tf.contrib.layers.fully_connected()

tf.contrib.layers.fully_connected(*args, **kwargs)

Adds a fully connected layer.

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 with at least rank 2 and 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, set 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.