Operazioni TensorFlow disponibili

Questa pagina elenca le API Python di TensorFlow e gli operatori di grafici disponibili su Cloud TPU.

API Python disponibili

Di seguito è riportata una guida all'insieme delle API TensorFlow Python disponibili. Questo elenco non è completo. Le funzioni di libreria non presenti in questo elenco possono funzionare se sono composte da primitive disponibili.

Consulta la guida alle prestazioni per suggerimenti su operatori specifici.

Modulo	API Python disponibile	Commenti
`tf`	`tf.abs`
	`tf.acosh`
	`tf.add`
	`tf.add_n`
	`tf.angle`
	`tf.arg_max`	L'argomento `dimension` deve essere una costante in tempo di compilazione.
	`tf.arg_min`	L'argomento `dimension` deve essere una costante in tempo di compilazione.
	`tf.asinh`
	`tf.assign`	Disponibile solo per la variabile della risorsa.
	`tf.assign_add`	Disponibile solo per la variabile della risorsa.
	`tf.assign_sub`	Disponibile solo per la variabile della risorsa.
	`tf.atan`
	`tf.atan2`
	`tf.atanh`
	`tf.batch_to_space`	Gli argomenti `crops` e `block_shape` devono essere una costante in tempo di compilazione.
	`tf.batch_to_space_nd`	L'argomento `crops` deve essere una costante in tempo di compilazione.
	`tf.broadcast_dynamic_shape`
	`tf.broadcast_static_shape`
	`tf.case`	Sperimentale (flusso di controllo). Potrebbe non funzionare ancora in modo affidabile.
	`tf.cast`
	`tf.ceil`
	`tf.cholesky`	Sperimentale. Potrebbe presentare problemi di precisione numerica.
	`tf.cholesky_solve`	Sperimentale. Potrebbe presentare problemi di precisione numerica.
	`tf.clip_by_average_norm`
	`tf.clip_by_global_norm`
	`tf.clip_by_norm`
	`tf.clip_by_value`
	`tf.complex`
	`tf.concat`	`concat_dim` deve essere una costante in tempo di compilazione.
	`tf.cond`	Sperimentale (flusso di controllo). Potrebbe non funzionare ancora in modo affidabile.
	`tf.conj`
	`tf.constant`
	`tf.convert_to_tensor`
	`tf.cos`
	`tf.cosh`
	`tf.cross`
	`tf.cumprod`	`axis` deve essere una costante in tempo di compilazione.
	`tf.cumsum`	`axis` deve essere una costante in tempo di compilazione.
	`tf.depth_to_space`
	`tf.diag`
	`tf.diag_part`
	`tf.div`	La divisione `int32` è più lenta rispetto ad altri tipi.
	`tf.divide`	La divisione `int32` è più lenta rispetto ad altri tipi.
	`tf.dynamic_stitch`	`indices` deve essere una costante in tempo di compilazione.
	`tf.einsum`
	`tf.equal`
	`tf.erf`
	`tf.erfc`
	`tf.exp`
	`tf.expand_dims`	`dims` deve essere una costante in tempo di compilazione.
	`tf.expm1`
	`tf.extract_image_patches`
	`tf.eye`
	`tf.fake_quant_with_min_max_args`
	`tf.fake_quant_with_min_max_args_gradient`
	`tf.fake_quant_with_min_max_vars`
	`tf.fake_quant_with_min_max_vars_gradient`
	`tf.fft`
	`tf.fft2d`
	`tf.fft3d`
	`tf.fill`	L'argomento `dims` deve essere una costante in tempo di compilazione.
	`tf.floor`
	`tf.floordiv`
	`tf.floormod`
	`tf.foldl`	Sperimentale (flusso di controllo).
	`tf.foldr`	Sperimentale (flusso di controllo).
	`tf.gather`	`axis` deve essere una costante in tempo di compilazione.
	`tf.gather_nd`
	`tf.greater`
	`tf.greater_equal`
	`tf.hessians`	Sperimentale (flusso di controllo.
	`tf.identity`
	`tf.identity_n`
	`tf.ifft`
	`tf.ifft2d`
	`tf.ifft3d`
	`tf.imag`
	`tf.invert_permutation`	L'argomento `x` deve essere una costante in tempo di compilazione.
	`tf.is_finite`
	`tf.is_inf`
	`tf.is_nan`
	`tf.is_non_decreasing`
	`tf.is_strictly_increasing`
	`tf.less`
	`tf.less_equal`
	`tf.linspace`	Gli argomenti `start`, `stop` e `num` devono essere costanti in tempo di compilazione.
