def cnn(height_a, height_q, count):
question_input = Input(shape=(height_q, 1), name='question_input')
embedding_q = Embedding(input_dim=count, output_dim=128, input_length=height_q)(question_input)
re_q = Reshape((height_q, 128, 1), input_shape=(height_q,))(embedding_q)
conv1_Q = Conv2D(128, (2, 128), activation='sigmoid', padding='valid',
kernel_regularizer=regularizers.l2(0.02),
kernel_initializer=initializers.random_normal(mean=0.0, stddev=0.05))(re_q)
Max1_Q = MaxPooling2D((29, 1), strides=(1, 1), padding='valid')(conv1_Q)
F1_Q = Flatten()(Max1_Q)
Drop1_Q = Dropout(0.5)(F1_Q)
predictQ = Dense(64, activation='relu',
kernel_regularizer=regularizers.l2(0.02),
kernel_initializer=initializers.random_normal(mean=0.0, stddev=0.05))(Drop1_Q)
# kernel_initializer=initializers.random_normal(mean=0.0, stddev=0.01)
answer_input = Input(shape=(height_a, 1), name='answer_input')
embedding_a = Embedding(input_dim=count, output_dim=128, input_length=height_a)(answer_input)
re_a = Reshape((height_a, 128, 1), input_shape=(height_a,))(embedding_a)
conv1_A = Conv2D(128, (2, 128), activation='sigmoid', padding='valid',
kernel_regularizer=regularizers.l2(0.02),
kernel_initializer=initializers.random_normal(mean=0.0, stddev=0.05))(re_a)
Max1_A = MaxPooling2D((399, 1), strides=(1, 1), padding='valid')(conv1_A)
F1_A = Flatten()(Max1_A)
Drop1_A = Dropout(0.5)(F1_A)
predictA = Dense(64, activation='relu',
kernel_regularizer=regularizers.l2(0.02),
kernel_initializer=initializers.random_normal(mean=0.0, stddev=0.05))(Drop1_A)
predictions = merge([predictA, predictQ], mode='dot')
model = Model(inputs=[question_input, answer_input],
outputs=predictions)
model.compile(loss='mean_squared_error',
optimizer=Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0))
# model.compile(loss='mean_squared_error',
# optimizer='nadam')
return model
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