Created
August 1, 2019 12:32
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from keras.layers import Input, Dense, Embedding, Concatenate, Activation, \ | |
Dropout, LSTM, Bidirectional, GlobalMaxPooling1D, GaussianNoise | |
from keras.models import Model | |
def buildModel(embeddings_matrix, sequence_length, lstm_dim, hidden_layer_dim, num_classes, | |
noise=0.1, dropout_lstm=0.2, dropout=0.2): | |
turn1_input = Input(shape=(sequence_length,), dtype='int32') | |
turn2_input = Input(shape=(sequence_length,), dtype='int32') | |
turn3_input = Input(shape=(sequence_length,), dtype='int32') | |
embedding_dim = embeddings_matrix.shape[1] | |
embeddingLayer = Embedding(embeddings_matrix.shape[0], | |
embedding_dim, | |
weights=[embeddings_matrix], | |
input_length=sequence_length, | |
trainable=False) | |
turn1_branch = embeddingLayer(turn1_input) | |
turn2_branch = embeddingLayer(turn2_input) | |
turn3_branch = embeddingLayer(turn3_input) | |
turn1_branch = GaussianNoise(noise, input_shape=(None, sequence_length, embedding_dim))(turn1_branch) | |
turn2_branch = GaussianNoise(noise, input_shape=(None, sequence_length, embedding_dim))(turn2_branch) | |
turn3_branch = GaussianNoise(noise, input_shape=(None, sequence_length, embedding_dim))(turn3_branch) | |
lstm1 = Bidirectional(LSTM(lstm_dim, dropout=dropout_lstm)) | |
lstm2 = Bidirectional(LSTM(lstm_dim, dropout=dropout_lstm)) | |
turn1_branch = lstm1(turn1_branch) | |
turn2_branch = lstm2(turn2_branch) | |
turn3_branch = lstm1(turn3_branch) | |
x = Concatenate(axis=-1)([turn1_branch, turn2_branch, turn3_branch]) | |
x = Dropout(dropout)(x) | |
x = Dense(hidden_layer_dim, activation='relu')(x) | |
output = Dense(num_classes, activation='softmax')(x) | |
model = Model(inputs=[turn1_input, turn2_input, turn3_input], outputs=output) | |
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc']) | |
return model | |
model = buildModel(embeddings_matrix, MAX_SEQUENCE_LENGTH, lstm_dim=64, hidden_layer_dim=30, num_classes=4) |
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