	`tf.log`
	`tf.log1p`
	`tf.log_sigmoid`
	`tf.logical_and`
	`tf.logical_or`
	`tf.logical_not`
	`tf.logical_xor`
	`tf.matmul`	Utilizza un matmul `bfloat16` con accumulo `float32`.
	`tf.matrix_band_part`
	`tf.matrix_diag`
	`tf.matrix_diag_part`
	`tf.matrix_set_diag`
	`tf.matrix_triangular_solve`	Sperimentale. Potrebbe presentare problemi di precisione numerica.
	`tf.maximum`
	`tf.meshgrid`
	`tf.minimum`
	`tf.mod`
	`tf.multinomial`	L'argomento `num_samples` deve essere una costante in tempo di compilazione.
	`tf.multiply`
	`tf.negative`
	`tf.no_op`
	`tf.norm`
	`tf.not_equal`
	`tf.one_hot`	`depth` deve essere una costante in tempo di compilazione.
	`tf.ones`
	`tf.ones_like`
	`tf.pad`	L'argomento `paddings` deve essere una costante in tempo di compilazione. Il gradiente di spaziatura interna `REFLECT` non è ancora disponibile.
	`tf.pow`
	`tf.random_normal`	`shape` deve essere una costante in tempo di compilazione.
	`tf.random_uniform`	`shape` deve essere una costante in tempo di compilazione.
	`tf.range`	Gli argomenti `start`, `limit` e `delta` devono essere costanti in tempo di compilazione.
	`tf.rank`
	`tf.real`
	`tf.realdiv`
	`tf.reciprocal`
	`tf.reduce_all`	`axis` deve essere una costante in tempo di compilazione.
	`tf.reduce_any`	`axis` deve essere una costante in tempo di compilazione.
	`tf.reduce_logsumexp`
	`tf.reduce_max`	`axis` deve essere una costante in tempo di compilazione.
	`tf.reduce_min`	`axis` deve essere una costante in tempo di compilazione.
	`tf.reduce_prod`	`axis` deve essere una costante in tempo di compilazione.
	`tf.reduce_sum`	`axis` deve essere una costante in tempo di compilazione.
	`tf.reshape`	L'argomento `shape` deve essere una costante in tempo di compilazione.
	`tf.reverse`	L'argomento `dims` deve essere una costante in tempo di compilazione.
	`tf.reverse_sequence`
	`tf.reverse_v2`	L'argomento `axis` deve essere una costante in tempo di compilazione.
	`tf.rint`
	`tf.round`
	`tf.rsqrt`
	`tf.saturate_cast`
	`tf.scalar_mul`
	`tf.scan`	Sperimentale (flusso di controllo).
	`tf.scatter_nd`
	`tf.sequence_mask`
	`tf.shape`
	`tf.shape_n`
	`tf.sigmoid`
	`tf.sign`
	`tf.sin`
	`tf.sinh`
	`tf.size`
	`tf.slice`	`size` deve essere una costante in tempo di compilazione. Inoltre, `begin` deve essere una costante in tempo di compilazione oppure `size` non deve essere un numero negativo. La retropropagazione è supportata solo se `begin` e `size` sono costanti in fase di compilazione.
	`tf.space_to_batch`	`paddings` e `block_shape` devono essere costanti in tempo di compilazione.
	`tf.space_to_batch_nd`	`paddings` deve essere una costante in tempo di compilazione.
	`tf.space_to_depth`
	`tf.split`	`axis` deve essere una costante in tempo di compilazione.
	`tf.sqrt`
	`tf.square`
	`tf.squared_difference`
	`tf.squeeze`
	`tf.stack`
	`tf.stop_gradient`
	`tf.strided_slice`
	`tf.tan`
	`tf.tanh`
	`tf.tensordot`
	`tf.tile`	L'argomento `multiples` deve essere una costante in tempo di compilazione.
	`tf.to_bfloat16`
	`tf.to_float`
	`tf.to_int32`
	`tf.to_int64`	L'assistenza `int64` è limitata.
	`tf.trace`
	`tf.transpose`	L'argomento `perm` deve essere una costante in tempo di compilazione.
	`tf.truediv`
	`tf.truncated_normal`	`shape` deve essere una costante in tempo di compilazione.
	`tf.truncatediv`
	`tf.truncatemod`
	`tf.unsorted_segment_sum`
	`tf.unstack`
	`tf.where`	Sia `x` che `y` devono essere diversi da `None`. Se entrambi `x` e `y` sono `None`, l'operatore non avrà una forma statica.
	`tf.while_loop`	Il calcolo del gradiente di un ciclo many richiede il passaggio dell'argomento `maximum_iterations`.
	`tf.zeros`
	`tf.zeros_like`
	`tf.Tensor.__getitem__`	L'inizio, la fine e gli intervalli di una sezione devono essere costanti di tempo di compilazione.
