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tf.raw_ops.ExperimentalUnbatchDataset A dataset that splits the elements of its input into multiple elements. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalUnbatchDataset
tf.raw_ops.ExperimentalUnbatchDataset(
input_dataset, output_types, output_shapes, name=None
)
Args
input_dataset A Tensor of type variant.
output_types A list of tf.DTypes that has length >= 1.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
name A name for the operation (optional).
Returns A Tensor of type variant. | tensorflow.raw_ops.experimentalunbatchdataset |
tf.raw_ops.ExperimentalUniqueDataset Creates a dataset that contains the unique elements of input_dataset. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalUniqueDataset
tf.raw_ops.ExperimentalUniqueDataset(
input_dataset, output_types, output_shapes, name=None
)
Args
input_dataset A Tensor of type variant.
output_types A list of tf.DTypes that has length >= 1.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
name A name for the operation (optional).
Returns A Tensor of type variant. | tensorflow.raw_ops.experimentaluniquedataset |
tf.raw_ops.Expint View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Expint
tf.raw_ops.Expint(
x, name=None
)
Args
x A Tensor. Must be one of the following types: bfloat16, half, float32, float64.
name A name for the operation (optional).
Returns A Tensor. Has the same type as x. | tensorflow.raw_ops.expint |
tf.raw_ops.Expm1 Computes exp(x) - 1 element-wise. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Expm1
tf.raw_ops.Expm1(
x, name=None
)
i.e. exp(x) - 1 or e^(x) - 1, where x is the input tensor. e denotes Euler's number and is approximately equal to 2.718281. x = tf.constant(2.0)
tf.math.expm1(x) ==> 6.389056
x = tf.constant([2.0, 8.0])
tf.math.expm1(x) ==> array([6.389056, 2979.958], dtype=float32)
x = tf.constant(1 + 1j)
tf.math.expm1(x) ==> (0.46869393991588515+2.2873552871788423j)
Args
x A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.
name A name for the operation (optional).
Returns A Tensor. Has the same type as x. | tensorflow.raw_ops.expm1 |
tf.raw_ops.ExtractGlimpse Extracts a glimpse from the input tensor. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExtractGlimpse
tf.raw_ops.ExtractGlimpse(
input, size, offsets, centered=True, normalized=True, uniform_noise=True,
noise='uniform', name=None
)
Returns a set of windows called glimpses extracted at location offsets from the input tensor. If the windows only partially overlaps the inputs, the non overlapping areas will be filled with random noise. The result is a 4-D tensor of shape [batch_size, glimpse_height, glimpse_width, channels]. The channels and batch dimensions are the same as that of the input tensor. The height and width of the output windows are specified in the size parameter. The argument normalized and centered controls how the windows are built: If the coordinates are normalized but not centered, 0.0 and 1.0 correspond to the minimum and maximum of each height and width dimension. If the coordinates are both normalized and centered, they range from -1.0 to 1.0. The coordinates (-1.0, -1.0) correspond to the upper left corner, the lower right corner is located at (1.0, 1.0) and the center is at (0, 0). If the coordinates are not normalized they are interpreted as numbers of pixels.
Args
input A Tensor of type float32. A 4-D float tensor of shape [batch_size, height, width, channels].
size A Tensor of type int32. A 1-D tensor of 2 elements containing the size of the glimpses to extract. The glimpse height must be specified first, following by the glimpse width.
offsets A Tensor of type float32. A 2-D integer tensor of shape [batch_size, 2] containing the y, x locations of the center of each window.
centered An optional bool. Defaults to True. indicates if the offset coordinates are centered relative to the image, in which case the (0, 0) offset is relative to the center of the input images. If false, the (0,0) offset corresponds to the upper left corner of the input images.
normalized An optional bool. Defaults to True. indicates if the offset coordinates are normalized.
uniform_noise An optional bool. Defaults to True. indicates if the noise should be generated using a uniform distribution or a Gaussian distribution.
noise An optional string. Defaults to "uniform". indicates if the noise should uniform, gaussian, or zero. The default is uniform which means the the noise type will be decided by uniform_noise.
name A name for the operation (optional).
Returns A Tensor of type float32. | tensorflow.raw_ops.extractglimpse |
tf.raw_ops.ExtractGlimpseV2 Extracts a glimpse from the input tensor. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExtractGlimpseV2
tf.raw_ops.ExtractGlimpseV2(
input, size, offsets, centered=True, normalized=True, uniform_noise=True,
noise='uniform', name=None
)
Returns a set of windows called glimpses extracted at location offsets from the input tensor. If the windows only partially overlaps the inputs, the non overlapping areas will be filled with random noise. The result is a 4-D tensor of shape [batch_size, glimpse_height, glimpse_width, channels]. The channels and batch dimensions are the same as that of the input tensor. The height and width of the output windows are specified in the size parameter. The argument normalized and centered controls how the windows are built: If the coordinates are normalized but not centered, 0.0 and 1.0 correspond to the minimum and maximum of each height and width dimension. If the coordinates are both normalized and centered, they range from -1.0 to 1.0. The coordinates (-1.0, -1.0) correspond to the upper left corner, the lower right corner is located at (1.0, 1.0) and the center is at (0, 0). If the coordinates are not normalized they are interpreted as numbers of pixels.
Args
input A Tensor of type float32. A 4-D float tensor of shape [batch_size, height, width, channels].
size A Tensor of type int32. A 1-D tensor of 2 elements containing the size of the glimpses to extract. The glimpse height must be specified first, following by the glimpse width.
offsets A Tensor of type float32. A 2-D integer tensor of shape [batch_size, 2] containing the y, x locations of the center of each window.
centered An optional bool. Defaults to True. indicates if the offset coordinates are centered relative to the image, in which case the (0, 0) offset is relative to the center of the input images. If false, the (0,0) offset corresponds to the upper left corner of the input images.
normalized An optional bool. Defaults to True. indicates if the offset coordinates are normalized.
uniform_noise An optional bool. Defaults to True. indicates if the noise should be generated using a uniform distribution or a Gaussian distribution.
noise An optional string. Defaults to "uniform". indicates if the noise should uniform, gaussian, or zero. The default is uniform which means the the noise type will be decided by uniform_noise.
name A name for the operation (optional).
Returns A Tensor of type float32. | tensorflow.raw_ops.extractglimpsev2 |
tf.raw_ops.ExtractImagePatches Extract patches from images and put them in the "depth" output dimension. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExtractImagePatches
tf.raw_ops.ExtractImagePatches(
images, ksizes, strides, rates, padding, name=None
)
Args
images A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, int16, int32, int64, uint8, uint16, uint32, uint64, complex64, complex128, bool. 4-D Tensor with shape [batch, in_rows, in_cols, depth].
ksizes A list of ints that has length >= 4. The size of the sliding window for each dimension of images.
strides A list of ints that has length >= 4. How far the centers of two consecutive patches are in the images. Must be: [1, stride_rows, stride_cols, 1].
rates A list of ints that has length >= 4. Must be: [1, rate_rows, rate_cols, 1]. This is the input stride, specifying how far two consecutive patch samples are in the input. Equivalent to extracting patches with patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1), followed by subsampling them spatially by a factor of rates. This is equivalent to rate in dilated (a.k.a. Atrous) convolutions.
padding A string from: "SAME", "VALID". The type of padding algorithm to use.
name A name for the operation (optional).
Returns A Tensor. Has the same type as images. | tensorflow.raw_ops.extractimagepatches |
tf.raw_ops.ExtractJpegShape Extract the shape information of a JPEG-encoded image. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExtractJpegShape
tf.raw_ops.ExtractJpegShape(
contents, output_type=tf.dtypes.int32, name=None
)
This op only parses the image header, so it is much faster than DecodeJpeg.
Args
contents A Tensor of type string. 0-D. The JPEG-encoded image.
output_type An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32. (Optional) The output type of the operation (int32 or int64). Defaults to int32.
name A name for the operation (optional).
Returns A Tensor of type output_type. | tensorflow.raw_ops.extractjpegshape |
tf.raw_ops.ExtractVolumePatches Extract patches from input and put them in the "depth" output dimension. 3D extension of extract_image_patches. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ExtractVolumePatches
tf.raw_ops.ExtractVolumePatches(
input, ksizes, strides, padding, name=None
)
Args
input A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. 5-D Tensor with shape [batch, in_planes, in_rows, in_cols, depth].
ksizes A list of ints that has length >= 5. The size of the sliding window for each dimension of input.
strides A list of ints that has length >= 5. 1-D of length 5. How far the centers of two consecutive patches are in input. Must be: [1, stride_planes, stride_rows, stride_cols, 1].
padding A string from: "SAME", "VALID". The type of padding algorithm to use. The size-related attributes are specified as follows: ksizes = [1, ksize_planes, ksize_rows, ksize_cols, 1]
strides = [1, stride_planes, strides_rows, strides_cols, 1]
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | tensorflow.raw_ops.extractvolumepatches |
tf.raw_ops.Fact Output a fact about factorials. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Fact
tf.raw_ops.Fact(
name=None
)
Args
name A name for the operation (optional).
Returns A Tensor of type string. | tensorflow.raw_ops.fact |
tf.raw_ops.FakeParam This op is used as a placeholder in If branch functions. It doesn't provide a View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FakeParam
tf.raw_ops.FakeParam(
dtype, shape, name=None
)
valid output when run, so must either be removed (e.g. replaced with a function input) or guaranteed not to be used (e.g. if mirroring an intermediate output needed for the gradient computation of the other branch).
Args
dtype A tf.DType. The type of the output.
shape A tf.TensorShape or list of ints. The purported shape of the output. This is only used for shape inference; the output will not necessarily have this shape. Can be a partial shape.
name A name for the operation (optional).
Returns A Tensor of type dtype. | tensorflow.raw_ops.fakeparam |
tf.raw_ops.FakeQuantWithMinMaxArgs Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQuantWithMinMaxArgs
tf.raw_ops.FakeQuantWithMinMaxArgs(
inputs, min=-6, max=6, num_bits=8, narrow_range=False, name=None
)
Attributes
[min; max] define the clamping range for the inputs data.
inputs values are quantized into the quantization range ( [0; 2^num_bits - 1] when narrow_range is false and [1; 2^num_bits - 1] when it is true) and then de-quantized and output as floats in [min; max] interval.
num_bits is the bitwidth of the quantization; between 2 and 16, inclusive. Before quantization, min and max values are adjusted with the following logic. It is suggested to have min <= 0 <= max. If 0 is not in the range of values, the behavior can be unexpected: If 0 < min < max: min_adj = 0 and max_adj = max - min. If min < max < 0: min_adj = min - max and max_adj = 0. If min <= 0 <= max: scale = (max - min) / (2^num_bits - 1), min_adj = scale * round(min / scale) and max_adj = max + min_adj - min. Quantization is called fake since the output is still in floating point.
Args
inputs A Tensor of type float32.
min An optional float. Defaults to -6.
max An optional float. Defaults to 6.
num_bits An optional int. Defaults to 8.
narrow_range An optional bool. Defaults to False.
name A name for the operation (optional).
Returns A Tensor of type float32. | tensorflow.raw_ops.fakequantwithminmaxargs |
tf.raw_ops.FakeQuantWithMinMaxArgsGradient Compute gradients for a FakeQuantWithMinMaxArgs operation. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQuantWithMinMaxArgsGradient
tf.raw_ops.FakeQuantWithMinMaxArgsGradient(
gradients, inputs, min=-6, max=6, num_bits=8, narrow_range=False, name=None
)
Args
gradients A Tensor of type float32. Backpropagated gradients above the FakeQuantWithMinMaxArgs operation.
inputs A Tensor of type float32. Values passed as inputs to the FakeQuantWithMinMaxArgs operation.
min An optional float. Defaults to -6.
max An optional float. Defaults to 6.
num_bits An optional int. Defaults to 8.
narrow_range An optional bool. Defaults to False.
name A name for the operation (optional).
Returns A Tensor of type float32. | tensorflow.raw_ops.fakequantwithminmaxargsgradient |
tf.raw_ops.FakeQuantWithMinMaxVars Fake-quantize the 'inputs' tensor of type float via global float scalars View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQuantWithMinMaxVars
tf.raw_ops.FakeQuantWithMinMaxVars(
inputs, min, max, num_bits=8, narrow_range=False, name=None
)
Fake-quantize the inputs tensor of type float via global float scalars min and max to outputs tensor of same shape as inputs. Attributes
[min; max] define the clamping range for the inputs data.
inputs values are quantized into the quantization range ( [0; 2^num_bits - 1] when narrow_range is false and [1; 2^num_bits - 1] when it is true) and then de-quantized and output as floats in [min; max] interval.
num_bits is the bitwidth of the quantization; between 2 and 16, inclusive. Before quantization, min and max values are adjusted with the following logic. It is suggested to have min <= 0 <= max. If 0 is not in the range of values, the behavior can be unexpected: If 0 < min < max: min_adj = 0 and max_adj = max - min. If min < max < 0: min_adj = min - max and max_adj = 0. If min <= 0 <= max: scale = (max - min) / (2^num_bits - 1), min_adj = scale * round(min / scale) and max_adj = max + min_adj - min. This operation has a gradient and thus allows for training min and max values.
