tf.contrib.model_pruning.masked_fully_connected

Adds a sparse fully connected layer. The weight matrix is masked.

tf.contrib.model_pruning.masked_fully_connected(
    inputs, num_outputs, 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
)

fully_connected creates a variable called weights, representing a fully connected weight matrix, which is multiplied by the inputs to produce a Tensor of hidden units. 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 hidden units. Finally, if activation_fn is not None, it is applied to the hidden units as well.

Note: that if inputs have a rank greater than 2, then inputs is flattened prior to the initial matrix multiply by weights.

Args
inputs	A tensor of at least rank 2 and static value for the last dimension; i.e. [batch_size, depth], [None, None, None, channels].
num_outputs	Integer or long, the number of output units in the layer.
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 collections 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
The tensor variable representing the result of the series of operations.

Raises
ValueError	If x has rank less than 2 or if its last dimension is not set.