`tf.bitwise`	`tf.bitwise_and`
	`tf.bitwise_or`
	`tf.bitwise_invert`
`tf.contrib.stateless`	`tf.contrib.stateless.stateless_random_normal`
	`tf.contrib.stateless.stateless_random_uniform`
`tf.image`	`tf.image.adjust_brightness`
	`tf.image.adjust_contrast`
	`tf.image.adjust_gamma`
	`tf.image.adjust_hue`
	`tf.image.adjust_saturation`
	`tf.image.central_crop`	Il fattore di ritaglio deve essere una costante di tempo di compilazione.
	`tf.image.convert_image_dtype`
	`tf.image.flip_left_right`
	`tf.image.flip_up_down`
	`tf.image.grayscale_to_rgb`
	`tf.image.hsv_to_rgb`
	`tf.image.resize_bilinear`	È disponibile solo `align_corners=True`. `size` deve essere una costante in tempo di compilazione.
	`tf.image.random_brightness`
	`tf.image.random_contrast`
	`tf.image.random_flip_left_right`
	`tf.image.random_flip_up_down`
	`tf.image.random_hue`
	`tf.image.random_saturation`
	`tf.image.rgb_to_hsv`
	`tf.image.rgb_to_grayscale`
	`tf.image.rot90`
	`tf.image.total_variation`
	`tf.image.transpose_image`
`tf.layers`	`tf.layers.average_pooling1d`
	`tf.layers.average_pooling2d`
	`tf.layers.average_pooling1d`
	`tf.layers.batch_normalization`
	`tf.layers.conv1d`
	`tf.layers.conv2d`
	`tf.layers.conv2d_transpose`
	`tf.layers.conv3d`
	`tf.layers.conv3d_transpose`
	`tf.layers.dense`
	`tf.layers.dropout`
	`tf.layers.flatten`
	`tf.layers.max_pooling1d`
	`tf.layers.max_pooling2d`
	`tf.layers.max_pooling3d`
	`tf.layers.separable_conv2d`
`tf.nn`	`tf.nn.atrous_conv2d`
	`tf.nn.atrous_conv2d_transpose`
	`tf.nn.avg_pool`
	`tf.nn.avg_pool3d`
	`tf.nn.batch_normalization`
	`tf.nn.bias_add`
	`tf.nn.conv1d`
	`tf.nn.conv2d`
	`tf.nn.conv2d_backprop_filter`
	`tf.nn.conv2d_backprop_input`
	`tf.nn.conv2d_transpose`
	`tf.nn.conv3d`
	`tf.nn.conv3d_backprop_filter`
	`tf.nn.conv3d_backprop_input`
	`tf.nn.conv3d_transpose`
	`tf.nn.convolution`
	`tf.nn.crelu`
	`tf.nn.depthwise_conv2d`
	`tf.nn.depthwise_conv2d_native`
	`tf.nn.depthwise_conv2d_native_backprop_filter`
	`tf.nn.depthwise_conv2d_native_backprop_input`
	`tf.nn.dropout`
	`tf.nn.dynamic_rnn`	Sperimentale.
	`tf.nn.elu`
	`tf.nn.fused_batch_norm`
	`tf.nn.l2_loss`
	`tf.nn.l2_normalize`
	`tf.nn.leaky_relu`
	`tf.nn.local_response_normalization`
	`tf.nn.log_poisson_loss`
	`tf.nn.log_softmax`
	`tf.nn.max_pool`
	`tf.nn.max_pool3d`
	`tf.nn.moments`
	`tf.nn.normalize_moments`
	`tf.nn.pool`
	`tf.nn.relu`
	`tf.nn.relu6`
	`tf.nn.relu_layer`
	`tf.nn.selu`
	`tf.nn.separable_conv2d`
	`tf.nn.sigmoid_cross_entropy_with_logits`
	`tf.nn.softmax`
	`tf.nn.softmax_cross_entropy_with_logits`
	`tf.nn.softplus`
	`tf.nn.softsign`
	`tf.nn.sparse_softmax_cross_entropy_with_logits`
	`tf.nn.static_bidirectional_rnn`	Sperimentale.
	`tf.nn.static_rnn`	Sperimentale.
	`tf.nn.weighted_cross_entropy_with_logits`	Sperimentale.
	`tf.nn.weighted_moments`
	`tf.nn.with_space_to_batch`
	`tf.nn.xw_plus_b`
	`tf.nn.zero_fraction`
`tf.spectral`	`tf.spectral.fft`
	`tf.spectral.fft2d`
	`tf.spectral.fft3d`
	`tf.spectral.ifft`
	`tf.spectral.ifft2d`
	`tf.spectral.ifft3d`
	`tf.spectral.irfft`	`fft_length` deve essere una costante in tempo di compilazione.
