tf.contrib.layers.convolution2d_in_plane()

tf.contrib.layers.convolution2d_in_plane(*args, **kwargs)

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, set 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 dictionay 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.