tf.contrib.layers.fully_connected详解

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博主

https://www.jianshu.com/p/673fd07954e9

tf.contrib.layers.fully_connected（F, num_outputs,activation_fn）

F ---[batch_size,images_pixels],tensor

num_outputs --- numbers of outputs,[batch_size,num_outputs]

activation_fn ---采用指定的非线性激励函数，默认不是None,如果不需要的话，要赋值None

 

 

API解释

https://docs.w3cub.com/tensorflow~python/tf/contrib/layers/fully_connected/

tf.contrib.layers.fully_connected

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
)

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

See the guide: Layers (contrib) > Higher level ops for building neural network layers

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 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.

 

 

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