	`tf.spectral.irfft2d`	`fft_length` deve essere una costante in tempo di compilazione.
	`tf.spectral.irfft3d`	`fft_length` deve essere una costante in tempo di compilazione.
	`tf.spectral.rfft`	`fft_length` deve essere una costante in tempo di compilazione.
	`tf.spectral.rfft2d`	`fft_length` deve essere una costante in tempo di compilazione.
	`tf.spectral.rfft3d`	`fft_length` deve essere una costante in tempo di compilazione.

API Python non disponibili

Questo elenco non è completo. Le operazioni non disponibili su Cloud TPU includono:

Modulo	API Python non disponibile	Commenti
`tf`	`tf.accumulate_n`	Utilizza le variabili Ref.
	`tf.acos`
	`tf.asin`
	`tf.betainc`
	`tf.bitcast`
	`tf.add_check_numerics_ops`	I programmi che contengono operatori di controllo numerico dovrebbero essere eseguiti, ma l'operatore di controllo numerico viene attualmente ignorato.
	`tf.assert_...`	I programmi contenenti le asserzioni dovrebbero essere eseguiti, ma queste vengono ignorate.
	`tf.check_numerics`	I programmi che contengono operatori di controllo numerico dovrebbero essere eseguiti, ma l'operatore di controllo numerico viene attualmente ignorato.
	`tf.confusion_matrix`
	`tf.count_nonzero`	Utilizza la riduzione di `int64`.
	`tf.count_up_to`
	`tf.create_partitioned_variables`
	`tf.dequantize`
	`tf.digamma`
	`tf.dynamic_partition`
	`tf.edit_distance`
	`tf.fake_quant_with_min_max_vars_per_channel`
	`tf.fake_quant_with_min_max_vars_per_channel_gradient`
	`tf.histogram_fixed_width`
	`tf.igamma`
	`tf.igammac`
	`tf.lbeta`
	`tf.lgamma`
	`tf.matrix_determinant`
	`tf.matrix_inverse`
	`tf.matrix_solve`
	`tf.matrix_solve_ls`
	`tf.polygamma`
	`tf.py_func`
	`tf.qr`
	`tf.quantize_v2`
	`tf.quantized_concat`
	`tf.random_crop`
	`tf.random_gamma`
	`tf.random_poisson`
	`tf.random_shuffle`
	`tf.scatter_add`
	`tf.scatter_div`
	`tf.scatter_mul`
	`tf.scatter_nd_add`
	`tf.scatter_nd_sub`
	`tf.scatter_nd_update`
	`tf.segment_mean`
	`tf.segment_max`
	`tf.segment_min`
	`tf.segment_prod`
	`tf.segment_sum`
	`tf.self_adjoint_eig`
	`tf.self_adjoint_eigvals`
	`tf.setdiff1d`
	`tf.sparse_...`
	`tf.string_...`
	`tf.substr`
	`tf.svd`
	`tf.to_double`
	`tf.unique`
	`tf.unsorted_segment_max`
	`tf.zeta`
	`tf.bitwise.bitwise_xor`
	`tf.contrib.stateless.stateless_truncated_normal`

Operatori dei grafici disponibili

Operatore	Tipo vincolo
`Abs`	`T={bfloat16,float,int32,int64}`
`Acos`	`T={bfloat16,complex64,float,int32,int64}`
`Acosh`	`T={bfloat16,complex64,float}`
`Add`	`T={bfloat16,complex64,float,int32,int64}`
`AddN`	`T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`AdjustContrastv2`	`T={float}`
`AdjustHue`	`T={float}`
`AdjustSaturation`	`T={float}`
`All`	`Tidx={int32,int64}`
`AllToAll`	`T={bfloat16,float}`
`Angle`	`Tout={float}` `T={complex64}`
`Any`	`Tidx={int32,int64}`
`ApproximateEqual`	`T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`ArgMax`	`Tidx={int32,int64}` `output_type={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`ArgMin`	`Tidx={int32,int64}` `output_type={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`Asin`	`T={bfloat16,complex64,float,int32,int64}`
`Asinh`	`T={bfloat16,complex64,float}`
`Assert`	`T={bfloat16,bool,complex64,float,int32,int64,string,uint32,uint64}`
`AssignAddVariableOp`	`dtype={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`AssignSubVariableOp`	`dtype={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`AssignVariableOp`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Atan`	`T={bfloat16,complex64,float,int32,int64}`
`Atan2`	`T={bfloat16,float}`
`Atanh`	`T={bfloat16,complex64,float}`
`AvgPool`	`T={bfloat16,float}`
`AvgPool3D`	`T={bfloat16,float}`
`AvgPool3DGrad`	`T={bfloat16,float}`
`AvgPoolGrad`	`T={bfloat16,float}`
`BatchMatMul`	`T={bfloat16,complex64,float,int32,int64}`
`BatchToSpace`	`Tidx={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`BatchToSpaceND`	`Tcrops={int32,int64}` `Tblock_shape={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`BiasAdd`	`T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`BiasAddGrad`	`T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`BiasAddV1`	`T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`Bitcast`	`type={bfloat16,complex64,float,int32,int64,uint32,uint64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`BitwiseAnd`	`T={int32,int64,uint32,uint64}`
`BitwiseOr`	`T={int32,int64,uint32,uint64}`
`BitwiseXor`	`T={int32,int64,uint32,uint64}`
`BroadcastArgs`	`T={int32,int64}`
`BroadcastGradientArgs`	`T={int32,int64}`
`BroadcastTo`	`Tidx={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Bucketize`	`T={float,int32,int64}`
