tf.contrib.model_pruning.masked_fully_connected

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
)

Defined in tensorflow/contrib/model_pruning/python/layers/layers.py.

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

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.