TensorProtoOrBuilder

interface publique TensorProtoOrBuilder
Sous-classes indirectes connues

Méthodes publiques

booléen abstrait
getBoolVal (index entier)
 DT_BOOL
repeated bool bool_val = 11 [packed = true];
abstrait entier
getBoolValCount ()
 DT_BOOL
repeated bool bool_val = 11 [packed = true];
Liste abstraite<Boolean>
getBoolValListe ()
 DT_BOOL
repeated bool bool_val = 11 [packed = true];
double abstrait
getDcomplexVal (index int)
 DT_COMPLEX128.
abstrait entier
getDcomplexValCount ()
 DT_COMPLEX128.
Liste abstraite<Double>
getDcomplexValList ()
 DT_COMPLEX128.
double abstrait
getDoubleVal (index entier)
 DT_DOUBLE.
abstrait entier
getDoubleValCount ()
 DT_DOUBLE.
Liste abstraite<Double>
getDoubleValListe ()
 DT_DOUBLE.
Type de données abstrait
getDtype ()
.tensorflow.DataType dtype = 1;
abstrait entier
getDtypeValue ()
.tensorflow.DataType dtype = 1;
flotteur abstrait
getFloatVal (index int)
 DT_FLOAT.
abstrait entier
getFloatValCount ()
 DT_FLOAT.
Liste abstraite<Float>
getFloatValList ()
 DT_FLOAT.
abstrait entier
getHalfVal (index int)
 DT_HALF, DT_BFLOAT16.
abstrait entier
getHalfValCount ()
 DT_HALF, DT_BFLOAT16.
Liste abstraite<Integer>
getHalfValListe ()
 DT_HALF, DT_BFLOAT16.
abstrait long
getInt64Val (index int)
 DT_INT64
repeated int64 int64_val = 10 [packed = true];
abstrait entier
getInt64ValCount ()
 DT_INT64
repeated int64 int64_val = 10 [packed = true];
Liste abstraite<Long>
getInt64ValListe ()
 DT_INT64
repeated int64 int64_val = 10 [packed = true];
abstrait entier
getIntVal (index entier)
 DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
abstrait entier
getIntValCount ()
 DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
Liste abstraite<Integer>
getIntValListe ()
 DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
résumé ResourceHandleProto
getResourceHandleVal (index int)
 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;
abstrait entier
getResourceHandleValCount ()
 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;
Liste abstraite < ResourceHandleProto >
getResourceHandleValList ()
 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;
résumé ResourceHandleProtoOrBuilder
getResourceHandleValOrBuilder (index int)
 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;
Liste abstraite <? étend ResourceHandleProtoOrBuilder >
getResourceHandleValOrBuilderList ()
 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;
flotteur abstrait
getScomplexVal (index int)
 DT_COMPLEX64.
abstrait entier
getScomplexValCount ()
 DT_COMPLEX64.
Liste abstraite<Float>
getScomplexValList ()
 DT_COMPLEX64.
résumé com.google.protobuf.ByteString
getStringVal (index int)
 DT_STRING
repeated bytes string_val = 8;
abstrait entier
getStringValCount ()
 DT_STRING
repeated bytes string_val = 8;
Liste abstraite<ByteString>
getStringValListe ()
 DT_STRING
repeated bytes string_val = 8;
résumé com.google.protobuf.ByteString
getTensorContent ()
 Serialized raw tensor content from either Tensor::AsProtoTensorContent or
 memcpy in tensorflow::grpc::EncodeTensorToByteBuffer.
TensorShapeProto abstrait
getTensorShape ()
 Shape of the tensor.
abstrait TensorShapeProtoOrBuilder
getTensorShapeOrBuilder ()
 Shape of the tensor.
abstrait entier
getUint32Val (index int)
 DT_UINT32
repeated uint32 uint32_val = 16 [packed = true];
abstrait entier
getUint32ValCount ()
 DT_UINT32
repeated uint32 uint32_val = 16 [packed = true];
Liste abstraite<Integer>
getUint32ValList ()
 DT_UINT32
repeated uint32 uint32_val = 16 [packed = true];
abstrait long
getUint64Val (index int)
 DT_UINT64
repeated uint64 uint64_val = 17 [packed = true];
abstrait entier
getUint64ValCount ()
 DT_UINT64
repeated uint64 uint64_val = 17 [packed = true];
Liste abstraite<Long>
getUint64ValList ()
 DT_UINT64
repeated uint64 uint64_val = 17 [packed = true];
abstrait VariantTensorDataProto
getVariantVal (index int)
 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;
abstrait entier
getVariantValCount ()
 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;
Liste abstraite < VariantTensorDataProto >
getVariantValList ()
 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;
abstrait VariantTensorDataProtoOrBuilder
getVariantValOrBuilder (index int)
 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;
Liste abstraite <? étend VariantTensorDataProtoOrBuilder >
getVariantValOrBuilderList ()
 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;
abstrait entier
obtenir le numéro de version ()
 Version number.
booléen abstrait
hasTensorShape ()
 Shape of the tensor.