`Cast`	`DstT={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}` `SrcT={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Ceil`	`T={bfloat16,float}`
`CheckNumerics`	`T={bfloat16,float}`
`Cholesky`	`T={float}`
`ClipByValue`	`T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`CollectivePermute`	`T={bfloat16,float}`
`Complex`	`Tout={complex64}` `T={float}`
`ComplexAbs`	`Tout={float}` `T={complex64}`
`Concat`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ConcatOffset`
`ConcatV2`	`Tidx={int32}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Conj`	`T={complex64}`
`ConjugateTranspose`	`Tperm={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Const`	`dtype={bfloat16,bool,complex64,float,int32,int64,string,uint32,uint64}`
`ControlTrigger`
`Conv2D`	`T={bfloat16,float}`
`Conv2DBackpropFilter`	`T={bfloat16,float}`
`Conv2DBackpropInput`	`T={bfloat16,float}`
`Conv3D`	`T={bfloat16,float}`
`Conv3DBackpropFilterV2`	`T={bfloat16,float}`
`Conv3DBackpropInputV2`	`Tshape={int32,int64}` `T={bfloat16,float}`
`Cos`	`T={bfloat16,complex64,float}`
`Cosh`	`T={bfloat16,complex64,float}`
`Cross`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`CrossReplicaSum`	`T={bfloat16,float}`
`Cumprod`	`Tidx={int32,int64}` `T={bfloat16,float,int32}`
`Cumsum`	`Tidx={int32,int64}` `T={bfloat16,float,int32}`
`DataFormatVecPermute`	`T={int32,int64}`
`DepthToSpace`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`DepthwiseConv2dNative`	`T={bfloat16,float}`
`DepthwiseConv2dNativeBackpropFilter`	`T={bfloat16,float}`
`DepthwiseConv2dNativeBackpropInput`	`T={bfloat16,float}`
`Diag`	`T={bfloat16,complex64,float,int32,int64}`
`DiagPart`	`T={bfloat16,complex64,float,int32,int64}`
`Digamma`	`T={bfloat16,float}`
`Div`	`T={bfloat16,complex64,float,int32,int64}`
`DivNoNan`	`T={float}`
`DynamicStitch`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Elu`	`T={bfloat16,float}`
`EluGrad`	`T={bfloat16,float}`
`Empty`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`EmptyTensorList`	`shape_type={int32,int64}` `element_dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Equal`	`T={bfloat16,bool,complex64,float,int32,int64}`
`Erf`	`T={bfloat16,float}`
`Erfc`	`T={bfloat16,float}`
`Exp`	`T={bfloat16,complex64,float}`
`ExpandDims`	`Tdim={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Expm1`	`T={bfloat16,complex64,float}`
`ExtractImagePatches`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`FFT`	`Tcomplex={complex64}`
`FFT2D`	`Tcomplex={complex64}`
`FFT3D`	`Tcomplex={complex64}`
`FakeParam`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`FakeQuantWithMinMaxArgs`
`FakeQuantWithMinMaxArgsGradient`
`FakeQuantWithMinMaxVars`
`FakeQuantWithMinMaxVarsGradient`
`Fill`	`index_type={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Floor`	`T={bfloat16,float}`
`FloorDiv`	`T={bfloat16,complex64,float,int32,int64}`
`FloorMod`	`T={bfloat16,float,int32,int64}`
`FusedBatchNorm`	`T={float}`
`FusedBatchNormGrad`	`T={float}`
`FusedBatchNormGradV2`	`U={float}` `T={bfloat16,float}`
`FusedBatchNormV2`	`U={float}` `T={bfloat16,float}`
`Gather`	`Tindices={int32,int64}` `Tparams={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`GatherNd`	`Tindices={int32,int64}` `Tparams={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`GatherV2`	`Taxis={int32,int64}` `Tindices={int32,int64}` `Tparams={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`GetItem`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Greater`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`GreaterEqual`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`HSVToRGB`	`T={bfloat16,float}`
`IFFT`	`Tcomplex={complex64}`
`IFFT2D`	`Tcomplex={complex64}`
`IFFT3D`	`Tcomplex={complex64}`
`IRFFT`
`IRFFT2D`
`IRFFT3D`
`Identity`	`T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`
`IdentityN`	`T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`
`If`	`Tout={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}` `Tin={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}` `Tcond={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`
`Imag`	`Tout={float}` `T={complex64}`
`InfeedDequeue`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`InfeedDequeueTuple`	`dtypes={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`InplaceAdd`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`InplaceUpdate`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Inv`	`T={bfloat16,complex64,float,int32,int64}`
`Invert`	`T={int32,int64,uint32,uint64}`
`InvertPermutation`	`T={int32}`
`IsFinite`	`T={bfloat16,float}`
`IsInf`	`T={bfloat16,float}`
`IsNan`	`T={bfloat16,float}`
`L2Loss`	`T={bfloat16,float}`
`LRN`	`T={bfloat16,float}`
