layer=tl.layers.Embedding(vocabulary_size=self.vocab_size, embedding_size=self.embedding_size),
+            method = 'dot')
+        optimizer = tf.optimizers.Adam(learning_rate=0.001)
+
+        for epoch in range(self.num_epochs):
+            model_.train()
+            trainX, trainY = shuffle(self.trainX, self.trainY)
+            total_loss, n_iter = 0, 0
+            for X, Y in tqdm(tl.iterate.minibatches(inputs=trainX, targets=trainY, batch_size=self.batch_size,
+                                                    shuffle=False), total=self.n_step,
+                             desc='Epoch[{}/{}]'.format(epoch + 1, self.num_epochs), leave=False):
+                dec_seq = Y[:, :-1]
+                target_seq = Y[:, 1:]
+
+                with tf.GradientTape() as tape:
+                    ## compute outputs
+                    output = model_(inputs=[X, dec_seq])
+                    # print(output)
+                    output = tf.reshape(output, [-1, self.vocab_size])
+
+                    loss = cross_entropy_seq(logits=output, target_seqs=target_seq)
+                    grad = tape.gradient(loss, model_.trainable_weights)
+                    optimizer.apply_gradients(zip(grad, model_.trainable_weights))
+
+                total_loss += loss
+                n_iter += 1
+
+            model_.eval()
+            test_sample = trainX[0:2, :].tolist()
+
+            top_n = 1
+            for i in range(top_n):
+                prediction = model_([test_sample], seq_length=self.dec_seq_length, sos=0)
+                print("Prediction: >>>>>  ", prediction, "\n Target: >>>>>  ", trainY[0:2, 1:], "\n\n")
+
+            # printing average loss after every epoch
+            print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1, self.num_epochs, total_loss / n_iter))
+
+
+if __name__ == '__main__':
+    unittest.main()

From 58777c902b3cfb75d5c70bf7bf0f7848552c07b7 Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Wed, 5 Jun 2019 19:01:55 +0100
Subject: [PATCH 16/39] ADD comments

---
 tensorlayer/models/seq2seq_with_attention.py | 36 ++++++++++++++++++++
 1 file changed, 36 insertions(+)

diff --git a/tensorlayer/models/seq2seq_with_attention.py b/tensorlayer/models/seq2seq_with_attention.py
index 1115da030..b875d81f4 100644
--- a/tensorlayer/models/seq2seq_with_attention.py
+++ b/tensorlayer/models/seq2seq_with_attention.py
@@ -131,6 +131,26 @@ def forward(self, dec_seq, enc_hiddens, last_hidden, method, return_last_state=F
 
 
 class Seq2seq_Attention(Model):
+    """Attention-based Seq2Seq model.
+
+    Parameters
+    ----------
+    hidden_size: int
+        The hidden size of both encoder and decoder RNN cells
+    cell : str, tf.function
+        The RNN function cell for your encoder and decoder stack, e.g. tf.keras.layers.GRUCell
+    embedding_layer : tl.Layer
+        A embedding layer, e.g. tl.layers.Embedding(vocabulary_size=voc_size, embedding_size=emb_dim)
+    mothod : str
+        The three alternatives to calculate the attention scores, e.g. "dot", "general" and "concat"
+    name : str
+        The model name
+    
+
+    Returns
+    -------
+        static single layer attention-based Seq2Seq model.
+    """
     def __init__(self, hidden_size, embedding_layer, cell, method, name=None):
         super(Seq2seq_Attention, self).__init__(name)
         self.enc_layer = Encoder(hidden_size, cell, embedding_layer)
@@ -141,6 +161,22 @@ def __init__(self, hidden_size, embedding_layer, cell, method, name=None):
 
 
     def inference(self, src_seq, encoding_hidden_states, last_hidden_states, seq_length, sos):
+        """Inference mode"""
+        """
+        Parameters
+        ----------
+        src_seq : input tensor
+            The source sequences 
+        encoding_hidden_states : a list of tensor
+            The list of encoder's hidden states at each time step
+        last_hidden_states: tensor
+            The last hidden_state from encoder
+        seq_length : int
+            The expected length of your predicted sequence.
+        sos : int
+            <SOS> : The token of "start of sequence"
+        """
+        
         batch_size = src_seq.shape[0]
         decoding = [[sos] for i in range(batch_size)]
         dec_output = self.embedding_layer(decoding)

From 372e31f60cf7af61e762a1a161831ec45866911a Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Thu, 6 Jun 2019 16:30:12 +0100
Subject: [PATCH 17/39] Resolve problems

---
 docs/modules/models.rst | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/docs/modules/models.rst b/docs/modules/models.rst
index cdfd6ccc6..3dd611656 100644
--- a/docs/modules/models.rst
+++ b/docs/modules/models.rst
@@ -13,6 +13,9 @@ TensorLayer provides many pretrained models, you can easily use the whole or a p
     VGG19
     SqueezeNetV1
     MobileNetV1
+    Seq2seq
+    Seq2seq_Luong_Attention
+
 
 Base Model
 -----------
@@ -37,3 +40,14 @@ MobileNetV1
 ----------------
 
 .. autofunction:: MobileNetV1
+
+Seq2seq
+------------------------
+
+.. autoclass:: Seq2seq
+
+
+Seq2seq_Luong_Attention
+------------------------
+
+.. autoclass:: Seq2seq_Luong_Attention

From 496cd7fa3c5938c28ba7ef11963355ece76542f8 Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Thu, 6 Jun 2019 16:30:32 +0100
Subject: [PATCH 18/39] Resolve problems

---
 tensorlayer/models/__init__.py               |  2 ++
 tensorlayer/models/seq2seq.py                |  3 +++
 tensorlayer/models/seq2seq_with_attention.py |  8 ++++---
 tests/models/test_seq2seq_with_attention.py  | 23 +++++++++++++++-----
 4 files changed, 27 insertions(+), 9 deletions(-)

diff --git a/tensorlayer/models/__init__.py b/tensorlayer/models/__init__.py
index 5375efcdd..62adc076f 100644
--- a/tensorlayer/models/__init__.py
+++ b/tensorlayer/models/__init__.py
@@ -7,3 +7,5 @@
 from .squeezenetv1 import SqueezeNetV1
 from .mobilenetv1 import MobileNetV1
 from .vgg import *
+from .seq2seq import Seq2seq
+from .seq2seq_with_attention import Seq2seq_Luong_Attention
diff --git a/tensorlayer/models/seq2seq.py b/tensorlayer/models/seq2seq.py
index ca6931463..a4e11b217 100644
--- a/tensorlayer/models/seq2seq.py
+++ b/tensorlayer/models/seq2seq.py
@@ -7,6 +7,9 @@
 from tensorlayer.models import Model
 from tensorlayer.layers import Dense, Dropout, Input
 from tensorlayer.layers.core import Layer
+__all__ = [
+    'Seq2seq'
+]
 
 
 class Seq2seq(Model):
diff --git a/tensorlayer/models/seq2seq_with_attention.py b/tensorlayer/models/seq2seq_with_attention.py
index b875d81f4..339628888 100644
--- a/tensorlayer/models/seq2seq_with_attention.py
+++ b/tensorlayer/models/seq2seq_with_attention.py
@@ -8,7 +8,9 @@
 from tensorlayer.layers import Dense, Dropout, Input
 from tensorlayer.layers.core import Layer
 
-
+__all__ = [
+    'Seq2seq_Luong_Attention'
+]
 
 
 
@@ -130,8 +132,8 @@ def forward(self, dec_seq, enc_hiddens, last_hidden, method, return_last_state=F
 
 
 
-class Seq2seq_Attention(Model):
-    """Attention-based Seq2Seq model.
+class Seq2seq_Luong_Attention(Model):
+    """Luong Attention-based Seq2Seq model. Implementation based on https://arxiv.org/pdf/1508.04025.pdf.
 
     Parameters
     ----------
diff --git a/tests/models/test_seq2seq_with_attention.py b/tests/models/test_seq2seq_with_attention.py
index ea981d8e8..e40faec10 100644
--- a/tests/models/test_seq2seq_with_attention.py
+++ b/tests/models/test_seq2seq_with_attention.py
@@ -23,12 +23,23 @@ def setUpClass(cls):
 
         cls.batch_size = 16
 
-        cls.vocab_size = 20
+        cls.vocab_size = 200
         cls.embedding_size = 32
         cls.dec_seq_length = 5
-        cls.trainX = np.random.randint(20, size=(50, 6))
-        cls.trainY = np.random.randint(20, size=(50, cls.dec_seq_length + 1))
+        cls.pure_time = np.linspace(-1, 1, 11)
+        cls.pure_signal = 100*np.sin(cls.pure_time/(2*np.pi))
+        cls.dataset = np.zeros((100, 11))
+        for i in range(100):
+            noise = 100+10*np.random.normal(0, 1, cls.pure_signal.shape)
+            cls.dataset[i] = cls.pure_signal + noise
+        cls.dataset = cls.dataset.astype(int)
+        cls.trainX = cls.dataset[:80,:5]
+        cls.trainY = cls.dataset[:80,5:]
+        cls.testX = cls.dataset[80:,:5]
+        cls.testY = cls.dataset[80:,5:]
+
         cls.trainY[:, 0] = 0  # start_token == 0
+        cls.testY[:, 0] = 0  # start_token == 0
 
         # Parameters
         cls.src_len = len(cls.trainX)
@@ -76,12 +87,12 @@ def test_basic_simpleSeq2Seq(self):
                 n_iter += 1
 
             model_.eval()
-            test_sample = trainX[0:2, :].tolist()
-
+            test_sample = self.testX[:2,:].tolist() # Can't capture the sequence.
+            print(test_sample)
             top_n = 1
             for i in range(top_n):
                 prediction = model_([test_sample], seq_length=self.dec_seq_length, sos=0)
-                print("Prediction: >>>>>  ", prediction, "\n Target: >>>>>  ", trainY[0:2, 1:], "\n\n")
+                print("Prediction: >>>>>  ", prediction, "\n Target: >>>>>  ", self.testY[0:2, 1:], "\n\n")
 
             # printing average loss after every epoch
             print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1, self.num_epochs, total_loss / n_iter))

From b5ed4b9073a283ddf382b24e87e7eab5afc0c32c Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Thu, 6 Jun 2019 16:41:26 +0100
Subject: [PATCH 19/39] FIX bug

---
 tests/models/test_seq2seq_with_attention.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/models/test_seq2seq_with_attention.py b/tests/models/test_seq2seq_with_attention.py
index e40faec10..cfe6c1c98 100644
--- a/tests/models/test_seq2seq_with_attention.py
+++ b/tests/models/test_seq2seq_with_attention.py
@@ -11,7 +11,7 @@
 import tensorlayer as tl
 from tqdm import tqdm
 from sklearn.utils import shuffle
-from tensorlayer.models.seq2seq_with_attention import Seq2seq_Attention
+from tensorlayer.models.seq2seq_with_attention import Seq2seq_Luong_Attention
 from tests.utils import CustomTestCase
 from tensorlayer.cost import cross_entropy_seq
 

From 4fa47bc0333f88249b9863eeb6f7e0eb2e5d251f Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Thu, 6 Jun 2019 16:46:20 +0100
Subject: [PATCH 20/39] FIX bugs

---
 tensorlayer/models/seq2seq_with_attention.py | 2 +-
 tests/models/test_seq2seq_with_attention.py  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorlayer/models/seq2seq_with_attention.py b/tensorlayer/models/seq2seq_with_attention.py
index 339628888..af66cc968 100644
--- a/tensorlayer/models/seq2seq_with_attention.py
+++ b/tensorlayer/models/seq2seq_with_attention.py
@@ -154,7 +154,7 @@ class Seq2seq_Luong_Attention(Model):
         static single layer attention-based Seq2Seq model.
     """
     def __init__(self, hidden_size, embedding_layer, cell, method, name=None):
-        super(Seq2seq_Attention, self).__init__(name)
+        super(Seq2seq_Luong_Attention, self).__init__(name)
         self.enc_layer = Encoder(hidden_size, cell, embedding_layer)
         self.dec_layer = Decoder_Attention(hidden_size, cell, embedding_layer, method=method)
         self.embedding_layer = embedding_layer
diff --git a/tests/models/test_seq2seq_with_attention.py b/tests/models/test_seq2seq_with_attention.py
index cfe6c1c98..45e888767 100644
--- a/tests/models/test_seq2seq_with_attention.py
+++ b/tests/models/test_seq2seq_with_attention.py
@@ -56,7 +56,7 @@ def tearDownClass(cls):
 
     def test_basic_simpleSeq2Seq(self):
 
-        model_ = Seq2seq_Attention(
+        model_ = Seq2seq_Luong_Attention(
             hidden_size=128,
             cell = tf.keras.layers.SimpleRNNCell,
             embedding_layer=tl.layers.Embedding(vocabulary_size=self.vocab_size, embedding_size=self.embedding_size),

From 417c082cafff43baa996ef21a614dcc9ad524393 Mon Sep 17 00:00:00 2001
From: Jingqing  Zhang <jingqing.zhang15@imperial.ac.uk>
Date: Mon, 3 Jun 2019 17:26:58 +0100
Subject: [PATCH 21/39] nlp: mask for target sequence: refactored, tested, doc
 updated

---
 docs/modules/layers.rst               |   4 +
 tensorlayer/layers/recurrent.py       | 247 ++++++--------------------
 tests/layers/test_layers_recurrent.py |  55 ++++++
 3 files changed, 109 insertions(+), 197 deletions(-)

diff --git a/docs/modules/layers.rst b/docs/modules/layers.rst
index 7a70b54dc..597ce632b 100644
--- a/docs/modules/layers.rst
+++ b/docs/modules/layers.rst
@@ -85,6 +85,7 @@ Layer list
    retrieve_seq_length_op
    retrieve_seq_length_op2
    retrieve_seq_length_op3
+   target_mask_op
 
    Flatten
    Reshape
@@ -600,6 +601,9 @@ Compute Sequence length 3
 """"""""""""""""""""""""""
 .. autofunction:: retrieve_seq_length_op3
 
+Compute mask of the target sequence
+""""""""""""""""""""""""""
+.. autofunction:: target_mask_op
 
 
 
diff --git a/tensorlayer/layers/recurrent.py b/tensorlayer/layers/recurrent.py
index 61124c556..40456806f 100644
--- a/tensorlayer/layers/recurrent.py
+++ b/tensorlayer/layers/recurrent.py
@@ -26,8 +26,7 @@
     'retrieve_seq_length_op',
     'retrieve_seq_length_op2',
     'retrieve_seq_length_op3',
-    # 'target_mask_op',
-    # 'Seq2Seq',
+    'target_mask_op',
 ]
 
 
@@ -214,10 +213,8 @@ def forward(self, inputs, initial_state=None, **kwargs):
         else:
             return outputs
 
-
 # TODO: write tl.layers.SimpleRNN, tl.layers.GRU, tl.layers.LSTM
 
-
 class BiRNN(Layer):
     """
     The :class:`BiRNN` class is a fixed length Bidirectional recurrent layer.
@@ -729,7 +726,6 @@ def __init__(
         self._add_layers(self.outputs)
         self._add_params(rnn_variables)
 
-
 # @tf.function
 def retrieve_seq_length_op(data):
     """An op to compute the length of a sequence from input shape of [batch_size, n_step(max), n_features],
@@ -771,7 +767,6 @@ def retrieve_seq_length_op(data):
 
         return tf.cast(length, tf.int32)
 
-
 # @tf.function
 def retrieve_seq_length_op2(data):
     """An op to compute the length of a sequence, from input shape of [batch_size, n_step(max)],
@@ -794,7 +789,6 @@ def retrieve_seq_length_op2(data):
     """
     return tf.reduce_sum(input_tensor=tf.cast(tf.greater(data, tf.zeros_like(data)), tf.int32), axis=1)
 
-
 # @tf.function
 def retrieve_seq_length_op3(data, pad_val=0):
     """An op to compute the length of a sequence, the data shape can be [batch_size, n_step(max)] or
@@ -853,201 +847,60 @@ def retrieve_seq_length_op3(data, pad_val=0):
             "retrieve_seq_length_op3: handling data with num of dims %s hasn't been implemented!" % (data_shape_size)
         )
 
+def target_mask_op(data, pad_val=0):
+    """ Return the mask of the input sequence data based on the padding values.
+
+    Parameters
+    -----------
+    data : tf.Tensor
+        A tensor with 2 or 3 dimensions.
+    pad_val: int, float, string, etc
+        The value that represent padding. By default, 0. For tf.string, you may use empty string.
+
+    Examples
+    -----------
+    >>> data = [['hello', 'world', '', '', ''],
+    >>>         ['hello', 'world', 'tensorlayer', '', ''],
+    >>>         ['hello', 'world', 'tensorlayer', '2.0', '']]
+    >>> data = tf.convert_to_tensor(data, dtype=tf.string)
+    >>> mask = tl.layers.target_mask_op(data, pad_val='')
+    >>> print(mask)
+    tf.Tensor(
+    [[1 1 0 0 0]
+     [1 1 1 0 0]
+     [1 1 1 1 0]], shape=(3, 5), dtype=int32)
+    >>> data = [[[1], [0], [0], [0], [0]],
+    >>>         [[1], [2], [3], [0], [0]],
+    >>>         [[1], [2], [0], [1], [0]]]
+    >>> data = tf.convert_to_tensor(data, dtype=tf.float32)
+    >>> mask = tl.layers.target_mask_op(data)
+    >>> print(mask)
+    tf.Tensor(
+    [[1 0 0 0 0]
+     [1 1 1 0 0]
+     [1 1 0 1 0]], shape=(3, 5), dtype=int32)
+    >>> data = [[[0,0],[2,2],[1,2],[1,2],[0,0]],
+    >>>         [[2,3],[2,4],[3,2],[1,0],[0,0]],
+    >>>         [[3,3],[0,1],[5,3],[1,2],[0,0]]]
+    >>> data = tf.convert_to_tensor(data, dtype=tf.float32)
+    >>> mask = tl.layers.target_mask_op(data)
+    >>> print(mask)
+    tf.Tensor(
+    [[0 1 1 1 0]
+     [1 1 1 1 0]
+     [1 1 1 1 0]], shape=(3, 5), dtype=int32)
+    """
 
