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tf.contrib.layers.conv2d_in_plane

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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])

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 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.

A Tensor representing the output of the operation.