Aliases:
tf.contrib.model_pruning.masked_conv2d
tf.contrib.model_pruning.masked_convolution
tf.contrib.model_pruning.masked_conv2d(
inputs,
num_outputs,
kernel_size,
stride=1,
padding='SAME',
data_format=None,
rate=1,
activation_fn=tf.nn.relu,
normalizer_fn=None,
normalizer_params=None,
weights_initializer=initializers.xavier_initializer(),
weights_regularizer=None,
biases_initializer=tf.zeros_initializer(),
biases_regularizer=None,
reuse=None,
variables_collections=None,
outputs_collections=None,
trainable=True,
scope=None
)
Defined in tensorflow/contrib/model_pruning/python/layers/layers.py
.
Adds an 2D convolution followed by an optional batch_norm layer. The layer creates a mask variable on top of the weight variable. The input to the convolution operation is the elementwise multiplication of the mask variable and the weigh
It is required that 1 <= N <= 3.
convolution
creates a variable called weights
, representing the
convolutional kernel, that is convolved (actually cross-correlated) with the
inputs
to produce a Tensor
of activations. If a normalizer_fn
is
provided (such as batch_norm
), it is then applied. Otherwise, if
normalizer_fn
is None and a biases_initializer
is provided then a biases
variable would be created and added the activations. Finally, if
activation_fn
is not None
, it is applied to the activations as well.
Performs atrous convolution with input stride/dilation rate equal to rate
if a value > 1 for any dimension of rate
is specified. In this case
stride
values != 1 are not supported.
Args:
inputs
: A Tensor of rank N+2 of shape[batch_size] + input_spatial_shape + [in_channels]
if data_format does not start with "NC" (default), or[batch_size, in_channels] + input_spatial_shape
if data_format starts with "NC".num_outputs
: Integer, the number of output filters.kernel_size
: A sequence of N positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.stride
: A sequence of N positive integers specifying the stride at which to compute output. Can be a single integer to specify the same value for all spatial dimensions. Specifying anystride
value != 1 is incompatible with specifying anyrate
value != 1.padding
: One of"VALID"
or"SAME"
.data_format
: A string or None. Specifies whether the channel dimension of theinput
and output is the last dimension (default, or ifdata_format
does not start with "NC"), or the second dimension (ifdata_format
starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".rate
: A sequence of N positive integers specifying the dilation rate to use for atrous convolution. Can be a single integer to specify the same value for all spatial dimensions. Specifying anyrate
value != 1 is incompatible with specifying anystride
value != 1.activation_fn
: Activation function. The default value is a ReLU function. Explicitly set it to None to skip it and maintain a linear activation.normalizer_fn
: Normalization function to use instead ofbiases
. Ifnormalizer_fn
is provided thenbiases_initializer
andbiases_regularizer
are ignored andbiases
are not created nor added. default set to None for no normalizer functionnormalizer_params
: Normalization function parameters.weights_initializer
: An initializer for the weights.weights_regularizer
: Optional regularizer for the weights.biases_initializer
: An initializer for the biases. If None skip biases.biases_regularizer
: Optional regularizer for the biases.reuse
: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.variables_collections
: Optional list of collections for all the variables or a dictionary containing a different list of collection per variable.outputs_collections
: Collection to add the outputs.trainable
: IfTrue
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable).scope
: Optional scope forvariable_scope
.
Returns:
A tensor representing the output of the operation.
Raises:
ValueError
: Ifdata_format
is invalid.ValueError
: Both 'rate' andstride
are not uniformly 1.