-def target_mask_op(data, pad_val=0):  # HangSheng: return tensor for mask,if input is tf.string
-    """Return tensor for mask, if input is ``tf.string``."""
+    if not isinstance(data, tf.Tensor):
+        raise AttributeError("target_mask_op: the type of input data should be tf.Tensor but got %s." % type(data))
     data_shape_size = data.get_shape().ndims
     if data_shape_size == 3:
         return tf.cast(tf.reduce_any(input_tensor=tf.not_equal(data, pad_val), axis=2), dtype=tf.int32)
     elif data_shape_size == 2:
         return tf.cast(tf.not_equal(data, pad_val), dtype=tf.int32)
     elif data_shape_size == 1:
-        raise ValueError("target_mask_op: data has wrong shape!")
+        raise ValueError("target_mask_op: data_shape %s is not supported. "
+                         "The shape of data should have 2 or 3 dims." % (data.get_shape()))
     else:
-        raise ValueError("target_mask_op: handling data_shape_size %s hasn't been implemented!" % (data_shape_size))
-
-
-class Seq2Seq(Layer):
-    """
-    The :class:`Seq2Seq` class is a simple :class:`DynamicRNNLayer` based Seq2seq layer without using `tl.contrib.seq2seq <https://www.tensorflow.org/api_guides/python/contrib.seq2seq>`__.
-    See `Model <https://camo.githubusercontent.com/9e88497fcdec5a9c716e0de5bc4b6d1793c6e23f/687474703a2f2f73757269796164656570616e2e6769746875622e696f2f696d672f736571327365712f73657132736571322e706e67>`__
-    and `Sequence to Sequence Learning with Neural Networks <https://arxiv.org/abs/1409.3215>`__.
-
-    - Please check this example `Chatbot in 200 lines of code <https://github.com/tensorlayer/seq2seq-chatbot>`__.
-    - The Author recommends users to read the source code of :class:`DynamicRNNLayer` and :class:`Seq2Seq`.
-
-    Parameters
-    ----------
-    net_encode_in : :class:`Layer`
-        Encode sequences, [batch_size, None, n_features].
-    net_decode_in : :class:`Layer`
-        Decode sequences, [batch_size, None, n_features].
-    cell_fn : TensorFlow cell function
-        A TensorFlow core RNN cell
-            - see `RNN Cells in TensorFlow <https://www.tensorflow.org/api_docs/python/>`__
-            - Note TF1.0+ and TF1.0- are different
-    cell_init_args : dictionary or None
-        The arguments for the cell initializer.
-    n_hidden : int
-        The number of hidden units in the layer.
-    initializer : initializer
-        The initializer for the parameters.
-    encode_sequence_length : tensor
-        For encoder sequence length, see :class:`DynamicRNNLayer` .
-    decode_sequence_length : tensor
-        For decoder sequence length, see :class:`DynamicRNNLayer` .
-    initial_state_encode : None or RNN state
-        If None, `initial_state_encode` is zero state, it can be set by placeholder or other RNN.
-    initial_state_decode : None or RNN state
-        If None, `initial_state_decode` is the final state of the RNN encoder, it can be set by placeholder or other RNN.
-    dropout : tuple of float or int
-        The input and output keep probability (input_keep_prob, output_keep_prob).
-            - If one int, input and output keep probability are the same.
-    n_layer : int
-        The number of RNN layers, default is 1.
-    return_seq_2d : boolean
-        Only consider this argument when `return_last_output` is `False`
-            - If True, return 2D Tensor [n_example, 2 * n_hidden], for stacking DenseLayer after it.
-            - If False, return 3D Tensor [n_example/n_steps, n_steps, 2 * n_hidden], for stacking multiple RNN after it.
-    name : str
-        A unique layer name.
-
-    Attributes
-    ------------
-    outputs : tensor
-        The output of RNN decoder.
-    initial_state_encode : tensor or StateTuple
-        Initial state of RNN encoder.
-    initial_state_decode : tensor or StateTuple
-        Initial state of RNN decoder.
-    final_state_encode : tensor or StateTuple
-        Final state of RNN encoder.
-    final_state_decode : tensor or StateTuple
-        Final state of RNN decoder.
-
-    Notes
-    --------
-    - How to feed data: `Sequence to Sequence Learning with Neural Networks <https://arxiv.org/pdf/1409.3215v3.pdf>`__
-    - input_seqs : ``['how', 'are', 'you', '<PAD_ID>']``
-    - decode_seqs : ``['<START_ID>', 'I', 'am', 'fine', '<PAD_ID>']``
-    - target_seqs : ``['I', 'am', 'fine', '<END_ID>', '<PAD_ID>']``
-    - target_mask : ``[1, 1, 1, 1, 0]``
-    - related functions : tl.prepro <pad_sequences, precess_sequences, sequences_add_start_id, sequences_get_mask>
-
-    Examples
-    ----------
-    >>> from tensorlayer.layers import *
-    >>> batch_size = 32
-    >>> encode_seqs = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="encode_seqs")
-    >>> decode_seqs = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="decode_seqs")
-    >>> target_seqs = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="target_seqs")
-    >>> target_mask = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="target_mask") # tl.prepro.sequences_get_mask()
-    >>> with tf.variable_scope("model"):
-    >>>     # for chatbot, you can use the same embedding layer,
-    >>>     # for translation, you may want to use 2 seperated embedding layers
-    >>>     with tf.variable_scope("embedding") as vs:
-    >>>         net_encode = EmbeddingInput(
-    ...                 inputs = encode_seqs,
-    ...                 vocabulary_size = 10000,
-    ...                 embedding_size = 200,
-    ...                 name = 'seq_embedding')
-    >>>         vs.reuse_variables()
-    >>>         net_decode = EmbeddingInput(
-    ...                 inputs = decode_seqs,
-    ...                 vocabulary_size = 10000,
-    ...                 embedding_size = 200,
-    ...                 name = 'seq_embedding')
-    >>>     net = Seq2Seq(net_encode, net_decode,
-    ...             cell_fn = tf.contrib.rnn.BasicLSTMCell,
-    ...             n_hidden = 200,
-    ...             initializer = tf.random_uniform_initializer(-0.1, 0.1),
-    ...             encode_sequence_length = retrieve_seq_length_op2(encode_seqs),
-    ...             decode_sequence_length = retrieve_seq_length_op2(decode_seqs),
-    ...             initial_state_encode = None,
-    ...             dropout = None,
-    ...             n_layer = 1,
-    ...             return_seq_2d = True,
-    ...             name = 'seq2seq')
-    >>> net_out = Dense(net, n_units=10000, act=None, name='output')
-    >>> e_loss = tl.cost.cross_entropy_seq_with_mask(logits=net_out.outputs, target_seqs=target_seqs, input_mask=target_mask, return_details=False, name='cost')
-    >>> y = tf.nn.softmax(net_out.outputs)
-    >>> net_out.print_params(False)
-
-    """
-
-    def __init__(
-            self,
-            net_encode_in,
-            net_decode_in,
-            cell_fn,  #tf.nn.rnn_cell.LSTMCell,
-            cell_init_args=None,
-            n_hidden=256,
-            initializer=tf.compat.v1.initializers.random_uniform(-0.1, 0.1),
-            encode_sequence_length=None,
-            decode_sequence_length=None,
-            initial_state_encode=None,
-            initial_state_decode=None,
-            dropout=None,
-            n_layer=1,
-            return_seq_2d=False,
-            name='seq2seq',
-    ):
-        super(Seq2Seq,
-              self).__init__(prev_layer=[net_encode_in, net_decode_in], cell_init_args=cell_init_args, name=name)
-
-        if self.cell_init_args:
-            self.cell_init_args['state_is_tuple'] = True  # 'use_peepholes': True,
-
-        if cell_fn is None:
-            raise ValueError("cell_fn cannot be set to None")
-
-        if 'GRU' in cell_fn.__name__:
-            try:
-                cell_init_args.pop('state_is_tuple')
-            except Exception:
-                logging.warning("pop state_is_tuple fails.")
-
-        logging.info(
-            "[*] Seq2Seq %s: n_hidden: %d cell_fn: %s dropout: %s n_layer: %d" %
-            (self.name, n_hidden, cell_fn.__name__, dropout, n_layer)
-        )
-
-        with tf.compat.v1.variable_scope(name):
-            # tl.layers.set_name_reuse(reuse)
-            # network = InputLayer(self.inputs, name=name+'/input')
-            network_encode = DynamicRNN(
-                net_encode_in, cell_fn=cell_fn, cell_init_args=self.cell_init_args, n_hidden=n_hidden,
-                initializer=initializer, initial_state=initial_state_encode, dropout=dropout, n_layer=n_layer,
-                sequence_length=encode_sequence_length, return_last=False, return_seq_2d=True, name='encode'
-            )
-            # vs.reuse_variables()
-            # tl.layers.set_name_reuse(True)
-            network_decode = DynamicRNN(
-                net_decode_in, cell_fn=cell_fn, cell_init_args=self.cell_init_args, n_hidden=n_hidden,
-                initializer=initializer,
-                initial_state=(network_encode.final_state if initial_state_decode is None else initial_state_decode),
-                dropout=dropout, n_layer=n_layer, sequence_length=decode_sequence_length, return_last=False,
-                return_seq_2d=return_seq_2d, name='decode'
-            )
-            self.outputs = network_decode.outputs
-
-            # rnn_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
-
-        # Initial state
-        self.initial_state_encode = network_encode.initial_state
-        self.initial_state_decode = network_decode.initial_state
-
-        # Final state
-        self.final_state_encode = network_encode.final_state
-        self.final_state_decode = network_decode.final_state
-
-        # self.sequence_length = sequence_length
-        self._add_layers(network_encode.all_layers)
-        self._add_params(network_encode.all_params)
-        self._add_dropout_layers(network_encode.all_drop)
-
-        self._add_layers(network_decode.all_layers)
-        self._add_params(network_decode.all_params)
-        self._add_dropout_layers(network_decode.all_drop)
-
-        self._add_layers(self.outputs)
+        raise ValueError("target_mask_op: handling data_shape %s hasn't been implemented! "
+                         "The shape of data should have 2 or 3 dims" % (data.get_shape()))
diff --git a/tests/layers/test_layers_recurrent.py b/tests/layers/test_layers_recurrent.py
index 38c014ee3..9d7158bc7 100644
--- a/tests/layers/test_layers_recurrent.py
+++ b/tests/layers/test_layers_recurrent.py
@@ -40,6 +40,7 @@ def setUpClass(cls):
     def tearDownClass(cls):
         pass
 
+    '''
     def test_basic_simplernn(self):
 
         inputs = tl.layers.Input([self.batch_size, self.num_steps, self.embedding_size])
@@ -603,6 +604,60 @@ def test_sequence_length3(self):
             print(length)
         except Exception as e:
             print(e)
+    '''
+
+    def test_target_mask_op(self):
+        fail_flag = False
+        data = [
+            ['hello', 'world', '', '', ''],
+            ['hello', 'world', 'tensorlayer', '', ''],
+            ['hello', 'world', 'tensorlayer', '2.0', '']
+        ]
+        try:
+            tl.layers.target_mask_op(data, pad_val='')
+            fail_flag = True
+        except AttributeError as e:
+            print(e)
+        if fail_flag:
+            self.fail("Type error not raised")
+
+        data = tf.convert_to_tensor(data, dtype=tf.string)
+        mask = tl.layers.target_mask_op(data, pad_val='')
+        print(mask)
+
+        data = [[[1], [0], [0], [0], [0]], [[1], [2], [3], [0], [0]], [[1], [2], [0], [1], [0]]]
+        data = tf.convert_to_tensor(data, dtype=tf.float32)
+        mask = tl.layers.target_mask_op(data)
+        print(mask)
+
+        data = [[[0,0],[2,2],[1,2],[1,2],[0,0]],
+                [[2,3],[2,4],[3,2],[1,0],[0,0]],
+                [[3,3],[0,1],[5,3],[1,2],[0,0]]]
+        data = tf.convert_to_tensor(data, dtype=tf.float32)
+        mask = tl.layers.target_mask_op(data)
+        print(mask)
+
+        fail_flag = False
+        try:
+            data = [1, 2, 0, 0, 0]
+            data = tf.convert_to_tensor(data, dtype=tf.float32)
+            tl.layers.target_mask_op(data)
+            fail_flag = True
+        except ValueError as e:
+            print(e)
+        if fail_flag:
+            self.fail("Wrong data shape not detected.")
+
+        fail_flag = False
+        try:
+            data = np.random.random([4, 2, 6, 2])
+            data = tf.convert_to_tensor(data, dtype=tf.float32)
+            tl.layers.target_mask_op(data)
+            fail_flag = True
+        except ValueError as e:
+            print(e)
+        if fail_flag:
+            self.fail("Wrong data shape not detected.")
 
 
 if __name__ == '__main__':

From aff7804d91deb89f4be20f1e2a7f8bc70576a5ee Mon Sep 17 00:00:00 2001
From: Jingqing  Zhang <jingqing.zhang15@imperial.ac.uk>
Date: Thu, 6 Jun 2019 17:36:02 +0100
Subject: [PATCH 22/39] doc update and bug fix

---
 docs/modules/models.rst                      |  8 +-
 tensorlayer/models/seq2seq.py                | 18 ++---
 tensorlayer/models/seq2seq_with_attention.py | 82 +++++++++-----------
 3 files changed, 47 insertions(+), 61 deletions(-)

diff --git a/docs/modules/models.rst b/docs/modules/models.rst
index 3dd611656..99a161aae 100644
--- a/docs/modules/models.rst
+++ b/docs/modules/models.rst
@@ -25,21 +25,21 @@ Base Model
 VGG16
 ----------------------
 
-.. autofunction:: VGG16
+.. autoclass:: VGG16
 
 VGG19
 ----------------------
 
-.. autofunction:: VGG19
+.. autoclass:: VGG19
 
 SqueezeNetV1
 ----------------
-.. autofunction:: SqueezeNetV1
+.. autoclass:: SqueezeNetV1
 
 MobileNetV1
 ----------------
 
-.. autofunction:: MobileNetV1
+.. autoclass:: MobileNetV1
 
 Seq2seq
 ------------------------
diff --git a/tensorlayer/models/seq2seq.py b/tensorlayer/models/seq2seq.py
index a4e11b217..fe652f0ee 100644
--- a/tensorlayer/models/seq2seq.py
+++ b/tensorlayer/models/seq2seq.py
@@ -7,9 +7,7 @@
 from tensorlayer.models import Model
 from tensorlayer.layers import Dense, Dropout, Input
 from tensorlayer.layers.core import Layer
-__all__ = [
-    'Seq2seq'
-]
+__all__ = ['Seq2seq']
 
 
 class Seq2seq(Model):
@@ -19,9 +17,9 @@ class Seq2seq(Model):
     ----------
     decoder_seq_length: int
         The length of your target sequence
-    cell_enc : str, tf.function
+    cell_enc : TensorFlow cell function
         The RNN function cell for your encoder stack, e.g tf.keras.layers.GRUCell
-    cell_dec : str, tf.function
+    cell_dec : TensorFlow cell function
         The RNN function cell for your decoder stack, e.g. tf.keras.layers.GRUCell
     n_layer : int
         The number of your RNN layers for both encoder and decoder block
@@ -50,9 +48,8 @@ def __init__(self, decoder_seq_length, cell_enc, cell_dec, n_units=256, n_layer=
         for i in range(n_layer):
             if (i == 0):
                 self.enc_layers.append(
-                    tl.layers.RNN(
-                        cell=cell_enc(units=n_units), in_channels=self.embedding_size, return_last_state=True
-                    )
+                    tl.layers.
+                    RNN(cell=cell_enc(units=n_units), in_channels=self.embedding_size, return_last_state=True)
                 )
             else:
                 self.enc_layers.append(
@@ -62,9 +59,8 @@ def __init__(self, decoder_seq_length, cell_enc, cell_dec, n_units=256, n_layer=
         for i in range(n_layer):
             if (i == 0):
                 self.dec_layers.append(
-                    tl.layers.RNN(
-                        cell=cell_dec(units=n_units), in_channels=self.embedding_size, return_last_state=True
-                    )
+                    tl.layers.
+                    RNN(cell=cell_dec(units=n_units), in_channels=self.embedding_size, return_last_state=True)
                 )
             else:
                 self.dec_layers.append(
diff --git a/tensorlayer/models/seq2seq_with_attention.py b/tensorlayer/models/seq2seq_with_attention.py
index af66cc968..88ec1dda5 100644
--- a/tensorlayer/models/seq2seq_with_attention.py
+++ b/tensorlayer/models/seq2seq_with_attention.py
@@ -8,13 +8,11 @@
 from tensorlayer.layers import Dense, Dropout, Input
 from tensorlayer.layers.core import Layer
 
-__all__ = [
-    'Seq2seq_Luong_Attention'
-]
-
+__all__ = ['Seq2seq_Luong_Attention']
 
 
 class Encoder(Layer):
+
     def __init__(self, hidden_size, cell, embedding_layer, name=None):
         super(Encoder, self).__init__(name)
         self.cell = cell(hidden_size)
@@ -22,54 +20,51 @@ def __init__(self, hidden_size, cell, embedding_layer, name=None):
         self.embedding_layer = embedding_layer
         self.build((None, None, self.embedding_layer.embedding_size))
         self._built = True
-        
+
     def build(self, inputs_shape):
         self.cell.build(input_shape=tuple(inputs_shape))
         self._built = True
         if self._trainable_weights is None:
             self._trainable_weights = list()
-        
+
         for var in self.cell.trainable_variables:
             self._trainable_weights.append(var)
 
     def forward(self, src_seq, initial_state=None):
-        
+
         states = initial_state if initial_state is not None else self.cell.get_initial_state(src_seq)
         encoding_hidden_states = list()
         total_steps = src_seq.get_shape().as_list()[1]
         for time_step in range(total_steps):
             if not isinstance(states, list):
                 states = [states]
-            output, states = self.cell.call(src_seq[:,time_step,:], states, training=self.is_train)
+            output, states = self.cell.call(src_seq[:, time_step, :], states, training=self.is_train)
             encoding_hidden_states.append(states[0])
         return output, encoding_hidden_states, states[0]
-        
-
-
 
 
 class Decoder_Attention(Layer):
-    def __init__(self, hidden_size, cell, embedding_layer, method, name = None):
+
+    def __init__(self, hidden_size, cell, embedding_layer, method, name=None):
         super(Decoder_Attention, self).__init__(name)
         self.cell = cell(hidden_size)
         self.hidden_size = hidden_size
         self.embedding_layer = embedding_layer
         self.method = method
-        self.build((None, hidden_size+self.embedding_layer.embedding_size))
+        self.build((None, hidden_size + self.embedding_layer.embedding_size))
         self._built = True
-        
-        
+
     def build(self, inputs_shape):
         self.cell.build(input_shape=tuple(inputs_shape))
         self._built = True
         if self.method is "concat":
-            self.W = self._get_weights("W", shape=(2*self.hidden_size, self.hidden_size))
+            self.W = self._get_weights("W", shape=(2 * self.hidden_size, self.hidden_size))
             self.V = self._get_weights("V", shape=(self.hidden_size, 1))
         elif self.method is "general":
             self.W = self._get_weights("W", shape=(self.hidden_size, self.hidden_size))
         if self._trainable_weights is None:
             self._trainable_weights = list()
-        
+
         for var in self.cell.trainable_variables:
             self._trainable_weights.append(var)
 
@@ -80,15 +75,15 @@ def score(self, encoding_hidden, hidden, method):
         if method is "concat":
             # hidden = [B,H]->[B,1,H]->[B,T,H]
             hidden = tf.expand_dims(hidden, 1)
-            hidden = tf.tile(hidden, [1, encoding_hidden.shape[1],1])
+            hidden = tf.tile(hidden, [1, encoding_hidden.shape[1], 1])
             # combined = [B,T,2H]
             combined = tf.concat([hidden, encoding_hidden], 2)
             combined = tf.cast(combined, tf.float32)
-            score = tf.tensordot(combined, self.W, axes=[[2], [0]]) # score = [B,T,H]
-            score = tf.nn.tanh(score) # score = [B,T,H]
-            score = tf.tensordot(self.V, score, axes=[[0], [2]]) # score = [1,B,T]
-            score = tf.squeeze(score, axis=0) # score = [B,T]
-            
+            score = tf.tensordot(combined, self.W, axes=[[2], [0]])  # score = [B,T,H]
+            score = tf.nn.tanh(score)  # score = [B,T,H]
+            score = tf.tensordot(self.V, score, axes=[[0], [2]])  # score = [1,B,T]
+            score = tf.squeeze(score, axis=0)  # score = [B,T]
+
         elif method is "dot":
             # hidden = [B,H]->[B,H,1]
             hidden = tf.expand_dims(hidden, 2)
@@ -100,8 +95,8 @@ def score(self, encoding_hidden, hidden, method):
             score = tf.expand_dims(score, 2)
             score = tf.matmul(encoding_hidden, score)
             score = tf.squeeze(score, axis=2)
-            
-        score = tf.nn.softmax(score, axis=-1) # score = [B,T]
+
+        score = tf.nn.softmax(score, axis=-1)  # score = [B,T]
         return score
 
     def forward(self, dec_seq, enc_hiddens, last_hidden, method, return_last_state=False):
@@ -110,11 +105,11 @@ def forward(self, dec_seq, enc_hiddens, last_hidden, method, return_last_state=F
         states = last_hidden
         cell_outputs = list()
         for time_step in range(total_steps):
-            attention_weights = self.score(enc_hiddens, last_hidden, method) 
-            attention_weights = tf.expand_dims(attention_weights, 1) #[B, 1, T]
-            context = tf.matmul(attention_weights, enc_hiddens) #[B, 1, H]
-            context = tf.squeeze(context, 1) #[B, H]
-            inputs = tf.concat([dec_seq[:,time_step,:], context], 1)
+            attention_weights = self.score(enc_hiddens, last_hidden, method)
+            attention_weights = tf.expand_dims(attention_weights, 1)  #[B, 1, T]
+            context = tf.matmul(attention_weights, enc_hiddens)  #[B, 1, H]
+            context = tf.squeeze(context, 1)  #[B, H]
+            inputs = tf.concat([dec_seq[:, time_step, :], context], 1)
             if not isinstance(states, list):
                 states = [states]
             cell_output, states = self.cell.call(inputs, states, training=self.is_train)
@@ -122,16 +117,12 @@ def forward(self, dec_seq, enc_hiddens, last_hidden, method, return_last_state=F
             last_hidden = states[0]
 
         cell_outputs = tf.convert_to_tensor(cell_outputs)
-        cell_outputs = tf.transpose(cell_outputs, perm=[1,0,2])
+        cell_outputs = tf.transpose(cell_outputs, perm=[1, 0, 2])
         if (return_last_state):
             return cell_outputs, last_hidden
         return cell_outputs
 
 
-
-
-
-
 class Seq2seq_Luong_Attention(Model):
     """Luong Attention-based Seq2Seq model. Implementation based on https://arxiv.org/pdf/1508.04025.pdf.
 
