tf.contrib.model_pruning.masked_conv2d

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 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 any stride value != 1 is incompatible with specifying any rate value != 1.
  • padding: One of "VALID" or "SAME".
  • data_format: A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_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 any rate value != 1 is incompatible with specifying any stride 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 of biases. If normalizer_fn is provided then biases_initializer and biases_regularizer are ignored and biases are not created nor added. default set to None for no normalizer function
  • normalizer_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: If True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
  • scope: Optional scope for variable_scope.

Returns:

A tensor representing the output of the operation.

Raises:

  • ValueError: If data_format is invalid.
  • ValueError: Both 'rate' and stride are not uniformly 1.