`LRNGrad`	`T={bfloat16,float}`
`LeakyRelu`	`T={bfloat16,float}`
`LeakyReluGrad`	`T={bfloat16,float}`
`LeftShift`	`T={int32,int64,uint32,uint64}`
`Less`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`LessEqual`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`Lgamma`	`T={bfloat16,float}`
`LinSpace`	`Tidx={int32,int64}` `T={bfloat16,float}`
`ListDiff`	`out_idx={int32,int64}` `T={int32,int64}`
`Log`	`T={bfloat16,complex64,float}`
`Log1p`	`T={bfloat16,complex64,float}`
`LogSoftmax`	`T={bfloat16,float}`
`LogicalAnd`
`LogicalNot`
`LogicalOr`
`MatMul`	`T={bfloat16,complex64,float}`
`MatrixBandPart`	`Tindex={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`MatrixDiag`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`MatrixDiagPart`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`MatrixSetDiag`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`MatrixTriangularSolve`	`T={complex64,float}`
`Max`	`Tidx={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`MaxPool`	`T={bfloat16,float,int32,int64}`
`MaxPool3D`	`T={bfloat16,float}`
`MaxPool3DGrad`	`TInput={bfloat16,float}` `T={bfloat16,float}`
`MaxPool3DGradGrad`	`T={float}`
`MaxPoolGrad`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`MaxPoolGradGrad`	`T={float}`
`MaxPoolGradGradV2`	`T={float}`
`MaxPoolGradV2`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`MaxPoolV2`	`T={bfloat16,float,int32,int64}`
`Maximum`	`T={bfloat16,float,int32,int64}`
`Mean`	`Tidx={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`Min`	`Tidx={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`Minimum`	`T={bfloat16,float,int32,int64}`
`MirrorPad`	`Tpaddings={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Mod`	`T={bfloat16,float,int32,int64}`
`Mul`	`T={bfloat16,complex64,float,int32,int64}`
`Multinomial`	`output_dtype={int32,int64}` `T={bfloat16,float,int32,int64,uint32,uint64}`
`Neg`	`T={bfloat16,complex64,float,int32,int64}`
`NoOp`
`NonMaxSuppressionV4`	`T={float}`
`NotEqual`	`T={bfloat16,bool,complex64,float,int32,int64}`
`OneHot`	`TI={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`OnesLike`	`T={bfloat16,bool,complex64,float,int32,int64}`
`OutfeedEnqueue`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`OutfeedEnqueueTuple`	`dtypes={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Pack`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Pad`	`Tpaddings={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`PadV2`	`Tpaddings={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ParallelDynamicStitch`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`PlaceholderWithDefault`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Pow`	`T={bfloat16,complex64,float,int32,int64}`
`PreventGradient`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Prod`	`Tidx={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`Qr`	`T={float}`
`QuantizeAndDequantizeV2`	`T={bfloat16,float}`
`QuantizeAndDequantizeV3`	`T={bfloat16,float}`
`RFFT`
`RFFT2D`
`RFFT3D`
`RGBToHSV`	`T={bfloat16,float}`
`RandomShuffle`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`RandomStandardNormal`	`T={int32,int64}` `dtype={bfloat16,float}`
`RandomUniform`	`T={int32,int64}` `dtype={bfloat16,float}`
`RandomUniformInt`	`T={int32,int64}` `Tout={int32,int64}`
`Range`	`Tidx={bfloat16,float,int32,int64}`
`Rank`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ReadVariableOp`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Real`	`Tout={float}` `T={complex64}`
`RealDiv`	`T={bfloat16,complex64,float,int32,int64}`
`Reciprocal`	`T={bfloat16,complex64,float,int32,int64}`
`ReciprocalGrad`	`T={bfloat16,complex64,float}`
`RecvTPUEmbeddingActivations`
`Relu`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`Relu6`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`Relu6Grad`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`ReluGrad`	`T={bfloat16,float,int32,int64,uint32,uint64}`
`Reshape`	`Tshape={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ResizeBilinear`	`T={bfloat16,float,int32,int64}`
`ResizeBilinearGrad`	`T={bfloat16,float}`
`ResizeNearestNeighbor`	`T={float,int32,int64}`
`ResourceApplyAdaMax`	`T={bfloat16,float}`
`ResourceApplyAdadelta`	`T={bfloat16,float}`
`ResourceApplyAdagrad`	`T={bfloat16,float}`
`ResourceApplyAdagradDA`	`T={bfloat16,float}`
`ResourceApplyAdam`	`T={bfloat16,float}`
`ResourceApplyAddSign`	`T={bfloat16,float}`
`ResourceApplyCenteredRMSProp`	`T={bfloat16,float}`
`ResourceApplyFtrl`	`T={bfloat16,float}`
`ResourceApplyFtrlV2`	`T={bfloat16,float}`
`ResourceApplyGradientDescent`	`T={bfloat16,float}`
`ResourceApplyKerasMomentum`	`T={bfloat16,float}`
