-
Notifications
You must be signed in to change notification settings - Fork 0
/
neuralmodel.py
172 lines (137 loc) · 4.69 KB
/
neuralmodel.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
from keras import Sequential, Model
from keras.models import model_from_json
from keras.metrics import categorical_accuracy
from keras.layers.recurrent import LSTM
from keras.layers.embeddings import Embedding
from keras.layers import Input, Conv1D, Concatenate,\
GlobalMaxPool1D, Dense, Lambda, Add, Multiply, Masking, Bidirectional,\
Dropout, TimeDistributed
import keras
from config import MAXLEN_SENTENCE, MAXLEN_WORD
CHAR_EMBEDDING_SIZE = 60
def build_input_network(character_dim):
model = Sequential()
model.add(
Embedding(
character_dim+1,
CHAR_EMBEDDING_SIZE,
input_length=MAXLEN_WORD)
)
model.trainable = True
return model
def build_convolutional_network():
filter_widths_per_layer = list(range(1, 8))
num_filters_per_layer = [
min(200, 50*width) for width in filter_widths_per_layer]
inputs = Input(shape=(MAXLEN_WORD, CHAR_EMBEDDING_SIZE))
layers = []
for index, num_filters in enumerate(num_filters_per_layer):
width = filter_widths_per_layer[index]
layer = Conv1D(num_filters,
width,
activation='tanh',
padding='causal')(inputs)
layers.append(layer)
convolutional_network = Model(
inputs=inputs,
outputs=layers)
return convolutional_network
def build_pooling_network(convolutional_output):
inputs = []
layers = []
for output_index, output in enumerate(convolutional_output):
inputs.append(
Input(
shape=(
output.shape[1],
output.shape[2]))
)
layer = GlobalMaxPool1D()(inputs[output_index])
layers.append(layer)
outputs = Concatenate()(layers)
pooling_network = Model(
inputs=inputs,
outputs=outputs)
return pooling_network
def build_highway_network(maxpooling_output):
inputs = Input(shape=(maxpooling_output.shape[1],))
transform_gate = Dense(maxpooling_output.shape[1],
activation='sigmoid')(inputs)
carry_gate = Lambda(lambda network_input: 1-network_input)(transform_gate)
network = Add()([
Multiply()([
transform_gate,
Dense(maxpooling_output.shape[1],
activation='relu')(inputs)
]),
Multiply()([carry_gate, inputs])
])
return Model(inputs=inputs, outputs=network)
def build_recurrent_network(feature_extract, tag_dim):
hidden_units = 900
rnn = Sequential()
rnn.add(Masking(mask_value=0., input_shape=(MAXLEN_SENTENCE,
MAXLEN_WORD)))
rnn.add(
TimeDistributed(
feature_extract,
input_shape=(MAXLEN_SENTENCE, MAXLEN_WORD)
)
)
rnn.add(
Bidirectional(LSTM(
hidden_units,
return_sequences=True,
dropout=0.5,
recurrent_dropout=0.5
))
)
rnn.add(
Dropout(0.5)
)
rnn.add(
Bidirectional(LSTM(
hidden_units,
return_sequences=True
))
)
rnn.add(
Dropout(0.5)
)
rnn.add(
TimeDistributed(
Dense(tag_dim, activation='sigmoid')
)
)
return rnn
def build_model(x_sample, character_dim, tag_dim):
input_network = build_input_network(character_dim)
embeddings_output = input_network.predict(x_sample)
convolutional_network = build_convolutional_network()
convolutional_output = convolutional_network.predict(embeddings_output)
pooling_network = build_pooling_network(convolutional_output)
maxpooling_output = pooling_network.predict(convolutional_output)
highway_network = build_highway_network(maxpooling_output)
inputs = input_network.inputs
model = input_network(inputs=inputs)
model = convolutional_network(inputs=model)
model = pooling_network(inputs=model)
model = highway_network(inputs=model)
feature_extract = Model(inputs=inputs, outputs=model)
return build_recurrent_network(feature_extract, tag_dim)
def bv_acc(y_true, y_pred):
return categorical_accuracy(y_true[:, :, :13], y_pred[:, :, :13])
def load_model(configuration_path, weights_path):
keras.metrics.bv_acc = bv_acc
json_file = open(configuration_path, 'r')
model_json = json_file.read()
json_file.close()
model = model_from_json(model_json)
model.compile(loss='binary_crossentropy', metrics=[bv_acc],
optimizer='adam')
model.load_weights(weights_path)
return model
def save_model(model, filename):
model_json = model.to_json()
with open(filename, "w") as json_file:
json_file.write(model_json)