Aliases:
tf.contrib.layers.conv2d_in_plane
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
)
Defined in tensorflow/contrib/layers/python/layers/layers.py
.
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 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
: 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.