Args
inputs A Tensor of type float32.
min A Tensor of type float32.
max A Tensor of type float32.
num_bits An optional int. Defaults to 8.
narrow_range An optional bool. Defaults to False.
name A name for the operation (optional).
Returns A Tensor of type float32. | tensorflow.raw_ops.fakequantwithminmaxvars |
tf.raw_ops.FakeQuantWithMinMaxVarsGradient Compute gradients for a FakeQuantWithMinMaxVars operation. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQuantWithMinMaxVarsGradient
tf.raw_ops.FakeQuantWithMinMaxVarsGradient(
gradients, inputs, min, max, num_bits=8, narrow_range=False, name=None
)
Args
gradients A Tensor of type float32. Backpropagated gradients above the FakeQuantWithMinMaxVars operation.
inputs A Tensor of type float32. Values passed as inputs to the FakeQuantWithMinMaxVars operation. min, max: Quantization interval, scalar floats.
min A Tensor of type float32.
max A Tensor of type float32.
num_bits An optional int. Defaults to 8. The bitwidth of the quantization; between 2 and 8, inclusive.
narrow_range An optional bool. Defaults to False. Whether to quantize into 2^num_bits - 1 distinct values.
name A name for the operation (optional).
Returns A tuple of Tensor objects (backprops_wrt_input, backprop_wrt_min, backprop_wrt_max). backprops_wrt_input A Tensor of type float32.
backprop_wrt_min A Tensor of type float32.
backprop_wrt_max A Tensor of type float32. | tensorflow.raw_ops.fakequantwithminmaxvarsgradient |
tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel Fake-quantize the 'inputs' tensor of type float via per-channel floats View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQuantWithMinMaxVarsPerChannel
tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel(
inputs, min, max, num_bits=8, narrow_range=False, name=None
)
Fake-quantize the inputs tensor of type float per-channel and one of the shapes: [d], [b, d] [b, h, w, d] via per-channel floats min and max of shape [d] to outputs tensor of same shape as inputs. Attributes
[min; max] define the clamping range for the inputs data.
inputs values are quantized into the quantization range ( [0; 2^num_bits - 1] when narrow_range is false and [1; 2^num_bits - 1] when it is true) and then de-quantized and output as floats in [min; max] interval.
num_bits is the bitwidth of the quantization; between 2 and 16, inclusive. Before quantization, min and max values are adjusted with the following logic. It is suggested to have min <= 0 <= max. If 0 is not in the range of values, the behavior can be unexpected: If 0 < min < max: min_adj = 0 and max_adj = max - min. If min < max < 0: min_adj = min - max and max_adj = 0. If min <= 0 <= max: scale = (max - min) / (2^num_bits - 1), min_adj = scale * round(min / scale) and max_adj = max + min_adj - min. This operation has a gradient and thus allows for training min and max values.
Args
inputs A Tensor of type float32.
min A Tensor of type float32.
max A Tensor of type float32.
num_bits An optional int. Defaults to 8.
narrow_range An optional bool. Defaults to False.
name A name for the operation (optional).
Returns A Tensor of type float32. | tensorflow.raw_ops.fakequantwithminmaxvarsperchannel |
tf.raw_ops.FakeQuantWithMinMaxVarsPerChannelGradient Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQuantWithMinMaxVarsPerChannelGradient
tf.raw_ops.FakeQuantWithMinMaxVarsPerChannelGradient(
gradients, inputs, min, max, num_bits=8, narrow_range=False, name=None
)
Args
gradients A Tensor of type float32. Backpropagated gradients above the FakeQuantWithMinMaxVars operation, shape one of: [d], [b, d], [b, h, w, d].
inputs A Tensor of type float32. Values passed as inputs to the FakeQuantWithMinMaxVars operation, shape same as gradients. min, max: Quantization interval, floats of shape [d].
min A Tensor of type float32.
max A Tensor of type float32.
num_bits An optional int. Defaults to 8. The bitwidth of the quantization; between 2 and 16, inclusive.
narrow_range An optional bool. Defaults to False. Whether to quantize into 2^num_bits - 1 distinct values.
name A name for the operation (optional).
Returns A tuple of Tensor objects (backprops_wrt_input, backprop_wrt_min, backprop_wrt_max). backprops_wrt_input A Tensor of type float32.
backprop_wrt_min A Tensor of type float32.
backprop_wrt_max A Tensor of type float32. | tensorflow.raw_ops.fakequantwithminmaxvarsperchannelgradient |
tf.raw_ops.FakeQueue Deprecated. Do not use. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FakeQueue
tf.raw_ops.FakeQueue(
resource, name=None
)
Args
resource A Tensor of type resource.
name A name for the operation (optional).
Returns A Tensor of type mutable string. | tensorflow.raw_ops.fakequeue |
tf.raw_ops.FFT Fast Fourier transform. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FFT
tf.raw_ops.FFT(
input, name=None
)
Computes the 1-dimensional discrete Fourier transform over the inner-most dimension of input.
Args
input A Tensor. Must be one of the following types: complex64, complex128. A complex tensor.
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | tensorflow.raw_ops.fft |
tf.raw_ops.FFT2D 2D fast Fourier transform. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FFT2D
tf.raw_ops.FFT2D(
input, name=None
)
Computes the 2-dimensional discrete Fourier transform over the inner-most 2 dimensions of input.
Args
input A Tensor. Must be one of the following types: complex64, complex128. A complex tensor.
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | tensorflow.raw_ops.fft2d |
tf.raw_ops.FFT3D 3D fast Fourier transform. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FFT3D
tf.raw_ops.FFT3D(
input, name=None
)
Computes the 3-dimensional discrete Fourier transform over the inner-most 3 dimensions of input.
Args
input A Tensor. Must be one of the following types: complex64, complex128. A complex tensor.
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | tensorflow.raw_ops.fft3d |
tf.raw_ops.FIFOQueue A queue that produces elements in first-in first-out order. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FIFOQueue
tf.raw_ops.FIFOQueue(
component_types, shapes=[], capacity=-1, container='',
shared_name='', name=None
)
Args
component_types A list of tf.DTypes that has length >= 1. The type of each component in a value.
shapes An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to []. The shape of each component in a value. The length of this attr must be either 0 or the same as the length of component_types. If the length of this attr is 0, the shapes of queue elements are not constrained, and only one element may be dequeued at a time.
capacity An optional int. Defaults to -1. The upper bound on the number of elements in this queue. Negative numbers mean no limit.
container An optional string. Defaults to "". If non-empty, this queue is placed in the given container. Otherwise, a default container is used.
shared_name An optional string. Defaults to "". If non-empty, this queue will be shared under the given name across multiple sessions.
name A name for the operation (optional).
Returns A Tensor of type mutable string. | tensorflow.raw_ops.fifoqueue |
tf.raw_ops.FIFOQueueV2 A queue that produces elements in first-in first-out order. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FIFOQueueV2
tf.raw_ops.FIFOQueueV2(
component_types, shapes=[], capacity=-1, container='',
shared_name='', name=None
)
Args
component_types A list of tf.DTypes that has length >= 1. The type of each component in a value.
shapes An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to []. The shape of each component in a value. The length of this attr must be either 0 or the same as the length of component_types. If the length of this attr is 0, the shapes of queue elements are not constrained, and only one element may be dequeued at a time.
capacity An optional int. Defaults to -1. The upper bound on the number of elements in this queue. Negative numbers mean no limit.
container An optional string. Defaults to "". If non-empty, this queue is placed in the given container. Otherwise, a default container is used.
shared_name An optional string. Defaults to "". If non-empty, this queue will be shared under the given name across multiple sessions.
name A name for the operation (optional).
Returns A Tensor of type resource. | tensorflow.raw_ops.fifoqueuev2 |
tf.raw_ops.Fill Creates a tensor filled with a scalar value. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Fill
tf.raw_ops.Fill(
dims, value, name=None
)
This operation creates a tensor of shape dims and fills it with value. For example: # Output tensor has shape [2, 3].
fill([2, 3], 9) ==> [[9, 9, 9]
[9, 9, 9]]
tf.fill differs from tf.constant in a few ways:
tf.fill only supports scalar contents, whereas tf.constant supports Tensor values.
tf.fill creates an Op in the computation graph that constructs the actual Tensor value at runtime. This is in contrast to tf.constant which embeds the entire Tensor into the graph with a Const node. Because tf.fill evaluates at graph runtime, it supports dynamic shapes based on other runtime Tensors, unlike tf.constant.
Args
dims A Tensor. Must be one of the following types: int32, int64. 1-D. Represents the shape of the output tensor.
value A Tensor. 0-D (scalar). Value to fill the returned tensor.
name A name for the operation (optional).
Returns A Tensor. Has the same type as value.
Numpy Compatibility Equivalent to np.full | tensorflow.raw_ops.fill |
tf.raw_ops.FilterByLastComponentDataset Creates a dataset containing elements of first component of input_dataset having true in the last component. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FilterByLastComponentDataset
tf.raw_ops.FilterByLastComponentDataset(
input_dataset, output_types, output_shapes, name=None
)
Args
input_dataset A Tensor of type variant.
output_types A list of tf.DTypes that has length >= 1.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
name A name for the operation (optional).
Returns A Tensor of type variant. | tensorflow.raw_ops.filterbylastcomponentdataset |
tf.raw_ops.FilterDataset Creates a dataset containing elements of input_dataset matching predicate. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FilterDataset
tf.raw_ops.FilterDataset(
input_dataset, other_arguments, predicate, output_types, output_shapes,
name=None
)
The predicate function must return a scalar boolean and accept the following arguments: One tensor for each component of an element of input_dataset. One tensor for each value in other_arguments.
Args
input_dataset A Tensor of type variant.
other_arguments A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for predicate.
predicate A function decorated with @Defun. A function returning a scalar boolean.
output_types A list of tf.DTypes that has length >= 1.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
name A name for the operation (optional).
Returns A Tensor of type variant. | tensorflow.raw_ops.filterdataset |
tf.raw_ops.Fingerprint Generates fingerprint values. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Fingerprint
tf.raw_ops.Fingerprint(
data, method, name=None
)
Generates fingerprint values of data. Fingerprint op considers the first dimension of data as the batch dimension, and output[i] contains the fingerprint value generated from contents in data[i, ...] for all i. Fingerprint op writes fingerprint values as byte arrays. For example, the default method farmhash64 generates a 64-bit fingerprint value at a time. This 8-byte value is written out as an uint8 array of size 8, in little-endian order. For example, suppose that data has data type DT_INT32 and shape (2, 3, 4), and that the fingerprint method is farmhash64. In this case, the output shape is (2, 8), where 2 is the batch dimension size of data, and 8 is the size of each fingerprint value in bytes. output[0, :] is generated from 12 integers in data[0, :, :] and similarly output[1, :] is generated from other 12 integers in data[1, :, :]. Note that this op fingerprints the raw underlying buffer, and it does not fingerprint Tensor's metadata such as data type and/or shape. For example, the fingerprint values are invariant under reshapes and bitcasts as long as the batch dimension remain the same: Fingerprint(data) == Fingerprint(Reshape(data, ...))
Fingerprint(data) == Fingerprint(Bitcast(data, ...))
For string data, one should expect Fingerprint(data) != Fingerprint(ReduceJoin(data)) in general.
Args
data A Tensor. Must have rank 1 or higher.
method A Tensor of type string. Fingerprint method used by this op. Currently available method is farmhash::fingerprint64.
name A name for the operation (optional).
Returns A Tensor of type uint8. | tensorflow.raw_ops.fingerprint |
tf.raw_ops.FixedLengthRecordDataset Creates a dataset that emits the records from one or more binary files. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FixedLengthRecordDataset
tf.raw_ops.FixedLengthRecordDataset(
filenames, header_bytes, record_bytes, footer_bytes, buffer_size, name=None
)
Args
filenames A Tensor of type string. A scalar or a vector containing the name(s) of the file(s) to be read.
header_bytes A Tensor of type int64. A scalar representing the number of bytes to skip at the beginning of a file.
record_bytes A Tensor of type int64. A scalar representing the number of bytes in each record.
footer_bytes A Tensor of type int64. A scalar representing the number of bytes to skip at the end of a file.
buffer_size A Tensor of type int64. A scalar representing the number of bytes to buffer. Must be > 0.
name A name for the operation (optional).
