contrib.layers.conv2d_in_plane
tf.contrib.layers.conv2d_in_plane
tf.contrib.layers.conv2d_in_plane
tf.contrib.layers.convolution2d_in_plane
conv2d_in_plane( inputs, kernel_size, stride=1, padding='SAME', 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
Performs the same in-plane convolution to each channel independently.
This is useful for performing various simple channel-independent convolution operations such as image gradients:
image = tf.constant(..., shape=(16, 240, 320, 3)) vert_gradients = layers.conv2d_in_plane(image, kernel=[1, -1], kernel_size=[2, 1]) horz_gradients = layers.conv2d_in_plane(image, kernel=[1, -1], kernel_size=[1, 2])
Args:
-
inputs
: A 4-D tensor with dimensions [batch_size, height, width, channels]. -
kernel_size
: A list of length 2 holding the [kernel_height, kernel_width] of of the pooling. Can be an int if both values are the same. -
stride
: A list of length 2[stride_height, stride_width]
. Can be an int if both strides are the same. Note that presently both strides must have the same value. -
padding
: The padding type to use, either 'SAME' or 'VALID'. -
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 ofbiases
. Ifnormalizer_fn
is provided thenbiases_initializer
andbiases_regularizer
are ignored andbiases
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
: IfTrue
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable). -
scope
: Optional scope forvariable_scope
.
Returns:
A Tensor
representing the output of the operation.
© 2017 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_in_plane