`ResourceApplyMomentum`	`T={bfloat16,float}`
`ResourceApplyPowerSign`	`T={bfloat16,float}`
`ResourceApplyProximalAdagrad`	`T={bfloat16,float}`
`ResourceApplyProximalGradientDescent`	`T={bfloat16,float}`
`ResourceApplyRMSProp`	`T={bfloat16,float}`
`ResourceGather`	`Tindices={int32,int64}` `dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterAdd`	`Tindices={int32,int64}` `dtype={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterDiv`	`Tindices={int32,int64}` `dtype={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterMax`	`Tindices={int32,int64}` `dtype={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterMin`	`Tindices={int32,int64}` `dtype={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterMul`	`Tindices={int32,int64}` `dtype={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterNdAdd`	`Tindices={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterNdSub`	`Tindices={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterNdUpdate`	`Tindices={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterSub`	`Tindices={int32,int64}` `dtype={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`ResourceScatterUpdate`	`Tindices={int32,int64}` `dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ResourceStridedSliceAssign`	`Index={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Reverse`	`T={bool,complex64,float,int32,int64}`
`ReverseSequence`	`Tlen={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ReverseV2`	`T={bfloat16,bool,complex64,float,int32,int64}` `Tidx={int32,int64}`
`RightShift`	`T={int32,int64,uint32,uint64}`
`Rint`	`T={bfloat16,float}`
`Round`	`T={bfloat16,complex64,float,int32,int64}`
`Rsqrt`	`T={bfloat16,complex64,float}`
`RsqrtGrad`	`T={bfloat16,complex64,float}`
`ScatterNd`	`Tindices={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Select`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Selu`	`T={bfloat16,float}`
`SeluGrad`	`T={bfloat16,float}`
`SendTPUEmbeddingGradients`
`Shape`	`out_type={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`ShapeN`	`out_type={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Sigmoid`	`T={bfloat16,complex64,float}`
`SigmoidGrad`	`T={bfloat16,complex64,float}`
`Sign`	`T={bfloat16,complex64,float,int32,int64}`
`Sin`	`T={bfloat16,complex64,float}`
`Sinh`	`T={bfloat16,complex64,float}`
`Size`	`out_type={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Slice`	`Index={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Snapshot`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Softmax`	`T={bfloat16,float}`
`SoftmaxCrossEntropyWithLogits`	`T={bfloat16,float}`
`Softplus`	`T={bfloat16,float}`
`SoftplusGrad`	`T={bfloat16,float}`
`Softsign`	`T={bfloat16,float}`
`SoftsignGrad`	`T={bfloat16,float}`
`SpaceToBatch`	`Tpaddings={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`SpaceToBatchND`	`Tblock_shape={int32,int64}` `Tpaddings={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`SpaceToDepth`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`SparseMatMul`	`Tb={bfloat16,float}` `Ta={bfloat16,float}`
`SparseSoftmaxCrossEntropyWithLogits`	`Tlabels={int32,int64}` `T={bfloat16,float}`
`SparseToDense`	`Tindices={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Split`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`SplitV`	`Tlen={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Sqrt`	`T={bfloat16,complex64,float}`
`SqrtGrad`	`T={bfloat16,complex64,float}`
`Square`	`T={bfloat16,complex64,float,int32,int64}`
`SquaredDifference`	`T={bfloat16,complex64,float,int32,int64}`
`Squeeze`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`StackCloseV2`
`StackPopV2`	`elem_type={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`StackPushV2`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`StackV2`	`elem_type={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`StatelessIf`	`Tout={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}` `Tin={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}` `Tcond={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`
`StatelessMultinomial`	`output_dtype={int32,int64}` `Tseed={int32}` `T={bfloat16,float}`
`StatelessRandomNormal`	`Tseed={int32}` `T={int32,int64}` `dtype={bfloat16,float}`
`StatelessRandomUniform`	`Tseed={int32}` `T={int32,int64}` `dtype={bfloat16,float}`
`StatelessRandomUniformInt`	`Tseed={int32}` `T={int32,int64}` `dtype={int32,int64}`
`StatelessTruncatedNormal`	`Tseed={int32}` `T={int32,int64}` `dtype={bfloat16,float}`
`StatelessWhile`	`T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`
