neuralnets.py 文件源码

def classifier_layers(x, input_shape, stage_num, trainable=False):

    # compile times on theano tend to be very high, so we use smaller ROI pooling regions to workaround
    # (hence a smaller stride in the region that follows the ROI pool)
    if K.backend() == 'tensorflow':
        x = conv_block_td(x, 3, [512, 512, 1024], stage=stage_num, block='a', input_shape=input_shape, strides=(1, 2), trainable=trainable)
    elif K.backend() == 'theano':
        x = conv_block_td(x, 3, [512, 512, 1024], stage=stage_num, block='a', input_shape=input_shape, strides=(1, 1), trainable=trainable)

    print 'INFO: Classifier layers x block a: ', x
    x = identity_block_td(x, 3, [512, 512, 1024], stage=stage_num, block='c', trainable=trainable)
    print 'INFO: Classifier layers x block b: ', x
    x = identity_block_td(x, 3, [512, 512, 1024], stage=stage_num, block='d', trainable=trainable)
    print 'INFO: Classifier layers x block c: ', x

    #x = TimeDistributed(AveragePooling2D((2, 1)), name='avg_pool')(x)

    return x