tf.contrib.layers.conv2d
tf.contrib.layers.conv3d
tf.contrib.layers.convolution2d
tf.contrib.layers.convolution3d
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 )
Defined in tensorflow/contrib/layers/python/layers/layers.py
.
See the guide: Layers (contrib) > Higher level ops for building neural network layers
Adds an N-D convolution followed by an optional batch_norm layer.
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.
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 functionnormalizer_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
.A tensor representing the output of the operation.
ValueError
: If data_format
is invalid.ValueError
: Both 'rate' and stride
are not uniformly 1.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/layers/conv2d