cnn_theano_plot_filters.py 文件源码

def convpool(X, W, b, poolsize=(2, 2)):
    conv_out = conv2d(input=X, filters=W)

    # downsample each feature map individually, using maxpooling
    pooled_out = downsample.max_pool_2d(
        input=conv_out,
        ds=poolsize,
        ignore_border=True
    )

    # add the bias term. Since the bias is a vector (1D array), we first
    # reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will
    # thus be broadcasted across mini-batches and feature map
    # width & height
    # return T.tanh(pooled_out + b.dimshuffle('x', 0, 'x', 'x'))
    return relu(pooled_out + b.dimshuffle('x', 0, 'x', 'x'))