Returns A Tensor of type variant. | tensorflow.raw_ops.fixedlengthrecorddataset |
tf.raw_ops.FixedLengthRecordDatasetV2 View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FixedLengthRecordDatasetV2
tf.raw_ops.FixedLengthRecordDatasetV2(
filenames, header_bytes, record_bytes, footer_bytes, buffer_size,
compression_type, name=None
)
Args
filenames A Tensor of type string.
header_bytes A Tensor of type int64.
record_bytes A Tensor of type int64.
footer_bytes A Tensor of type int64.
buffer_size A Tensor of type int64.
compression_type A Tensor of type string.
name A name for the operation (optional).
Returns A Tensor of type variant. | tensorflow.raw_ops.fixedlengthrecorddatasetv2 |
tf.raw_ops.FixedLengthRecordReader A Reader that outputs fixed-length records from a file. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FixedLengthRecordReader
tf.raw_ops.FixedLengthRecordReader(
record_bytes, header_bytes=0, footer_bytes=0, hop_bytes=0,
container='', shared_name='', name=None
)
Args
record_bytes An int. Number of bytes in the record.
header_bytes An optional int. Defaults to 0. Number of bytes in the header, defaults to 0.
footer_bytes An optional int. Defaults to 0. Number of bytes in the footer, defaults to 0.
hop_bytes An optional int. Defaults to 0. Number of bytes to hop before each read. Default of 0 means using record_bytes.
container An optional string. Defaults to "". If non-empty, this reader is placed in the given container. Otherwise, a default container is used.
shared_name An optional string. Defaults to "". If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead.
name A name for the operation (optional).
Returns A Tensor of type mutable string. | tensorflow.raw_ops.fixedlengthrecordreader |
tf.raw_ops.FixedLengthRecordReaderV2 A Reader that outputs fixed-length records from a file. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FixedLengthRecordReaderV2
tf.raw_ops.FixedLengthRecordReaderV2(
record_bytes, header_bytes=0, footer_bytes=0, hop_bytes=0,
container='', shared_name='', encoding='',
name=None
)
Args
record_bytes An int. Number of bytes in the record.
header_bytes An optional int. Defaults to 0. Number of bytes in the header, defaults to 0.
footer_bytes An optional int. Defaults to 0. Number of bytes in the footer, defaults to 0.
hop_bytes An optional int. Defaults to 0. Number of bytes to hop before each read. Default of 0 means using record_bytes.
container An optional string. Defaults to "". If non-empty, this reader is placed in the given container. Otherwise, a default container is used.
shared_name An optional string. Defaults to "". If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead.
encoding An optional string. Defaults to "". The type of encoding for the file. Currently ZLIB and GZIP are supported. Defaults to none.
name A name for the operation (optional).
Returns A Tensor of type resource. | tensorflow.raw_ops.fixedlengthrecordreaderv2 |
tf.raw_ops.FixedUnigramCandidateSampler Generates labels for candidate sampling with a learned unigram distribution. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FixedUnigramCandidateSampler
tf.raw_ops.FixedUnigramCandidateSampler(
true_classes, num_true, num_sampled, unique, range_max, vocab_file='',
distortion=1, num_reserved_ids=0, num_shards=1, shard=0, unigrams=[], seed=0,
seed2=0, name=None
)
A unigram sampler could use a fixed unigram distribution read from a file or passed in as an in-memory array instead of building up the distribution from data on the fly. There is also an option to skew the distribution by applying a distortion power to the weights. The vocabulary file should be in CSV-like format, with the last field being the weight associated with the word. For each batch, this op picks a single set of sampled candidate labels. The advantages of sampling candidates per-batch are simplicity and the possibility of efficient dense matrix multiplication. The disadvantage is that the sampled candidates must be chosen independently of the context and of the true labels.
Args
true_classes A Tensor of type int64. A batch_size * num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label.
num_true An int that is >= 1. Number of true labels per context.
num_sampled An int that is >= 1. Number of candidates to randomly sample.
unique A bool. If unique is true, we sample with rejection, so that all sampled candidates in a batch are unique. This requires some approximation to estimate the post-rejection sampling probabilities.
range_max An int that is >= 1. The sampler will sample integers from the interval [0, range_max).
vocab_file An optional string. Defaults to "". Each valid line in this file (which should have a CSV-like format) corresponds to a valid word ID. IDs are in sequential order, starting from num_reserved_ids. The last entry in each line is expected to be a value corresponding to the count or relative probability. Exactly one of vocab_file and unigrams needs to be passed to this op.
distortion An optional float. Defaults to 1. The distortion is used to skew the unigram probability distribution. Each weight is first raised to the distortion's power before adding to the internal unigram distribution. As a result, distortion = 1.0 gives regular unigram sampling (as defined by the vocab file), and distortion = 0.0 gives a uniform distribution.
num_reserved_ids An optional int. Defaults to 0. Optionally some reserved IDs can be added in the range [0, ..., num_reserved_ids) by the users. One use case is that a special unknown word token is used as ID 0. These IDs will have a sampling probability of 0.
num_shards An optional int that is >= 1. Defaults to 1. A sampler can be used to sample from a subset of the original range in order to speed up the whole computation through parallelism. This parameter (together with 'shard') indicates the number of partitions that are being used in the overall computation.
shard An optional int that is >= 0. Defaults to 0. A sampler can be used to sample from a subset of the original range in order to speed up the whole computation through parallelism. This parameter (together with 'num_shards') indicates the particular partition number of a sampler op, when partitioning is being used.
unigrams An optional list of floats. Defaults to []. A list of unigram counts or probabilities, one per ID in sequential order. Exactly one of vocab_file and unigrams should be passed to this op.
seed An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
seed2 An optional int. Defaults to 0. An second seed to avoid seed collision.
name A name for the operation (optional).
Returns A tuple of Tensor objects (sampled_candidates, true_expected_count, sampled_expected_count). sampled_candidates A Tensor of type int64.
true_expected_count A Tensor of type float32.
sampled_expected_count A Tensor of type float32. | tensorflow.raw_ops.fixedunigramcandidatesampler |
tf.raw_ops.FlatMapDataset Creates a dataset that applies f to the outputs of input_dataset. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FlatMapDataset
tf.raw_ops.FlatMapDataset(
input_dataset, other_arguments, f, output_types, output_shapes, name=None
)
Unlike MapDataset, the f in FlatMapDataset is expected to return a Dataset variant, and FlatMapDataset will flatten successive results into a single Dataset.
Args
input_dataset A Tensor of type variant.
other_arguments A list of Tensor objects.
f A function decorated with @Defun. A function mapping elements of input_dataset, concatenated with other_arguments, to a Dataset variant that contains elements matching output_types and output_shapes.
output_types A list of tf.DTypes that has length >= 1.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
name A name for the operation (optional).
Returns A Tensor of type variant. | tensorflow.raw_ops.flatmapdataset |
tf.raw_ops.Floor Returns element-wise largest integer not greater than x. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Floor
tf.raw_ops.Floor(
x, name=None
)
Args
x A Tensor. Must be one of the following types: bfloat16, half, float32, float64.
name A name for the operation (optional).
Returns A Tensor. Has the same type as x. | tensorflow.raw_ops.floor |
tf.raw_ops.FloorDiv Returns x // y element-wise. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FloorDiv
tf.raw_ops.FloorDiv(
x, y, name=None
)
Note: floor_div supports broadcasting. More about broadcasting here
Args
x A Tensor. Must be one of the following types: bfloat16, half, float32, float64, uint8, int8, uint16, int16, int32, int64, complex64, complex128.
y A Tensor. Must have the same type as x.
name A name for the operation (optional).
Returns A Tensor. Has the same type as x. | tensorflow.raw_ops.floordiv |
tf.raw_ops.FloorMod Returns element-wise remainder of division. When x < 0 xor y < 0 is View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FloorMod
tf.raw_ops.FloorMod(
x, y, name=None
)
true, this follows Python semantics in that the result here is consistent with a flooring divide. E.g. floor(x / y) * y + mod(x, y) = x.
Note: math.floormod supports broadcasting. More about broadcasting here
Args
x A Tensor. Must be one of the following types: int32, int64, uint64, bfloat16, half, float32, float64.
y A Tensor. Must have the same type as x.
name A name for the operation (optional).
Returns A Tensor. Has the same type as x. | tensorflow.raw_ops.floormod |
tf.raw_ops.FlushSummaryWriter View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FlushSummaryWriter
tf.raw_ops.FlushSummaryWriter(
writer, name=None
)
Args
writer A Tensor of type resource.
name A name for the operation (optional).
Returns The created Operation. | tensorflow.raw_ops.flushsummarywriter |
tf.raw_ops.For
<section class="expandable">
<h4 class="showalways">View aliases</h4>
<p>
<b>Compat aliases for migration</b>
<p>See
<a href="https://www.tensorflow.org/guide/migrate">Migration guide</a> for
more details.</p>
<p>`tf.compat.v1.raw_ops.For`</p>
</p>
</section>
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link">
<code>tf.raw_ops.For(
start, limit, delta, input, body, name=None
)
</code></pre>
<!-- Placeholder for "Used in" -->
output = input;
for i in range(start, limit, delta)
output = body(i, output);
Args
start A Tensor of type int32. The lower bound. An int32
limit A Tensor of type int32. The upper bound. An int32
delta A Tensor of type int32. The increment. An int32
input A list of Tensor objects. A list of input tensors whose types are T.
body A function decorated with @Defun. A function that takes a list of tensors (int32, T) and returns another list of tensors (T).
name A name for the operation (optional).
Returns A list of Tensor objects. Has the same type as input. | tensorflow.raw_ops.for |
tf.raw_ops.FractionalAvgPool Performs fractional average pooling on the input. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FractionalAvgPool
tf.raw_ops.FractionalAvgPool(
value, pooling_ratio, pseudo_random=False, overlapping=False,
deterministic=False, seed=0, seed2=0, name=None
)
Fractional average pooling is similar to Fractional max pooling in the pooling region generation step. The only difference is that after pooling regions are generated, a mean operation is performed instead of a max operation in each pooling region.
Args
value A Tensor. Must be one of the following types: float32, float64, int32, int64. 4-D with shape [batch, height, width, channels].
pooling_ratio A list of floats that has length >= 4. Pooling ratio for each dimension of value, currently only supports row and col dimension and should be >= 1.0. For example, a valid pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements must be 1.0 because we don't allow pooling on batch and channels dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions respectively.
pseudo_random An optional bool. Defaults to False. When set to True, generates the pooling sequence in a pseudorandom fashion, otherwise, in a random fashion. Check paper Benjamin Graham, Fractional Max-Pooling for difference between pseudorandom and random.
overlapping An optional bool. Defaults to False. When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: index 0 1 2 3 4 value 20 5 16 3 7 If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [41/3, 26/3] for fractional avg pooling.
deterministic An optional bool. Defaults to False. When set to True, a fixed pooling region will be used when iterating over a FractionalAvgPool node in the computation graph. Mainly used in unit test to make FractionalAvgPool deterministic.
seed An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
seed2 An optional int. Defaults to 0. An second seed to avoid seed collision.
name A name for the operation (optional).
Returns A tuple of Tensor objects (output, row_pooling_sequence, col_pooling_sequence). output A Tensor. Has the same type as value.
row_pooling_sequence A Tensor of type int64.
col_pooling_sequence A Tensor of type int64. | tensorflow.raw_ops.fractionalavgpool |
tf.raw_ops.FractionalAvgPoolGrad Computes gradient of the FractionalAvgPool function. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FractionalAvgPoolGrad
tf.raw_ops.FractionalAvgPoolGrad(
orig_input_tensor_shape, out_backprop, row_pooling_sequence,
col_pooling_sequence, overlapping=False, name=None
)
Unlike FractionalMaxPoolGrad, we don't need to find arg_max for FractionalAvgPoolGrad, we just need to evenly back-propagate each element of out_backprop to those indices that form the same pooling cell. Therefore, we just need to know the shape of original input tensor, instead of the whole tensor.
Args
orig_input_tensor_shape A Tensor of type int64. Original input tensor shape for fractional_avg_pool
out_backprop A Tensor. Must be one of the following types: float32, float64, int32, int64. 4-D with shape [batch, height, width, channels]. Gradients w.r.t. the output of fractional_avg_pool.
row_pooling_sequence A Tensor of type int64. row pooling sequence, form pooling region with col_pooling_sequence.
col_pooling_sequence A Tensor of type int64. column pooling sequence, form pooling region with row_pooling sequence.
overlapping An optional bool. Defaults to False. When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: index 0 1 2 3 4 value 20 5 16 3 7 If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [41/3, 26/3] for fractional avg pooling.
name A name for the operation (optional).
