example.py 文件源码

def design_for_residual_blocks(num_channel_input=1):
    ''''''
    model = Sequential() # it's a CONTAINER, not MODEL
    # set numbers
    num_big_blocks = 3
    image_patch_sizes = [[3,3]]*num_big_blocks
    pool_sizes = [(2,2)]*num_big_blocks
    n_features = [128, 256, 512, 512, 1024]
    n_features_next = [256, 512, 512, 512, 1024]
    height_input = 32
    width_input = 32
    for conv_idx in range(num_big_blocks):    
        n_feat_here = n_features[conv_idx]
        # residual block 0
        model.add(residual_blocks.building_residual_block(  (num_channel_input, height_input, width_input),
                                                            n_feat_here,
                                                            kernel_sizes=image_patch_sizes[conv_idx]
                                                            ))

        # residual block 1 (you can add it as you want (and your resources allow..))
        if False:
            model.add(residual_blocks.building_residual_block(  (n_feat_here, height_input, width_input),
                                                                n_feat_here,
                                                                kernel_sizes=image_patch_sizes[conv_idx]
                                                                ))

        # the last residual block N-1
        # the last one : pad zeros, subsamples, and increase #channels
        pad_height = compute_padding_length(height_input, pool_sizes[conv_idx][0], image_patch_sizes[conv_idx][0])
        pad_width = compute_padding_length(width_input, pool_sizes[conv_idx][1], image_patch_sizes[conv_idx][1])
        model.add(ZeroPadding2D(padding=(pad_height,pad_width))) 
        height_input += 2*pad_height
        width_input += 2*pad_width
        n_feat_next = n_features_next[conv_idx]
        model.add(residual_blocks.building_residual_block(  (n_feat_here, height_input, width_input),
                                                            n_feat_next,
                                                            kernel_sizes=image_patch_sizes[conv_idx],
                                                            is_subsample=True,
                                                            subsample=pool_sizes[conv_idx]
                                                            ))

        height_input, width_input = model.output_shape[2:]
        # width_input  = int(width_input/pool_sizes[conv_idx][1])
        num_channel_input = n_feat_next

    # Add average pooling at the end:
    print('Average pooling, from (%d,%d) to (1,1)' % (height_input, width_input))
    model.add(AveragePooling2D(pool_size=(height_input, width_input)))

    return model