models.py 文件源码

def MyCNN_Keras2(X, nb_classes, nb_layers=4):
    from keras import backend as K
    K.set_image_data_format('channels_first')

    nb_filters = 32  # number of convolutional filters = "feature maps"
    kernel_size = (3, 3)  # convolution kernel size
    pool_size = (2, 2)  # size of pooling area for max pooling
    cl_dropout = 0.5    # conv. layer dropout
    dl_dropout = 0.8    # dense layer dropout

    channels = X.shape[1]   # channels = 1 for mono, 2 for stereo

    print(" MyCNN_Keras2: X.shape = ",X.shape,", channels = ",channels)
    input_shape = (channels, X.shape[2], X.shape[3])
    model = Sequential()
    #model.add(Conv2D(nb_filters, kernel_size, border_mode='valid', input_shape=input_shape))
    model.add(Conv2D(nb_filters, kernel_size, border_mode='valid', input_shape=input_shape))
    model.add(BatchNormalization(axis=1))
    model.add(Activation('relu'))

    for layer in range(nb_layers-1):   # add more layers than just the first
        model.add(Conv2D(nb_filters, kernel_size))
        model.add(BatchNormalization(axis=1))
        model.add(ELU(alpha=1.0))
        model.add(MaxPooling2D(pool_size=pool_size))
        model.add(Dropout(cl_dropout))

    model.add(Flatten())
    model.add(Dense(128))
    model.add(Activation('relu'))
    model.add(Dropout(dl_dropout))
    model.add(Dense(nb_classes))
    model.add(Activation("softmax"))
    model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])
    return model