Returns A Tensor. Has the same type as out_backprop. | tensorflow.raw_ops.fractionalavgpoolgrad |
tf.raw_ops.FractionalMaxPool Performs fractional max pooling on the input. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FractionalMaxPool
tf.raw_ops.FractionalMaxPool(
value, pooling_ratio, pseudo_random=False, overlapping=False,
deterministic=False, seed=0, seed2=0, name=None
)
Fractional max pooling is slightly different than regular max pooling. In regular max pooling, you downsize an input set by taking the maximum value of smaller N x N subsections of the set (often 2x2), and try to reduce the set by a factor of N, where N is an integer. Fractional max pooling, as you might expect from the word "fractional", means that the overall reduction ratio N does not have to be an integer. The sizes of the pooling regions are generated randomly but are fairly uniform. For example, let's look at the height dimension, and the constraints on the list of rows that will be pool boundaries. First we define the following: input_row_length : the number of rows from the input set output_row_length : which will be smaller than the input alpha = input_row_length / output_row_length : our reduction ratio K = floor(alpha) row_pooling_sequence : this is the result list of pool boundary rows Then, row_pooling_sequence should satisfy: a[0] = 0 : the first value of the sequence is 0 a[end] = input_row_length : the last value of the sequence is the size K <= (a[i+1] - a[i]) <= K+1 : all intervals are K or K+1 size length(row_pooling_sequence) = output_row_length+1 For more details on fractional max pooling, see this paper: Benjamin Graham, Fractional Max-Pooling
Args
value A Tensor. Must be one of the following types: float32, float64, int32, int64. 4-D with shape [batch, height, width, channels].
pooling_ratio A list of floats that has length >= 4. Pooling ratio for each dimension of value, currently only supports row and col dimension and should be >= 1.0. For example, a valid pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements must be 1.0 because we don't allow pooling on batch and channels dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions respectively.
pseudo_random An optional bool. Defaults to False. When set to True, generates the pooling sequence in a pseudorandom fashion, otherwise, in a random fashion. Check paper Benjamin Graham, Fractional Max-Pooling for difference between pseudorandom and random.
overlapping An optional bool. Defaults to False. When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: index 0 1 2 3 4 value 20 5 16 3 7 If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [20, 16] for fractional max pooling.
deterministic An optional bool. Defaults to False. When set to True, a fixed pooling region will be used when iterating over a FractionalMaxPool node in the computation graph. Mainly used in unit test to make FractionalMaxPool deterministic.
seed An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
seed2 An optional int. Defaults to 0. An second seed to avoid seed collision.
name A name for the operation (optional).
Returns A tuple of Tensor objects (output, row_pooling_sequence, col_pooling_sequence). output A Tensor. Has the same type as value.
row_pooling_sequence A Tensor of type int64.
col_pooling_sequence A Tensor of type int64. | tensorflow.raw_ops.fractionalmaxpool |
tf.raw_ops.FractionalMaxPoolGrad Computes gradient of the FractionalMaxPool function. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FractionalMaxPoolGrad
tf.raw_ops.FractionalMaxPoolGrad(
orig_input, orig_output, out_backprop, row_pooling_sequence,
col_pooling_sequence, overlapping=False, name=None
)
Args
orig_input A Tensor. Must be one of the following types: float32, float64, int32, int64. Original input for fractional_max_pool
orig_output A Tensor. Must have the same type as orig_input. Original output for fractional_max_pool
out_backprop A Tensor. Must have the same type as orig_input. 4-D with shape [batch, height, width, channels]. Gradients w.r.t. the output of fractional_max_pool.
row_pooling_sequence A Tensor of type int64. row pooling sequence, form pooling region with col_pooling_sequence.
col_pooling_sequence A Tensor of type int64. column pooling sequence, form pooling region with row_pooling sequence.
overlapping An optional bool. Defaults to False. When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: index 0 1 2 3 4 value 20 5 16 3 7 If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [20, 16] for fractional max pooling.
name A name for the operation (optional).
Returns A Tensor. Has the same type as orig_input. | tensorflow.raw_ops.fractionalmaxpoolgrad |
tf.raw_ops.FresnelCos View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FresnelCos
tf.raw_ops.FresnelCos(
x, name=None
)
Args
x A Tensor. Must be one of the following types: bfloat16, half, float32, float64.
name A name for the operation (optional).
Returns A Tensor. Has the same type as x. | tensorflow.raw_ops.fresnelcos |
tf.raw_ops.FresnelSin View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FresnelSin
tf.raw_ops.FresnelSin(
x, name=None
)
Args
x A Tensor. Must be one of the following types: bfloat16, half, float32, float64.
name A name for the operation (optional).
Returns A Tensor. Has the same type as x. | tensorflow.raw_ops.fresnelsin |
tf.raw_ops.FusedBatchNorm Batch normalization. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FusedBatchNorm
tf.raw_ops.FusedBatchNorm(
x, scale, offset, mean, variance, epsilon=0.0001, exponential_avg_factor=1,
data_format='NHWC', is_training=True, name=None
)
Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.
Args
x A Tensor. Must be one of the following types: float32. A 4D Tensor for input data.
scale A Tensor. Must have the same type as x. A 1D Tensor for scaling factor, to scale the normalized x.
offset A Tensor. Must have the same type as x. A 1D Tensor for offset, to shift to the normalized x.
mean A Tensor. Must have the same type as x. A 1D Tensor for population mean. Used for inference only; must be empty for training.
variance A Tensor. Must have the same type as x. A 1D Tensor for population variance. Used for inference only; must be empty for training.
epsilon An optional float. Defaults to 0.0001. A small float number added to the variance of x.
exponential_avg_factor An optional float. Defaults to 1.
data_format An optional string from: "NHWC", "NCHW". Defaults to "NHWC". The data format for x and y. Either "NHWC" (default) or "NCHW".
is_training An optional bool. Defaults to True. A bool value to indicate the operation is for training (default) or inference.
name A name for the operation (optional).
Returns A tuple of Tensor objects (y, batch_mean, batch_variance, reserve_space_1, reserve_space_2). y A Tensor. Has the same type as x.
batch_mean A Tensor. Has the same type as x.
batch_variance A Tensor. Has the same type as x.
reserve_space_1 A Tensor. Has the same type as x.
reserve_space_2 A Tensor. Has the same type as x. | tensorflow.raw_ops.fusedbatchnorm |
tf.raw_ops.FusedBatchNormGrad Gradient for batch normalization. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FusedBatchNormGrad
tf.raw_ops.FusedBatchNormGrad(
y_backprop, x, scale, reserve_space_1, reserve_space_2, epsilon=0.0001,
data_format='NHWC', is_training=True, name=None
)
Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.
Args
y_backprop A Tensor. Must be one of the following types: float32. A 4D Tensor for the gradient with respect to y.
x A Tensor. Must have the same type as y_backprop. A 4D Tensor for input data.
scale A Tensor. Must have the same type as y_backprop. A 1D Tensor for scaling factor, to scale the normalized x.
reserve_space_1 A Tensor. Must have the same type as y_backprop. When is_training is True, a 1D Tensor for the computed batch mean to be reused in gradient computation. When is_training is False, a 1D Tensor for the population mean to be reused in both 1st and 2nd order gradient computation.
reserve_space_2 A Tensor. Must have the same type as y_backprop. When is_training is True, a 1D Tensor for the computed batch variance (inverted variance in the cuDNN case) to be reused in gradient computation. When is_training is False, a 1D Tensor for the population variance to be reused in both 1st and 2nd order gradient computation.
epsilon An optional float. Defaults to 0.0001. A small float number added to the variance of x.
data_format An optional string from: "NHWC", "NCHW". Defaults to "NHWC". The data format for y_backprop, x, x_backprop. Either "NHWC" (default) or "NCHW".
is_training An optional bool. Defaults to True. A bool value to indicate the operation is for training (default) or inference.
name A name for the operation (optional).
Returns A tuple of Tensor objects (x_backprop, scale_backprop, offset_backprop, reserve_space_3, reserve_space_4). x_backprop A Tensor. Has the same type as y_backprop.
scale_backprop A Tensor. Has the same type as y_backprop.
offset_backprop A Tensor. Has the same type as y_backprop.
reserve_space_3 A Tensor. Has the same type as y_backprop.
reserve_space_4 A Tensor. Has the same type as y_backprop. | tensorflow.raw_ops.fusedbatchnormgrad |
tf.raw_ops.FusedBatchNormGradV2 Gradient for batch normalization. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FusedBatchNormGradV2
tf.raw_ops.FusedBatchNormGradV2(
y_backprop, x, scale, reserve_space_1, reserve_space_2, epsilon=0.0001,
data_format='NHWC', is_training=True, name=None
)
Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.
Args
y_backprop A Tensor. Must be one of the following types: half, bfloat16, float32. A 4D Tensor for the gradient with respect to y.
x A Tensor. Must have the same type as y_backprop. A 4D Tensor for input data.
scale A Tensor of type float32. A 1D Tensor for scaling factor, to scale the normalized x.
reserve_space_1 A Tensor. Must be one of the following types: float32. When is_training is True, a 1D Tensor for the computed batch mean to be reused in gradient computation. When is_training is False, a 1D Tensor for the population mean to be reused in both 1st and 2nd order gradient computation.
reserve_space_2 A Tensor. Must have the same type as reserve_space_1. When is_training is True, a 1D Tensor for the computed batch variance (inverted variance in the cuDNN case) to be reused in gradient computation. When is_training is False, a 1D Tensor for the population variance to be reused in both 1st and 2nd order gradient computation.
epsilon An optional float. Defaults to 0.0001. A small float number added to the variance of x.
data_format An optional string from: "NHWC", "NCHW". Defaults to "NHWC". The data format for y_backprop, x, x_backprop. Either "NHWC" (default) or "NCHW".
is_training An optional bool. Defaults to True. A bool value to indicate the operation is for training (default) or inference.
name A name for the operation (optional).
Returns A tuple of Tensor objects (x_backprop, scale_backprop, offset_backprop, reserve_space_3, reserve_space_4). x_backprop A Tensor. Has the same type as y_backprop.
scale_backprop A Tensor. Has the same type as reserve_space_1.
offset_backprop A Tensor. Has the same type as reserve_space_1.
reserve_space_3 A Tensor. Has the same type as reserve_space_1.
reserve_space_4 A Tensor. Has the same type as reserve_space_1. | tensorflow.raw_ops.fusedbatchnormgradv2 |
tf.raw_ops.FusedBatchNormGradV3 Gradient for batch normalization. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FusedBatchNormGradV3
tf.raw_ops.FusedBatchNormGradV3(
y_backprop, x, scale, reserve_space_1, reserve_space_2, reserve_space_3,
epsilon=0.0001, data_format='NHWC', is_training=True, name=None
)
Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.
Args
y_backprop A Tensor. Must be one of the following types: half, bfloat16, float32. A 4D Tensor for the gradient with respect to y.
x A Tensor. Must have the same type as y_backprop. A 4D Tensor for input data.
scale A Tensor of type float32. A 1D Tensor for scaling factor, to scale the normalized x.
reserve_space_1 A Tensor. Must be one of the following types: float32. When is_training is True, a 1D Tensor for the computed batch mean to be reused in gradient computation. When is_training is False, a 1D Tensor for the population mean to be reused in both 1st and 2nd order gradient computation.
reserve_space_2 A Tensor. Must have the same type as reserve_space_1. When is_training is True, a 1D Tensor for the computed batch variance (inverted variance in the cuDNN case) to be reused in gradient computation. When is_training is False, a 1D Tensor for the population variance to be reused in both 1st and 2nd order gradient computation.
reserve_space_3 A Tensor. Must have the same type as reserve_space_1. When is_training is True, a 1D Tensor for some intermediate results to be reused in gradient computation. When is_training is False, a dummy empty Tensor will be created.
epsilon An optional float. Defaults to 0.0001. A small float number added to the variance of x.
data_format An optional string from: "NHWC", "NCHW", "NDHWC", "NCDHW". Defaults to "NHWC". The data format for y_backprop, x, x_backprop. Either "NHWC" (default) or "NCHW".
is_training An optional bool. Defaults to True. A bool value to indicate the operation is for training (default) or inference.
name A name for the operation (optional).
Returns A tuple of Tensor objects (x_backprop, scale_backprop, offset_backprop, reserve_space_4, reserve_space_5). x_backprop A Tensor. Has the same type as y_backprop.
scale_backprop A Tensor. Has the same type as reserve_space_1.
offset_backprop A Tensor. Has the same type as reserve_space_1.
reserve_space_4 A Tensor. Has the same type as reserve_space_1.
reserve_space_5 A Tensor. Has the same type as reserve_space_1. | tensorflow.raw_ops.fusedbatchnormgradv3 |
tf.raw_ops.FusedBatchNormV2 Batch normalization. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FusedBatchNormV2
tf.raw_ops.FusedBatchNormV2(
x, scale, offset, mean, variance, epsilon=0.0001, exponential_avg_factor=1,
data_format='NHWC', is_training=True, name=None
)
Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.