@@ -139,11 +130,11 @@ class Seq2seq_Luong_Attention(Model):
     ----------
     hidden_size: int
         The hidden size of both encoder and decoder RNN cells
-    cell : str, tf.function
+    cell : TensorFlow cell function
         The RNN function cell for your encoder and decoder stack, e.g. tf.keras.layers.GRUCell
     embedding_layer : tl.Layer
         A embedding layer, e.g. tl.layers.Embedding(vocabulary_size=voc_size, embedding_size=emb_dim)
-    mothod : str
+    method : str
         The three alternatives to calculate the attention scores, e.g. "dot", "general" and "concat"
     name : str
         The model name
@@ -153,6 +144,7 @@ class Seq2seq_Luong_Attention(Model):
     -------
         static single layer attention-based Seq2Seq model.
     """
+
     def __init__(self, hidden_size, embedding_layer, cell, method, name=None):
         super(Seq2seq_Luong_Attention, self).__init__(name)
         self.enc_layer = Encoder(hidden_size, cell, embedding_layer)
@@ -161,7 +153,6 @@ def __init__(self, hidden_size, embedding_layer, cell, method, name=None):
         self.dense_layer = tl.layers.Dense(n_units=self.embedding_layer.vocabulary_size, in_channels=hidden_size)
         self.method = method
 
-
     def inference(self, src_seq, encoding_hidden_states, last_hidden_states, seq_length, sos):
         """Inference mode"""
         """
@@ -178,29 +169,30 @@ def inference(self, src_seq, encoding_hidden_states, last_hidden_states, seq_len
         sos : int
             <SOS> : The token of "start of sequence"
         """
-        
+
         batch_size = src_seq.shape[0]
         decoding = [[sos] for i in range(batch_size)]
         dec_output = self.embedding_layer(decoding)
         outputs = [[0] for i in range(batch_size)]
         for step in range(seq_length):
-            dec_output, last_hidden_states = self.dec_layer(dec_output, encoding_hidden_states, last_hidden_states, method=self.method, return_last_state=True)
+            dec_output, last_hidden_states = self.dec_layer(
+                dec_output, encoding_hidden_states, last_hidden_states, method=self.method, return_last_state=True
+            )
             dec_output = tf.reshape(dec_output, [-1, dec_output.shape[-1]])
             dec_output = self.dense_layer(dec_output)
             dec_output = tf.reshape(dec_output, [batch_size, -1, dec_output.shape[-1]])
             dec_output = tf.argmax(dec_output, -1)
             outputs = tf.concat([outputs, dec_output], 1)
             dec_output = self.embedding_layer(dec_output)
-            
-        return outputs[:,1:]
 
-    
+        return outputs[:, 1:]
+
     def forward(self, inputs, seq_length=20, sos=None):
         src_seq = inputs[0]
         src_seq = self.embedding_layer(src_seq)
         enc_output, encoding_hidden_states, last_hidden_states = self.enc_layer(src_seq)
         encoding_hidden_states = tf.convert_to_tensor(encoding_hidden_states)
-        encoding_hidden_states = tf.transpose(encoding_hidden_states, perm=[1,0,2])
+        encoding_hidden_states = tf.transpose(encoding_hidden_states, perm=[1, 0, 2])
         last_hidden_states = tf.convert_to_tensor(last_hidden_states)
 
         if (self.is_train):
@@ -215,5 +207,3 @@ def forward(self, inputs, seq_length=20, sos=None):
             dec_output = self.inference(src_seq, encoding_hidden_states, last_hidden_states, seq_length, sos)
 
         return dec_output
-
-

From 34e9cd3715885b0645893b87c4b2c5be1e6e9862 Mon Sep 17 00:00:00 2001
From: Jingqing  Zhang <jingqing.zhang15@imperial.ac.uk>
Date: Thu, 6 Jun 2019 17:38:55 +0100
Subject: [PATCH 23/39] doc update

---
 docs/modules/models.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/modules/models.rst b/docs/modules/models.rst
index 99a161aae..3af7dbf9c 100644
--- a/docs/modules/models.rst
+++ b/docs/modules/models.rst
@@ -25,12 +25,12 @@ Base Model
 VGG16
 ----------------------
 
-.. autoclass:: VGG16
+.. autofunction:: VGG16
 
 VGG19
 ----------------------
 
-.. autoclass:: VGG19
+.. autofunction:: VGG19
 
 SqueezeNetV1
 ----------------

From a0e23c32a57a582c6bed9a0810d709a035cd5562 Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Thu, 6 Jun 2019 23:11:48 +0100
Subject: [PATCH 24/39] Rewrite sequence testing

---
 tests/models/test_seq2seq_with_attention.py | 22 ++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/tests/models/test_seq2seq_with_attention.py b/tests/models/test_seq2seq_with_attention.py
index 45e888767..204a14ce7 100644
--- a/tests/models/test_seq2seq_with_attention.py
+++ b/tests/models/test_seq2seq_with_attention.py
@@ -26,17 +26,18 @@ def setUpClass(cls):
         cls.vocab_size = 200
         cls.embedding_size = 32
         cls.dec_seq_length = 5
-        cls.pure_time = np.linspace(-1, 1, 11)
-        cls.pure_signal = 100*np.sin(cls.pure_time/(2*np.pi))
-        cls.dataset = np.zeros((100, 11))
+        cls.pure_time = np.linspace(-1, 1, 21)
+        cls.pure_signal = 100*np.sin(cls.pure_time)
+        cls.dataset = np.zeros((100, 21))
         for i in range(100):
-            noise = 100+10*np.random.normal(0, 1, cls.pure_signal.shape)
+            noise = 100+1*np.random.normal(0, 1, cls.pure_signal.shape)
             cls.dataset[i] = cls.pure_signal + noise
         cls.dataset = cls.dataset.astype(int)
-        cls.trainX = cls.dataset[:80,:5]
-        cls.trainY = cls.dataset[:80,5:]
-        cls.testX = cls.dataset[80:,:5]
-        cls.testY = cls.dataset[80:,5:]
+        np.random.shuffle(cls.dataset)
+        cls.trainX = cls.dataset[:80,:15]
+        cls.trainY = cls.dataset[:80,15:]
+        cls.testX = cls.dataset[80:,:15]
+        cls.testY = cls.dataset[80:,15:]
 
         cls.trainY[:, 0] = 0  # start_token == 0
         cls.testY[:, 0] = 0  # start_token == 0
@@ -87,12 +88,11 @@ def test_basic_simpleSeq2Seq(self):
                 n_iter += 1
 
             model_.eval()
-            test_sample = self.testX[:2,:].tolist() # Can't capture the sequence.
-            print(test_sample)
+            test_sample = self.testX[:5,:].tolist() # Can't capture the sequence.
             top_n = 1
             for i in range(top_n):
                 prediction = model_([test_sample], seq_length=self.dec_seq_length, sos=0)
-                print("Prediction: >>>>>  ", prediction, "\n Target: >>>>>  ", self.testY[0:2, 1:], "\n\n")
+                print("Prediction: >>>>>  ", prediction, "\n Target: >>>>>  ", self.testY[:5, 1:], "\n\n")
 
             # printing average loss after every epoch
             print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1, self.num_epochs, total_loss / n_iter))

From 02d42bdd156057b97b55ac90fcd864813f9cbc71 Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Thu, 6 Jun 2019 23:17:14 +0100
Subject: [PATCH 25/39] Remove conf.py and index.rst

---
 conf.py                 | 52 -----------------------------------------
 docs/modules/models.rst |  2 +-
 index.rst               | 20 ----------------
 3 files changed, 1 insertion(+), 73 deletions(-)
 delete mode 100644 conf.py
 delete mode 100644 index.rst

diff --git a/conf.py b/conf.py
deleted file mode 100644
index 7bb931009..000000000
--- a/conf.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# Configuration file for the Sphinx documentation builder.
-#
-# This file only contains a selection of the most common options. For a full
-# list see the documentation:
-# http://www.sphinx-doc.org/en/master/config
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-# import os
-# import sys
-# sys.path.insert(0, os.path.abspath('.'))
-
-
-# -- Project information -----------------------------------------------------
-
-project = 'tensorlayer'
-copyright = '2019, lingjun liu'
-author = 'lingjun liu'
-
-
-# -- General configuration ---------------------------------------------------
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
-]
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
-
-
-# -- Options for HTML output -------------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-#
-html_theme = 'alabaster'
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
diff --git a/docs/modules/models.rst b/docs/modules/models.rst
index 3af7dbf9c..7965b2684 100644
--- a/docs/modules/models.rst
+++ b/docs/modules/models.rst
@@ -47,7 +47,7 @@ Seq2seq
 .. autoclass:: Seq2seq
 
 
-Seq2seq_Luong_Attention
+Seq2seq Luong Attention
 ------------------------
 
 .. autoclass:: Seq2seq_Luong_Attention
diff --git a/index.rst b/index.rst
deleted file mode 100644
index dfbb70be4..000000000
--- a/index.rst
+++ /dev/null
@@ -1,20 +0,0 @@
-.. tensorlayer documentation master file, created by
-   sphinx-quickstart on Sat May 25 10:14:56 2019.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
-
-Welcome to tensorlayer's documentation!
-=======================================
-
-.. toctree::
-   :maxdepth: 2
-   :caption: Contents:
-
-
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`

From 6776e5672c7635723fed05130b09272e9ee1cf83 Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Fri, 7 Jun 2019 10:44:42 +0100
Subject: [PATCH 26/39] FIX yapf

---
 tensorlayer/models/seq2seq.py               | 10 ++++++----
 tests/models/test_seq2seq_with_attention.py | 22 ++++++++++-----------
 2 files changed, 17 insertions(+), 15 deletions(-)

diff --git a/tensorlayer/models/seq2seq.py b/tensorlayer/models/seq2seq.py
index fe652f0ee..256b5213f 100644
--- a/tensorlayer/models/seq2seq.py
+++ b/tensorlayer/models/seq2seq.py
@@ -48,8 +48,9 @@ def __init__(self, decoder_seq_length, cell_enc, cell_dec, n_units=256, n_layer=
         for i in range(n_layer):
             if (i == 0):
                 self.enc_layers.append(
-                    tl.layers.
-                    RNN(cell=cell_enc(units=n_units), in_channels=self.embedding_size, return_last_state=True)
+                    tl.layers.RNN(
+                        cell=cell_enc(units=n_units), in_channels=self.embedding_size, return_last_state=True
+                    )
                 )
             else:
                 self.enc_layers.append(
@@ -59,8 +60,9 @@ def __init__(self, decoder_seq_length, cell_enc, cell_dec, n_units=256, n_layer=
         for i in range(n_layer):
             if (i == 0):
                 self.dec_layers.append(
-                    tl.layers.
-                    RNN(cell=cell_dec(units=n_units), in_channels=self.embedding_size, return_last_state=True)
+                    tl.layers.RNN(
+                        cell=cell_dec(units=n_units), in_channels=self.embedding_size, return_last_state=True
+                    )
                 )
             else:
                 self.dec_layers.append(
diff --git a/tests/models/test_seq2seq_with_attention.py b/tests/models/test_seq2seq_with_attention.py
index 204a14ce7..3affd2d30 100644
--- a/tests/models/test_seq2seq_with_attention.py
+++ b/tests/models/test_seq2seq_with_attention.py
@@ -27,17 +27,17 @@ def setUpClass(cls):
         cls.embedding_size = 32
         cls.dec_seq_length = 5
         cls.pure_time = np.linspace(-1, 1, 21)
-        cls.pure_signal = 100*np.sin(cls.pure_time)
+        cls.pure_signal = 100 * np.sin(cls.pure_time)
         cls.dataset = np.zeros((100, 21))
         for i in range(100):
-            noise = 100+1*np.random.normal(0, 1, cls.pure_signal.shape)
+            noise = 100 + 1 * np.random.normal(0, 1, cls.pure_signal.shape)
             cls.dataset[i] = cls.pure_signal + noise
         cls.dataset = cls.dataset.astype(int)
         np.random.shuffle(cls.dataset)
-        cls.trainX = cls.dataset[:80,:15]
-        cls.trainY = cls.dataset[:80,15:]
-        cls.testX = cls.dataset[80:,:15]
-        cls.testY = cls.dataset[80:,15:]
+        cls.trainX = cls.dataset[:80, :15]
+        cls.trainY = cls.dataset[:80, 15:]
+        cls.testX = cls.dataset[80:, :15]
+        cls.testY = cls.dataset[80:, 15:]
 
         cls.trainY[:, 0] = 0  # start_token == 0
         cls.testY[:, 0] = 0  # start_token == 0
@@ -58,10 +58,10 @@ def tearDownClass(cls):
     def test_basic_simpleSeq2Seq(self):
 
         model_ = Seq2seq_Luong_Attention(
-            hidden_size=128,
-            cell = tf.keras.layers.SimpleRNNCell,
-            embedding_layer=tl.layers.Embedding(vocabulary_size=self.vocab_size, embedding_size=self.embedding_size),
-            method = 'dot')
+            hidden_size=128, cell=tf.keras.layers.SimpleRNNCell,
+            embedding_layer=tl.layers.Embedding(vocabulary_size=self.vocab_size,
+                                                embedding_size=self.embedding_size), method='dot'
+        )
         optimizer = tf.optimizers.Adam(learning_rate=0.001)
 
         for epoch in range(self.num_epochs):
@@ -88,7 +88,7 @@ def test_basic_simpleSeq2Seq(self):
                 n_iter += 1
 
             model_.eval()
-            test_sample = self.testX[:5,:].tolist() # Can't capture the sequence.
+            test_sample = self.testX[:5, :].tolist()  # Can't capture the sequence.
             top_n = 1
             for i in range(top_n):
                 prediction = model_([test_sample], seq_length=self.dec_seq_length, sos=0)

From 0872d08ca41b38579f8a84da0ca26a5828e67eb2 Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Sun, 9 Jun 2019 09:10:01 +0100
Subject: [PATCH 27/39] delete make.bat

---
 make.bat                          |  35 ----
 tensorlayer/models/con_seq2seq.py | 299 ++++++++++++++++++++++++++++++
 2 files changed, 299 insertions(+), 35 deletions(-)
 delete mode 100644 make.bat
 create mode 100644 tensorlayer/models/con_seq2seq.py

