MatMul, BatchMatMulV2

tf.linalg.matmul

tf.matmul

adjoint_a, adjoint_b, a_is_sparse, b_is_sparse not supported

AvgPool

tf.nn.avg_pool

MaxPool

tf.nn.max_pool

Conv2D

tf.nn.conv2d

Filter has to be constant

DepthwiseConv2dNative

tf.nn.depthwise_conv2d

Conv2DBackpropInput

tf.nn.conv2d_backprop_input

Assumptions:

1. ignoring input_sizes tensor

2. order of tensors

Conv2DBackpropFilter

tf.nn.conv2d_backprop_filter

Assumptions:

1. ignoring input_sizes tensor

2. order of tensors

LRN

tf.nn.local_response_normalization

Softmax

tf.nn.softmax

Reshape

tf.reshape

Pre- and Post- transpose ops are added in case reshape involves depth dim

Sigmoid

tf.sigmoid

Tanh

tf.nn.tanh

tf.nn.atrous_conv2d

Expands to SpaceToBatchND-> Conv2D -> BatchToSpaceND

SpaceToBatchND

tf.space_to_batch_nd

[SpaceToBatchND -> Conv2D -> BatchToSpaceND] works

BatchToSpaceND

tf.batch_to_space_nd

[SpaceToBatchND -> Conv2D -> BatchToSpaceND] works

Const

tf.constant

Placeholder

tf.placeholder

Max

tf.reduce_max

keepdims supported using transpose node

Min

tf.reduce_min

keepdims supported using transpose node

Sum

tf.reduce_sum

keepdims supported using transpose node

Prod

tf.reduce_prod

keepdims supported using transpose node

Mean

tf.reduce_mean

keepdims supported using transpose node

Squeeze

tf.squeeze

axis parameter not supported

BatchNorm

tf.nn.batch_normalization

FusedBatchNorm

tf.nn.fused_batch_norm

Relu

tf.nn.relu

Elu

tf.nn.elu

Relu6

tf.nn.relu6

LeakyRelu

tf.nn.leaky_relu

Concat

tf.concat

ConcatV2

tf.concatv2

Add

tf.add

BiasAdd

tf.nn.bias_add

AddN

tf.addn

Sub

tf.subtract

Abs

tf.abs

Neg

tf.negative

Ceil

tf.ceil

Floor

tf.floor

Exp

tf.exp

Log

tf.log

Reciprocal

tf.reciprocal

Pow

tf.pow

Mul

tf.multiply

Square

tf.square

Sqrt

tf.sqrt

Rsqrt

tf.rqrt

Div

tf.div

TrueDiv

tf.truediv

RealDiv

tf.realdiv

Divide

tf.divide

Maximum

tf.maximum

Minimum

tf.minimum

Pad, MirrorPad

tf.pad

Limited support.
VP can do zero replicate (CONSTANT with const value 0) or edge pixel replicate (SYMMETRIC with pad length 1). Data might not match in other modes.

Transpose

tf.transpose

conjugate = True not supported

ResizeBilinear

tf.image.resize_bilinear

ResizeNearestNeighbor

tf.image.resize_nearest_neighbor

align_corners = True not supported half_center_pixels = True not supported

Resamp

tf.contrib.resampler

Slice

tf.slice

begin and size dtype int64 not supported

StridedSlice

tf.strided_slice

• ellipsis_mask and new_axis_mask not supported

• begin and strides cannot have negative indices

Split, SplitV

tf.split

only "num_split" attr is supported

ArgMin

tf.argmin

ArgMax

tf.argmax

OneHot

tf.one_hot

SquaredDifference

tf.squared_difference

Pack

tf.stack

Unpack

tf.unstack

Range

tf.range

ClipByValue

tf.clip_by_value

ScatterND

tf.scatter_nd

Duplicate indices values will be overwritten to the last index value.

Gather

tf.gather

Only axis = 0 is supported

tf.nn.l2_normalize

Expands to math operators

TopKV2

tf.math.top_k

Cast

tf.dtypes.saturate_cast

tf.cast might fail.
Use only saturate cast

ZerosLike

tf.zeros_like

OnesLike

tf.ones_like

Tile

tf.tile

Softplus

tf.math.softplus

Identity

tf.identity

ExpandDims

tf.expand_dims

FakeQuantWithMinMaxArgs

tf.fake_quant_with_min_max_args

FakeQuantWithMinMaxVars

tf.fake_quant_with_min_max_vars

Shape

tf.Shape

CheckNumerics

tf.check_numerics

NoOp

tf.no_op

Assert

tf.Assert

Mish

Swish

Sign

tf.math.sign

Level Curve

Custom op

FloorMod

tf.math.floormod