Args
x A Tensor. Must be one of the following types: half, bfloat16, float32. A 4D Tensor for input data.
scale A Tensor. Must be one of the following types: float32. A 1D Tensor for scaling factor, to scale the normalized x.
offset A Tensor. Must have the same type as scale. A 1D Tensor for offset, to shift to the normalized x.
mean A Tensor. Must have the same type as scale. A 1D Tensor for population mean. Used for inference only; must be empty for training.
variance A Tensor. Must have the same type as scale. A 1D Tensor for population variance. Used for inference only; must be empty for training.
epsilon An optional float. Defaults to 0.0001. A small float number added to the variance of x.
exponential_avg_factor An optional float. Defaults to 1.
data_format An optional string from: "NHWC", "NCHW". Defaults to "NHWC". The data format for x and y. Either "NHWC" (default) or "NCHW".
is_training An optional bool. Defaults to True. A bool value to indicate the operation is for training (default) or inference.
name A name for the operation (optional).
Returns A tuple of Tensor objects (y, batch_mean, batch_variance, reserve_space_1, reserve_space_2). y A Tensor. Has the same type as x.
batch_mean A Tensor. Has the same type as scale.
batch_variance A Tensor. Has the same type as scale.
reserve_space_1 A Tensor. Has the same type as scale.
reserve_space_2 A Tensor. Has the same type as scale. | tensorflow.raw_ops.fusedbatchnormv2 |
tf.raw_ops.FusedBatchNormV3 Batch normalization. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FusedBatchNormV3
tf.raw_ops.FusedBatchNormV3(
x, scale, offset, mean, variance, epsilon=0.0001, exponential_avg_factor=1,
data_format='NHWC', is_training=True, name=None
)
Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.
Args
x A Tensor. Must be one of the following types: half, bfloat16, float32. A 4D Tensor for input data.
scale A Tensor. Must be one of the following types: float32. A 1D Tensor for scaling factor, to scale the normalized x.
offset A Tensor. Must have the same type as scale. A 1D Tensor for offset, to shift to the normalized x.
mean A Tensor. Must have the same type as scale. A 1D Tensor for population mean. Used for inference only; must be empty for training.
variance A Tensor. Must have the same type as scale. A 1D Tensor for population variance. Used for inference only; must be empty for training.
epsilon An optional float. Defaults to 0.0001. A small float number added to the variance of x.
exponential_avg_factor An optional float. Defaults to 1.
data_format An optional string from: "NHWC", "NCHW", "NDHWC", "NCDHW". Defaults to "NHWC". The data format for x and y. Either "NHWC" (default) or "NCHW".
is_training An optional bool. Defaults to True. A bool value to indicate the operation is for training (default) or inference.
name A name for the operation (optional).
Returns A tuple of Tensor objects (y, batch_mean, batch_variance, reserve_space_1, reserve_space_2, reserve_space_3). y A Tensor. Has the same type as x.
batch_mean A Tensor. Has the same type as scale.
batch_variance A Tensor. Has the same type as scale.
reserve_space_1 A Tensor. Has the same type as scale.
reserve_space_2 A Tensor. Has the same type as scale.
reserve_space_3 A Tensor. Has the same type as scale. | tensorflow.raw_ops.fusedbatchnormv3 |
tf.raw_ops.FusedPadConv2D Performs a padding as a preprocess during a convolution. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FusedPadConv2D
tf.raw_ops.FusedPadConv2D(
input, paddings, filter, mode, strides, padding, name=None
)
Similar to FusedResizeAndPadConv2d, this op allows for an optimized implementation where the spatial padding transformation stage is fused with the im2col lookup, but in this case without the bilinear filtering required for resizing. Fusing the padding prevents the need to write out the intermediate results as whole tensors, reducing memory pressure, and we can get some latency gains by merging the transformation calculations. The data_format attribute for Conv2D isn't supported by this op, and 'NHWC' order is used instead. Internally this op uses a single per-graph scratch buffer, which means that it will block if multiple versions are being run in parallel. This is because this operator is primarily an optimization to minimize memory usage.
Args
input A Tensor. Must be one of the following types: half, float32, float64. 4-D with shape [batch, in_height, in_width, in_channels].
paddings A Tensor of type int32. A two-column matrix specifying the padding sizes. The number of rows must be the same as the rank of input.
filter A Tensor. Must have the same type as input. 4-D with shape [filter_height, filter_width, in_channels, out_channels].
mode A string from: "REFLECT", "SYMMETRIC".
strides A list of ints. 1-D of length 4. The stride of the sliding window for each dimension of input. Must be in the same order as the dimension specified with format.
padding A string from: "SAME", "VALID". The type of padding algorithm to use.
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | tensorflow.raw_ops.fusedpadconv2d |
tf.raw_ops.FusedResizeAndPadConv2D Performs a resize and padding as a preprocess during a convolution. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.FusedResizeAndPadConv2D
tf.raw_ops.FusedResizeAndPadConv2D(
input, size, paddings, filter, mode, strides, padding,
resize_align_corners=False, name=None
)
It's often possible to do spatial transformations more efficiently as part of the packing stage of a convolution, so this op allows for an optimized implementation where these stages are fused together. This prevents the need to write out the intermediate results as whole tensors, reducing memory pressure, and we can get some latency gains by merging the transformation calculations. The data_format attribute for Conv2D isn't supported by this op, and defaults to 'NHWC' order. Internally this op uses a single per-graph scratch buffer, which means that it will block if multiple versions are being run in parallel. This is because this operator is primarily an optimization to minimize memory usage.
Args
input A Tensor. Must be one of the following types: half, float32, float64. 4-D with shape [batch, in_height, in_width, in_channels].
size A Tensor of type int32. A 1-D int32 Tensor of 2 elements: new_height, new_width. The new size for the images.
paddings A Tensor of type int32. A two-column matrix specifying the padding sizes. The number of rows must be the same as the rank of input.
filter A Tensor. Must have the same type as input. 4-D with shape [filter_height, filter_width, in_channels, out_channels].
mode A string from: "REFLECT", "SYMMETRIC".
strides A list of ints. 1-D of length 4. The stride of the sliding window for each dimension of input. Must be in the same order as the dimension specified with format.
padding A string from: "SAME", "VALID". The type of padding algorithm to use.
resize_align_corners An optional bool. Defaults to False. If true, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels. Defaults to false.
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | tensorflow.raw_ops.fusedresizeandpadconv2d |
tf.raw_ops.Gather Gather slices from params according to indices. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Gather
tf.raw_ops.Gather(
params, indices, validate_indices=True, name=None
)
indices must be an integer tensor of any dimension (usually 0-D or 1-D). Produces an output tensor with shape indices.shape + params.shape[1:] where: # Scalar indices
output[:, ..., :] = params[indices, :, ... :]
# Vector indices
output[i, :, ..., :] = params[indices[i], :, ... :]
# Higher rank indices
output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]
If indices is a permutation and len(indices) == params.shape[0] then this operation will permute params accordingly. validate_indices: DEPRECATED. If this operation is assigned to CPU, values in indices are always validated to be within range. If assigned to GPU, out-of-bound indices result in safe but unspecified behavior, which may include raising an error.
Args
params A Tensor.
indices A Tensor. Must be one of the following types: int32, int64.
validate_indices An optional bool. Defaults to True.
name A name for the operation (optional).
Returns A Tensor. Has the same type as params. | tensorflow.raw_ops.gather |
tf.raw_ops.GatherNd Gather slices from params into a Tensor with shape specified by indices. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.GatherNd
tf.raw_ops.GatherNd(
params, indices, name=None
)
indices is a K-dimensional integer tensor, best thought of as a (K-1)-dimensional tensor of indices into params, where each element defines a slice of params: output[\\(i_0, ..., i_{K-2}\\)] = params[indices[\\(i_0, ..., i_{K-2}\\)]]
Whereas in tf.gather indices defines slices into the axis dimension of params, in tf.gather_nd, indices defines slices into the first N dimensions of params, where N = indices.shape[-1]. The last dimension of indices can be at most the rank of params: indices.shape[-1] <= params.rank
The last dimension of indices corresponds to elements (if indices.shape[-1] == params.rank) or slices (if indices.shape[-1] < params.rank) along dimension indices.shape[-1] of params. The output tensor has shape indices.shape[:-1] + params.shape[indices.shape[-1]:]
Note that on CPU, if an out of bound index is found, an error is returned. On GPU, if an out of bound index is found, a 0 is stored in the corresponding output value. Some examples below. Simple indexing into a matrix: indices = [[0, 0], [1, 1]]
params = [['a', 'b'], ['c', 'd']]
output = ['a', 'd']
Slice indexing into a matrix: indices = [[1], [0]]
params = [['a', 'b'], ['c', 'd']]
output = [['c', 'd'], ['a', 'b']]
Indexing into a 3-tensor: indices = [[1]]
params = [[['a0', 'b0'], ['c0', 'd0']],
[['a1', 'b1'], ['c1', 'd1']]]
output = [[['a1', 'b1'], ['c1', 'd1']]]
indices = [[0, 1], [1, 0]]
params = [[['a0', 'b0'], ['c0', 'd0']],
[['a1', 'b1'], ['c1', 'd1']]]
output = [['c0', 'd0'], ['a1', 'b1']]
indices = [[0, 0, 1], [1, 0, 1]]
params = [[['a0', 'b0'], ['c0', 'd0']],
[['a1', 'b1'], ['c1', 'd1']]]
output = ['b0', 'b1']
Batched indexing into a matrix: indices = [[[0, 0]], [[0, 1]]]
params = [['a', 'b'], ['c', 'd']]
output = [['a'], ['b']]
Batched slice indexing into a matrix: indices = [[[1]], [[0]]]
params = [['a', 'b'], ['c', 'd']]
output = [[['c', 'd']], [['a', 'b']]]
Batched indexing into a 3-tensor: indices = [[[1]], [[0]]]
params = [[['a0', 'b0'], ['c0', 'd0']],
[['a1', 'b1'], ['c1', 'd1']]]
output = [[[['a1', 'b1'], ['c1', 'd1']]],
[[['a0', 'b0'], ['c0', 'd0']]]]
indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]]
params = [[['a0', 'b0'], ['c0', 'd0']],
[['a1', 'b1'], ['c1', 'd1']]]
output = [[['c0', 'd0'], ['a1', 'b1']],
[['a0', 'b0'], ['c1', 'd1']]]
indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]]
params = [[['a0', 'b0'], ['c0', 'd0']],
[['a1', 'b1'], ['c1', 'd1']]]
output = [['b0', 'b1'], ['d0', 'c1']]
See also tf.gather and tf.batch_gather.
Args
params A Tensor. The tensor from which to gather values.
indices A Tensor. Must be one of the following types: int32, int64. Index tensor.
name A name for the operation (optional).
Returns A Tensor. Has the same type as params. | tensorflow.raw_ops.gathernd |
tf.raw_ops.GatherV2 Gather slices from params axis axis according to indices. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.GatherV2
tf.raw_ops.GatherV2(
params, indices, axis, batch_dims=0, name=None
)
indices must be an integer tensor of any dimension (usually 0-D or 1-D). Produces an output tensor with shape params.shape[:axis] + indices.shape[batch_dims:] + params.shape[axis + 1:] where: # Scalar indices (output is rank(params) - 1).
output[a_0, ..., a_n, b_0, ..., b_n] =
params[a_0, ..., a_n, indices, b_0, ..., b_n]
# Vector indices (output is rank(params)).
output[a_0, ..., a_n, i, b_0, ..., b_n] =
params[a_0, ..., a_n, indices[i], b_0, ..., b_n]
# Higher rank indices (output is rank(params) + rank(indices) - 1).
output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] =
params[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n]
Note that on CPU, if an out of bound index is found, an error is returned. On GPU, if an out of bound index is found, a 0 is stored in the corresponding output value. See also tf.batch_gather and tf.gather_nd.
Args
params A Tensor. The tensor from which to gather values. Must be at least rank axis + 1.
indices A Tensor. Must be one of the following types: int32, int64. Index tensor. Must be in range [0, params.shape[axis]).
axis A Tensor. Must be one of the following types: int32, int64. The axis in params to gather indices from. Defaults to the first dimension. Supports negative indexes.
batch_dims An optional int. Defaults to 0.
name A name for the operation (optional).
Returns A Tensor. Has the same type as params. | tensorflow.raw_ops.gatherv2 |
tf.raw_ops.GenerateBoundingBoxProposals This op produces Region of Interests from given bounding boxes(bbox_deltas) encoded wrt anchors according to eq.2 in arXiv:1506.01497 View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.GenerateBoundingBoxProposals
tf.raw_ops.GenerateBoundingBoxProposals(
scores, bbox_deltas, image_info, anchors, nms_threshold, pre_nms_topn, min_size,
post_nms_topn=300, name=None
)
The op selects top `pre_nms_topn` scoring boxes, decodes them with respect to anchors,
applies non-maximal suppression on overlapping boxes with higher than
`nms_threshold` intersection-over-union (iou) value, discarding boxes where shorter
side is less than `min_size`.
Inputs:
`scores`: A 4D tensor of shape [Batch, Height, Width, Num Anchors] containing the scores per anchor at given position
`bbox_deltas`: is a tensor of shape [Batch, Height, Width, 4 x Num Anchors] boxes encoded to each anchor
`anchors`: A 1D tensor of shape [4 x Num Anchors], representing the anchors.
