Nn Ops

Performs average pooling on the input.

Performs 3D average pooling on the input.

Computes gradients of average pooling function.

Adds bias to value.

The backward operation for "BiasAdd" on the "bias" tensor.

Computes a 2-D convolution given 4-D input and filter tensors.

Computes the gradients of convolution with respect to the filter.

Computes the gradients of convolution with respect to the input.

Computes a 3-D convolution given 5-D input and filter tensors.

Computes the gradients of 3-D convolution with respect to the filter.

Computes the gradients of 3-D convolution with respect to the input.

Returns the dimension index in the destination data format given the one in.

Returns the permuted vector/tensor in the destination data format given the.

Computes a 2-D depthwise convolution given 4-D input and filter tensors.

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

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

Computes the grayscale dilation of 4-D input and 3-D filter tensors.

Computes the gradient of morphological 2-D dilation with respect to the filter.

Computes the gradient of morphological 2-D dilation with respect to the input.

Computes exponential linear: exp(features) - 1 if < 0, features otherwise.

Performs fractional average pooling on the input.

Performs fractional max pooling on the input.

Batch normalization.

Gradient for batch normalization.

Gradient for batch normalization.

Gradient for batch normalization.

Batch normalization.

Batch normalization.

Performs a padding as a preprocess during a convolution.

Performs a resize and padding as a preprocess during a convolution.

Says whether the targets are in the top K predictions.

Says whether the targets are in the top K predictions.

L2 Loss.

Local Response Normalization.

Computes log softmax activations.

Performs max pooling on the input.

Performs 3D max pooling on the input.

Computes gradients of max pooling function.

Computes second-order gradients of the maxpooling function.

Computes second-order gradients of the maxpooling function.

Computes second-order gradients of the maxpooling function.

Computes second-order gradients of the maxpooling function.

Computes gradients of the maxpooling function.

Performs max pooling on the input.

Performs max pooling on the input and outputs both max values and indices.

Finds values of the n-th order statistic for the last dimension.

Produces the average pool of the input tensor for quantized types.

Quantized Batch normalization.

Adds Tensor 'bias' to Tensor 'input' for Quantized types.

Computes a 2D convolution given quantized 4D input and filter tensors.

Produces the max pool of the input tensor for quantized types.

Computes Quantized Rectified Linear: max(features, 0)

Computes Quantized Rectified Linear 6: min(max(features, 0), 6)

Computes Quantized Rectified Linear X: min(max(features, 0), max_value)

Computes rectified linear: max(features, 0).

Computes rectified linear 6: min(max(features, 0), 6).

Computes scaled exponential linear: scale * alpha * (exp(features) - 1)

Computes softmax activations.

Computes softmax cross entropy cost and gradients to backpropagate.

Computes softplus: log(exp(features) + 1).

Computes softsign: features / (abs(features) + 1).

Computes softmax cross entropy cost and gradients to backpropagate.

Finds values and indices of the k largest elements for the last dimension.