tf.contrib.layers.fully_connected

Adds a fully connected layer.

tf.contrib.layers.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.

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.

tf.contrib.layers.fully_connected

Args

Returns

Raises