Outputs:
`rois`: output RoIs, a 3D tensor of shape [Batch, post_nms_topn, 4], padded by 0 if less than post_nms_topn candidates found.
`roi_probabilities`: probability scores of each roi in 'rois', a 2D tensor of shape [Batch,post_nms_topn], padded with 0 if needed, sorted by scores.
Args
scores A Tensor of type float32. A 4-D float tensor of shape [num_images, height, width, num_achors] containing scores of the boxes for given anchors, can be unsorted.
bbox_deltas A Tensor of type float32. A 4-D float tensor of shape [num_images, height, width, 4 x num_anchors]. encoding boxes with respec to each anchor. Coordinates are given in the form [dy, dx, dh, dw].
image_info A Tensor of type float32. A 2-D float tensor of shape [num_images, 5] containing image information Height, Width, Scale.
anchors A Tensor of type float32. A 2-D float tensor of shape [num_anchors, 4] describing the anchor boxes. Boxes are formatted in the form [y1, x1, y2, x2].
nms_threshold A Tensor of type float32. A scalar float tensor for non-maximal-suppression threshold.
pre_nms_topn A Tensor of type int32. A scalar int tensor for the number of top scoring boxes to be used as input.
min_size A Tensor of type float32. A scalar float tensor. Any box that has a smaller size than min_size will be discarded.
post_nms_topn An optional int. Defaults to 300. An integer. Maximum number of rois in the output.
name A name for the operation (optional).
Returns A tuple of Tensor objects (rois, roi_probabilities). rois A Tensor of type float32.
roi_probabilities A Tensor of type float32. | tensorflow.raw_ops.generateboundingboxproposals |
tf.raw_ops.GenerateVocabRemapping Given a path to new and old vocabulary files, returns a remapping Tensor of View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.GenerateVocabRemapping
tf.raw_ops.GenerateVocabRemapping(
new_vocab_file, old_vocab_file, new_vocab_offset, num_new_vocab,
old_vocab_size=-1, name=None
)
length num_new_vocab, where remapping[i] contains the row number in the old vocabulary that corresponds to row i in the new vocabulary (starting at line new_vocab_offset and up to num_new_vocab entities), or -1 if entry i in the new vocabulary is not in the old vocabulary. The old vocabulary is constrained to the first old_vocab_size entries if old_vocab_size is not the default value of -1. num_vocab_offset enables use in the partitioned variable case, and should generally be set through examining partitioning info. The format of the files should be a text file, with each line containing a single entity within the vocabulary. For example, with new_vocab_file a text file containing each of the following elements on a single line: [f0, f1, f2, f3], old_vocab_file = [f1, f0, f3], num_new_vocab = 3, new_vocab_offset = 1, the returned remapping would be [0, -1, 2]. The op also returns a count of how many entries in the new vocabulary were present in the old vocabulary, which is used to calculate the number of values to initialize in a weight matrix remapping This functionality can be used to remap both row vocabularies (typically, features) and column vocabularies (typically, classes) from TensorFlow checkpoints. Note that the partitioning logic relies on contiguous vocabularies corresponding to div-partitioned variables. Moreover, the underlying remapping uses an IndexTable (as opposed to an inexact CuckooTable), so client code should use the corresponding index_table_from_file() as the FeatureColumn framework does (as opposed to tf.feature_to_id(), which uses a CuckooTable).
Args
new_vocab_file A Tensor of type string. Path to the new vocab file.
old_vocab_file A Tensor of type string. Path to the old vocab file.
new_vocab_offset An int that is >= 0. How many entries into the new vocab file to start reading.
num_new_vocab An int that is >= 0. Number of entries in the new vocab file to remap.
old_vocab_size An optional int that is >= -1. Defaults to -1. Number of entries in the old vocab file to consider. If -1, use the entire old vocabulary.
name A name for the operation (optional).
Returns A tuple of Tensor objects (remapping, num_present). remapping A Tensor of type int64.
num_present A Tensor of type int32. | tensorflow.raw_ops.generatevocabremapping |
tf.raw_ops.GeneratorDataset Creates a dataset that invokes a function to generate elements. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.GeneratorDataset
tf.raw_ops.GeneratorDataset(
init_func_other_args, next_func_other_args, finalize_func_other_args, init_func,
next_func, finalize_func, output_types, output_shapes, name=None
)
Args
init_func_other_args A list of Tensor objects.
next_func_other_args A list of Tensor objects.
finalize_func_other_args A list of Tensor objects.
init_func A function decorated with @Defun.
next_func A function decorated with @Defun.
finalize_func A function decorated with @Defun.
output_types A list of tf.DTypes that has length >= 1.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
name A name for the operation (optional).
Returns A Tensor of type variant. | tensorflow.raw_ops.generatordataset |
tf.raw_ops.GetSessionHandle Store the input tensor in the state of the current session. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.GetSessionHandle
tf.raw_ops.GetSessionHandle(
value, name=None
)
Args
value A Tensor. The tensor to be stored.
name A name for the operation (optional).
Returns A Tensor of type string. | tensorflow.raw_ops.getsessionhandle |
tf.raw_ops.GetSessionHandleV2 Store the input tensor in the state of the current session. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.GetSessionHandleV2
tf.raw_ops.GetSessionHandleV2(
value, name=None
)
Args
value A Tensor. The tensor to be stored.
name A name for the operation (optional).
Returns A Tensor of type resource. | tensorflow.raw_ops.getsessionhandlev2 |
tf.raw_ops.GetSessionTensor Get the value of the tensor specified by its handle. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.GetSessionTensor
tf.raw_ops.GetSessionTensor(
handle, dtype, name=None
)
Args
handle A Tensor of type string. The handle for a tensor stored in the session state.
dtype A tf.DType. The type of the output value.
name A name for the operation (optional).
Returns A Tensor of type dtype. | tensorflow.raw_ops.getsessiontensor |
tf.raw_ops.Greater Returns the truth value of (x > y) element-wise. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Greater
tf.raw_ops.Greater(
x, y, name=None
)
Note: math.greater supports broadcasting. More about broadcasting here
Example: x = tf.constant([5, 4, 6])
y = tf.constant([5, 2, 5])
tf.math.greater(x, y) ==> [False, True, True]
x = tf.constant([5, 4, 6])
y = tf.constant([5])
tf.math.greater(x, y) ==> [False, False, True]
Args
x A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.
y A Tensor. Must have the same type as x.
name A name for the operation (optional).
Returns A Tensor of type bool. | tensorflow.raw_ops.greater |
tf.raw_ops.GreaterEqual Returns the truth value of (x >= y) element-wise. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.GreaterEqual
tf.raw_ops.GreaterEqual(
x, y, name=None
)
Note: math.greater_equal supports broadcasting. More about broadcasting here
Example: x = tf.constant([5, 4, 6, 7])
y = tf.constant([5, 2, 5, 10])
tf.math.greater_equal(x, y) ==> [True, True, True, False]
x = tf.constant([5, 4, 6, 7])
y = tf.constant([5])
tf.math.greater_equal(x, y) ==> [True, False, True, True]
Args
x A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.
y A Tensor. Must have the same type as x.
name A name for the operation (optional).
Returns A Tensor of type bool. | tensorflow.raw_ops.greaterequal |
tf.raw_ops.GroupByReducerDataset Creates a dataset that computes a group-by on input_dataset. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.GroupByReducerDataset
tf.raw_ops.GroupByReducerDataset(
input_dataset, key_func_other_arguments, init_func_other_arguments,
reduce_func_other_arguments, finalize_func_other_arguments, key_func, init_func,
reduce_func, finalize_func, output_types, output_shapes, name=None
)
Creates a dataset that computes a group-by on input_dataset.
Args
input_dataset A Tensor of type variant. A variant tensor representing the input dataset.
key_func_other_arguments A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for key_func.
init_func_other_arguments A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for init_func.
reduce_func_other_arguments A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for reduce_func.
finalize_func_other_arguments A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for finalize_func.
key_func A function decorated with @Defun. A function mapping an element of input_dataset, concatenated with key_func_other_arguments to a scalar value of type DT_INT64.
init_func A function decorated with @Defun. A function mapping a key of type DT_INT64, concatenated with init_func_other_arguments to the initial reducer state.
reduce_func A function decorated with @Defun. A function mapping the current reducer state and an element of input_dataset, concatenated with reduce_func_other_arguments to a new reducer state.
finalize_func A function decorated with @Defun. A function mapping the final reducer state to an output element.
output_types A list of tf.DTypes that has length >= 1.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
name A name for the operation (optional).
Returns A Tensor of type variant. | tensorflow.raw_ops.groupbyreducerdataset |
tf.raw_ops.GroupByWindowDataset Creates a dataset that computes a windowed group-by on input_dataset. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.GroupByWindowDataset
tf.raw_ops.GroupByWindowDataset(
input_dataset, key_func_other_arguments, reduce_func_other_arguments,
window_size_func_other_arguments, key_func, reduce_func, window_size_func,
output_types, output_shapes, name=None
)
//
Args
input_dataset A Tensor of type variant.
key_func_other_arguments A list of Tensor objects.
reduce_func_other_arguments A list of Tensor objects.
window_size_func_other_arguments A list of Tensor objects.
key_func A function decorated with @Defun. A function mapping an element of input_dataset, concatenated with key_func_other_arguments to a scalar value of type DT_INT64.
reduce_func A function decorated with @Defun.
window_size_func A function decorated with @Defun.
output_types A list of tf.DTypes that has length >= 1.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
name A name for the operation (optional).
Returns A Tensor of type variant. | tensorflow.raw_ops.groupbywindowdataset |
tf.raw_ops.GRUBlockCell Computes the GRU cell forward propagation for 1 time step. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.GRUBlockCell
tf.raw_ops.GRUBlockCell(
x, h_prev, w_ru, w_c, b_ru, b_c, name=None
)
Args x: Input to the GRU cell. h_prev: State input from the previous GRU cell. w_ru: Weight matrix for the reset and update gate. w_c: Weight matrix for the cell connection gate. b_ru: Bias vector for the reset and update gate. b_c: Bias vector for the cell connection gate. Returns r: Output of the reset gate. u: Output of the update gate. c: Output of the cell connection gate. h: Current state of the GRU cell. Note on notation of the variables: Concatenation of a and b is represented by a_b Element-wise dot product of a and b is represented by ab Element-wise dot product is represented by \circ Matrix multiplication is represented by * Biases are initialized with : b_ru - constant_initializer(1.0) b_c - constant_initializer(0.0) This kernel op implements the following mathematical equations: x_h_prev = [x, h_prev]
[r_bar u_bar] = x_h_prev * w_ru + b_ru
r = sigmoid(r_bar)
u = sigmoid(u_bar)
h_prevr = h_prev \circ r
x_h_prevr = [x h_prevr]
c_bar = x_h_prevr * w_c + b_c
c = tanh(c_bar)
h = (1-u) \circ c + u \circ h_prev
Args
x A Tensor. Must be one of the following types: float32.
h_prev A Tensor. Must have the same type as x.
w_ru A Tensor. Must have the same type as x.
w_c A Tensor. Must have the same type as x.
b_ru A Tensor. Must have the same type as x.
b_c A Tensor. Must have the same type as x.
name A name for the operation (optional).
