tf.contrib.layers.separable_convolution2d()

tf.contrib.layers.separable_convolution2d(*args, **kwargs)

Adds a depth-separable 2D convolution with optional batch_norm layer.

This op first performs a depthwise convolution that acts separately on channels, creating a variable called depthwise_weights. If num_outputs is not None, it adds a pointwise convolution that mixes channels, creating a variable called pointwise_weights. Then, if batch_norm_params is None, it adds bias to the result, creating a variable called 'biases', otherwise it adds a batch normalization layer. It finally applies an activation function to produce the end result.

Args:

• inputs: a tensor of size [batch_size, height, width, channels].
• num_outputs: the number of pointwise convolution output filters. If is None, then we skip the pointwise convolution stage.
• kernel_size: a list of length 2: [kernel_height, kernel_width] of of the filters. Can be an int if both values are the same.
• depth_multiplier: the number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to num_filters_in * depth_multiplier.
• stride: a list of length 2: [stride_height, stride_width], specifying the depthwise convolution stride. Can be an int if both strides are the same.
• padding: one of 'VALID' or 'SAME'.
• 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: whether or not the variables should be trainable or not.
• scope: Optional scope for variable_scope.

Returns:

A Tensor representing the output of the operation.