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September 10, 2020 23:41
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from tensorflow.keras.layers import ( | |
Attention, | |
Bidirectional, | |
Concatenate, | |
Dropout, | |
Embedding, | |
Dense, | |
GRU, | |
TimeDistributed, | |
Input | |
) | |
from tensorflow.keras import Sequential, Model | |
from sklearn.model_selection import train_test_split | |
from train.attention import AttentionLayer | |
EMBED_SIZE = 100 | |
MAX_SENTENCE_LEN = 580 | |
MAX_WORD_LEN = 100 | |
def get_attention_model(vocab_size, embedding_matrix, embed_size=100): | |
word_input = Input(shape=(MAX_WORD_LEN,), dtype='int32', name='word_input') | |
word_sequence = Embedding( | |
vocab_size, | |
embed_size, | |
input_length=MAX_WORD_LEN, # length of sentences in doc | |
weights=[embedding_matrix], | |
trainable=False)(word_input) | |
## attention at words | |
word_gru = Bidirectional(GRU( | |
50, | |
activation="tanh", | |
recurrent_activation="sigmoid", | |
use_bias=True, | |
kernel_initializer="glorot_uniform", | |
recurrent_initializer="orthogonal", | |
return_sequences=True))(word_sequence) | |
word_dense = Dense(100, activation='relu', name="word_dense")(word_gru) | |
word_att, word_coeff = AttentionLayer(embed_size, True, name="word_attention")(word_dense) | |
word_encoder = Model(inputs=word_input, outputs=word_att, name="word_encoder") | |
## attention at sentence level | |
sent_input = Input(shape=(MAX_SENTENCE_LEN, MAX_WORD_LEN), dtype='int32', name='sent_input') | |
sent_encoder = TimeDistributed(word_encoder, name="sent_linking")(sent_input) | |
sent_gru = Bidirectional(GRU( | |
50, | |
return_sequences=True))(sent_encoder) | |
sent_dense = Dense(100, activation="relu", name="sent_dense")(sent_gru) | |
sent_att, sent_coeff = AttentionLayer(embed_size, True, name="sent_attention")(sent_dense) | |
sent_drop = Dropout(0.5)(sent_att) | |
preds = Dense(1, activation="softmax", name="prediction")(sent_drop) | |
# # model | |
han_model = Model(sent_input, preds, name="han_model") | |
han_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) | |
print(word_encoder.summary()) | |
print(han_model.summary()) | |
return han_model |
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