def maxout(inputs,
num_units,
axis=None,
outputs_collections=None,
scope=None):
"""Adds a maxout op which is a max pooling performed in filter/channel
dimension. This can also be used after fully-connected layers to reduce
number of features.
Args:
inputs: A Tensor on which maxout will be performed
num_units: Specifies how many features will remain after max pooling at the
channel dimension. This must be multiple of number of channels.
axis: The dimension where max pooling will be performed. Default is the
last dimension.
outputs_collections: The collections to which the outputs are added.
scope: Optional scope for name_scope.
Returns:
A `Tensor` representing the results of the pooling operation.
Raises:
ValueError: if num_units is not multiple of number of features.
"""
with ops.name_scope(scope, 'MaxOut', [inputs]) as sc:
inputs = ops.convert_to_tensor(inputs)
shape = inputs.get_shape().as_list()
if axis is None:
# Assume that channel is the last dimension
axis = -1
num_channels = shape[axis]
if num_channels % num_units:
raise ValueError('number of features({}) is not '
'a multiple of num_units({})'
.format(num_channels, num_units))
shape[axis] = -1
shape += [num_channels // num_units]
outputs = math_ops.reduce_max(gen_array_ops.reshape(inputs, shape), -1,
keep_dims=False)
return utils.collect_named_outputs(outputs_collections, sc, outputs)
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