ecog_1d_model.py 文件源码

def ecog_1d_model(channels=None, weights=None):

    input_tensor = Input(shape=(1, channels, 1000))
    # Block 1
    x = AveragePooling2D((1, 5), name='pre_pool')(input_tensor)
    x = Convolution2D(4, 1, 3, border_mode='same', name='block1_conv1')(x)
    # x = BatchNormalization(axis=1)(x)
    x = Activation('relu')(x)
    x = MaxPooling2D((1, 3), name='block1_pool')(x)

    # Block 2
    x = Convolution2D(8, 1, 3, border_mode='same', name='block2_conv1')(x)
    # x = BatchNormalization(axis=1)(x)
    x = Activation('relu')(x)
    x = MaxPooling2D((1, 3), name='block2_pool')(x)

    # Block 3
    x = Convolution2D(16, 1, 3, border_mode='same', name='block3_conv1')(x)
    # x = BatchNormalization(axis=1)(x)
    x = Activation('relu')(x)
    x = MaxPooling2D((1, 2), name='block3_pool')(x)

    # Block 4
    # x = Convolution2D(32, 1, 3, border_mode='same', name='block4_conv1')(x)
    # x = BatchNormalization(axis=1)(x)
    # x = Activation('relu')(x)
    # x = MaxPooling2D((1, 2), name='block4_pool')(x)

    x = Flatten(name='flatten')(x)
    x = Dropout(0.5)(x)
    x = Dense(64, W_regularizer=l2(0.01), name='fc1')(x)
    #x = BatchNormalization()(x)
    #x = Activation('relu')(x)
    #x = Dropout(0.5)(x)
    #x = Dense(1, name='predictions')(x)
    # x = BatchNormalization()(x)
    predictions = Activation('sigmoid')(x)

    # for layer in base_model.layers[:10]:
    #    layer.trainable = False
    model = Model(input=input_tensor, output=predictions)
    if weights is not None:
        model.load_weights(weights)

    return model