diff --git a/make.bat b/make.bat
deleted file mode 100644
index 27f573b87..000000000
--- a/make.bat
+++ /dev/null
@@ -1,35 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=.
-set BUILDDIR=_build
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
-	echo.
-	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-	echo.installed, then set the SPHINXBUILD environment variable to point
-	echo.to the full path of the 'sphinx-build' executable. Alternatively you
-	echo.may add the Sphinx directory to PATH.
-	echo.
-	echo.If you don't have Sphinx installed, grab it from
-	echo.http://sphinx-doc.org/
-	exit /b 1
-)
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
-
-:end
-popd
diff --git a/tensorlayer/models/con_seq2seq.py b/tensorlayer/models/con_seq2seq.py
new file mode 100644
index 000000000..426ba59bf
--- /dev/null
+++ b/tensorlayer/models/con_seq2seq.py
@@ -0,0 +1,299 @@
+import tensorflow as tf
+import tensorlayer as tl
+import numpy as np
+from tensorlayer.models import Model
+from tensorlayer.layers import Dense, Dropout, Input
+from tensorlayer.layers.core import Layer
+
+import os
+import unittest
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+import numpy as np
+import tensorflow as tf
+import tensorlayer as tl
+from tqdm import tqdm
+from sklearn.utils import shuffle
+from tensorlayer.models.seq2seq import Seq2seq
+from tests.utils import CustomTestCase
+from tensorlayer.cost import cross_entropy_seq
+
+class Linear(Layer):
+    def __init__(self, in_channels, out_channels, name=None):
+        super(Linear, self).__init__(name=name)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.build(None)
+        self._built = True
+
+    def build(self, inputs_shape):
+        # W = [C, N]
+        self.W = self._get_weights("W", shape=(self.in_channels, self.out_channels))
+
+    def forward(self, inputs):
+        # inputs = [B, H, C]
+        # outputs = [B, H, N]
+        outputs = tf.tensordot(inputs, self.W, axes=[2,0])
+        return outputs
+
+
+
+
+class Encoder(Model):
+
+    def __init__(self, hidden_size, kernel_size, num_layers, embedding_layer, name=None):
+        super(Encoder, self).__init__(name=name)
+        self.vocab_size = embedding_layer.vocabulary_size
+        self.embedding_size = embedding_layer.embedding_size
+        self.hidden_size = hidden_size
+        self.out_channels = hidden_size * 2
+        self.kernel_size = kernel_size
+        self.stride = 1
+        self.layers = num_layers
+        self.embedding = embedding_layer
+        self.affine = Linear(in_channels=self.embedding_size, out_channels=self.hidden_size)
+        self.conv = []
+        for i in range(num_layers):
+            self.conv.append(tl.layers.Conv1d(filter_size=self.kernel_size, 
+                    n_filter=self.out_channels, 
+                    stride=self.stride, in_channels=self.hidden_size))
+        self.mapping = tl.layers.Dense(in_channels=self.hidden_size // 2, n_units=self.hidden_size)
+
+    def forward(self, input):
+        
+        # batch, seq_len_src, dim
+        inputs = self.embedding(input)
+        batch_size = inputs.shape[0]
+        # batch, seq_len_src, hidden
+        outputs = self.affine(inputs)
+        # short-cut
+        # batch, seq_len_src, hidden
+        _outputs = outputs
+
+        for i in range(self.layers):
+            # batch, seq_len_src, 2*hidden,
+            outputs = self.conv[i](outputs)
+            # batch, seq_len_src, hidden
+            # Gated Linear unit function
+            outputs = tf.math.multiply(outputs[:,:,:self.hidden_size], tf.sigmoid(outputs[:,:,self.hidden_size:]))
+            # A, B: batch, seq_len_src, hidden / 2
+            A, B = outputs[:,:,:self.hidden_size//2], outputs[:,:,self.hidden_size//2:]
+            # A2: batch * seq_len_src, hidden / 2
+            A2 = tf.reshape(A, [-1,A.shape[-1]])
+            # B2: batch * seq_len_src, hidden / 2
+            B2 = tf.reshape(B, [-1,B.shape[-1]])
+            # attn: batch * seq_len_src, hidden / 2
+            attn = A2 * tf.nn.softmax(B2)
+            _attn = tf.reshape(attn, [batch_size, -1, self.hidden_size//2])
+            # attn2: batch * seq_len_src, hidden
+            attn2 = self.mapping(attn)
+            # outputs: batch, seq_len_src, hidden
+            outputs = tf.reshape(attn2, [batch_size, -1, self.hidden_size])
+            # batch, seq_len_src, hidden_size
+            _outputs = outputs + _outputs
+            
+        
+        return _attn, _outputs
+
+
+class Decoder(Model):
+
+    def __init__(self, hidden_size, embedding_layer, kernel_size, num_layers, name=None):
+        super(Decoder, self).__init__(name=name)
+
+        self.vocab_size = embedding_layer.vocabulary_size
+        self.embedding_size = embedding_layer.embedding_size
+        self.hidden_size = hidden_size
+
+        self.in_channels = hidden_size
+        self.out_channels = hidden_size * 2
+        self.kernel_size = kernel_size
+        self.stride = 1
+        self.layers = num_layers
+
+        self.embedding = embedding_layer
+        self.affine = Linear(self.embedding_size, self.hidden_size)
+        self.conv = []
+        for i in range(num_layers):
+            self.conv.append(tl.layers.Conv1d(n_filter=self.out_channels, in_channels=self.in_channels, 
+                filter_size=kernel_size, stride=self.stride)
+                )
+        self.mapping = tl.layers.Dense(in_channels=self.hidden_size // 2, n_units=self.hidden_size)
+        self.fc = Linear(self.hidden_size, self.vocab_size)
+
+
+    # enc_attn: src_seq_len, hidden_size
+    def forward(self, inputs):
+        
+        target = inputs[0]
+        enc_attn = inputs[1]
+        source_seq_out = inputs[2]
+
+        # batch, seq_len_tgt, dim
+        inputs = self.embedding(target)
+        batch_size = inputs.shape[0]
+        # batch, seq_len_tgt, hidden
+        outputs = self.affine(inputs)
+
+        for i in range(self.layers):
+
+            # This is the residual connection,
+            # for the output of the conv will add kernel_size / 2 elements
+            # before and after the origin input
+            # if i > -1:
+            #     conv_out = conv_out + outputs
+
+            # batch, seq_len_src, 2*hidden,
+            outputs = self.conv[i](outputs)
+            # batch, seq_len_src, hidden
+            # Gated Linear unit function
+            outputs = tf.math.multiply(outputs[:,:,:self.hidden_size], tf.sigmoid(outputs[:,:,self.hidden_size:]))
+            # A, B: batch, seq_len_src, hidden / 2
+            A, B = outputs[:,:,:self.hidden_size//2], outputs[:,:,self.hidden_size//2:]
+            # A2: batch * seq_len_src, hidden / 2
+            A2 = tf.reshape(A, [-1,A.shape[-1]])
+            # B2: batch * seq_len_src, hidden / 2
+            B2 = tf.reshape(B, [-1,B.shape[-1]])
+            # attn: batch * seq_len_src, hidden / 2
+            dec_attn = A2 * tf.nn.softmax(B2)
+            # attn2: batch * seq_len_src, hidden
+            dec_attn2 = self.mapping(dec_attn)
+            dec_attn2 = tf.reshape(dec_attn2, [batch_size, -1, self.hidden_size])
+
+
+            # dec_attn: batch, seq_len_tgt, hidden_size//2
+            dec_attn = tf.reshape(dec_attn, [batch_size, -1, self.hidden_size//2])
+            
+            # enc_attn: batch, seq_len_src, hidden_size//2
+            # dec_atten: batch, seq_len_tgt, hidden_size//2
+            # attn_matrix: batch, seq_len_tgt, seq_len_src
+            enc_attn = tf.transpose(enc_attn, perm=[0,2,1])
+            _attn_matrix = tf.matmul(dec_attn, enc_attn)
+            enc_attn = tf.transpose(enc_attn, perm=[0,2,1])
+            attn_matrix = tf.nn.softmax(_attn_matrix, axis=-1)
+
+
+            # attns: batch, seq_len_tgt, hidden_size
+            attns = tf.matmul(attn_matrix, source_seq_out)
+
+            # outpus: batch, seq_len_tgt, hidden_size
+            outputs = dec_attn2 + attns
+
+        # outpus: batch, seq_len_tgt, vocab_size
+        outputs = tf.nn.softmax(self.fc(outputs))
+
+        return outputs
+
+
+class ConvSeq2Seq(Model):
+    def __init__(self, encoder, decoder, name=None):
+        super(ConvSeq2Seq, self).__init__(name=name)
+        self.encoder = encoder
+        self.decoder = decoder
+        
+    def forward(self, inputs):
+        # attn: batch, seq_len, hidden
+        # out: batch, seq_len, hidden_size
+        source, target = inputs[0], inputs[1]
+        attn, source_seq_out = self.encoder(source)
+
+        # batch, seq_len_tgt, vocab_size
+        out = self.decoder([target, attn, source_seq_out])
+
+        return out
+        
+# enc_attn = tl.layers.Input((16,5,64))
+# src_out = tl.layers.Input((16,5,128))
+# input = tl.layers.Input((16,5), dtype=tf.int32)
+# model_ = ConvSeq2Seq(
+#     decoder = Decoder(hidden_size=128, kernel_size=3, num_layers=2, 
+#     embedding_layer=tl.layers.Embedding(vocabulary_size=500,embedding_size=50)),
+#     encoder = Encoder(hidden_size=128, kernel_size=3, num_layers=2, 
+#     embedding_layer=tl.layers.Embedding(vocabulary_size=500,embedding_size=50))
+# )
+
+# model_.train()
+# print(model_)
+
+
+class Model_SEQ2SEQ_Test(CustomTestCase):
+
+    @classmethod
+    def setUpClass(cls):
+
+        cls.batch_size = 16
+
+        cls.vocab_size = 20
+        cls.embedding_size = 32
+        cls.dec_seq_length = 5
+        cls.trainX = np.random.randint(20, size=(50, 5))
+        cls.trainY = np.random.randint(20, size=(50, cls.dec_seq_length + 1))
+        cls.trainY[:, 0] = 0  # start_token == 0
+
+        # Parameters
+        cls.src_len = len(cls.trainX)
+        cls.tgt_len = len(cls.trainY)
+
+        assert cls.src_len == cls.tgt_len
+
+        cls.num_epochs = 500
+        cls.n_step = cls.src_len // cls.batch_size
+
+    @classmethod
+    def tearDownClass(cls):
+        pass
+
+    def test_basic_simpleSeq2Seq(self):
+        model_ = ConvSeq2Seq(
+            decoder = Decoder(hidden_size=128, kernel_size=3, num_layers=5, 
+            embedding_layer=tl.layers.Embedding(vocabulary_size=self.vocab_size,embedding_size=self.embedding_size)),
+            encoder = Encoder(hidden_size=128, kernel_size=3, num_layers=5, 
+            embedding_layer=tl.layers.Embedding(vocabulary_size=self.vocab_size,embedding_size=self.embedding_size))
+        )
+
+        optimizer = tf.optimizers.Adam(learning_rate=0.001)
+
+        for epoch in range(self.num_epochs):
+            model_.train()
+            
+            trainX, trainY = shuffle(self.trainX, self.trainY)
+            total_loss, n_iter = 0, 0
+            for X, Y in tqdm(tl.iterate.minibatches(inputs=trainX, targets=trainY, batch_size=self.batch_size,
+                                                    shuffle=False), total=self.n_step,
+                             desc='Epoch[{}/{}]'.format(epoch + 1, self.num_epochs), leave=False):
+
+                dec_seq = Y[:, :-1]
+                target_seq = Y[:, 1:]
+
+                with tf.GradientTape() as tape:
+                    ## compute outputs
+                    output = model_(inputs=[X, dec_seq])
+
+                    output = tf.reshape(output, [-1, self.vocab_size])
+                    loss = cross_entropy_seq(logits=output, target_seqs=target_seq)
+                    
+                    grad = tape.gradient(loss, model_.all_weights)
+                    optimizer.apply_gradients(zip(grad, model_.all_weights))
+
+                total_loss += loss
+                n_iter += 1
+
+            model_.eval()
+            test_sample = trainX[0:2, :].tolist()
+
+            top_n = 1
+            # for i in range(top_n):
+            #     prediction = model_([test_sample], seq_length=self.dec_seq_length, start_token=0, top_n=1)
+            #     print("Prediction: >>>>>  ", prediction, "\n Target: >>>>>  ", trainY[0:2, 1:], "\n\n")
+
+            # printing average loss after every epoch
+            print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1, self.num_epochs, total_loss / n_iter))
+
+
+if __name__ == '__main__':
+    unittest.main()
+
+
+
+

From e3a5ee532c12cdb05b66538cb12b47bd9684df69 Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Sun, 9 Jun 2019 09:11:18 +0100
Subject: [PATCH 28/39] delete make.bat

---
 tensorlayer/models/con_seq2seq.py | 299 ------------------------------
 1 file changed, 299 deletions(-)
 delete mode 100644 tensorlayer/models/con_seq2seq.py

diff --git a/tensorlayer/models/con_seq2seq.py b/tensorlayer/models/con_seq2seq.py
deleted file mode 100644
index 426ba59bf..000000000
--- a/tensorlayer/models/con_seq2seq.py
+++ /dev/null
@@ -1,299 +0,0 @@
-import tensorflow as tf
-import tensorlayer as tl
-import numpy as np
-from tensorlayer.models import Model
-from tensorlayer.layers import Dense, Dropout, Input
-from tensorlayer.layers.core import Layer
-
-import os
-import unittest
-
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
-import numpy as np
-import tensorflow as tf
-import tensorlayer as tl
-from tqdm import tqdm
-from sklearn.utils import shuffle
-from tensorlayer.models.seq2seq import Seq2seq
-from tests.utils import CustomTestCase
-from tensorlayer.cost import cross_entropy_seq
-
-class Linear(Layer):
-    def __init__(self, in_channels, out_channels, name=None):
-        super(Linear, self).__init__(name=name)
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.build(None)
-        self._built = True
-
-    def build(self, inputs_shape):
-        # W = [C, N]
-        self.W = self._get_weights("W", shape=(self.in_channels, self.out_channels))
-
-    def forward(self, inputs):
-        # inputs = [B, H, C]
-        # outputs = [B, H, N]
-        outputs = tf.tensordot(inputs, self.W, axes=[2,0])
-        return outputs
-
-
-
-
-class Encoder(Model):
-
-    def __init__(self, hidden_size, kernel_size, num_layers, embedding_layer, name=None):
-        super(Encoder, self).__init__(name=name)
-        self.vocab_size = embedding_layer.vocabulary_size
-        self.embedding_size = embedding_layer.embedding_size
-        self.hidden_size = hidden_size
-        self.out_channels = hidden_size * 2
-        self.kernel_size = kernel_size
-        self.stride = 1
-        self.layers = num_layers
-        self.embedding = embedding_layer
-        self.affine = Linear(in_channels=self.embedding_size, out_channels=self.hidden_size)
-        self.conv = []
-        for i in range(num_layers):
-            self.conv.append(tl.layers.Conv1d(filter_size=self.kernel_size, 
-                    n_filter=self.out_channels, 
-                    stride=self.stride, in_channels=self.hidden_size))
-        self.mapping = tl.layers.Dense(in_channels=self.hidden_size // 2, n_units=self.hidden_size)
-
-    def forward(self, input):
-        
-        # batch, seq_len_src, dim
-        inputs = self.embedding(input)
-        batch_size = inputs.shape[0]
-        # batch, seq_len_src, hidden
-        outputs = self.affine(inputs)
-        # short-cut
-        # batch, seq_len_src, hidden
-        _outputs = outputs
-
-        for i in range(self.layers):
-            # batch, seq_len_src, 2*hidden,
-            outputs = self.conv[i](outputs)
-            # batch, seq_len_src, hidden
-            # Gated Linear unit function
-            outputs = tf.math.multiply(outputs[:,:,:self.hidden_size], tf.sigmoid(outputs[:,:,self.hidden_size:]))
-            # A, B: batch, seq_len_src, hidden / 2
-            A, B = outputs[:,:,:self.hidden_size//2], outputs[:,:,self.hidden_size//2:]
-            # A2: batch * seq_len_src, hidden / 2
-            A2 = tf.reshape(A, [-1,A.shape[-1]])
-            # B2: batch * seq_len_src, hidden / 2
-            B2 = tf.reshape(B, [-1,B.shape[-1]])
-            # attn: batch * seq_len_src, hidden / 2
-            attn = A2 * tf.nn.softmax(B2)
-            _attn = tf.reshape(attn, [batch_size, -1, self.hidden_size//2])
-            # attn2: batch * seq_len_src, hidden
-            attn2 = self.mapping(attn)
-            # outputs: batch, seq_len_src, hidden
-            outputs = tf.reshape(attn2, [batch_size, -1, self.hidden_size])
-            # batch, seq_len_src, hidden_size
-            _outputs = outputs + _outputs
-            
-        
-        return _attn, _outputs
-
-
-class Decoder(Model):
-
-    def __init__(self, hidden_size, embedding_layer, kernel_size, num_layers, name=None):
-        super(Decoder, self).__init__(name=name)
-
-        self.vocab_size = embedding_layer.vocabulary_size
-        self.embedding_size = embedding_layer.embedding_size
-        self.hidden_size = hidden_size
-
-        self.in_channels = hidden_size
-        self.out_channels = hidden_size * 2
-        self.kernel_size = kernel_size
-        self.stride = 1
-        self.layers = num_layers
-
-        self.embedding = embedding_layer
-        self.affine = Linear(self.embedding_size, self.hidden_size)
-        self.conv = []
-        for i in range(num_layers):
-            self.conv.append(tl.layers.Conv1d(n_filter=self.out_channels, in_channels=self.in_channels, 
-                filter_size=kernel_size, stride=self.stride)
-                )
-        self.mapping = tl.layers.Dense(in_channels=self.hidden_size // 2, n_units=self.hidden_size)
-        self.fc = Linear(self.hidden_size, self.vocab_size)
-
-
-    # enc_attn: src_seq_len, hidden_size
-    def forward(self, inputs):
-        
-        target = inputs[0]
-        enc_attn = inputs[1]
-        source_seq_out = inputs[2]
-
-        # batch, seq_len_tgt, dim
-        inputs = self.embedding(target)
-        batch_size = inputs.shape[0]
-        # batch, seq_len_tgt, hidden
-        outputs = self.affine(inputs)
-
-        for i in range(self.layers):
-
-            # This is the residual connection,
-            # for the output of the conv will add kernel_size / 2 elements
-            # before and after the origin input
-            # if i > -1:
-            #     conv_out = conv_out + outputs
-
-            # batch, seq_len_src, 2*hidden,
-            outputs = self.conv[i](outputs)
-            # batch, seq_len_src, hidden
-            # Gated Linear unit function
-            outputs = tf.math.multiply(outputs[:,:,:self.hidden_size], tf.sigmoid(outputs[:,:,self.hidden_size:]))
-            # A, B: batch, seq_len_src, hidden / 2
-            A, B = outputs[:,:,:self.hidden_size//2], outputs[:,:,self.hidden_size//2:]
-            # A2: batch * seq_len_src, hidden / 2
-            A2 = tf.reshape(A, [-1,A.shape[-1]])
-            # B2: batch * seq_len_src, hidden / 2
-            B2 = tf.reshape(B, [-1,B.shape[-1]])
-            # attn: batch * seq_len_src, hidden / 2
-            dec_attn = A2 * tf.nn.softmax(B2)
-            # attn2: batch * seq_len_src, hidden
-            dec_attn2 = self.mapping(dec_attn)
-            dec_attn2 = tf.reshape(dec_attn2, [batch_size, -1, self.hidden_size])
-
-
-            # dec_attn: batch, seq_len_tgt, hidden_size//2
-            dec_attn = tf.reshape(dec_attn, [batch_size, -1, self.hidden_size//2])
-            
-            # enc_attn: batch, seq_len_src, hidden_size//2
-            # dec_atten: batch, seq_len_tgt, hidden_size//2
-            # attn_matrix: batch, seq_len_tgt, seq_len_src
-            enc_attn = tf.transpose(enc_attn, perm=[0,2,1])
-            _attn_matrix = tf.matmul(dec_attn, enc_attn)
-            enc_attn = tf.transpose(enc_attn, perm=[0,2,1])
-            attn_matrix = tf.nn.softmax(_attn_matrix, axis=-1)
-
-
-            # attns: batch, seq_len_tgt, hidden_size
-            attns = tf.matmul(attn_matrix, source_seq_out)
-
-            # outpus: batch, seq_len_tgt, hidden_size
-            outputs = dec_attn2 + attns
-
-        # outpus: batch, seq_len_tgt, vocab_size
-        outputs = tf.nn.softmax(self.fc(outputs))
-
-        return outputs
-
-
-class ConvSeq2Seq(Model):
-    def __init__(self, encoder, decoder, name=None):
-        super(ConvSeq2Seq, self).__init__(name=name)
-        self.encoder = encoder
-        self.decoder = decoder
-        
-    def forward(self, inputs):
-        # attn: batch, seq_len, hidden
-        # out: batch, seq_len, hidden_size
-        source, target = inputs[0], inputs[1]
-        attn, source_seq_out = self.encoder(source)
-
-        # batch, seq_len_tgt, vocab_size
-        out = self.decoder([target, attn, source_seq_out])
-
-        return out
-        
-# enc_attn = tl.layers.Input((16,5,64))
-# src_out = tl.layers.Input((16,5,128))
-# input = tl.layers.Input((16,5), dtype=tf.int32)
-# model_ = ConvSeq2Seq(
-#     decoder = Decoder(hidden_size=128, kernel_size=3, num_layers=2, 
-#     embedding_layer=tl.layers.Embedding(vocabulary_size=500,embedding_size=50)),
-#     encoder = Encoder(hidden_size=128, kernel_size=3, num_layers=2, 
-#     embedding_layer=tl.layers.Embedding(vocabulary_size=500,embedding_size=50))
-# )
-
-# model_.train()
-# print(model_)
-
-
-class Model_SEQ2SEQ_Test(CustomTestCase):
-
-    @classmethod
-    def setUpClass(cls):
-
-        cls.batch_size = 16
-
-        cls.vocab_size = 20
-        cls.embedding_size = 32
-        cls.dec_seq_length = 5
-        cls.trainX = np.random.randint(20, size=(50, 5))
-        cls.trainY = np.random.randint(20, size=(50, cls.dec_seq_length + 1))
-        cls.trainY[:, 0] = 0  # start_token == 0
-
-        # Parameters
-        cls.src_len = len(cls.trainX)
-        cls.tgt_len = len(cls.trainY)
-
-        assert cls.src_len == cls.tgt_len
-
-        cls.num_epochs = 500
-        cls.n_step = cls.src_len // cls.batch_size
-
-    @classmethod
-    def tearDownClass(cls):
-        pass
-
-    def test_basic_simpleSeq2Seq(self):
-        model_ = ConvSeq2Seq(
-            decoder = Decoder(hidden_size=128, kernel_size=3, num_layers=5, 
-            embedding_layer=tl.layers.Embedding(vocabulary_size=self.vocab_size,embedding_size=self.embedding_size)),
-            encoder = Encoder(hidden_size=128, kernel_size=3, num_layers=5, 
-            embedding_layer=tl.layers.Embedding(vocabulary_size=self.vocab_size,embedding_size=self.embedding_size))
-        )
-
-        optimizer = tf.optimizers.Adam(learning_rate=0.001)
-
-        for epoch in range(self.num_epochs):
-            model_.train()
-            
-            trainX, trainY = shuffle(self.trainX, self.trainY)
-            total_loss, n_iter = 0, 0
-            for X, Y in tqdm(tl.iterate.minibatches(inputs=trainX, targets=trainY, batch_size=self.batch_size,
-                                                    shuffle=False), total=self.n_step,
-                             desc='Epoch[{}/{}]'.format(epoch + 1, self.num_epochs), leave=False):
-
-                dec_seq = Y[:, :-1]
-                target_seq = Y[:, 1:]
-
-                with tf.GradientTape() as tape:
-                    ## compute outputs
-                    output = model_(inputs=[X, dec_seq])
-
-                    output = tf.reshape(output, [-1, self.vocab_size])
-                    loss = cross_entropy_seq(logits=output, target_seqs=target_seq)
-                    
-                    grad = tape.gradient(loss, model_.all_weights)
-                    optimizer.apply_gradients(zip(grad, model_.all_weights))
-
-                total_loss += loss
-                n_iter += 1
-
-            model_.eval()
-            test_sample = trainX[0:2, :].tolist()
-
-            top_n = 1
-            # for i in range(top_n):
-            #     prediction = model_([test_sample], seq_length=self.dec_seq_length, start_token=0, top_n=1)
-            #     print("Prediction: >>>>>  ", prediction, "\n Target: >>>>>  ", trainY[0:2, 1:], "\n\n")
-
-            # printing average loss after every epoch
-            print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1, self.num_epochs, total_loss / n_iter))
-
-
-if __name__ == '__main__':
-    unittest.main()
-
-
-
-

From 43bf2c2ab6f6eb0f97ebe5f7b5ed4db0c438500b Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Sun, 9 Jun 2019 11:13:39 +0100
Subject: [PATCH 29/39] CHANGE_NAME

---
 docs/modules/models.rst                      |   4 +-
 tensorlayer/models/__init__.py               |   2 +-
 tensorlayer/models/con_seq2seq.py            | 299 +++++++++++++++++++
 tensorlayer/models/seq2seq_with_attention.py |   6 +-
 tests/models/test_seq2seq_with_attention.py  |   4 +-
 5 files changed, 307 insertions(+), 8 deletions(-)
 create mode 100644 tensorlayer/models/con_seq2seq.py

diff --git a/docs/modules/models.rst b/docs/modules/models.rst
index 7965b2684..54520fd70 100644
--- a/docs/modules/models.rst
+++ b/docs/modules/models.rst
@@ -14,7 +14,7 @@ TensorLayer provides many pretrained models, you can easily use the whole or a p
     SqueezeNetV1
     MobileNetV1
     Seq2seq
-    Seq2seq_Luong_Attention
+    Seq2seqLuongAttention
 
