audiomodels.py 文件源码

def cnn_melspect_1D(input_shape):
    kernel_size = 3
    #activation_func = LeakyReLU()
    activation_func = Activation('relu')
    inputs = Input(input_shape)

    # Convolutional block_1
    conv1 = Conv1D(32, kernel_size)(inputs)
    act1 = activation_func(conv1)
    bn1 = BatchNormalization()(act1)
    pool1 = MaxPooling1D(pool_size=2, strides=2)(bn1)

    # Convolutional block_2
    conv2 = Conv1D(64, kernel_size)(pool1)
    act2 = activation_func(conv2)
    bn2 = BatchNormalization()(act2)
    pool2 = MaxPooling1D(pool_size=2, strides=2)(bn2)

    # Convolutional block_3
    conv3 = Conv1D(128, kernel_size)(pool2)
    act3 = activation_func(conv3)
    bn3 = BatchNormalization()(act3)

    # Global Layers
    gmaxpl = GlobalMaxPooling1D()(bn3)
    gmeanpl = GlobalAveragePooling1D()(bn3)
    mergedlayer = concatenate([gmaxpl, gmeanpl], axis=1)

    # Regular MLP
    dense1 = Dense(512,
        kernel_initializer='glorot_normal',
        bias_initializer='glorot_normal')(mergedlayer)
    actmlp = activation_func(dense1)
    reg = Dropout(0.5)(actmlp)

    dense2 = Dense(512,
        kernel_initializer='glorot_normal',
        bias_initializer='glorot_normal')(reg)
    actmlp = activation_func(dense2)
    reg = Dropout(0.5)(actmlp)

    dense2 = Dense(10, activation='softmax')(reg)

    model = Model(inputs=[inputs], outputs=[dense2])
    return model