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}