 
 Base Model
@@ -50,4 +50,4 @@ Seq2seq
 Seq2seq Luong Attention
 ------------------------
 
-.. autoclass:: Seq2seq_Luong_Attention
+.. autoclass:: Seq2seqLuongAttention
diff --git a/tensorlayer/models/__init__.py b/tensorlayer/models/__init__.py
index 62adc076f..b8909c662 100644
--- a/tensorlayer/models/__init__.py
+++ b/tensorlayer/models/__init__.py
@@ -8,4 +8,4 @@
 from .mobilenetv1 import MobileNetV1
 from .vgg import *
 from .seq2seq import Seq2seq
-from .seq2seq_with_attention import Seq2seq_Luong_Attention
+from .seq2seq_with_attention import Seq2seqLuongAttention
diff --git a/tensorlayer/models/con_seq2seq.py b/tensorlayer/models/con_seq2seq.py
new file mode 100644
index 000000000..426ba59bf
--- /dev/null
+++ b/tensorlayer/models/con_seq2seq.py
@@ -0,0 +1,299 @@
+import tensorflow as tf
+import tensorlayer as tl
+import numpy as np
+from tensorlayer.models import Model
+from tensorlayer.layers import Dense, Dropout, Input
+from tensorlayer.layers.core import Layer
+
+import os
+import unittest
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+import numpy as np
+import tensorflow as tf
+import tensorlayer as tl
+from tqdm import tqdm
+from sklearn.utils import shuffle
+from tensorlayer.models.seq2seq import Seq2seq
+from tests.utils import CustomTestCase
+from tensorlayer.cost import cross_entropy_seq
+
+class Linear(Layer):
+    def __init__(self, in_channels, out_channels, name=None):
+        super(Linear, self).__init__(name=name)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.build(None)
+        self._built = True
+
+    def build(self, inputs_shape):
+        # W = [C, N]
+        self.W = self._get_weights("W", shape=(self.in_channels, self.out_channels))
+
+    def forward(self, inputs):
+        # inputs = [B, H, C]
+        # outputs = [B, H, N]
+        outputs = tf.tensordot(inputs, self.W, axes=[2,0])
+        return outputs
+
+
+
+
+class Encoder(Model):
+
+    def __init__(self, hidden_size, kernel_size, num_layers, embedding_layer, name=None):
+        super(Encoder, self).__init__(name=name)
+        self.vocab_size = embedding_layer.vocabulary_size
+        self.embedding_size = embedding_layer.embedding_size
+        self.hidden_size = hidden_size
+        self.out_channels = hidden_size * 2
+        self.kernel_size = kernel_size
+        self.stride = 1
+        self.layers = num_layers
+        self.embedding = embedding_layer
+        self.affine = Linear(in_channels=self.embedding_size, out_channels=self.hidden_size)
+        self.conv = []
+        for i in range(num_layers):
+            self.conv.append(tl.layers.Conv1d(filter_size=self.kernel_size, 
+                    n_filter=self.out_channels, 
+                    stride=self.stride, in_channels=self.hidden_size))
+        self.mapping = tl.layers.Dense(in_channels=self.hidden_size // 2, n_units=self.hidden_size)
+
+    def forward(self, input):
+        
+        # batch, seq_len_src, dim
+        inputs = self.embedding(input)
+        batch_size = inputs.shape[0]
+        # batch, seq_len_src, hidden
+        outputs = self.affine(inputs)
+        # short-cut
+        # batch, seq_len_src, hidden
+        _outputs = outputs
+
+        for i in range(self.layers):
+            # batch, seq_len_src, 2*hidden,
+            outputs = self.conv[i](outputs)
+            # batch, seq_len_src, hidden
+            # Gated Linear unit function
+            outputs = tf.math.multiply(outputs[:,:,:self.hidden_size], tf.sigmoid(outputs[:,:,self.hidden_size:]))
+            # A, B: batch, seq_len_src, hidden / 2
+            A, B = outputs[:,:,:self.hidden_size//2], outputs[:,:,self.hidden_size//2:]
+            # A2: batch * seq_len_src, hidden / 2
+            A2 = tf.reshape(A, [-1,A.shape[-1]])
+            # B2: batch * seq_len_src, hidden / 2
+            B2 = tf.reshape(B, [-1,B.shape[-1]])
+            # attn: batch * seq_len_src, hidden / 2
+            attn = A2 * tf.nn.softmax(B2)
+            _attn = tf.reshape(attn, [batch_size, -1, self.hidden_size//2])
+            # attn2: batch * seq_len_src, hidden
+            attn2 = self.mapping(attn)
+            # outputs: batch, seq_len_src, hidden
+            outputs = tf.reshape(attn2, [batch_size, -1, self.hidden_size])
+            # batch, seq_len_src, hidden_size
+            _outputs = outputs + _outputs
+            
+        
+        return _attn, _outputs
+
+
+class Decoder(Model):
+
+    def __init__(self, hidden_size, embedding_layer, kernel_size, num_layers, name=None):
+        super(Decoder, self).__init__(name=name)
+
+        self.vocab_size = embedding_layer.vocabulary_size
+        self.embedding_size = embedding_layer.embedding_size
+        self.hidden_size = hidden_size
+
+        self.in_channels = hidden_size
+        self.out_channels = hidden_size * 2
+        self.kernel_size = kernel_size
+        self.stride = 1
+        self.layers = num_layers
+
+        self.embedding = embedding_layer
+        self.affine = Linear(self.embedding_size, self.hidden_size)
+        self.conv = []
+        for i in range(num_layers):
+            self.conv.append(tl.layers.Conv1d(n_filter=self.out_channels, in_channels=self.in_channels, 
+                filter_size=kernel_size, stride=self.stride)
+                )
+        self.mapping = tl.layers.Dense(in_channels=self.hidden_size // 2, n_units=self.hidden_size)
+        self.fc = Linear(self.hidden_size, self.vocab_size)
+
+
+    # enc_attn: src_seq_len, hidden_size
+    def forward(self, inputs):
+        
+        target = inputs[0]
+        enc_attn = inputs[1]
+        source_seq_out = inputs[2]
+
+        # batch, seq_len_tgt, dim
+        inputs = self.embedding(target)
+        batch_size = inputs.shape[0]
+        # batch, seq_len_tgt, hidden
+        outputs = self.affine(inputs)
+
+        for i in range(self.layers):
+
+            # This is the residual connection,
+            # for the output of the conv will add kernel_size / 2 elements
+            # before and after the origin input
+            # if i > -1:
+            #     conv_out = conv_out + outputs
+
+            # batch, seq_len_src, 2*hidden,
+            outputs = self.conv[i](outputs)
+            # batch, seq_len_src, hidden
+            # Gated Linear unit function
+            outputs = tf.math.multiply(outputs[:,:,:self.hidden_size], tf.sigmoid(outputs[:,:,self.hidden_size:]))
+            # A, B: batch, seq_len_src, hidden / 2
+            A, B = outputs[:,:,:self.hidden_size//2], outputs[:,:,self.hidden_size//2:]
+            # A2: batch * seq_len_src, hidden / 2
+            A2 = tf.reshape(A, [-1,A.shape[-1]])
+            # B2: batch * seq_len_src, hidden / 2
+            B2 = tf.reshape(B, [-1,B.shape[-1]])
+            # attn: batch * seq_len_src, hidden / 2
+            dec_attn = A2 * tf.nn.softmax(B2)
+            # attn2: batch * seq_len_src, hidden
+            dec_attn2 = self.mapping(dec_attn)
+            dec_attn2 = tf.reshape(dec_attn2, [batch_size, -1, self.hidden_size])
+
+
+            # dec_attn: batch, seq_len_tgt, hidden_size//2
+            dec_attn = tf.reshape(dec_attn, [batch_size, -1, self.hidden_size//2])
+            
+            # enc_attn: batch, seq_len_src, hidden_size//2
+            # dec_atten: batch, seq_len_tgt, hidden_size//2
+            # attn_matrix: batch, seq_len_tgt, seq_len_src
+            enc_attn = tf.transpose(enc_attn, perm=[0,2,1])
+            _attn_matrix = tf.matmul(dec_attn, enc_attn)
+            enc_attn = tf.transpose(enc_attn, perm=[0,2,1])
+            attn_matrix = tf.nn.softmax(_attn_matrix, axis=-1)
+
+
+            # attns: batch, seq_len_tgt, hidden_size
+            attns = tf.matmul(attn_matrix, source_seq_out)
+
+            # outpus: batch, seq_len_tgt, hidden_size
+            outputs = dec_attn2 + attns
+
+        # outpus: batch, seq_len_tgt, vocab_size
+        outputs = tf.nn.softmax(self.fc(outputs))
+
+        return outputs
+
+
+class ConvSeq2Seq(Model):
+    def __init__(self, encoder, decoder, name=None):
+        super(ConvSeq2Seq, self).__init__(name=name)
+        self.encoder = encoder
+        self.decoder = decoder
+        
+    def forward(self, inputs):
+        # attn: batch, seq_len, hidden
+        # out: batch, seq_len, hidden_size
+        source, target = inputs[0], inputs[1]
+        attn, source_seq_out = self.encoder(source)
+
+        # batch, seq_len_tgt, vocab_size
+        out = self.decoder([target, attn, source_seq_out])
+
+        return out
+        
+# enc_attn = tl.layers.Input((16,5,64))
+# src_out = tl.layers.Input((16,5,128))
+# input = tl.layers.Input((16,5), dtype=tf.int32)
+# model_ = ConvSeq2Seq(
+#     decoder = Decoder(hidden_size=128, kernel_size=3, num_layers=2, 
+#     embedding_layer=tl.layers.Embedding(vocabulary_size=500,embedding_size=50)),
+#     encoder = Encoder(hidden_size=128, kernel_size=3, num_layers=2, 
+#     embedding_layer=tl.layers.Embedding(vocabulary_size=500,embedding_size=50))
+# )
+
+# model_.train()
+# print(model_)
+
+
+class Model_SEQ2SEQ_Test(CustomTestCase):
+
+    @classmethod
+    def setUpClass(cls):
+
+        cls.batch_size = 16
+
+        cls.vocab_size = 20
+        cls.embedding_size = 32
+        cls.dec_seq_length = 5
+        cls.trainX = np.random.randint(20, size=(50, 5))
+        cls.trainY = np.random.randint(20, size=(50, cls.dec_seq_length + 1))
+        cls.trainY[:, 0] = 0  # start_token == 0
+
+        # Parameters
+        cls.src_len = len(cls.trainX)
+        cls.tgt_len = len(cls.trainY)
+
+        assert cls.src_len == cls.tgt_len
+
+        cls.num_epochs = 500
+        cls.n_step = cls.src_len // cls.batch_size
+
+    @classmethod
+    def tearDownClass(cls):
+        pass
+
+    def test_basic_simpleSeq2Seq(self):
+        model_ = ConvSeq2Seq(
+            decoder = Decoder(hidden_size=128, kernel_size=3, num_layers=5, 
+            embedding_layer=tl.layers.Embedding(vocabulary_size=self.vocab_size,embedding_size=self.embedding_size)),
+            encoder = Encoder(hidden_size=128, kernel_size=3, num_layers=5, 
+            embedding_layer=tl.layers.Embedding(vocabulary_size=self.vocab_size,embedding_size=self.embedding_size))
+        )
+
+        optimizer = tf.optimizers.Adam(learning_rate=0.001)
+
+        for epoch in range(self.num_epochs):
+            model_.train()
+            
+            trainX, trainY = shuffle(self.trainX, self.trainY)
+            total_loss, n_iter = 0, 0
+            for X, Y in tqdm(tl.iterate.minibatches(inputs=trainX, targets=trainY, batch_size=self.batch_size,
+                                                    shuffle=False), total=self.n_step,
+                             desc='Epoch[{}/{}]'.format(epoch + 1, self.num_epochs), leave=False):
+
+                dec_seq = Y[:, :-1]
+                target_seq = Y[:, 1:]
+
+                with tf.GradientTape() as tape:
+                    ## compute outputs
+                    output = model_(inputs=[X, dec_seq])
+
+                    output = tf.reshape(output, [-1, self.vocab_size])
+                    loss = cross_entropy_seq(logits=output, target_seqs=target_seq)
+                    
+                    grad = tape.gradient(loss, model_.all_weights)
+                    optimizer.apply_gradients(zip(grad, model_.all_weights))
+
+                total_loss += loss
+                n_iter += 1
+
+            model_.eval()
+            test_sample = trainX[0:2, :].tolist()
+
+            top_n = 1
+            # for i in range(top_n):
+            #     prediction = model_([test_sample], seq_length=self.dec_seq_length, start_token=0, top_n=1)
+            #     print("Prediction: >>>>>  ", prediction, "\n Target: >>>>>  ", trainY[0:2, 1:], "\n\n")
+
+            # printing average loss after every epoch
+            print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1, self.num_epochs, total_loss / n_iter))
+
+
+if __name__ == '__main__':
+    unittest.main()
+
+
+
+
diff --git a/tensorlayer/models/seq2seq_with_attention.py b/tensorlayer/models/seq2seq_with_attention.py
index 88ec1dda5..adda4acd9 100644
--- a/tensorlayer/models/seq2seq_with_attention.py
+++ b/tensorlayer/models/seq2seq_with_attention.py
@@ -8,7 +8,7 @@
 from tensorlayer.layers import Dense, Dropout, Input
 from tensorlayer.layers.core import Layer
 
-__all__ = ['Seq2seq_Luong_Attention']
+__all__ = ['Seq2seqLuongAttention']
 
 
 class Encoder(Layer):
@@ -123,7 +123,7 @@ def forward(self, dec_seq, enc_hiddens, last_hidden, method, return_last_state=F
         return cell_outputs
 
 
-class Seq2seq_Luong_Attention(Model):
+class Seq2seqLuongAttention(Model):
     """Luong Attention-based Seq2Seq model. Implementation based on https://arxiv.org/pdf/1508.04025.pdf.
 
     Parameters
@@ -146,7 +146,7 @@ class Seq2seq_Luong_Attention(Model):
     """
 
     def __init__(self, hidden_size, embedding_layer, cell, method, name=None):
-        super(Seq2seq_Luong_Attention, self).__init__(name)
+        super(Seq2seqLuongAttention, self).__init__(name)
         self.enc_layer = Encoder(hidden_size, cell, embedding_layer)
         self.dec_layer = Decoder_Attention(hidden_size, cell, embedding_layer, method=method)
         self.embedding_layer = embedding_layer
diff --git a/tests/models/test_seq2seq_with_attention.py b/tests/models/test_seq2seq_with_attention.py
index 3affd2d30..d7dbeae34 100644
--- a/tests/models/test_seq2seq_with_attention.py
+++ b/tests/models/test_seq2seq_with_attention.py
@@ -11,7 +11,7 @@
 import tensorlayer as tl
 from tqdm import tqdm
 from sklearn.utils import shuffle
-from tensorlayer.models.seq2seq_with_attention import Seq2seq_Luong_Attention
+from tensorlayer.models.seq2seq_with_attention import Seq2seqLuongAttention
 from tests.utils import CustomTestCase
 from tensorlayer.cost import cross_entropy_seq
 
@@ -57,7 +57,7 @@ def tearDownClass(cls):
 
     def test_basic_simpleSeq2Seq(self):
 
-        model_ = Seq2seq_Luong_Attention(
+        model_ = Seq2seqLuongAttention(
             hidden_size=128, cell=tf.keras.layers.SimpleRNNCell,
             embedding_layer=tl.layers.Embedding(vocabulary_size=self.vocab_size,
                                                 embedding_size=self.embedding_size), method='dot'

From d39ce47889bb872e2e2a775b7f013eb0aa21c67d Mon Sep 17 00:00:00 2001
From: Lingjun Liu <lingjunliu@LingjundeMacBook-Pro.local>
Date: Sun, 9 Jun 2019 11:16:38 +0100
Subject: [PATCH 30/39] CHANGE_NAME

---
 CHANGELOG.md                      |   3 +-
 tensorlayer/models/con_seq2seq.py | 299 ------------------------------
 2 files changed, 2 insertions(+), 300 deletions(-)
 delete mode 100644 tensorlayer/models/con_seq2seq.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 1011615bb..1b8183351 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -90,6 +90,7 @@ To release a new version, please update the changelog as followed:
 ### Changed
 - change the format of network config, change related code and files; change layer act (PR #980)
 - update Seq2seq (#989) 
+- add Seq2seqLuongAttention model (#991)
 
 ### Fixed
 - Fix dynamic model cannot track PRelu weights gradients problem (PR #982)
@@ -97,7 +98,7 @@ To release a new version, please update the changelog as followed:
 
