TensorProtoOrBuilder

antarmuka publik TensorProtoOrBuilder
Subkelas Tidak Langsung yang Diketahui

Metode Publik

boolean abstrak
getBoolVal (indeks int)
 DT_BOOL
repeated bool bool_val = 11 [packed = true];
abstrak ke dalam
dapatkanBoolValCount ()
 DT_BOOL
repeated bool bool_val = 11 [packed = true];
Daftar abstrak<Boolean>
dapatkanBoolValList ()
 DT_BOOL
repeated bool bool_val = 11 [packed = true];
abstrak ganda
getDcomplexVal (indeks int)
 DT_COMPLEX128.
abstrak ke dalam
dapatkanDcomplexValCount ()
 DT_COMPLEX128.
Daftar abstrak<Ganda>
dapatkanDcomplexValList ()
 DT_COMPLEX128.
abstrak ganda
getDoubleVal (indeks int)
 DT_DOUBLE.
abstrak ke dalam
dapatkanDoubleValCount ()
 DT_DOUBLE.
Daftar abstrak<Ganda>
dapatkanDoubleValList ()
 DT_DOUBLE.
Tipe Data abstrak
dapatkan tipe D ()
.tensorflow.DataType dtype = 1;
abstrak ke dalam
dapatkanDtypeValue ()
.tensorflow.DataType dtype = 1;
pelampung abstrak
getFloatVal (indeks int)
 DT_FLOAT.
abstrak ke dalam
dapatkanFloatValCount ()
 DT_FLOAT.
Daftar abstrak<Float>
dapatkanFloatValList ()
 DT_FLOAT.
abstrak ke dalam
getHalfVal (indeks int)
 DT_HALF, DT_BFLOAT16.
abstrak ke dalam
dapatkanHalfValCount ()
 DT_HALF, DT_BFLOAT16.
Daftar abstrak<Bilangan Bulat>
dapatkanHalfValList ()
 DT_HALF, DT_BFLOAT16.
abstrak panjang
getInt64Val (indeks int)
 DT_INT64
repeated int64 int64_val = 10 [packed = true];
abstrak ke dalam
dapatkanInt64ValCount ()
 DT_INT64
repeated int64 int64_val = 10 [packed = true];
Daftar abstrak<Panjang>
dapatkanInt64ValList ()
 DT_INT64
repeated int64 int64_val = 10 [packed = true];
abstrak ke dalam
getIntVal (indeks int)
 DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
abstrak ke dalam
dapatkanIntValCount ()
 DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
Daftar abstrak<Bilangan Bulat>
dapatkanIntValList ()
 DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
ResourceHandleProto abstrak
getResourceHandleVal (indeks int)
 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;
abstrak ke dalam
dapatkanResourceHandleValCount ()
 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;
Daftar abstrak< ResourceHandleProto >
dapatkanResourceHandleValList ()
 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;
abstrak ResourceHandleProtoOrBuilder
getResourceHandleValOrBuilder (indeks int)
 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;
Daftar abstrak<? memperluas ResourceHandleProtoOrBuilder >
getResourceHandleValOrBuilderList ()
 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;
pelampung abstrak
getScomplexVal (indeks int)
 DT_COMPLEX64.
abstrak ke dalam
dapatkanScomplexValCount ()
 DT_COMPLEX64.
Daftar abstrak<Float>
dapatkanScomplexValList ()
 DT_COMPLEX64.
abstrak com.google.protobuf.ByteString
getStringVal (indeks int)
 DT_STRING
repeated bytes string_val = 8;
abstrak ke dalam
dapatkanStringValCount ()
 DT_STRING
repeated bytes string_val = 8;
Daftar abstrak<ByteString>
dapatkanStringValList ()
 DT_STRING
repeated bytes string_val = 8;
abstrak com.google.protobuf.ByteString
dapatkanTensorContent ()
 Serialized raw tensor content from either Tensor::AsProtoTensorContent or
 memcpy in tensorflow::grpc::EncodeTensorToByteBuffer.
TensorShapeProto abstrak
dapatkanTensorBentuk ()
 Shape of the tensor.
TensorShapeProtoOrBuilder abstrak
dapatkanTensorShapeOrBuilder ()
 Shape of the tensor.
abstrak ke dalam
getUint32Val (indeks int)
 DT_UINT32
repeated uint32 uint32_val = 16 [packed = true];
abstrak ke dalam
dapatkanUint32ValCount ()
 DT_UINT32
repeated uint32 uint32_val = 16 [packed = true];
Daftar abstrak<Bilangan Bulat>
dapatkanUint32ValList ()
 DT_UINT32
repeated uint32 uint32_val = 16 [packed = true];
abstrak panjang
getUint64Val (indeks int)
 DT_UINT64
repeated uint64 uint64_val = 17 [packed = true];
abstrak ke dalam
dapatkanUint64ValCount ()
 DT_UINT64
repeated uint64 uint64_val = 17 [packed = true];
Daftar abstrak<Panjang>
dapatkanUint64ValList ()
 DT_UINT64
repeated uint64 uint64_val = 17 [packed = true];
VariantTensorDataProto abstrak
getVariantVal (indeks int)
 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;
abstrak ke dalam
dapatkanVariantValCount ()
 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;
Daftar abstrak< VariantTensorDataProto >
dapatkanVariantValList ()
 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;
abstrak VariantTensorDataProtoOrBuilder
getVariantValOrBuilder (indeks int)
 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;
Daftar abstrak<? memperluas VariantTensorDataProtoOrBuilder >
dapatkanVariantValOrBuilderList ()
 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;
abstrak ke dalam
dapatkanVersionNumber ()
 Version number.
boolean abstrak
hasTensorShape ()
 Shape of the tensor.

