tf.contrib.layers.conv2d

Adds an N-D convolution followed by an optional batch_norm layer.

View aliases

Main aliases

`tf.contrib.layers.convolution2d`

tf.contrib.layers.conv2d(
    inputs, num_outputs, kernel_size, stride=1, padding='SAME', data_format=None,
    rate=1, 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
)

It is required that 1 <= N <= 3.

convolution creates a variable called weights, representing the convolutional kernel, that is convolved (actually cross-correlated) with the inputs to produce a Tensor of activations. 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 activations. Finally, if activation_fn is not None, it is applied to the activations as well.

Performs atrous convolution with input stride/dilation rate equal to rate if a value > 1 for any dimension of rate is specified. In this case stride values != 1 are not supported.

Args
inputs	A Tensor of rank N+2 of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC".
num_outputs	Integer, the number of output filters.
kernel_size	A sequence of N positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
stride	A sequence of N positive integers specifying the stride at which to compute output. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any rate value != 1.
padding	One of "VALID" or "SAME".
data_format	A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".
rate	A sequence of N positive integers specifying the dilation rate to use for atrous convolution. Can be a single integer to specify the same value for all spatial dimensions. Specifying any rate value != 1 is incompatible with specifying any stride value != 1.
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 collection 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.
conv_dims	Optional convolution dimensionality, when set it would use the corresponding convolution (e.g. 2 for Conv 2D, 3 for Conv 3D, ..). When leaved to None it would select the convolution dimensionality based on the input rank (i.e. Conv ND, with N = input_rank - 2).

Returns
A tensor representing the output of the operation.

Raises
ValueError	If data_format is invalid.
ValueError	Both 'rate' and stride are not uniformly 1.