CNNModel3.py 文件源码

def cnn(height, width):
    question_input = Input(shape=(height, width, 1), name='question_input')
    conv1_Q = Conv2D(512, (2, 320), activation='sigmoid', padding='valid',
                     kernel_regularizer=regularizers.l2(0.01),
                     kernel_initializer=initializers.random_normal(mean=0.0, stddev=0.02))(question_input)
    Max1_Q = MaxPooling2D((29, 1), strides=(1, 1), padding='valid')(conv1_Q)
    F1_Q = Flatten()(Max1_Q)
    Drop1_Q = Dropout(0.25)(F1_Q)
    predictQ = Dense(32, activation='relu',
                     kernel_regularizer=regularizers.l2(0.01),
                     kernel_initializer=initializers.random_normal(mean=0.0, stddev=0.02))(Drop1_Q)
    prediction2 = Dropout(0.25)(predictQ)
    predictions = Dense(1, activation='relu')(prediction2)
    model = Model(inputs=[question_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