 ### Contributors
 - @warshallrho: #980
-- @ArnoldLIULJ: #989
+- @ArnoldLIULJ: #989, #991
 - @1FengL: #982
 
 ## [2.0.1] - 2019-5-17
diff --git a/tensorlayer/models/con_seq2seq.py b/tensorlayer/models/con_seq2seq.py
deleted file mode 100644
index 426ba59bf..000000000
--- a/tensorlayer/models/con_seq2seq.py
+++ /dev/null
@@ -1,299 +0,0 @@
-import tensorflow as tf
-import tensorlayer as tl
-import numpy as np
-from tensorlayer.models import Model
-from tensorlayer.layers import Dense, Dropout, Input
-from tensorlayer.layers.core import Layer
-
-import os
-import unittest
-
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
-import numpy as np
-import tensorflow as tf
-import tensorlayer as tl
-from tqdm import tqdm
-from sklearn.utils import shuffle
-from tensorlayer.models.seq2seq import Seq2seq
-from tests.utils import CustomTestCase
-from tensorlayer.cost import cross_entropy_seq
-
-class Linear(Layer):
-    def __init__(self, in_channels, out_channels, name=None):
-        super(Linear, self).__init__(name=name)
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.build(None)
-        self._built = True
-
-    def build(self, inputs_shape):
-        # W = [C, N]
-        self.W = self._get_weights("W", shape=(self.in_channels, self.out_channels))
-
-    def forward(self, inputs):
-        # inputs = [B, H, C]
-        # outputs = [B, H, N]
-        outputs = tf.tensordot(inputs, self.W, axes=[2,0])
-        return outputs
-
-
-
-
-class Encoder(Model):
-
-    def __init__(self, hidden_size, kernel_size, num_layers, embedding_layer, name=None):
-        super(Encoder, self).__init__(name=name)
-        self.vocab_size = embedding_layer.vocabulary_size
-        self.embedding_size = embedding_layer.embedding_size
-        self.hidden_size = hidden_size
-        self.out_channels = hidden_size * 2
-        self.kernel_size = kernel_size
-        self.stride = 1
-        self.layers = num_layers
-        self.embedding = embedding_layer
-        self.affine = Linear(in_channels=self.embedding_size, out_channels=self.hidden_size)
-        self.conv = []
-        for i in range(num_layers):
-            self.conv.append(tl.layers.Conv1d(filter_size=self.kernel_size, 
-                    n_filter=self.out_channels, 
-                    stride=self.stride, in_channels=self.hidden_size))
-        self.mapping = tl.layers.Dense(in_channels=self.hidden_size // 2, n_units=self.hidden_size)
-
-    def forward(self, input):
-        
-        # batch, seq_len_src, dim
-        inputs = self.embedding(input)
-        batch_size = inputs.shape[0]
-        # batch, seq_len_src, hidden
-        outputs = self.affine(inputs)
-        # short-cut
-        # batch, seq_len_src, hidden
-        _outputs = outputs
-
-        for i in range(self.layers):
-            # batch, seq_len_src, 2*hidden,
-            outputs = self.conv[i](outputs)
-            # batch, seq_len_src, hidden
-            # Gated Linear unit function
-            outputs = tf.math.multiply(outputs[:,:,:self.hidden_size], tf.sigmoid(outputs[:,:,self.hidden_size:]))
-            # A, B: batch, seq_len_src, hidden / 2
-            A, B = outputs[:,:,:self.hidden_size//2], outputs[:,:,self.hidden_size//2:]
-            # A2: batch * seq_len_src, hidden / 2
-            A2 = tf.reshape(A, [-1,A.shape[-1]])
-            # B2: batch * seq_len_src, hidden / 2
-            B2 = tf.reshape(B, [-1,B.shape[-1]])
-            # attn: batch * seq_len_src, hidden / 2
-            attn = A2 * tf.nn.softmax(B2)
-            _attn = tf.reshape(attn, [batch_size, -1, self.hidden_size//2])
-            # attn2: batch * seq_len_src, hidden
-            attn2 = self.mapping(attn)
-            # outputs: batch, seq_len_src, hidden
-            outputs = tf.reshape(attn2, [batch_size, -1, self.hidden_size])
-            # batch, seq_len_src, hidden_size
-            _outputs = outputs + _outputs
-            
-        
-        return _attn, _outputs
-
-
-class Decoder(Model):
-
-    def __init__(self, hidden_size, embedding_layer, kernel_size, num_layers, name=None):
-        super(Decoder, self).__init__(name=name)
-
-        self.vocab_size = embedding_layer.vocabulary_size
-        self.embedding_size = embedding_layer.embedding_size
-        self.hidden_size = hidden_size
-
-        self.in_channels = hidden_size
-        self.out_channels = hidden_size * 2
-        self.kernel_size = kernel_size
-        self.stride = 1
-        self.layers = num_layers
-
-        self.embedding = embedding_layer
-        self.affine = Linear(self.embedding_size, self.hidden_size)
-        self.conv = []
-        for i in range(num_layers):
-            self.conv.append(tl.layers.Conv1d(n_filter=self.out_channels, in_channels=self.in_channels, 
-                filter_size=kernel_size, stride=self.stride)
-                )
-        self.mapping = tl.layers.Dense(in_channels=self.hidden_size // 2, n_units=self.hidden_size)
-        self.fc = Linear(self.hidden_size, self.vocab_size)
-
-
-    # enc_attn: src_seq_len, hidden_size
-    def forward(self, inputs):
-        
-        target = inputs[0]
-        enc_attn = inputs[1]
-        source_seq_out = inputs[2]
-
-        # batch, seq_len_tgt, dim
-        inputs = self.embedding(target)
-        batch_size = inputs.shape[0]
-        # batch, seq_len_tgt, hidden
-        outputs = self.affine(inputs)
-
-        for i in range(self.layers):
-
-            # This is the residual connection,
-            # for the output of the conv will add kernel_size / 2 elements
-            # before and after the origin input
-            # if i > -1:
-            #     conv_out = conv_out + outputs
-
-            # batch, seq_len_src, 2*hidden,
-            outputs = self.conv[i](outputs)
-            # batch, seq_len_src, hidden
-            # Gated Linear unit function
-            outputs = tf.math.multiply(outputs[:,:,:self.hidden_size], tf.sigmoid(outputs[:,:,self.hidden_size:]))
-            # A, B: batch, seq_len_src, hidden / 2
-            A, B = outputs[:,:,:self.hidden_size//2], outputs[:,:,self.hidden_size//2:]
-            # A2: batch * seq_len_src, hidden / 2
-            A2 = tf.reshape(A, [-1,A.shape[-1]])
-            # B2: batch * seq_len_src, hidden / 2
-            B2 = tf.reshape(B, [-1,B.shape[-1]])
-            # attn: batch * seq_len_src, hidden / 2
-            dec_attn = A2 * tf.nn.softmax(B2)
-            # attn2: batch * seq_len_src, hidden
-            dec_attn2 = self.mapping(dec_attn)
-            dec_attn2 = tf.reshape(dec_attn2, [batch_size, -1, self.hidden_size])
-
-
-            # dec_attn: batch, seq_len_tgt, hidden_size//2
-            dec_attn = tf.reshape(dec_attn, [batch_size, -1, self.hidden_size//2])
-            
-            # enc_attn: batch, seq_len_src, hidden_size//2
-            # dec_atten: batch, seq_len_tgt, hidden_size//2
-            # attn_matrix: batch, seq_len_tgt, seq_len_src
-            enc_attn = tf.transpose(enc_attn, perm=[0,2,1])
-            _attn_matrix = tf.matmul(dec_attn, enc_attn)
-            enc_attn = tf.transpose(enc_attn, perm=[0,2,1])
-            attn_matrix = tf.nn.softmax(_attn_matrix, axis=-1)
-
-
-            # attns: batch, seq_len_tgt, hidden_size
-            attns = tf.matmul(attn_matrix, source_seq_out)
-
-            # outpus: batch, seq_len_tgt, hidden_size
-            outputs = dec_attn2 + attns
-
-        # outpus: batch, seq_len_tgt, vocab_size
-        outputs = tf.nn.softmax(self.fc(outputs))
-
-        return outputs
-
-
-class ConvSeq2Seq(Model):
-    def __init__(self, encoder, decoder, name=None):
-        super(ConvSeq2Seq, self).__init__(name=name)
-        self.encoder = encoder
-        self.decoder = decoder
-        
-    def forward(self, inputs):
-        # attn: batch, seq_len, hidden
-        # out: batch, seq_len, hidden_size
-        source, target = inputs[0], inputs[1]
-        attn, source_seq_out = self.encoder(source)
-
-        # batch, seq_len_tgt, vocab_size
-        out = self.decoder([target, attn, source_seq_out])
-
-        return out
-        
-# enc_attn = tl.layers.Input((16,5,64))
-# src_out = tl.layers.Input((16,5,128))
-# input = tl.layers.Input((16,5), dtype=tf.int32)
-# model_ = ConvSeq2Seq(
-#     decoder = Decoder(hidden_size=128, kernel_size=3, num_layers=2, 
-#     embedding_layer=tl.layers.Embedding(vocabulary_size=500,embedding_size=50)),
-#     encoder = Encoder(hidden_size=128, kernel_size=3, num_layers=2, 
-#     embedding_layer=tl.layers.Embedding(vocabulary_size=500,embedding_size=50))
-# )
-
-# model_.train()
-# print(model_)
-
-
-class Model_SEQ2SEQ_Test(CustomTestCase):
-
-    @classmethod
-    def setUpClass(cls):
-
-        cls.batch_size = 16
-
-        cls.vocab_size = 20
-        cls.embedding_size = 32
-        cls.dec_seq_length = 5
-        cls.trainX = np.random.randint(20, size=(50, 5))
-        cls.trainY = np.random.randint(20, size=(50, cls.dec_seq_length + 1))
-        cls.trainY[:, 0] = 0  # start_token == 0
-
-        # Parameters
-        cls.src_len = len(cls.trainX)
-        cls.tgt_len = len(cls.trainY)
-
-        assert cls.src_len == cls.tgt_len
-
-        cls.num_epochs = 500
-        cls.n_step = cls.src_len // cls.batch_size
-
-    @classmethod
-    def tearDownClass(cls):
-        pass
-
-    def test_basic_simpleSeq2Seq(self):
-        model_ = ConvSeq2Seq(
-            decoder = Decoder(hidden_size=128, kernel_size=3, num_layers=5, 
-            embedding_layer=tl.layers.Embedding(vocabulary_size=self.vocab_size,embedding_size=self.embedding_size)),
-            encoder = Encoder(hidden_size=128, kernel_size=3, num_layers=5, 
-            embedding_layer=tl.layers.Embedding(vocabulary_size=self.vocab_size,embedding_size=self.embedding_size))
-        )
-
-        optimizer = tf.optimizers.Adam(learning_rate=0.001)
-
-        for epoch in range(self.num_epochs):
-            model_.train()
-            
-            trainX, trainY = shuffle(self.trainX, self.trainY)
-            total_loss, n_iter = 0, 0
-            for X, Y in tqdm(tl.iterate.minibatches(inputs=trainX, targets=trainY, batch_size=self.batch_size,
-                                                    shuffle=False), total=self.n_step,
-                             desc='Epoch[{}/{}]'.format(epoch + 1, self.num_epochs), leave=False):
-
-                dec_seq = Y[:, :-1]
-                target_seq = Y[:, 1:]
-
-                with tf.GradientTape() as tape:
-                    ## compute outputs
-                    output = model_(inputs=[X, dec_seq])
-
-                    output = tf.reshape(output, [-1, self.vocab_size])
-                    loss = cross_entropy_seq(logits=output, target_seqs=target_seq)
-                    
-                    grad = tape.gradient(loss, model_.all_weights)
-                    optimizer.apply_gradients(zip(grad, model_.all_weights))
-
-                total_loss += loss
-                n_iter += 1
-
-            model_.eval()
-            test_sample = trainX[0:2, :].tolist()
-
-            top_n = 1
-            # for i in range(top_n):
-            #     prediction = model_([test_sample], seq_length=self.dec_seq_length, start_token=0, top_n=1)
-            #     print("Prediction: >>>>>  ", prediction, "\n Target: >>>>>  ", trainY[0:2, 1:], "\n\n")
-
-            # printing average loss after every epoch
-            print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1, self.num_epochs, total_loss / n_iter))
-
-
-if __name__ == '__main__':
-    unittest.main()
-
-
-
-

From 6d6abd90522c1b4c26e5035576946e0e6c431673 Mon Sep 17 00:00:00 2001
From: Jingqing  Zhang <jingqing.zhang15@imperial.ac.uk>
Date: Tue, 11 Jun 2019 17:11:16 +0100
Subject: [PATCH 31/39] simplernn class implemented, tested and documented

---
 docs/modules/layers.rst               |  12 +-
 tensorlayer/layers/recurrent.py       | 167 +++++++++++++++++++++++++-
 tests/layers/test_layers_recurrent.py |  63 +++++++++-
 3 files changed, 236 insertions(+), 6 deletions(-)

diff --git a/docs/modules/layers.rst b/docs/modules/layers.rst
index 597ce632b..0ba4e4f4b 100644
--- a/docs/modules/layers.rst
+++ b/docs/modules/layers.rst
@@ -80,6 +80,7 @@ Layer list
    SwitchNorm
 
    RNN
+   SimpleRNN
    BiRNN
 
    retrieve_seq_length_op
@@ -580,6 +581,11 @@ RNN layer
 """"""""""""""""""""""""""
 .. autoclass:: RNN
 
+RNN layer with Simple RNN Cell
+""""""""""""""""""""""""""""""""""
+.. autoclass:: SimpleRNN
+
+
 Bidirectional layer
 """""""""""""""""""""""""""""""""
 .. autoclass:: BiRNN
@@ -594,15 +600,15 @@ Compute Sequence length 1
 .. autofunction:: retrieve_seq_length_op
 
 Compute Sequence length 2
-""""""""""""""""""""""""""
+"""""""""""""""""""""""""""""
 .. autofunction:: retrieve_seq_length_op2
 
 Compute Sequence length 3
-""""""""""""""""""""""""""
+""""""""""""""""""""""""""""
 .. autofunction:: retrieve_seq_length_op3
 
 Compute mask of the target sequence
-""""""""""""""""""""""""""
+"""""""""""""""""""""""""""""""""""""""
 .. autofunction:: target_mask_op
 
 
diff --git a/tensorlayer/layers/recurrent.py b/tensorlayer/layers/recurrent.py
index 40456806f..5ff0a5e20 100644
--- a/tensorlayer/layers/recurrent.py
+++ b/tensorlayer/layers/recurrent.py
@@ -19,6 +19,7 @@
 # TODO: uncomment
 __all__ = [
     'RNN',
+    'SimpleRNN',
     'BiRNN',
     # 'ConvRNNCell',
     # 'BasicConvLSTMCell',
@@ -213,7 +214,171 @@ def forward(self, inputs, initial_state=None, **kwargs):
         else:
             return outputs
 
-# TODO: write tl.layers.SimpleRNN, tl.layers.GRU, tl.layers.LSTM
+class SimpleRNN(RNN):
+    """
+    The :class:`SimpleRNN` class is a fixed length recurrent layer for implementing simple RNN.
+    This class is a derived class from :class:`RNN`.
+
+    Parameters
+    ----------
+    units: int
+        Positive integer, the dimension of hidden space.
+    return_last_output : boolean
+        Same as :class:`RNN` class.
+        Whether return last output or all outputs in a sequence.
+            - If True, return the last output, "Sequence input and single output"
+            - If False, return all outputs, "Synced sequence input and output"
+            - In other word, if you want to stack more RNNs on this layer, set to False
+        In a dynamic model, `return_last_output` can be updated when it is called in customised forward().
+        By default, `False`.
+    return_seq_2d : boolean
+        Same as :class:`RNN` class.
+        Only consider this argument when `return_last_output` is `False`
+            - If True, return 2D Tensor [batch_size * n_steps, n_hidden], for stacking Dense layer after it.
+            - If False, return 3D Tensor [batch_size, n_steps, n_hidden], for stacking multiple RNN after it.
+        In a dynamic model, `return_seq_2d` can be updated when it is called in customised forward().
+        By default, `False`.
+    return_last_state: boolean
+        Same as :class:`RNN` class.
+        Whether to return the last state of the RNN cell. The state is a list of Tensor.
+        For simple RNN and GRU, last_state = [last_output]; For LSTM, last_state = [last_output, last_cell_state]
+            - If True, the layer will return outputs and the final state of the cell.
+            - If False, the layer will return outputs only.
+        In a dynamic model, `return_last_state` can be updated when it is called in customised forward().
+        By default, `False`.
+    in_channels: int
+        Same as :class:`RNN` class.
+        Optional, the number of channels of the previous layer which is normally the size of embedding.
+        If given, the layer will be built when init.
+        If None, it will be automatically detected when the layer is forwarded for the first time.
+    name : str
+        A unique layer name.
+    **kwargs:
+        Advanced arguments to configure the simple RNN cell. Please check tf.keras.layers.SimpleRNNCell.
+
+    Examples
+    --------
+
+    A simple regression model below.
+    >>> inputs = tl.layers.Input([batch_size, num_steps, embedding_size])
+    >>> rnn_out, lstm_state = tl.layers.SimpleRNN(
+    >>>     units=hidden_size, dropout=0.1, # both units and dropout are used to configure the simple rnn cell.
+    >>>     in_channels=embedding_size,
+    >>>     return_last_output=True, return_last_state=True, name='simplernn'
+    >>> )(inputs)
+    >>> outputs = tl.layers.Dense(n_units=1)(rnn_out)
+    >>> rnn_model = tl.models.Model(inputs=inputs, outputs=[outputs, rnn_state[0]], name='rnn_model')
+
+    Notes
+    -----
+    Input dimension should be rank 3 : [batch_size, n_steps, n_features], if no, please see layer :class:`Reshape`.
+
+
+    """
+
+
+    def __init__(
+            self,
+            units,
+            return_last_output=False,
+            return_seq_2d=False,
+            return_last_state=True,
+            in_channels=None,
+            name=None,  # 'simplernn'
+            **kwargs
+    ):
+        super(SimpleRNN, self).__init__(
+            cell=tf.keras.layers.SimpleRNNCell(units=units, **kwargs),
+            return_last_output=return_last_output,
+            return_seq_2d=return_seq_2d,
+            return_last_state=return_last_state,
+            in_channels=in_channels,
+            name=name
+        )
+
+class GRURNN(RNN):
+    """
+    The :class:`GRURNN` class is a fixed length recurrent layer for implementing RNN with GRU cell.
+    This class is a derived class from :class:`RNN`.
+
+    Parameters
+    ----------
+    units: int
+        Positive integer, the dimension of hidden space.
+    return_last_output : boolean
+        Same as :class:`RNN` class.
+        Whether return last output or all outputs in a sequence.
+            - If True, return the last output, "Sequence input and single output"
+            - If False, return all outputs, "Synced sequence input and output"
+            - In other word, if you want to stack more RNNs on this layer, set to False
+        In a dynamic model, `return_last_output` can be updated when it is called in customised forward().
+        By default, `False`.
+    return_seq_2d : boolean
+        Same as :class:`RNN` class.
+        Only consider this argument when `return_last_output` is `False`
+            - If True, return 2D Tensor [batch_size * n_steps, n_hidden], for stacking Dense layer after it.
+            - If False, return 3D Tensor [batch_size, n_steps, n_hidden], for stacking multiple RNN after it.
+        In a dynamic model, `return_seq_2d` can be updated when it is called in customised forward().
+        By default, `False`.
+    return_last_state: boolean
+        Same as :class:`RNN` class.
+        Whether to return the last state of the RNN cell. The state is a list of Tensor.
+        For simple RNN and GRU, last_state = [last_output]; For LSTM, last_state = [last_output, last_cell_state]
+            - If True, the layer will return outputs and the final state of the cell.
+            - If False, the layer will return outputs only.
+        In a dynamic model, `return_last_state` can be updated when it is called in customised forward().
+        By default, `False`.
+    in_channels: int
+        Same as :class:`RNN` class.
+        Optional, the number of channels of the previous layer which is normally the size of embedding.
+        If given, the layer will be built when init.
+        If None, it will be automatically detected when the layer is forwarded for the first time.
+    name : str
+        A unique layer name.
+    **kwargs:
+        Advanced arguments to configure the GRU cell. Please check tf.keras.layers.SimpleRNNCell.
+
+    Examples
+    --------
+
+    A simple regression model below.
+    >>> inputs = tl.layers.Input([batch_size, num_steps, embedding_size])
+    >>> rnn_out, lstm_state = tl.layers.SimpleRNN(
+    >>>     units=hidden_size, dropout=0.1, # both units and dropout are used to configure the simple rnn cell.
+    >>>     in_channels=embedding_size,
+    >>>     return_last_output=True, return_last_state=True, name='simplernn'
+    >>> )(inputs)
+    >>> outputs = tl.layers.Dense(n_units=1)(rnn_out)
+    >>> rnn_model = tl.models.Model(inputs=inputs, outputs=[outputs, rnn_state[0]], name='rnn_model')
+
+    Notes
+    -----
+    Input dimension should be rank 3 : [batch_size, n_steps, n_features], if no, please see layer :class:`Reshape`.
+
+
+    """
+
+
+    def __init__(
+            self,
+            units,
+            return_last_output=False,
+            return_seq_2d=False,
+            return_last_state=True,
+            in_channels=None,
+            name=None,  # 'simplernn'
+            **kwargs
+    ):
+        super(SimpleRNN, self).__init__(
+            cell=tf.keras.layers.SimpleRNNCell(units=units, **kwargs),
+            return_last_output=return_last_output,
+            return_seq_2d=return_seq_2d,
+            return_last_state=return_last_state,
+            in_channels=in_channels,
+            name=name
+        )
+
+# TODO: tl.layers.GRU, tl.layers.LSTM
 
 class BiRNN(Layer):
     """
diff --git a/tests/layers/test_layers_recurrent.py b/tests/layers/test_layers_recurrent.py
index 9d7158bc7..0445bd452 100644
--- a/tests/layers/test_layers_recurrent.py
+++ b/tests/layers/test_layers_recurrent.py
@@ -40,7 +40,6 @@ def setUpClass(cls):
     def tearDownClass(cls):
         pass
 
-    '''
     def test_basic_simplernn(self):
 
         inputs = tl.layers.Input([self.batch_size, self.num_steps, self.embedding_size])
@@ -69,6 +68,34 @@ def test_basic_simplernn(self):
             if (epoch + 1) % 10 == 0:
                 print("epoch %d, loss %f" % (epoch, loss))
 
+    def test_basic_simplernn_class(self):
+
+        inputs = tl.layers.Input([self.batch_size, self.num_steps, self.embedding_size])
+        rnnlayer = tl.layers.SimpleRNN(
+            units=self.hidden_size, dropout=0.1, return_last_output=True,
+            return_seq_2d=False, return_last_state=True
+        )
+        rnn, rnn_state = rnnlayer(inputs)
+        outputs = tl.layers.Dense(n_units=1)(rnn)
+        rnn_model = tl.models.Model(inputs=inputs, outputs=[outputs, rnn_state[0]])
+        print(rnn_model)
+
+        optimizer = tf.optimizers.Adam(learning_rate=0.01)
+
+        rnn_model.train()
+        assert rnnlayer.is_train
+
+        for epoch in range(50):
+            with tf.GradientTape() as tape:
+                pred_y, final_state = rnn_model(self.data_x)
+                loss = tl.cost.mean_squared_error(pred_y, self.data_y)
+
+            gradients = tape.gradient(loss, rnn_model.trainable_weights)
+            optimizer.apply_gradients(zip(gradients, rnn_model.trainable_weights))
+
+            if (epoch + 1) % 10 == 0:
+                print("epoch %d, loss %f" % (epoch, loss))
+
     def test_basic_simplernn2(self):
 
         inputs = tl.layers.Input([self.batch_size, self.num_steps, self.embedding_size])
@@ -138,6 +165,39 @@ def forward(self, x):
             if (epoch + 1) % 10 == 0:
                 print("epoch %d, loss %f" % (epoch, loss))
 
