-
Notifications
You must be signed in to change notification settings - Fork 0
/
Model.py
195 lines (135 loc) · 7.67 KB
/
Model.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
from keras.models import Model
from datetime import datetime
import tensorflow as tf
from tensorflow.keras.layers import Input, LSTM, Embedding, Dense, TimeDistributed
from keras.callbacks import EarlyStopping
class LSTM_Model:
def __init__(self,max_len_, x_max_words, y_max_words, x_data, y_data):
self.max_len_ = max_len_
self.x_max_words = x_max_words
self.y_max_words = y_max_words
self.x_data = x_data
self.y_data = y_data
self.embedding_dim = 300
self.latent_dim = 200
def print_func(self,msg):
print(f"[+] {msg} | {datetime.now().strftime('%d/%m/%Y %H:%M:%S')}\n")
def main_model(self):
inputs = Input(shape=[self.max_len_],)
enc_emb = Embedding(self.x_max_words, self.embedding_dim,trainable=True)(inputs)
# Encoder LSTM 1
encoder_lstm1 = LSTM(self.latent_dim, return_sequences=True,return_state=True, dropout=0.4,recurrent_dropout=0.4)
(encoder_output1, state_h1, state_c1) = encoder_lstm1(enc_emb)
# Encoder LSTM 2
encoder_lstm2 = LSTM(self.latent_dim, return_sequences=True,return_state=True, dropout=0.4,recurrent_dropout=0.4)
(encoder_output2, state_h2, state_c2) = encoder_lstm2(encoder_output1)
# Encoder LSTM 3
encoder_lstm3 = LSTM(self.latent_dim, return_state=True,return_sequences=True, dropout=0.4,recurrent_dropout=0.4)
(encoder_outputs3, state_h3, state_c3) = encoder_lstm3(encoder_output2)
# Encoder LSTM 4
encoder_lstm4 = LSTM(self.latent_dim, return_state=True,return_sequences=True, dropout=0.4,recurrent_dropout=0.4)
(encoder_output4, state_h4, state_c4) = encoder_lstm4(encoder_outputs3)
# Encoder LSTM 5
encoder_lstm5 = LSTM(self.latent_dim, return_state=True,return_sequences=True, dropout=0.4,recurrent_dropout=0.4)
(encoder_output5, state_h5, state_c5) = encoder_lstm5(encoder_output4)
# Encoder LSTM 6
encoder_lstm6 = LSTM(self.latent_dim, return_state=True,return_sequences=True, dropout=0.4,recurrent_dropout=0.4)
(encoder_output6, state_h6, state_c6) = encoder_lstm6(encoder_output5)
# Encoder LSTM 7
encoder_lstm7 = LSTM(self.latent_dim, return_state=True,return_sequences=True, dropout=0.4,recurrent_dropout=0.4)
(encoder_outputs, state_h, state_c) = encoder_lstm7(encoder_output6)
# DECODER
decoder_inputs = Input(shape=(None, ))
# Embedding layer
dec_emb_layer = Embedding(self.y_max_words, self.embedding_dim, trainable=True)
dec_emb = dec_emb_layer(decoder_inputs)
# Decoder LSTM
decoder_lstm = LSTM(self.latent_dim, return_sequences=True, return_state=True, dropout=0.4,recurrent_dropout=0.2)
(decoder_outputs, decoder_fwd_state, decoder_back_state) = decoder_lstm(dec_emb, initial_state=[state_h, state_c])
# Dense layer
decoder_dense = TimeDistributed(Dense(self.y_max_words, activation='softmax'))
decoder_outputs = decoder_dense(decoder_outputs)
# Define the model
model = Model([inputs, decoder_inputs], decoder_outputs)
# =============================================
# saving model.summry
self.print_func('* saving model summary...')
with open('./report/modelsummary.txt', 'w') as f:
model.summary(print_fn=lambda x: f.write(x + '\n'))
self.print_func('* saving model summary...')
self.print_func('* saving model before training...')
model.save('./report/model_before_training_v1.h5')
self.print_func('* model saved.')
self.model = model
self.Training()
# ENCODER MODEL
self.encoder_model(inputs, encoder_outputs, state_h, state_c)
# DECODER MODEL
self.decoder_model(dec_emb_layer, decoder_inputs, decoder_lstm, decoder_dense)
def Training(self):
"""
compailing and training the model
"""
# compiling the model
self.model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy')
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=2)
# ================================
self.print_func('STARTING TO TRAIN')
self.model.fit(
[self.x_data['x_tr'], self.x_data['y_tr'][:, :-1]],
self.x_data['y_tr'].reshape(self.x_data['y_tr'].shape[0], self.x_data['y_tr'].shape[1], 1)[:, 1:],
epochs=100,
callbacks=[es],
batch_size=128,
validation_data=([self.y_data['x_val'], self.y_data['y_val'][:, :-1]],
self.y_data['y_val'].reshape(self.y_data['y_val'].shape[0], self.y_data['y_val'].shape[1], 1)[:,1:]),
)
# ================================
self.print_func('DONE TRAINING')
self.print_func('* saving model after training... ')
self.model.save('./report/model_after_training_v1.h5')
self.print_func('* model saved.')
self.print_func('* starting to evaluate')
model_evaluation = self.model.evaluate([self.y_data['x_val'], self.y_data['y_val'][:, :-1]],self.y_data['y_val'].reshape(self.y_data['y_val'].shape[0], self.y_data['y_val'].shape[1], 1)[:,1:])
print(f'model Test accuracy: {model_evaluation * 100} \n')
with open('./report/model_evaluation.txt', 'w+') as f:
f.write('================================================')
f.write(f'model Test accuracy: {model_evaluation * 100} \n')
f.write('================================================')
f.close()
# encoder model function
def encoder_model(self, inputs, encoder_outputs, state_h, state_c):
self.print_func('CREATING ENCODER MODEL...')
encoder_model = Model(inputs=inputs, outputs=[encoder_outputs,state_h, state_c])
self.print_func('ENCODER MODEL CREATED.')
self.print_func('saving encoder model...')
encoder_model.save('./report/encoder_model_v1.h5')
self.print_func('encoder model saved.')
return encoder_model
# decoder model function
def decoder_model(self, dec_emb_layer, decoder_inputs, decoder_lstm, decoder_dense):
self.print_func('CREATING DECODER MODEL...')
# Below tensors will hold the states of the previous time step
decoder_state_input_h = Input(shape=(self.latent_dim,))
decoder_state_input_c = Input(shape=(self.latent_dim,))
decoder_hidden_state_input = Input(shape=(self.max_len_, self.latent_dim))
# Get the embeddings of the decoder sequence
dec_emb2 = dec_emb_layer(decoder_inputs)
# To predict the next word in the sequence, set the initial states to the states from the previous time step
(decoder_outputs2, state_h2, state_c2) = decoder_lstm(dec_emb2,
initial_state=[decoder_state_input_h, decoder_state_input_c])
# A dense softmax layer to generate prob dist. over the target vocabulary
decoder_outputs2 = decoder_dense(decoder_outputs2)
# Final decoder model
decoder_model = Model([decoder_inputs] + [decoder_hidden_state_input,
decoder_state_input_h, decoder_state_input_c],
[decoder_outputs2] + [state_h2, state_c2])
self.print_func('DECODER MODEL CREATED. ')
self.print_func('saving decoder model...')
decoder_model.save('./report/decoder_model_v1.h5')
self.print_func('decoder model saved.')
return decoder_model
def run(self):
self.print_func('starting to create models and training progress saving models as well')
self.main_model()
self.print_func('model trainging and saving is done')