Returns A tuple of Tensor objects (r, u, c, h). r A Tensor. Has the same type as x.
u A Tensor. Has the same type as x.
c A Tensor. Has the same type as x.
h A Tensor. Has the same type as x. | tensorflow.raw_ops.grublockcell |
tf.raw_ops.GRUBlockCellGrad Computes the GRU cell back-propagation for 1 time step. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.GRUBlockCellGrad
tf.raw_ops.GRUBlockCellGrad(
x, h_prev, w_ru, w_c, b_ru, b_c, r, u, c, d_h, name=None
)
Args x: Input to the GRU cell. h_prev: State input from the previous GRU cell. w_ru: Weight matrix for the reset and update gate. w_c: Weight matrix for the cell connection gate. b_ru: Bias vector for the reset and update gate. b_c: Bias vector for the cell connection gate. r: Output of the reset gate. u: Output of the update gate. c: Output of the cell connection gate. d_h: Gradients of the h_new wrt to objective function. Returns d_x: Gradients of the x wrt to objective function. d_h_prev: Gradients of the h wrt to objective function. d_c_bar Gradients of the c_bar wrt to objective function. d_r_bar_u_bar Gradients of the r_bar & u_bar wrt to objective function. This kernel op implements the following mathematical equations: Note on notation of the variables: Concatenation of a and b is represented by a_b Element-wise dot product of a and b is represented by ab Element-wise dot product is represented by \circ Matrix multiplication is represented by * Additional notes for clarity: w_ru can be segmented into 4 different matrices. w_ru = [w_r_x w_u_x
w_r_h_prev w_u_h_prev]
Similarly, w_c can be segmented into 2 different matrices. w_c = [w_c_x w_c_h_prevr]
Same goes for biases. b_ru = [b_ru_x b_ru_h]
b_c = [b_c_x b_c_h]
Another note on notation: d_x = d_x_component_1 + d_x_component_2
where d_x_component_1 = d_r_bar * w_r_x^T + d_u_bar * w_r_x^T
and d_x_component_2 = d_c_bar * w_c_x^T
d_h_prev = d_h_prev_component_1 + d_h_prevr \circ r + d_h \circ u
where d_h_prev_componenet_1 = d_r_bar * w_r_h_prev^T + d_u_bar * w_r_h_prev^T
Mathematics behind the Gradients below: d_c_bar = d_h \circ (1-u) \circ (1-c \circ c)
d_u_bar = d_h \circ (h-c) \circ u \circ (1-u)
d_r_bar_u_bar = [d_r_bar d_u_bar]
[d_x_component_1 d_h_prev_component_1] = d_r_bar_u_bar * w_ru^T
[d_x_component_2 d_h_prevr] = d_c_bar * w_c^T
d_x = d_x_component_1 + d_x_component_2
d_h_prev = d_h_prev_component_1 + d_h_prevr \circ r + u
Below calculation is performed in the python wrapper for the Gradients (not in the gradient kernel.) d_w_ru = x_h_prevr^T * d_c_bar
d_w_c = x_h_prev^T * d_r_bar_u_bar
d_b_ru = sum of d_r_bar_u_bar along axis = 0
d_b_c = sum of d_c_bar along axis = 0
Args
x A Tensor. Must be one of the following types: float32.
h_prev A Tensor. Must have the same type as x.
w_ru A Tensor. Must have the same type as x.
w_c A Tensor. Must have the same type as x.
b_ru A Tensor. Must have the same type as x.
b_c A Tensor. Must have the same type as x.
r A Tensor. Must have the same type as x.
u A Tensor. Must have the same type as x.
c A Tensor. Must have the same type as x.
d_h A Tensor. Must have the same type as x.
name A name for the operation (optional).
Returns A tuple of Tensor objects (d_x, d_h_prev, d_c_bar, d_r_bar_u_bar). d_x A Tensor. Has the same type as x.
d_h_prev A Tensor. Has the same type as x.
d_c_bar A Tensor. Has the same type as x.
d_r_bar_u_bar A Tensor. Has the same type as x. | tensorflow.raw_ops.grublockcellgrad |
tf.raw_ops.GuaranteeConst Gives a guarantee to the TF runtime that the input tensor is a constant. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.GuaranteeConst
tf.raw_ops.GuaranteeConst(
input, name=None
)
The runtime is then free to make optimizations based on this. Only accepts value typed tensors as inputs and rejects resource variable handles as input. Returns the input tensor without modification.
Args
input A Tensor.
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | tensorflow.raw_ops.guaranteeconst |
tf.raw_ops.HashTable Creates a non-initialized hash table. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.HashTable
tf.raw_ops.HashTable(
key_dtype, value_dtype, container='', shared_name='',
use_node_name_sharing=False, name=None
)
This op creates a hash table, specifying the type of its keys and values. Before using the table you will have to initialize it. After initialization the table will be immutable.
Args
key_dtype A tf.DType. Type of the table keys.
value_dtype A tf.DType. Type of the table values.
container An optional string. Defaults to "". If non-empty, this table is placed in the given container. Otherwise, a default container is used.
shared_name An optional string. Defaults to "". If non-empty, this table is shared under the given name across multiple sessions.
use_node_name_sharing An optional bool. Defaults to False. If true and shared_name is empty, the table is shared using the node name.
name A name for the operation (optional).
Returns A Tensor of type mutable string. | tensorflow.raw_ops.hashtable |
tf.raw_ops.HashTableV2 Creates a non-initialized hash table. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.HashTableV2
tf.raw_ops.HashTableV2(
key_dtype, value_dtype, container='', shared_name='',
use_node_name_sharing=False, name=None
)
This op creates a hash table, specifying the type of its keys and values. Before using the table you will have to initialize it. After initialization the table will be immutable.
Args
key_dtype A tf.DType. Type of the table keys.
value_dtype A tf.DType. Type of the table values.
container An optional string. Defaults to "". If non-empty, this table is placed in the given container. Otherwise, a default container is used.
shared_name An optional string. Defaults to "". If non-empty, this table is shared under the given name across multiple sessions.
use_node_name_sharing An optional bool. Defaults to False. If true and shared_name is empty, the table is shared using the node name.
name A name for the operation (optional).
Returns A Tensor of type resource. | tensorflow.raw_ops.hashtablev2 |
tf.raw_ops.HistogramFixedWidth Return histogram of values. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.HistogramFixedWidth
tf.raw_ops.HistogramFixedWidth(
values, value_range, nbins, dtype=tf.dtypes.int32, name=None
)
Given the tensor values, this operation returns a rank 1 histogram counting the number of entries in values that fall into every bin. The bins are equal width and determined by the arguments value_range and nbins. # Bins will be: (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
nbins = 5
value_range = [0.0, 5.0]
new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
with tf.get_default_session() as sess:
hist = tf.histogram_fixed_width(new_values, value_range, nbins=5)
variables.global_variables_initializer().run()
sess.run(hist) => [2, 1, 1, 0, 2]
Args
values A Tensor. Must be one of the following types: int32, int64, float32, float64. Numeric Tensor.
value_range A Tensor. Must have the same type as values. Shape [2] Tensor of same dtype as values. values <= value_range[0] will be mapped to hist[0], values >= value_range[1] will be mapped to hist[-1].
nbins A Tensor of type int32. Scalar int32 Tensor. Number of histogram bins.
dtype An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.
name A name for the operation (optional).
Returns A Tensor of type dtype. | tensorflow.raw_ops.histogramfixedwidth |
tf.raw_ops.HistogramSummary Outputs a Summary protocol buffer with a histogram. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.HistogramSummary
tf.raw_ops.HistogramSummary(
tag, values, name=None
)
The generated Summary has one summary value containing a histogram for values. This op reports an InvalidArgument error if any value is not finite.
Args
tag A Tensor of type string. Scalar. Tag to use for the Summary.Value.
values A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. Any shape. Values to use to build the histogram.
name A name for the operation (optional).
Returns A Tensor of type string. | tensorflow.raw_ops.histogramsummary |
tf.raw_ops.HSVToRGB Convert one or more images from HSV to RGB. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.HSVToRGB
tf.raw_ops.HSVToRGB(
images, name=None
)
Outputs a tensor of the same shape as the images tensor, containing the RGB value of the pixels. The output is only well defined if the value in images are in [0,1]. See rgb_to_hsv for a description of the HSV encoding.
Args
images A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 1-D or higher rank. HSV data to convert. Last dimension must be size 3.
name A name for the operation (optional).
Returns A Tensor. Has the same type as images. | tensorflow.raw_ops.hsvtorgb |
tf.raw_ops.Identity Return a tensor with the same shape and contents as the input tensor or value. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Identity
tf.raw_ops.Identity(
input, name=None
)
Args
input A Tensor.
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | tensorflow.raw_ops.identity |
tf.raw_ops.IdentityN Returns a list of tensors with the same shapes and contents as the input View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.IdentityN
tf.raw_ops.IdentityN(
input, name=None
)
tensors. This op can be used to override the gradient for complicated functions. For example, suppose y = f(x) and we wish to apply a custom function g for backprop such that dx = g(dy). In Python, with tf.get_default_graph().gradient_override_map(
{'IdentityN': 'OverrideGradientWithG'}):
y, _ = identity_n([f(x), x])
@tf.RegisterGradient('OverrideGradientWithG')
def ApplyG(op, dy, _):
return [None, g(dy)] # Do not backprop to f(x).
Args
input A list of Tensor objects.
name A name for the operation (optional).
Returns A list of Tensor objects. Has the same type as input. | tensorflow.raw_ops.identityn |
tf.raw_ops.IdentityReader A Reader that outputs the queued work as both the key and value. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.IdentityReader
tf.raw_ops.IdentityReader(
container='', shared_name='', name=None
)
To use, enqueue strings in a Queue. ReaderRead will take the front work string and output (work, work).
Args
container An optional string. Defaults to "". If non-empty, this reader is placed in the given container. Otherwise, a default container is used.
shared_name An optional string. Defaults to "". If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead.
name A name for the operation (optional).
Returns A Tensor of type mutable string. | tensorflow.raw_ops.identityreader |
tf.raw_ops.IdentityReaderV2 A Reader that outputs the queued work as both the key and value. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.IdentityReaderV2
tf.raw_ops.IdentityReaderV2(
container='', shared_name='', name=None
)
To use, enqueue strings in a Queue. ReaderRead will take the front work string and output (work, work).
Args
container An optional string. Defaults to "". If non-empty, this reader is placed in the given container. Otherwise, a default container is used.
shared_name An optional string. Defaults to "". If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead.
name A name for the operation (optional).
Returns A Tensor of type resource. | tensorflow.raw_ops.identityreaderv2 |
tf.raw_ops.If output = cond ? then_branch(input) : else_branch(input) View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.If
tf.raw_ops.If(
cond, input, Tout, then_branch, else_branch, output_shapes=[], name=None
)
Args
cond A Tensor. A Tensor. If the tensor is a scalar of non-boolean type, the scalar is converted to a boolean according to the following rule: if the scalar is a numerical value, non-zero means True and zero means False; if the scalar is a string, non-empty means True and empty means False. If the tensor is not a scalar, being empty means False and being non-empty means True.
input A list of Tensor objects. A list of input tensors.
Tout A list of tf.DTypes. A list of output types.
then_branch A function decorated with @Defun. A function that takes 'inputs' and returns a list of tensors, whose types are the same as what else_branch returns.
else_branch A function decorated with @Defun. A function that takes 'inputs' and returns a list of tensors, whose types are the same as what then_branch returns.
output_shapes An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to [].
name A name for the operation (optional).
Returns A list of Tensor objects of type Tout. | tensorflow.raw_ops.if |
tf.raw_ops.IFFT Inverse fast Fourier transform. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.IFFT
tf.raw_ops.IFFT(
input, name=None
)
Computes the inverse 1-dimensional discrete Fourier transform over the inner-most dimension of input.
Args
input A Tensor. Must be one of the following types: complex64, complex128. A complex tensor.
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | tensorflow.raw_ops.ifft |
tf.raw_ops.IFFT2D Inverse 2D fast Fourier transform. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.IFFT2D
tf.raw_ops.IFFT2D(
input, name=None
)
Computes the inverse 2-dimensional discrete Fourier transform over the inner-most 2 dimensions of input.
Args
input A Tensor. Must be one of the following types: complex64, complex128. A complex tensor.
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | tensorflow.raw_ops.ifft2d |
tf.raw_ops.IFFT3D Inverse 3D fast Fourier transform. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.IFFT3D
tf.raw_ops.IFFT3D(
input, name=None
)
Computes the inverse 3-dimensional discrete Fourier transform over the inner-most 3 dimensions of input.
Args
input A Tensor. Must be one of the following types: complex64, complex128. A complex tensor.
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | tensorflow.raw_ops.ifft3d |
tf.raw_ops.Igamma Compute the lower regularized incomplete Gamma function P(a, x). View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Igamma
tf.raw_ops.Igamma(
a, x, name=None
)
The lower regularized incomplete Gamma function is defined as: \(P(a, x) = gamma(a, x) / Gamma(a) = 1 - Q(a, x)\) where \(gamma(a, x) = \\int_{0}^{x} t^{a-1} exp(-t) dt\) is the lower incomplete Gamma function. Note, above Q(a, x) (Igammac) is the upper regularized complete Gamma function.
Args
a A Tensor. Must be one of the following types: float32, float64.
x A Tensor. Must have the same type as a.
name A name for the operation (optional).
Returns A Tensor. Has the same type as a. | tensorflow.raw_ops.igamma |
tf.raw_ops.Igammac Compute the upper regularized incomplete Gamma function Q(a, x). View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Igammac
tf.raw_ops.Igammac(
a, x, name=None
)
The upper regularized incomplete Gamma function is defined as: \(Q(a, x) = Gamma(a, x) / Gamma(a) = 1 - P(a, x)\) where \(Gamma(a, x) = int_{x}^{\infty} t^{a-1} exp(-t) dt\) is the upper incomplete Gama function. Note, above P(a, x) (Igamma) is the lower regularized complete Gamma function.
Args
a A Tensor. Must be one of the following types: float32, float64.
x A Tensor. Must have the same type as a.
name A name for the operation (optional).
Returns A Tensor. Has the same type as a. | tensorflow.raw_ops.igammac |
tf.raw_ops.IgammaGradA Computes the gradient of igamma(a, x) wrt a. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.IgammaGradA
tf.raw_ops.IgammaGradA(
a, x, name=None
)
Args
a A Tensor. Must be one of the following types: float32, float64.
x A Tensor. Must have the same type as a.
name A name for the operation (optional).