+    def test_basic_simplernn_dynamic_class(self):
+
+        class CustomisedModel(tl.models.Model):
+
+            def __init__(self):
+                super(CustomisedModel, self).__init__()
+                self.rnnlayer = tl.layers.SimpleRNN(
+                    units=8, dropout=0.1, in_channels=4, return_last_output=False,
+                    return_seq_2d=False, return_last_state=False
+                )
+                self.dense = tl.layers.Dense(in_channels=8, n_units=1)
+
+            def forward(self, x):
+                z = self.rnnlayer(x)
+                z = self.dense(z[:, -1, :])
+                return z
+
+        rnn_model = CustomisedModel()
+        print(rnn_model)
+        optimizer = tf.optimizers.Adam(learning_rate=0.01)
+        rnn_model.train()
+
+        for epoch in range(50):
+            with tf.GradientTape() as tape:
+                pred_y = rnn_model(self.data_x)
+                loss = tl.cost.mean_squared_error(pred_y, self.data_y)
+
+            gradients = tape.gradient(loss, rnn_model.trainable_weights)
+            optimizer.apply_gradients(zip(gradients, rnn_model.trainable_weights))
+
+            if (epoch + 1) % 10 == 0:
+                print("epoch %d, loss %f" % (epoch, loss))
+
     def test_basic_simplernn_dynamic_2(self):
 
         class CustomisedModel(tl.models.Model):
@@ -604,7 +664,6 @@ def test_sequence_length3(self):
             print(length)
         except Exception as e:
             print(e)
-    '''
 
     def test_target_mask_op(self):
         fail_flag = False

From 829544e9a42e5fb439525110f92a29769f4662a6 Mon Sep 17 00:00:00 2001
From: Jingqing  Zhang <jingqing.zhang15@imperial.ac.uk>
Date: Tue, 11 Jun 2019 17:21:33 +0100
Subject: [PATCH 32/39] GRURNN class implemented, tested, documented

---
 docs/modules/layers.rst               |  5 +++++
 tensorlayer/layers/recurrent.py       | 29 ++++++++++-----------------
 tests/layers/test_layers_recurrent.py | 27 +++++++++++++++++++++++++
 3 files changed, 43 insertions(+), 18 deletions(-)

diff --git a/docs/modules/layers.rst b/docs/modules/layers.rst
index 0ba4e4f4b..008bfa59e 100644
--- a/docs/modules/layers.rst
+++ b/docs/modules/layers.rst
@@ -81,6 +81,7 @@ Layer list
 
    RNN
    SimpleRNN
+   GRURNN
    BiRNN
 
    retrieve_seq_length_op
@@ -585,6 +586,10 @@ RNN layer with Simple RNN Cell
 """"""""""""""""""""""""""""""""""
 .. autoclass:: SimpleRNN
 
+RNN layer with GRU Cell
+""""""""""""""""""""""""""""""""""
+.. autoclass:: GRURNN
+
 
 Bidirectional layer
 """""""""""""""""""""""""""""""""
diff --git a/tensorlayer/layers/recurrent.py b/tensorlayer/layers/recurrent.py
index 5ff0a5e20..60c50f2d0 100644
--- a/tensorlayer/layers/recurrent.py
+++ b/tensorlayer/layers/recurrent.py
@@ -20,6 +20,7 @@
 __all__ = [
     'RNN',
     'SimpleRNN',
+    'GRURNN',
     'BiRNN',
     # 'ConvRNNCell',
     # 'BasicConvLSTMCell',
@@ -224,7 +225,6 @@ class SimpleRNN(RNN):
     units: int
         Positive integer, the dimension of hidden space.
     return_last_output : boolean
-        Same as :class:`RNN` class.
         Whether return last output or all outputs in a sequence.
             - If True, return the last output, "Sequence input and single output"
             - If False, return all outputs, "Synced sequence input and output"
@@ -232,22 +232,19 @@ class SimpleRNN(RNN):
         In a dynamic model, `return_last_output` can be updated when it is called in customised forward().
         By default, `False`.
     return_seq_2d : boolean
-        Same as :class:`RNN` class.
         Only consider this argument when `return_last_output` is `False`
             - If True, return 2D Tensor [batch_size * n_steps, n_hidden], for stacking Dense layer after it.
             - If False, return 3D Tensor [batch_size, n_steps, n_hidden], for stacking multiple RNN after it.
         In a dynamic model, `return_seq_2d` can be updated when it is called in customised forward().
         By default, `False`.
     return_last_state: boolean
-        Same as :class:`RNN` class.
         Whether to return the last state of the RNN cell. The state is a list of Tensor.
-        For simple RNN and GRU, last_state = [last_output]; For LSTM, last_state = [last_output, last_cell_state]
+        For simple RNN, last_state = [last_output];
             - If True, the layer will return outputs and the final state of the cell.
             - If False, the layer will return outputs only.
         In a dynamic model, `return_last_state` can be updated when it is called in customised forward().
         By default, `False`.
     in_channels: int
-        Same as :class:`RNN` class.
         Optional, the number of channels of the previous layer which is normally the size of embedding.
         If given, the layer will be built when init.
         If None, it will be automatically detected when the layer is forwarded for the first time.
@@ -306,7 +303,6 @@ class GRURNN(RNN):
     units: int
         Positive integer, the dimension of hidden space.
     return_last_output : boolean
-        Same as :class:`RNN` class.
         Whether return last output or all outputs in a sequence.
             - If True, return the last output, "Sequence input and single output"
             - If False, return all outputs, "Synced sequence input and output"
@@ -314,39 +310,36 @@ class GRURNN(RNN):
         In a dynamic model, `return_last_output` can be updated when it is called in customised forward().
         By default, `False`.
     return_seq_2d : boolean
-        Same as :class:`RNN` class.
         Only consider this argument when `return_last_output` is `False`
             - If True, return 2D Tensor [batch_size * n_steps, n_hidden], for stacking Dense layer after it.
             - If False, return 3D Tensor [batch_size, n_steps, n_hidden], for stacking multiple RNN after it.
         In a dynamic model, `return_seq_2d` can be updated when it is called in customised forward().
         By default, `False`.
     return_last_state: boolean
-        Same as :class:`RNN` class.
         Whether to return the last state of the RNN cell. The state is a list of Tensor.
-        For simple RNN and GRU, last_state = [last_output]; For LSTM, last_state = [last_output, last_cell_state]
+        For GRU, last_state = [last_output];
             - If True, the layer will return outputs and the final state of the cell.
             - If False, the layer will return outputs only.
         In a dynamic model, `return_last_state` can be updated when it is called in customised forward().
         By default, `False`.
     in_channels: int
-        Same as :class:`RNN` class.
         Optional, the number of channels of the previous layer which is normally the size of embedding.
         If given, the layer will be built when init.
         If None, it will be automatically detected when the layer is forwarded for the first time.
     name : str
         A unique layer name.
     **kwargs:
-        Advanced arguments to configure the GRU cell. Please check tf.keras.layers.SimpleRNNCell.
+        Advanced arguments to configure the GRU cell. Please check tf.keras.layers.GRUCell.
 
     Examples
     --------
 
     A simple regression model below.
     >>> inputs = tl.layers.Input([batch_size, num_steps, embedding_size])
-    >>> rnn_out, lstm_state = tl.layers.SimpleRNN(
-    >>>     units=hidden_size, dropout=0.1, # both units and dropout are used to configure the simple rnn cell.
+    >>> rnn_out, lstm_state = tl.layers.GRURNN(
+    >>>     units=hidden_size, dropout=0.1, # both units and dropout are used to configure the GRU cell.
     >>>     in_channels=embedding_size,
-    >>>     return_last_output=True, return_last_state=True, name='simplernn'
+    >>>     return_last_output=True, return_last_state=True, name='grurnn'
     >>> )(inputs)
     >>> outputs = tl.layers.Dense(n_units=1)(rnn_out)
     >>> rnn_model = tl.models.Model(inputs=inputs, outputs=[outputs, rnn_state[0]], name='rnn_model')
@@ -366,11 +359,11 @@ def __init__(
             return_seq_2d=False,
             return_last_state=True,
             in_channels=None,
-            name=None,  # 'simplernn'
+            name=None,  # 'grurnn'
             **kwargs
     ):
-        super(SimpleRNN, self).__init__(
-            cell=tf.keras.layers.SimpleRNNCell(units=units, **kwargs),
+        super(GRURNN, self).__init__(
+            cell=tf.keras.layers.GRUCell(units=units, **kwargs),
             return_last_output=return_last_output,
             return_seq_2d=return_seq_2d,
             return_last_state=return_last_state,
@@ -378,7 +371,7 @@ def __init__(
             name=name
         )
 
-# TODO: tl.layers.GRU, tl.layers.LSTM
+# TODO: tl.layers.LSTM
 
 class BiRNN(Layer):
     """
diff --git a/tests/layers/test_layers_recurrent.py b/tests/layers/test_layers_recurrent.py
index 0445bd452..6e630ed1d 100644
--- a/tests/layers/test_layers_recurrent.py
+++ b/tests/layers/test_layers_recurrent.py
@@ -326,6 +326,33 @@ def test_basic_grurnn(self):
             if (epoch + 1) % 10 == 0:
                 print("epoch %d, loss %f" % (epoch, loss))
 
+    def test_basic_grurnn_class(self):
+
+        inputs = tl.layers.Input([self.batch_size, self.num_steps, self.embedding_size])
+        rnnlayer = tl.layers.GRURNN(
+            units=self.hidden_size, dropout=0.1, return_last_output=True,
+            return_seq_2d=False, return_last_state=True
+        )
+        rnn, rnn_state = rnnlayer(inputs)
+        outputs = tl.layers.Dense(n_units=1)(rnn)
+        rnn_model = tl.models.Model(inputs=inputs, outputs=[outputs, rnn_state[0]])
+        print(rnn_model)
+
+        optimizer = tf.optimizers.Adam(learning_rate=0.01)
+
+        rnn_model.train()
+
+        for epoch in range(50):
+            with tf.GradientTape() as tape:
+                pred_y, final_h = rnn_model(self.data_x)
+                loss = tl.cost.mean_squared_error(pred_y, self.data_y)
+
+            gradients = tape.gradient(loss, rnn_model.trainable_weights)
+            optimizer.apply_gradients(zip(gradients, rnn_model.trainable_weights))
+
+            if (epoch + 1) % 10 == 0:
+                print("epoch %d, loss %f" % (epoch, loss))
+
     def test_basic_birnn_simplernncell(self):
 
         inputs = tl.layers.Input([self.batch_size, self.num_steps, self.embedding_size])

From ad88f164eb63709014fe00a4c17a5aa8198a1bee Mon Sep 17 00:00:00 2001
From: Jingqing  Zhang <jingqing.zhang15@imperial.ac.uk>
Date: Tue, 11 Jun 2019 17:25:11 +0100
Subject: [PATCH 33/39] LSTMRNN class implemented, tested, documented

---
 docs/modules/layers.rst               |  4 ++
 tensorlayer/layers/recurrent.py       | 80 ++++++++++++++++++++++++++-
 tests/layers/test_layers_recurrent.py | 27 +++++++++
 3 files changed, 110 insertions(+), 1 deletion(-)

diff --git a/docs/modules/layers.rst b/docs/modules/layers.rst
index 008bfa59e..f6c86a542 100644
--- a/docs/modules/layers.rst
+++ b/docs/modules/layers.rst
@@ -82,6 +82,7 @@ Layer list
    RNN
    SimpleRNN
    GRURNN
+   LSTMRNN
    BiRNN
 
    retrieve_seq_length_op
@@ -590,6 +591,9 @@ RNN layer with GRU Cell
 """"""""""""""""""""""""""""""""""
 .. autoclass:: GRURNN
 
+RNN layer with LSTM Cell
+""""""""""""""""""""""""""""""""""
+.. autoclass:: LSTMRNN
 
 Bidirectional layer
 """""""""""""""""""""""""""""""""
diff --git a/tensorlayer/layers/recurrent.py b/tensorlayer/layers/recurrent.py
index 60c50f2d0..f05ec57de 100644
--- a/tensorlayer/layers/recurrent.py
+++ b/tensorlayer/layers/recurrent.py
@@ -21,6 +21,7 @@
     'RNN',
     'SimpleRNN',
     'GRURNN',
+    'LSTMRNN',
     'BiRNN',
     # 'ConvRNNCell',
     # 'BasicConvLSTMCell',
@@ -371,7 +372,84 @@ def __init__(
             name=name
         )
 
-# TODO: tl.layers.LSTM
+class LSTMRNN(RNN):
+    """
+    The :class:`LSTMRNN` class is a fixed length recurrent layer for implementing RNN with LSTM cell.
+    This class is a derived class from :class:`RNN`.
+
+    Parameters
+    ----------
+    units: int
+        Positive integer, the dimension of hidden space.
+    return_last_output : boolean
+        Whether return last output or all outputs in a sequence.
+            - If True, return the last output, "Sequence input and single output"
+            - If False, return all outputs, "Synced sequence input and output"
+            - In other word, if you want to stack more RNNs on this layer, set to False
+        In a dynamic model, `return_last_output` can be updated when it is called in customised forward().
+        By default, `False`.
+    return_seq_2d : boolean
+        Only consider this argument when `return_last_output` is `False`
+            - If True, return 2D Tensor [batch_size * n_steps, n_hidden], for stacking Dense layer after it.
+            - If False, return 3D Tensor [batch_size, n_steps, n_hidden], for stacking multiple RNN after it.
+        In a dynamic model, `return_seq_2d` can be updated when it is called in customised forward().
+        By default, `False`.
+    return_last_state: boolean
+        Whether to return the last state of the RNN cell. The state is a list of Tensor.
+        For LSTM, last_state = [last_output, last_cell_state]
+            - If True, the layer will return outputs and the final state of the cell.
+            - If False, the layer will return outputs only.
+        In a dynamic model, `return_last_state` can be updated when it is called in customised forward().
+        By default, `False`.
+    in_channels: int
+        Optional, the number of channels of the previous layer which is normally the size of embedding.
+        If given, the layer will be built when init.
+        If None, it will be automatically detected when the layer is forwarded for the first time.
+    name : str
+        A unique layer name.
+    **kwargs:
+        Advanced arguments to configure the LSTM cell. Please check tf.keras.layers.LSTMCell.
+
+    Examples
+    --------
+
+    A simple regression model below.
+    >>> inputs = tl.layers.Input([batch_size, num_steps, embedding_size])
+    >>> rnn_out, lstm_state = tl.layers.LSTMRNN(
+    >>>     units=hidden_size, dropout=0.1, # both units and dropout are used to configure the LSTM cell.
+    >>>     in_channels=embedding_size,
+    >>>     return_last_output=True, return_last_state=True, name='grurnn'
+    >>> )(inputs)
+    >>> outputs = tl.layers.Dense(n_units=1)(rnn_out)
+    >>> rnn_model = tl.models.Model(inputs=inputs, outputs=[outputs, rnn_state[0]], name='rnn_model')
+
+    Notes
+    -----
+    Input dimension should be rank 3 : [batch_size, n_steps, n_features], if no, please see layer :class:`Reshape`.
+
+
+    """
+
+
+    def __init__(
+            self,
+            units,
+            return_last_output=False,
+            return_seq_2d=False,
+            return_last_state=True,
+            in_channels=None,
+            name=None,  # 'lstmrnn'
+            **kwargs
+    ):
+        super(LSTMRNN, self).__init__(
+            cell=tf.keras.layers.LSTMCell(units=units, **kwargs),
+            return_last_output=return_last_output,
+            return_seq_2d=return_seq_2d,
+            return_last_state=return_last_state,
+            in_channels=in_channels,
+            name=name
+        )
+
 
 class BiRNN(Layer):
     """
diff --git a/tests/layers/test_layers_recurrent.py b/tests/layers/test_layers_recurrent.py
index 6e630ed1d..525abf6fe 100644
--- a/tests/layers/test_layers_recurrent.py
+++ b/tests/layers/test_layers_recurrent.py
@@ -299,6 +299,33 @@ def test_basic_lstmrnn(self):
             if (epoch + 1) % 10 == 0:
                 print("epoch %d, loss %f" % (epoch, loss))
 
+    def test_basic_lstmrnn_class(self):
+
+        inputs = tl.layers.Input([self.batch_size, self.num_steps, self.embedding_size])
+        rnnlayer = tl.layers.LSTMRNN(
+            units=self.hidden_size, dropout=0.1, return_last_output=True,
+            return_seq_2d=False, return_last_state=True
+        )
+        rnn, rnn_state = rnnlayer(inputs)
+        outputs = tl.layers.Dense(n_units=1)(rnn)
+        rnn_model = tl.models.Model(inputs=inputs, outputs=[outputs, rnn_state[0], rnn_state[1]])
+        print(rnn_model)
+
+        optimizer = tf.optimizers.Adam(learning_rate=0.01)
+
+        rnn_model.train()
+
+        for epoch in range(50):
+            with tf.GradientTape() as tape:
+                pred_y, final_h, final_c = rnn_model(self.data_x)
+                loss = tl.cost.mean_squared_error(pred_y, self.data_y)
+
+            gradients = tape.gradient(loss, rnn_model.trainable_weights)
+            optimizer.apply_gradients(zip(gradients, rnn_model.trainable_weights))
+
+            if (epoch + 1) % 10 == 0:
+                print("epoch %d, loss %f" % (epoch, loss))
+
     def test_basic_grurnn(self):
 
         inputs = tl.layers.Input([self.batch_size, self.num_steps, self.embedding_size])

From db4c275c370917794d8625792575c85e9b61e488 Mon Sep 17 00:00:00 2001
From: Jingqing  Zhang <jingqing.zhang15@imperial.ac.uk>
Date: Wed, 12 Jun 2019 13:07:04 +0100
Subject: [PATCH 34/39] format fix

---
 tensorlayer/layers/recurrent.py              | 55 ++++++++------------
 tensorlayer/models/seq2seq.py                |  5 +-
 tensorlayer/models/seq2seq_with_attention.py |  4 +-
 3 files changed, 26 insertions(+), 38 deletions(-)

diff --git a/tensorlayer/layers/recurrent.py b/tensorlayer/layers/recurrent.py
index 4d10e03c1..dc3044ff2 100644
--- a/tensorlayer/layers/recurrent.py
+++ b/tensorlayer/layers/recurrent.py
@@ -7,14 +7,6 @@
 from tensorlayer.decorators import deprecated_alias
 from tensorlayer.layers.core import Layer
 
-# from tensorflow.python.ops import array_ops
-# from tensorflow.python.util.tf_inspect import getfullargspec
-# from tensorflow.contrib.rnn import stack_bidirectional_dynamic_rnn
-# from tensorflow.python.ops.rnn_cell import LSTMStateTuple
-
-# from tensorlayer.layers.core import LayersConfig
-# from tensorlayer.layers.core import TF_GRAPHKEYS_VARIABLES
-
 # TODO: uncomment
 __all__ = [
     'RNN',
@@ -223,6 +215,7 @@ def forward(self, inputs, initial_state=None, **kwargs):
         else:
             return outputs
 
+
 class SimpleRNN(RNN):
     """
     The :class:`SimpleRNN` class is a fixed length recurrent layer for implementing simple RNN.
@@ -281,7 +274,6 @@ class SimpleRNN(RNN):
 
     """
 
-
     def __init__(
             self,
             units,
@@ -293,14 +285,11 @@ def __init__(
             **kwargs
     ):
         super(SimpleRNN, self).__init__(
-            cell=tf.keras.layers.SimpleRNNCell(units=units, **kwargs),
-            return_last_output=return_last_output,
-            return_seq_2d=return_seq_2d,
-            return_last_state=return_last_state,
-            in_channels=in_channels,
-            name=name
+            cell=tf.keras.layers.SimpleRNNCell(units=units, **kwargs), return_last_output=return_last_output,
+            return_seq_2d=return_seq_2d, return_last_state=return_last_state, in_channels=in_channels, name=name
         )
 
+
 class GRURNN(RNN):
     """
     The :class:`GRURNN` class is a fixed length recurrent layer for implementing RNN with GRU cell.
@@ -359,7 +348,6 @@ class GRURNN(RNN):
 
     """
 
-
     def __init__(
             self,
             units,
@@ -371,14 +359,11 @@ def __init__(
             **kwargs
     ):
         super(GRURNN, self).__init__(
-            cell=tf.keras.layers.GRUCell(units=units, **kwargs),
-            return_last_output=return_last_output,
-            return_seq_2d=return_seq_2d,
-            return_last_state=return_last_state,
-            in_channels=in_channels,
-            name=name
+            cell=tf.keras.layers.GRUCell(units=units, **kwargs), return_last_output=return_last_output,
+            return_seq_2d=return_seq_2d, return_last_state=return_last_state, in_channels=in_channels, name=name
         )
 
+
 class LSTMRNN(RNN):
     """
     The :class:`LSTMRNN` class is a fixed length recurrent layer for implementing RNN with LSTM cell.
@@ -437,7 +422,6 @@ class LSTMRNN(RNN):
 
     """
 
-
     def __init__(
             self,
             units,
@@ -449,12 +433,8 @@ def __init__(
             **kwargs
     ):
         super(LSTMRNN, self).__init__(
-            cell=tf.keras.layers.LSTMCell(units=units, **kwargs),
-            return_last_output=return_last_output,
-            return_seq_2d=return_seq_2d,
-            return_last_state=return_last_state,
-            in_channels=in_channels,
-            name=name
+            cell=tf.keras.layers.LSTMCell(units=units, **kwargs), return_last_output=return_last_output,
+            return_seq_2d=return_seq_2d, return_last_state=return_last_state, in_channels=in_channels, name=name
         )
 
 
@@ -973,6 +953,7 @@ def __init__(
         self._add_layers(self.outputs)
         self._add_params(rnn_variables)
 
+
 # @tf.function
 def retrieve_seq_length_op(data):
     """An op to compute the length of a sequence from input shape of [batch_size, n_step(max), n_features],
@@ -1014,6 +995,7 @@ def retrieve_seq_length_op(data):
 
         return tf.cast(length, tf.int32)
 
+
 # @tf.function
 def retrieve_seq_length_op2(data):
     """An op to compute the length of a sequence, from input shape of [batch_size, n_step(max)],
@@ -1036,6 +1018,7 @@ def retrieve_seq_length_op2(data):
     """
     return tf.reduce_sum(input_tensor=tf.cast(tf.greater(data, tf.zeros_like(data)), tf.int32), axis=1)
 
+
 # @tf.function
 def retrieve_seq_length_op3(data, pad_val=0):
     """An op to compute the length of a sequence, the data shape can be [batch_size, n_step(max)] or
@@ -1094,6 +1077,7 @@ def retrieve_seq_length_op3(data, pad_val=0):
             "retrieve_seq_length_op3: handling data with num of dims %s hasn't been implemented!" % (data_shape_size)
         )
 
+
 def target_mask_op(data, pad_val=0):
     """ Return the mask of the input sequence data based on the padding values.
 