Metode Publik

boolean abstrak publik getBoolVal (indeks int) 

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

abstrak publik int getBoolValCount () 

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

Daftar abstrak publik<Boolean> getBoolValList () 

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

abstrak publik ganda getDcomplexVal (int indeks) 

 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];

abstrak publik 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];

Daftar abstrak publik<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];

abstrak publik getDoubleVal ganda (indeks int) 

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

abstrak publik int getDoubleValCount () 

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

Daftar abstrak publik<Double> getDoubleValList () 

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

Tipe Data abstrak publik getDtype ()

.tensorflow.DataType dtype = 1;

abstrak publik int getDtypeValue ()

.tensorflow.DataType dtype = 1;

abstrak publik float getFloatVal (int indeks) 

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

abstrak publik int getFloatValCount () 

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

Daftar abstrak publik<Float> getFloatValList () 

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

abstrak publik int getHalfVal (indeks 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];

abstrak publik 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];

Daftar abstrak publik<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];

abstrak publik getInt64Val panjang (indeks int) 

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

abstrak publik int getInt64ValCount () 

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

Daftar abstrak publik<Panjang> getInt64ValList () 

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

abstrak publik int getIntVal (indeks int) 

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

abstrak publik int getIntValCount () 

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

Daftar abstrak publik<Integer> getIntValList () 

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

abstrak publik ResourceHandleProto getResourceHandleVal (int indeks) 

 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;

abstrak publik int getResourceHandleValCount () 

 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;

Daftar abstrak publik< ResourceHandleProto > getResourceHandleValList () 

 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;

abstrak publik ResourceHandleProtoOrBuilder getResourceHandleValOrBuilder (int indeks) 

 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;

Daftar abstrak publik<? memperluas ResourceHandleProtoOrBuilder > getResourceHandleValOrBuilderList () 

 DT_RESOURCE
repeated .tensorflow.ResourceHandleProto resource_handle_val = 14;

float abstrak publik getScomplexVal (indeks 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];

abstrak publik 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];

Daftar abstrak publik<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];

abstrak publik com.google.protobuf.ByteString getStringVal (indeks int) 

 DT_STRING
repeated bytes string_val = 8;

abstrak publik int getStringValCount () 

 DT_STRING
repeated bytes string_val = 8;

Daftar abstrak publik<ByteString> getStringValList () 

 DT_STRING
repeated bytes string_val = 8;

abstrak publik 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;

abstrak publik TensorShapeProto getTensorShape () 

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

abstrak publik TensorShapeProtoOrBuilder getTensorShapeOrBuilder () 

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

abstrak publik int getUint32Val (indeks int) 

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

abstrak publik int getUint32ValCount () 

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

Daftar abstrak publik<Integer> getUint32ValList () 

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

getUint64Val abstrak publik yang panjang (indeks int) 

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

abstrak publik int getUint64ValCount () 

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

Daftar abstrak publik<Panjang> getUint64ValList () 

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

abstrak publik VariantTensorDataProto getVariantVal (indeks int) 

 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;

abstrak publik int getVariantValCount () 

 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;

Daftar abstrak publik< VariantTensorDataProto > getVariantValList () 

 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;

abstrak publik VariantTensorDataProtoOrBuilder getVariantValOrBuilder (indeks int) 

 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;

Daftar abstrak publik<? memperluas VariantTensorDataProtoOrBuilder > getVariantValOrBuilderList () 

 DT_VARIANT
repeated .tensorflow.VariantTensorDataProto variant_val = 15;

abstrak publik 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;

boolean abstrak publik hasTensorShape () 

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