tf.contrib.layers.conv2d_in_plane

Performs the same in-plane convolution to each channel independently.

View aliases

Main aliases

`tf.contrib.layers.convolution2d_in_plane`

tf.contrib.layers.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
)

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 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.

Returns
A Tensor representing the output of the operation.