# HG changeset patch # User James Bergstra # Date 1256585738 14400 # Node ID cfdaa56c66e804bb21241ce8c77f4c8577674b80 # Parent 77e6b2d3e5e576ccc243a6fba991357798848480 added function documentation to lecun layer diff -r 77e6b2d3e5e5 -r cfdaa56c66e8 pylearn/shared/layers/lecun1998.py --- a/pylearn/shared/layers/lecun1998.py Thu Oct 22 19:10:15 2009 -0400 +++ b/pylearn/shared/layers/lecun1998.py Mon Oct 26 15:35:38 2009 -0400 @@ -24,6 +24,16 @@ # - more? def __init__(self, input, w, b, conv_op, ds_op, squash_op, params): + """ + :param input: symbolic images. Shape: (n_examples, n_images, n_rows, n_cols) + :param w: symbolic kernels. Shape: (n_kernels, n_images, filter_height, filter_width) + :param b: symbolic biases Shape (n_kernels) + :param conv_op: Typically, an instantiation of ConvOp. + :param ds_op: A downsampling op instance (such as of DownsampleFactorMax) + :param squash_op: an elemwise squashing function (typically tanh) + :param params: a list of shared variables that parametrize this layer (typically w and + b) + """ if input.ndim != 4: raise TypeError(input) if w.ndim != 4: @@ -40,6 +50,28 @@ ignore_border=True, conv_subsample=(1,1), dtype=None, conv_mode='valid', pool_type='max', squash_fn=tensor.tanh): """ + Allocate a LeNetConvPool layer with shared variable internal parameters. + + :param rng: a random number generator used to initialize weights + :param input: symbolic images. Shape: (n_examples, n_imgs, img_shape[0], img_shape[1]) + :param n_examples: input's shape[0] at runtime + :param n_imgs: input's shape[1] at runtime + :param img_shape: input's shape[2:4] at runtime + :param n_filters: the number of filters to apply to the image. + :param filter_shape: the size of the filters to apply + :type filter_shape: pair (rows, cols) + :param poolsize: the downsampling factor + :type poolsize: pair (rows, cols) + :param ignore_border: True means the downsampling should skip the scrap around the + edges if there is any. + :param conv_subsample: by how much should the convolution subsample the image? + :type conv_subsample: pair (rows, cols) + :param dtype: the dtype for the internally allocated parameters. This defaults to the + input's dtype. + :param conv_mode: The convolution mode ('full' or 'valid') + :param pool_type: Must be 'max' for now (reserved for different kinds of pooling) + :param squash_fn: The activation function for this layer + :type squash_fn: A one-to-one elemwise function such as tanh or logistic sigmoid. """ if pool_type != 'max': # LeNet5 actually used averaging filters. Consider implementing 'mean'