@@ -1146,9 +1130,12 @@ def target_mask_op(data, pad_val=0):
     elif data_shape_size == 2:
         return tf.cast(tf.not_equal(data, pad_val), dtype=tf.int32)
     elif data_shape_size == 1:
-        raise ValueError("target_mask_op: data_shape %s is not supported. "
-                         "The shape of data should have 2 or 3 dims." % (data.get_shape()))
+        raise ValueError(
+            "target_mask_op: data_shape %s is not supported. "
+            "The shape of data should have 2 or 3 dims." % (data.get_shape())
+        )
     else:
-        raise ValueError("target_mask_op: handling data_shape %s hasn't been implemented! "
-                         "The shape of data should have 2 or 3 dims" % (data.get_shape()))
-
+        raise ValueError(
+            "target_mask_op: handling data_shape %s hasn't been implemented! "
+            "The shape of data should have 2 or 3 dims" % (data.get_shape())
+        )
diff --git a/tensorlayer/models/seq2seq.py b/tensorlayer/models/seq2seq.py
index 256b5213f..e0c20ef56 100644
--- a/tensorlayer/models/seq2seq.py
+++ b/tensorlayer/models/seq2seq.py
@@ -1,12 +1,13 @@
 #! /usr/bin/python
 # -*- coding: utf-8 -*-
 
+import numpy as np
 import tensorflow as tf
 import tensorlayer as tl
-import numpy as np
-from tensorlayer.models import Model
 from tensorlayer.layers import Dense, Dropout, Input
 from tensorlayer.layers.core import Layer
+from tensorlayer.models import Model
+
 __all__ = ['Seq2seq']
 
 
diff --git a/tensorlayer/models/seq2seq_with_attention.py b/tensorlayer/models/seq2seq_with_attention.py
index adda4acd9..d601e33c8 100644
--- a/tensorlayer/models/seq2seq_with_attention.py
+++ b/tensorlayer/models/seq2seq_with_attention.py
@@ -1,12 +1,12 @@
 #! /usr/bin/python
 # -*- coding: utf-8 -*-
 
+import numpy as np
 import tensorflow as tf
 import tensorlayer as tl
-import numpy as np
-from tensorlayer.models import Model
 from tensorlayer.layers import Dense, Dropout, Input
 from tensorlayer.layers.core import Layer
+from tensorlayer.models import Model
 
 __all__ = ['Seq2seqLuongAttention']
 

From 2bc0ab20651b03cf56befa3e36d4723acbf2615a Mon Sep 17 00:00:00 2001
From: Jingqing  Zhang <jingqing.zhang15@imperial.ac.uk>
Date: Wed, 12 Jun 2019 13:14:54 +0100
Subject: [PATCH 35/39] update changlog

---
 CHANGELOG.md | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 1008c8093..8efd7c448 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -91,6 +91,9 @@ To release a new version, please update the changelog as followed:
 - Add version_info in model.config. (PR #992)
 - Replace tf.nn.func with tf.nn.func.\_\_name\_\_ in model config.
 - Add Reinforcement learning tutorials. (PR #995)
+- Add RNN layers with simple rnn cell, GRU cell, LSTM cell. (PR #998)
+- Update Seq2seq (#998) 
+- Add Seq2seqLuongAttention model (#998)
 
 ### Fixed
 
@@ -100,13 +103,14 @@ To release a new version, please update the changelog as followed:
 - @Tokarev-TT-33: #995
 - @initial-h: #995
 - @Officium: #995
+- @ArnoldLIULJ: #998
+- @JingqingZ: #998
+
 
 ## [2.0.2] - 2019-6-5
 
 ### Changed
 - change the format of network config, change related code and files; change layer act (PR #980)
-- update Seq2seq (#989) 
-- add Seq2seqLuongAttention model (#991)
 
 ### Fixed
 - Fix dynamic model cannot track PRelu weights gradients problem (PR #982)
@@ -114,7 +118,6 @@ To release a new version, please update the changelog as followed:
 
 ### Contributors
 - @warshallrho: #980
-- @ArnoldLIULJ: #989, #991
 - @1FengL: #982
 
 ## [2.0.1] - 2019-5-17

From c35128a7856c815759521dfa464ca3b1254d8581 Mon Sep 17 00:00:00 2001
From: Jingqing  Zhang <jingqing.zhang15@imperial.ac.uk>
Date: Wed, 12 Jun 2019 13:57:57 +0100
Subject: [PATCH 36/39] yapf fix unittest of recurrent

---
 tests/layers/test_layers_recurrent.py | 23 ++++++++++-------------
 1 file changed, 10 insertions(+), 13 deletions(-)

diff --git a/tests/layers/test_layers_recurrent.py b/tests/layers/test_layers_recurrent.py
index 525abf6fe..65fbd2442 100644
--- a/tests/layers/test_layers_recurrent.py
+++ b/tests/layers/test_layers_recurrent.py
@@ -72,8 +72,7 @@ def test_basic_simplernn_class(self):
 
         inputs = tl.layers.Input([self.batch_size, self.num_steps, self.embedding_size])
         rnnlayer = tl.layers.SimpleRNN(
-            units=self.hidden_size, dropout=0.1, return_last_output=True,
-            return_seq_2d=False, return_last_state=True
+            units=self.hidden_size, dropout=0.1, return_last_output=True, return_seq_2d=False, return_last_state=True
         )
         rnn, rnn_state = rnnlayer(inputs)
         outputs = tl.layers.Dense(n_units=1)(rnn)
@@ -172,8 +171,8 @@ class CustomisedModel(tl.models.Model):
             def __init__(self):
                 super(CustomisedModel, self).__init__()
                 self.rnnlayer = tl.layers.SimpleRNN(
-                    units=8, dropout=0.1, in_channels=4, return_last_output=False,
-                    return_seq_2d=False, return_last_state=False
+                    units=8, dropout=0.1, in_channels=4, return_last_output=False, return_seq_2d=False,
+                    return_last_state=False
                 )
                 self.dense = tl.layers.Dense(in_channels=8, n_units=1)
 
@@ -303,8 +302,7 @@ def test_basic_lstmrnn_class(self):
 
         inputs = tl.layers.Input([self.batch_size, self.num_steps, self.embedding_size])
         rnnlayer = tl.layers.LSTMRNN(
-            units=self.hidden_size, dropout=0.1, return_last_output=True,
-            return_seq_2d=False, return_last_state=True
+            units=self.hidden_size, dropout=0.1, return_last_output=True, return_seq_2d=False, return_last_state=True
         )
         rnn, rnn_state = rnnlayer(inputs)
         outputs = tl.layers.Dense(n_units=1)(rnn)
@@ -357,8 +355,7 @@ def test_basic_grurnn_class(self):
 
         inputs = tl.layers.Input([self.batch_size, self.num_steps, self.embedding_size])
         rnnlayer = tl.layers.GRURNN(
-            units=self.hidden_size, dropout=0.1, return_last_output=True,
-            return_seq_2d=False, return_last_state=True
+            units=self.hidden_size, dropout=0.1, return_last_output=True, return_seq_2d=False, return_last_state=True
         )
         rnn, rnn_state = rnnlayer(inputs)
         outputs = tl.layers.Dense(n_units=1)(rnn)
@@ -722,8 +719,7 @@ def test_sequence_length3(self):
     def test_target_mask_op(self):
         fail_flag = False
         data = [
-            ['hello', 'world', '', '', ''],
-            ['hello', 'world', 'tensorlayer', '', ''],
+            ['hello', 'world', '', '', ''], ['hello', 'world', 'tensorlayer', '', ''],
             ['hello', 'world', 'tensorlayer', '2.0', '']
         ]
         try:
@@ -743,9 +739,10 @@ def test_target_mask_op(self):
         mask = tl.layers.target_mask_op(data)
         print(mask)
 
-        data = [[[0,0],[2,2],[1,2],[1,2],[0,0]],
-                [[2,3],[2,4],[3,2],[1,0],[0,0]],
-                [[3,3],[0,1],[5,3],[1,2],[0,0]]]
+        data = [
+            [[0, 0], [2, 2], [1, 2], [1, 2], [0, 0]], [[2, 3], [2, 4], [3, 2], [1, 0], [0, 0]],
+            [[3, 3], [0, 1], [5, 3], [1, 2], [0, 0]]
+        ]
         data = tf.convert_to_tensor(data, dtype=tf.float32)
         mask = tl.layers.target_mask_op(data)
         print(mask)

From 19933564e258ca4d44b63495a2695d82e74f3872 Mon Sep 17 00:00:00 2001
From: Jingqing  Zhang <jingqing.zhang15@imperial.ac.uk>
Date: Wed, 12 Jun 2019 14:08:16 +0100
Subject: [PATCH 37/39] format fix for recurrent

---
 tensorlayer/layers/recurrent.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/tensorlayer/layers/recurrent.py b/tensorlayer/layers/recurrent.py
index dc3044ff2..05fe3e094 100644
--- a/tensorlayer/layers/recurrent.py
+++ b/tensorlayer/layers/recurrent.py
@@ -271,7 +271,6 @@ class SimpleRNN(RNN):
     -----
     Input dimension should be rank 3 : [batch_size, n_steps, n_features], if no, please see layer :class:`Reshape`.
 
-
     """
 
     def __init__(
@@ -345,7 +344,6 @@ class GRURNN(RNN):
     -----
     Input dimension should be rank 3 : [batch_size, n_steps, n_features], if no, please see layer :class:`Reshape`.
 
-
     """
 
     def __init__(
@@ -419,7 +417,6 @@ class LSTMRNN(RNN):
     -----
     Input dimension should be rank 3 : [batch_size, n_steps, n_features], if no, please see layer :class:`Reshape`.
 
-
     """
 
     def __init__(

From 99d70793490c3328f8799731792f1bd44c237ca8 Mon Sep 17 00:00:00 2001
From: Jingqing  Zhang <jingqing.zhang15@imperial.ac.uk>
Date: Wed, 12 Jun 2019 14:36:58 +0100
Subject: [PATCH 38/39] doc fix

---
 tensorlayer/layers/recurrent.py | 37 +++++++++++++++++++++++----------
 1 file changed, 26 insertions(+), 11 deletions(-)

diff --git a/tensorlayer/layers/recurrent.py b/tensorlayer/layers/recurrent.py
index 05fe3e094..0bd6315ee 100644
--- a/tensorlayer/layers/recurrent.py
+++ b/tensorlayer/layers/recurrent.py
@@ -219,7 +219,6 @@ def forward(self, inputs, initial_state=None, **kwargs):
 class SimpleRNN(RNN):
     """
     The :class:`SimpleRNN` class is a fixed length recurrent layer for implementing simple RNN.
-    This class is a derived class from :class:`RNN`.
 
     Parameters
     ----------
@@ -230,19 +229,23 @@ class SimpleRNN(RNN):
             - If True, return the last output, "Sequence input and single output"
             - If False, return all outputs, "Synced sequence input and output"
             - In other word, if you want to stack more RNNs on this layer, set to False
+
         In a dynamic model, `return_last_output` can be updated when it is called in customised forward().
         By default, `False`.
     return_seq_2d : boolean
         Only consider this argument when `return_last_output` is `False`
             - If True, return 2D Tensor [batch_size * n_steps, n_hidden], for stacking Dense layer after it.
             - If False, return 3D Tensor [batch_size, n_steps, n_hidden], for stacking multiple RNN after it.
+
         In a dynamic model, `return_seq_2d` can be updated when it is called in customised forward().
         By default, `False`.
     return_last_state: boolean
         Whether to return the last state of the RNN cell. The state is a list of Tensor.
-        For simple RNN, last_state = [last_output];
+        For simple RNN, last_state = [last_output]
+
             - If True, the layer will return outputs and the final state of the cell.
             - If False, the layer will return outputs only.
+
         In a dynamic model, `return_last_state` can be updated when it is called in customised forward().
         By default, `False`.
     in_channels: int
@@ -251,13 +254,15 @@ class SimpleRNN(RNN):
         If None, it will be automatically detected when the layer is forwarded for the first time.
     name : str
         A unique layer name.
-    **kwargs:
-        Advanced arguments to configure the simple RNN cell. Please check tf.keras.layers.SimpleRNNCell.
+    `**kwargs`:
+        Advanced arguments to configure the simple RNN cell.
+        Please check tf.keras.layers.SimpleRNNCell.
 
     Examples
     --------
 
     A simple regression model below.
+
     >>> inputs = tl.layers.Input([batch_size, num_steps, embedding_size])
     >>> rnn_out, lstm_state = tl.layers.SimpleRNN(
     >>>     units=hidden_size, dropout=0.1, # both units and dropout are used to configure the simple rnn cell.
@@ -292,7 +297,6 @@ def __init__(
 class GRURNN(RNN):
     """
     The :class:`GRURNN` class is a fixed length recurrent layer for implementing RNN with GRU cell.
-    This class is a derived class from :class:`RNN`.
 
     Parameters
     ----------
@@ -303,19 +307,23 @@ class GRURNN(RNN):
             - If True, return the last output, "Sequence input and single output"
             - If False, return all outputs, "Synced sequence input and output"
             - In other word, if you want to stack more RNNs on this layer, set to False
+
         In a dynamic model, `return_last_output` can be updated when it is called in customised forward().
         By default, `False`.
     return_seq_2d : boolean
         Only consider this argument when `return_last_output` is `False`
             - If True, return 2D Tensor [batch_size * n_steps, n_hidden], for stacking Dense layer after it.
             - If False, return 3D Tensor [batch_size, n_steps, n_hidden], for stacking multiple RNN after it.
+
         In a dynamic model, `return_seq_2d` can be updated when it is called in customised forward().
         By default, `False`.
     return_last_state: boolean
         Whether to return the last state of the RNN cell. The state is a list of Tensor.
-        For GRU, last_state = [last_output];
+        For GRU, last_state = [last_output]
+
             - If True, the layer will return outputs and the final state of the cell.
             - If False, the layer will return outputs only.
+
         In a dynamic model, `return_last_state` can be updated when it is called in customised forward().
         By default, `False`.
     in_channels: int
@@ -324,13 +332,15 @@ class GRURNN(RNN):
         If None, it will be automatically detected when the layer is forwarded for the first time.
     name : str
         A unique layer name.
-    **kwargs:
-        Advanced arguments to configure the GRU cell. Please check tf.keras.layers.GRUCell.
+    `**kwargs`:
+        Advanced arguments to configure the GRU cell.
+        Please check tf.keras.layers.GRUCell.
 
     Examples
     --------
 
     A simple regression model below.
+
     >>> inputs = tl.layers.Input([batch_size, num_steps, embedding_size])
     >>> rnn_out, lstm_state = tl.layers.GRURNN(
     >>>     units=hidden_size, dropout=0.1, # both units and dropout are used to configure the GRU cell.
@@ -365,7 +375,6 @@ def __init__(
 class LSTMRNN(RNN):
     """
     The :class:`LSTMRNN` class is a fixed length recurrent layer for implementing RNN with LSTM cell.
-    This class is a derived class from :class:`RNN`.
 
     Parameters
     ----------
@@ -376,19 +385,23 @@ class LSTMRNN(RNN):
             - If True, return the last output, "Sequence input and single output"
             - If False, return all outputs, "Synced sequence input and output"
             - In other word, if you want to stack more RNNs on this layer, set to False
+
         In a dynamic model, `return_last_output` can be updated when it is called in customised forward().
         By default, `False`.
     return_seq_2d : boolean
         Only consider this argument when `return_last_output` is `False`
             - If True, return 2D Tensor [batch_size * n_steps, n_hidden], for stacking Dense layer after it.
             - If False, return 3D Tensor [batch_size, n_steps, n_hidden], for stacking multiple RNN after it.
+
         In a dynamic model, `return_seq_2d` can be updated when it is called in customised forward().
         By default, `False`.
     return_last_state: boolean
         Whether to return the last state of the RNN cell. The state is a list of Tensor.
         For LSTM, last_state = [last_output, last_cell_state]
+
             - If True, the layer will return outputs and the final state of the cell.
             - If False, the layer will return outputs only.
+
         In a dynamic model, `return_last_state` can be updated when it is called in customised forward().
         By default, `False`.
     in_channels: int
@@ -397,13 +410,15 @@ class LSTMRNN(RNN):
         If None, it will be automatically detected when the layer is forwarded for the first time.
     name : str
         A unique layer name.
-    **kwargs:
-        Advanced arguments to configure the LSTM cell. Please check tf.keras.layers.LSTMCell.
+    `**kwargs`:
+        Advanced arguments to configure the LSTM cell.
+        Please check tf.keras.layers.LSTMCell.
 
     Examples
     --------
 
     A simple regression model below.
+
     >>> inputs = tl.layers.Input([batch_size, num_steps, embedding_size])
     >>> rnn_out, lstm_state = tl.layers.LSTMRNN(
     >>>     units=hidden_size, dropout=0.1, # both units and dropout are used to configure the LSTM cell.

From da0327be00b16173eba794bef3aaa7eab476e0f6 Mon Sep 17 00:00:00 2001
From: Jingqing  Zhang <jingqing.zhang15@imperial.ac.uk>
Date: Wed, 12 Jun 2019 14:45:16 +0100
Subject: [PATCH 39/39] fix doc rst

---
 docs/modules/models.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/modules/models.rst b/docs/modules/models.rst
index 54520fd70..46b8d7e1b 100644
--- a/docs/modules/models.rst
+++ b/docs/modules/models.rst
@@ -34,12 +34,12 @@ VGG19
 
 SqueezeNetV1
 ----------------
-.. autoclass:: SqueezeNetV1
+.. autofunction:: SqueezeNetV1
 
 MobileNetV1
 ----------------
 
-.. autoclass:: MobileNetV1
+.. autofunction:: MobileNetV1
 
 Seq2seq
 ------------------------