`StopGradient`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`StridedSlice`	`Index={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`StridedSliceGrad`	`Index={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Sub`	`T={bfloat16,complex64,float,int32,int64}`
`Sum`	`Tidx={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`SymbolicGradient`	`Tout={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}` `Tin={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TPUEmbeddingActivations`
`Tan`	`T={bfloat16,complex64,float,int32,int64}`
`Tanh`	`T={bfloat16,complex64,float}`
`TanhGrad`	`T={bfloat16,complex64,float}`
`TensorArrayCloseV3`
`TensorArrayConcatV3`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TensorArrayGatherV3`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TensorArrayGradV3`
`TensorArrayReadV3`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TensorArrayScatterV3`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TensorArraySizeV3`
`TensorArraySplitV3`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TensorArrayV3`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TensorArrayWriteV3`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TensorListElementShape`	`shape_type={int32,int64}`
`TensorListPopBack`	`element_dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TensorListPushBack`	`element_dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TensorListReserve`	`shape_type={int32,int64}` `element_dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`Tile`	`Tmultiples={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TopKV2`	`T={bfloat16,float,int32,uint32}`
`Transpose`	`Tperm={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`TruncateDiv`	`T={bfloat16,complex64,float,int32,int64}`
`TruncateMod`	`T={bfloat16,float,int32,int64}`
`TruncatedNormal`	`T={int32,int64}` `dtype={float}`
`Unpack`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`UnsortedSegmentMax`	`Tnumsegments={int32,int64}` `Tindices={int32,int64}` `T={bfloat16,float,int32,int64,uint32,uint64}`
`UnsortedSegmentMin`	`Tnumsegments={int32,int64}` `Tindices={int32,int64}` `T={bfloat16,float,int32,int64,uint32,uint64}`
`UnsortedSegmentProd`	`Tnumsegments={int32,int64}` `Tindices={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`UnsortedSegmentSum`	`Tnumsegments={int32,int64}` `Tindices={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`VarIsInitializedOp`
`VariableShape`	`out_type={int32,int64}`
`While`	`T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`
`Xdivy`	`T={complex64,float}`
`XlaBroadcastHelper`	`Tindices={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`XlaConv`	`Tindices={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`XlaDequantize`
`XlaDot`	`T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`XlaDynamicSlice`	`Tindices={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`XlaDynamicUpdateSlice`	`Tindices={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`XlaHostCompute`	`Toutputs={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}` `Tinputs={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`XlaIf`	`Tout={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}` `Tin={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}` `Tcond={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`
`XlaKeyValueSort`	`V={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}` `K={bfloat16,float,int32,int64,uint32,uint64}`
`XlaPad`	`Tindices={int32,int64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`XlaRecv`	`dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`XlaRecvFromHost`	`Toutput={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`XlaReduce`	`T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`XlaReduceWindow`	`Tindices={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`XlaSelectAndScatter`	`Tindices={int32,int64}` `T={bfloat16,complex64,float,int32,int64,uint32,uint64}`
`XlaSend`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`XlaSendToHost`	`Tinput={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`XlaSort`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`XlaWhile`	`T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`
`Xlogy`	`T={complex64,float}`
`ZerosLike`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`_Arg`	`T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`
`_ArrayToList`	`out_types={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}` `T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`_ListToArray`	`T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}` `Tin={bfloat16,bool,complex64,float,int32,int64,uint32,uint64}`
`_Retval`	`T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64}`