tf.nn.depthwise_conv2d_native_backprop_input

tf.nn.depthwise_conv2d_native_backprop_input

tf.nn.depthwise_conv2d_native_backprop_input

depthwise_conv2d_native_backprop_input(
    input_sizes,
    filter,
    out_backprop,
    strides,
    padding,
    data_format=None,
    name=None
)

Defined in tensorflow/python/ops/gen_nn_ops.py.

See the guide: Neural Network > Convolution

Computes the gradients of depthwise convolution with respect to the input.

Args:

• input_sizes: A Tensor of type int32. An integer vector representing the shape of input, based on data_format. For example, if data_format is 'NHWC' then input is a 4-D [batch, height, width, channels] tensor.
• filter: A Tensor. Must be one of the following types: float32, float64. 4-D with shape [filter_height, filter_width, in_channels, depthwise_multiplier].
• out_backprop: A Tensor. Must have the same type as filter. 4-D with shape based on data_format. For example, if data_format is 'NHWC' then out_backprop shape is [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.
• strides: A list of ints. The stride of the sliding window for each dimension of the input of the convolution.
• padding: A string from: "SAME", "VALID". The type of padding algorithm to use.
• data_format: An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
• name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as filter. 4-D with shape according to data_format. For example, if data_format is 'NHWC', output shape is [batch, in_height, in_width, in_channels]. Gradient w.r.t. the input of the convolution.