Nn Ops

Summary

Classes
tensorflow::ops::AvgPool	Performs average pooling on the input.
tensorflow::ops::AvgPool3D	Performs 3D average pooling on the input.
tensorflow::ops::AvgPool3DGrad	Computes gradients of average pooling function.
tensorflow::ops::BiasAdd	Adds bias to value.
tensorflow::ops::BiasAddGrad	The backward operation for "BiasAdd" on the "bias" tensor.
tensorflow::ops::Conv2D	Computes a 2-D convolution given 4-D input and filter tensors.
tensorflow::ops::Conv2DBackpropFilter	Computes the gradients of convolution with respect to the filter.
tensorflow::ops::Conv2DBackpropInput	Computes the gradients of convolution with respect to the input.
tensorflow::ops::Conv3D	Computes a 3-D convolution given 5-D input and filter tensors.
tensorflow::ops::Conv3DBackpropFilterV2	Computes the gradients of 3-D convolution with respect to the filter.
tensorflow::ops::Conv3DBackpropInputV2	Computes the gradients of 3-D convolution with respect to the input.
tensorflow::ops::DataFormatDimMap	Returns the dimension index in the destination data format given the one in.
tensorflow::ops::DataFormatVecPermute	Permute input tensor from src_format to dst_format.
tensorflow::ops::DepthwiseConv2dNative	Computes a 2-D depthwise convolution given 4-D input and filter tensors.
tensorflow::ops::DepthwiseConv2dNativeBackpropFilter	Computes the gradients of depthwise convolution with respect to the filter.
tensorflow::ops::DepthwiseConv2dNativeBackpropInput	Computes the gradients of depthwise convolution with respect to the input.
tensorflow::ops::Dilation2D	Computes the grayscale dilation of 4-D input and 3-D filter tensors.
tensorflow::ops::Dilation2DBackpropFilter	Computes the gradient of morphological 2-D dilation with respect to the filter.
tensorflow::ops::Dilation2DBackpropInput	Computes the gradient of morphological 2-D dilation with respect to the input.
tensorflow::ops::Elu	Computes exponential linear: exp(features) - 1 if < 0, features otherwise.
tensorflow::ops::FractionalAvgPool	Performs fractional average pooling on the input.
tensorflow::ops::FractionalMaxPool	Performs fractional max pooling on the input.
tensorflow::ops::FusedBatchNorm	Batch normalization.
tensorflow::ops::FusedBatchNormGrad	Gradient for batch normalization.
tensorflow::ops::FusedBatchNormGradV2	Gradient for batch normalization.
tensorflow::ops::FusedBatchNormGradV3	Gradient for batch normalization.
tensorflow::ops::FusedBatchNormV2	Batch normalization.
tensorflow::ops::FusedBatchNormV3	Batch normalization.
tensorflow::ops::FusedPadConv2D	Performs a padding as a preprocess during a convolution.
tensorflow::ops::FusedResizeAndPadConv2D	Performs a resize and padding as a preprocess during a convolution.
tensorflow::ops::InTopK	Says whether the targets are in the top K predictions.
tensorflow::ops::InTopKV2	Says whether the targets are in the top K predictions.
tensorflow::ops::L2Loss	L2 Loss.
tensorflow::ops::LRN	Local Response Normalization.
tensorflow::ops::LogSoftmax	Computes log softmax activations.
tensorflow::ops::MaxPool	Performs max pooling on the input.
tensorflow::ops::MaxPool3D	Performs 3D max pooling on the input.
tensorflow::ops::MaxPool3DGrad	Computes gradients of 3D max pooling function.
tensorflow::ops::MaxPool3DGradGrad	Computes second-order gradients of the maxpooling function.
tensorflow::ops::MaxPoolGradGrad	Computes second-order gradients of the maxpooling function.
tensorflow::ops::MaxPoolGradGradV2	Computes second-order gradients of the maxpooling function.
tensorflow::ops::MaxPoolGradGradWithArgmax	Computes second-order gradients of the maxpooling function.
tensorflow::ops::MaxPoolGradV2	Computes gradients of the maxpooling function.
tensorflow::ops::MaxPoolV2	Performs max pooling on the input.
tensorflow::ops::MaxPoolWithArgmax	Performs max pooling on the input and outputs both max values and indices.
tensorflow::ops::NthElement	Finds values of the n-th order statistic for the last dimension.
tensorflow::ops::QuantizedAvgPool	Produces the average pool of the input tensor for quantized types.
tensorflow::ops::QuantizedBatchNormWithGlobalNormalization	Quantized Batch normalization.
tensorflow::ops::QuantizedBiasAdd	Adds Tensor 'bias' to Tensor 'input' for Quantized types.
tensorflow::ops::QuantizedConv2D	Computes a 2D convolution given quantized 4D input and filter tensors.
tensorflow::ops::QuantizedMaxPool	Produces the max pool of the input tensor for quantized types.
tensorflow::ops::QuantizedRelu	Computes Quantized Rectified Linear: max(features, 0)
tensorflow::ops::QuantizedRelu6	Computes Quantized Rectified Linear 6: min(max(features, 0), 6)
tensorflow::ops::QuantizedReluX	Computes Quantized Rectified Linear X: min(max(features, 0), max_value)
tensorflow::ops::Relu	Computes rectified linear: max(features, 0).
tensorflow::ops::Relu6	Computes rectified linear 6: min(max(features, 0), 6).
tensorflow::ops::Selu	Computes scaled exponential linear: scale * alpha * (exp(features) - 1)
tensorflow::ops::Softmax	Computes softmax activations.
tensorflow::ops::SoftmaxCrossEntropyWithLogits	Computes softmax cross entropy cost and gradients to backpropagate.
tensorflow::ops::Softplus	Computes softplus: log(exp(features) + 1).
tensorflow::ops::Softsign	Computes softsign: features / (abs(features) + 1).
tensorflow::ops::SparseSoftmaxCrossEntropyWithLogits	Computes softmax cross entropy cost and gradients to backpropagate.
tensorflow::ops::TopK	Finds values and indices of the k largest elements for the last dimension.