Méthodes publiques

public abstrait booléen getBoolVal (index int) 

 DT_BOOL
repeated bool bool_val = 11 [packed = true];

public abstrait int getBoolValCount () 

 DT_BOOL
repeated bool bool_val = 11 [packed = true];

liste abstraite publique<Boolean> getBoolValList () 

 DT_BOOL
repeated bool bool_val = 11 [packed = true];

public abstrait double getDcomplexVal (index int) 

 DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real
 and imaginary parts of i-th double precision complex.
repeated double dcomplex_val = 12 [packed = true];

public abstrait int getDcomplexValCount () 

 DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real
 and imaginary parts of i-th double precision complex.
repeated double dcomplex_val = 12 [packed = true];

liste abstraite publique<Double> getDcomplexValList () 

 DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real
 and imaginary parts of i-th double precision complex.
repeated double dcomplex_val = 12 [packed = true];

public abstrait double getDoubleVal (index int) 

 DT_DOUBLE.
repeated double double_val = 6 [packed = true];

public abstrait int getDoubleValCount () 

 DT_DOUBLE.
repeated double double_val = 6 [packed = true];

liste abstraite publique<Double> getDoubleValList () 

 DT_DOUBLE.
repeated double double_val = 6 [packed = true];

Type de données abstrait public getDtype ()

.tensorflow.DataType dtype = 1;

public abstrait int getDtypeValue ()

.tensorflow.DataType dtype = 1;

public abstrait float getFloatVal (index int) 

 DT_FLOAT.
repeated float float_val = 5 [packed = true];

public abstrait int getFloatValCount () 

 DT_FLOAT.
repeated float float_val = 5 [packed = true];

liste abstraite publique<Float> getFloatValList () 

 DT_FLOAT.
repeated float float_val = 5 [packed = true];

public abstrait int getHalfVal (index int) 

 DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll
 have some pointless zero padding for each value here.
repeated int32 half_val = 13 [packed = true];

public abstrait int getHalfValCount () 

 DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll
 have some pointless zero padding for each value here.
repeated int32 half_val = 13 [packed = true];

liste abstraite publique<Integer> getHalfValList () 

 DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll
 have some pointless zero padding for each value here.
repeated int32 half_val = 13 [packed = true];

public abstrait long getInt64Val (index int) 

 DT_INT64
repeated int64 int64_val = 10 [packed = true];

public abstrait int getInt64ValCount () 

 DT_INT64
repeated int64 int64_val = 10 [packed = true];

liste abstraite publique<Long> getInt64ValList () 

 DT_INT64
repeated int64 int64_val = 10 [packed = true];

public abstrait int getIntVal (index int) 

 DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
repeated int32 int_val = 7 [packed = true];

public abstrait int getIntValCount () 

 DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
repeated int32 int_val = 7 [packed = true];

liste abstraite publique<Integer> getIntValList () 

 DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
repeated int32 int_val = 7 [packed = true];

résumé public ResourceHandleProto getResourceHandleVal (index int) 

 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;

public abstrait int getResourceHandleValCount () 

 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;

Liste abstraite publique < ResourceHandleProto > getResourceHandleValList () 

 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;

résumé public ResourceHandleProtoOrBuilder getResourceHandleValOrBuilder (index int) 

 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;

Liste des résumés publics<? étend ResourceHandleProtoOrBuilder > getResourceHandleValOrBuilderList () 

 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;

public abstrait float getScomplexVal (index int) 

 DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
 and imaginary parts of i-th single precision complex.
repeated float scomplex_val = 9 [packed = true];

public abstrait int getScomplexValCount () 

 DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
 and imaginary parts of i-th single precision complex.
repeated float scomplex_val = 9 [packed = true];

liste abstraite publique<Float> getScomplexValList () 

 DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
 and imaginary parts of i-th single precision complex.
repeated float scomplex_val = 9 [packed = true];

résumé public com.google.protobuf.ByteString getStringVal (index int) 

 DT_STRING
repeated bytes string_val = 8;

public abstrait int getStringValCount () 

 DT_STRING
repeated bytes string_val = 8;

liste abstraite publique<ByteString> getStringValList () 

 DT_STRING
repeated bytes string_val = 8;

résumé public com.google.protobuf.ByteString getTensorContent () 

 Serialized raw tensor content from either Tensor::AsProtoTensorContent or
 memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation
 can be used for all tensor types. The purpose of this representation is to
 reduce serialization overhead during RPC call by avoiding serialization of
 many repeated small items.
bytes tensor_content = 4;

résumé public TensorShapeProto getTensorShape () 

 Shape of the tensor.  TODO(touts): sort out the 0-rank issues.
.tensorflow.TensorShapeProto tensor_shape = 2;

résumé public TensorShapeProtoOrBuilder getTensorShapeOrBuilder () 

 Shape of the tensor.  TODO(touts): sort out the 0-rank issues.
.tensorflow.TensorShapeProto tensor_shape = 2;

public abstrait int getUint32Val (index int) 

 DT_UINT32
repeated uint32 uint32_val = 16 [packed = true];

public abstrait int getUint32ValCount () 

 DT_UINT32
repeated uint32 uint32_val = 16 [packed = true];

liste abstraite publique<Integer> getUint32ValList () 

 DT_UINT32
repeated uint32 uint32_val = 16 [packed = true];

public abstrait long getUint64Val (index int) 

 DT_UINT64
repeated uint64 uint64_val = 17 [packed = true];

public abstrait int getUint64ValCount () 

 DT_UINT64
repeated uint64 uint64_val = 17 [packed = true];

liste abstraite publique<Long> getUint64ValList () 

 DT_UINT64
repeated uint64 uint64_val = 17 [packed = true];

résumé public VariantTensorDataProto getVariantVal (index int) 

 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;

public abstrait int getVariantValCount () 

 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;

Liste abstraite publique < VariantTensorDataProto > getVariantValList () 

 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;

résumé public VariantTensorDataProtoOrBuilder getVariantValOrBuilder (index int) 

 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;

Liste des résumés publics<? étend VariantTensorDataProtoOrBuilder > getVariantValOrBuilderList () 

 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;

public abstrait int getVersionNumber () 

 Version number.
 In version 0, if the "repeated xxx" representations contain only one
 element, that element is repeated to fill the shape.  This makes it easy
 to represent a constant Tensor with a single value.
int32 version_number = 3;

public abstrait booléen hasTensorShape () 

 Shape of the tensor.  TODO(touts): sort out the 0-rank issues.
.tensorflow.TensorShapeProto tensor_shape = 2;