Returns A Tensor. Has the same type as a. | tensorflow.raw_ops.igammagrada |
tf.raw_ops.IgnoreErrorsDataset Creates a dataset that contains the elements of input_dataset ignoring errors. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.IgnoreErrorsDataset
tf.raw_ops.IgnoreErrorsDataset(
input_dataset, output_types, output_shapes, log_warning=False, name=None
)
Args
input_dataset A Tensor of type variant.
output_types A list of tf.DTypes that has length >= 1.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
log_warning An optional bool. Defaults to False.
name A name for the operation (optional).
Returns A Tensor of type variant. | tensorflow.raw_ops.ignoreerrorsdataset |
tf.raw_ops.Imag Returns the imaginary part of a complex number. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Imag
tf.raw_ops.Imag(
input, Tout=tf.dtypes.float32, name=None
)
Given a tensor input of complex numbers, this operation returns a tensor of type float that is the imaginary part of each element in input. All elements in input must be complex numbers of the form \(a + bj\), where a is the real part and b is the imaginary part returned by this operation. For example: # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
tf.imag(input) ==> [4.75, 5.75]
Args
input A Tensor. Must be one of the following types: complex64, complex128.
Tout An optional tf.DType from: tf.float32, tf.float64. Defaults to tf.float32.
name A name for the operation (optional).
Returns A Tensor of type Tout. | tensorflow.raw_ops.imag |
tf.raw_ops.ImageProjectiveTransformV2 Applies the given transform to each of the images. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ImageProjectiveTransformV2
tf.raw_ops.ImageProjectiveTransformV2(
images, transforms, output_shape, interpolation, fill_mode='CONSTANT',
name=None
)
If one row of transforms is [a0, a1, a2, b0, b1, b2, c0, c1], then it maps the output point (x, y) to a transformed input point (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k), where k = c0 x + c1 y + 1. If the transformed point lays outside of the input image, the output pixel is set to 0.
Args
images A Tensor. Must be one of the following types: uint8, int32, int64, half, float32, float64. 4-D with shape [batch, height, width, channels].
transforms A Tensor of type float32. 2-D Tensor, [batch, 8] or [1, 8] matrix, where each row corresponds to a 3 x 3 projective transformation matrix, with the last entry assumed to be 1. If there is one row, the same transformation will be applied to all images.
output_shape A Tensor of type int32. 1-D Tensor [new_height, new_width].
interpolation A string. Interpolation method, "NEAREST" or "BILINEAR".
fill_mode An optional string. Defaults to "CONSTANT". Fill mode, "REFLECT", "WRAP", or "CONSTANT".
name A name for the operation (optional).
Returns A Tensor. Has the same type as images. | tensorflow.raw_ops.imageprojectivetransformv2 |
tf.raw_ops.ImageProjectiveTransformV3 Applies the given transform to each of the images. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ImageProjectiveTransformV3
tf.raw_ops.ImageProjectiveTransformV3(
images, transforms, output_shape, fill_value, interpolation,
fill_mode='CONSTANT', name=None
)
If one row of transforms is [a0, a1, a2, b0, b1, b2, c0, c1], then it maps the output point (x, y) to a transformed input point (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k), where k = c0 x + c1 y + 1. If the transformed point lays outside of the input image, the output pixel is set to fill_value.
Args
images A Tensor. Must be one of the following types: uint8, int32, int64, half, float32, float64. 4-D with shape [batch, height, width, channels].
transforms A Tensor of type float32. 2-D Tensor, [batch, 8] or [1, 8] matrix, where each row corresponds to a 3 x 3 projective transformation matrix, with the last entry assumed to be 1. If there is one row, the same transformation will be applied to all images.
output_shape A Tensor of type int32. 1-D Tensor [new_height, new_width].
fill_value A Tensor of type float32. float, the value to be filled when fill_mode is constant".
interpolation A string. Interpolation method, "NEAREST" or "BILINEAR".
fill_mode An optional string. Defaults to "CONSTANT". Fill mode, "REFLECT", "WRAP", or "CONSTANT".
name A name for the operation (optional).
Returns A Tensor. Has the same type as images. | tensorflow.raw_ops.imageprojectivetransformv3 |
tf.raw_ops.ImageSummary Outputs a Summary protocol buffer with images. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ImageSummary
tf.raw_ops.ImageSummary(
tag, tensor, max_images=3, bad_color=_execute.make_tensor(\n 'dtype:
DT_UINT8 tensor_shape { dim { size: 4 } } int_val: 255 int_val: 0 int_val: 0
int_val: 255'\n , 'bad_color'), name=None
)
The summary has up to max_images summary values containing images. The images are built from tensor which must be 4-D with shape [batch_size, height, width, channels] and where channels can be: 1: tensor is interpreted as Grayscale. 3: tensor is interpreted as RGB. 4: tensor is interpreted as RGBA. The images have the same number of channels as the input tensor. For float input, the values are normalized one image at a time to fit in the range [0, 255]. uint8 values are unchanged. The op uses two different normalization algorithms: If the input values are all positive, they are rescaled so the largest one is 255. If any input value is negative, the values are shifted so input value 0.0 is at 127. They are then rescaled so that either the smallest value is 0, or the largest one is 255. The tag argument is a scalar Tensor of type string. It is used to build the tag of the summary values: If max_images is 1, the summary value tag is 'tag/image'. If max_images is greater than 1, the summary value tags are generated sequentially as 'tag/image/0', 'tag/image/1', etc. The bad_color argument is the color to use in the generated images for non-finite input values. It is a uint8 1-D tensor of length channels. Each element must be in the range [0, 255] (It represents the value of a pixel in the output image). Non-finite values in the input tensor are replaced by this tensor in the output image. The default value is the color red.
Args
tag A Tensor of type string. Scalar. Used to build the tag attribute of the summary values.
tensor A Tensor. Must be one of the following types: uint8, float32, half, float64. 4-D of shape [batch_size, height, width, channels] where channels is 1, 3, or 4.
max_images An optional int that is >= 1. Defaults to 3. Max number of batch elements to generate images for.
bad_color An optional tf.TensorProto. Defaults to dtype: DT_UINT8 tensor_shape { dim { size: 4 } } int_val: 255 int_val: 0 int_val: 0 int_val: 255. Color to use for pixels with non-finite values.
name A name for the operation (optional).
Returns A Tensor of type string. | tensorflow.raw_ops.imagesummary |
tf.raw_ops.ImmutableConst Returns immutable tensor from memory region. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ImmutableConst
tf.raw_ops.ImmutableConst(
dtype, shape, memory_region_name, name=None
)
The current implementation memmaps the tensor from a file.
Args
dtype A tf.DType. Type of the returned tensor.
shape A tf.TensorShape or list of ints. Shape of the returned tensor.
memory_region_name A string. Name of readonly memory region used by the tensor, see NewReadOnlyMemoryRegionFromFile in tensorflow::Env.
name A name for the operation (optional).
Returns A Tensor of type dtype. | tensorflow.raw_ops.immutableconst |
tf.raw_ops.ImportEvent View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ImportEvent
tf.raw_ops.ImportEvent(
writer, event, name=None
)
Args
writer A Tensor of type resource.
event A Tensor of type string.
name A name for the operation (optional).
Returns The created Operation. | tensorflow.raw_ops.importevent |
tf.raw_ops.InfeedDequeue A placeholder op for a value that will be fed into the computation. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.InfeedDequeue
tf.raw_ops.InfeedDequeue(
dtype, shape, name=None
)
Args
dtype A tf.DType. The type of elements in the tensor.
shape A tf.TensorShape or list of ints. The shape of the tensor.
name A name for the operation (optional).
Returns A Tensor of type dtype. | tensorflow.raw_ops.infeeddequeue |
tf.raw_ops.InfeedDequeueTuple Fetches multiple values from infeed as an XLA tuple. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.InfeedDequeueTuple
tf.raw_ops.InfeedDequeueTuple(
dtypes, shapes, name=None
)
Args
dtypes A list of tf.DTypes that has length >= 1. The element types of each element in outputs.
shapes A list of shapes (each a tf.TensorShape or list of ints). The shapes of each tensor in outputs.
name A name for the operation (optional).
Returns A list of Tensor objects of type dtypes. | tensorflow.raw_ops.infeeddequeuetuple |
tf.raw_ops.InfeedEnqueue An op which feeds a single Tensor value into the computation. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.InfeedEnqueue
tf.raw_ops.InfeedEnqueue(
input, shape=[], layout=[], device_ordinal=-1, name=None
)
Args
input A Tensor. A tensor that will be provided using the infeed mechanism.
shape An optional tf.TensorShape or list of ints. Defaults to []. The shape of the tensor.
layout An optional list of ints. Defaults to []. A vector holding the requested layout in minor-to-major sequence. If a layout attribute is passed, but its values are all -1, the layout will be computed by the infeed operation.
device_ordinal An optional int. Defaults to -1. The TPU device to use. This should be -1 when the Op is running on a TPU device, and >= 0 when the Op is running on the CPU device.
name A name for the operation (optional).
Returns The created Operation. | tensorflow.raw_ops.infeedenqueue |
tf.raw_ops.InfeedEnqueuePrelinearizedBuffer An op which enqueues prelinearized buffer into TPU infeed. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.InfeedEnqueuePrelinearizedBuffer
tf.raw_ops.InfeedEnqueuePrelinearizedBuffer(
input, device_ordinal=-1, name=None
)
Args
input A Tensor of type variant. A variant tensor representing linearized output.
device_ordinal An optional int. Defaults to -1. The TPU device to use. This should be -1 when the Op is running on a TPU device and = 0 when the Op is running on the CPU device.
name A name for the operation (optional).
Returns The created Operation. | tensorflow.raw_ops.infeedenqueueprelinearizedbuffer |
tf.raw_ops.InfeedEnqueueTuple Feeds multiple Tensor values into the computation as an XLA tuple. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.InfeedEnqueueTuple
tf.raw_ops.InfeedEnqueueTuple(
inputs, shapes, layouts=[], device_ordinal=-1, name=None
)
Args
inputs A list of Tensor objects. A list of tensors that will be provided using the infeed mechanism.
shapes A list of shapes (each a tf.TensorShape or list of ints). The shapes of each tensor in inputs.
layouts An optional list of ints. Defaults to []. A vector holding the requested layout in minor-to-major sequence for all the tuple shapes, in the order the shapes appear in the "shapes" input. The layout elements for a sub-shape can be set to -1, in which case the corresponding layout will be computed by the infeed operation.
device_ordinal An optional int. Defaults to -1. The TPU device to use. This should be -1 when the Op is running on a TPU device, and >= 0 when the Op is running on the CPU device.
name A name for the operation (optional).
Returns The created Operation. | tensorflow.raw_ops.infeedenqueuetuple |
tf.raw_ops.InitializeTable Table initializer that takes two tensors for keys and values respectively. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.InitializeTable
tf.raw_ops.InitializeTable(
table_handle, keys, values, name=None
)
Args
table_handle A Tensor of type mutable string. Handle to a table which will be initialized.
keys A Tensor. Keys of type Tkey.
values A Tensor. Values of type Tval.
name A name for the operation (optional).
Returns The created Operation. | tensorflow.raw_ops.initializetable |
tf.raw_ops.InitializeTableFromDataset View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.InitializeTableFromDataset
tf.raw_ops.InitializeTableFromDataset(
table_handle, dataset, name=None
)
Args
table_handle A Tensor of type resource.
dataset A Tensor of type variant.
name A name for the operation (optional).
Returns The created Operation. | tensorflow.raw_ops.initializetablefromdataset |
tf.raw_ops.InitializeTableFromTextFile Initializes a table from a text file. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.InitializeTableFromTextFile
tf.raw_ops.InitializeTableFromTextFile(
table_handle, filename, key_index, value_index, vocab_size=-1,
delimiter='\t', name=None
)
It inserts one key-value pair into the table for each line of the file. The key and value is extracted from the whole line content, elements from the split line based on delimiter or the line number (starting from zero). Where to extract the key and value from a line is specified by key_index and value_index. A value of -1 means use the line number(starting from zero), expects int64. A value of -2 means use the whole line content, expects string. A value >= 0 means use the index (starting at zero) of the split line based on delimiter.
Args
table_handle A Tensor of type mutable string. Handle to a table which will be initialized.
filename A Tensor of type string. Filename of a vocabulary text file.
key_index An int that is >= -2. Column index in a line to get the table key values from.
value_index An int that is >= -2. Column index that represents information of a line to get the table value values from.
vocab_size An optional int that is >= -1. Defaults to -1. Number of elements of the file, use -1 if unknown.
delimiter An optional string. Defaults to "\t". Delimiter to separate fields in a line.
name A name for the operation (optional).
Returns The created Operation. | tensorflow.raw_ops.initializetablefromtextfile |
tf.raw_ops.InitializeTableFromTextFileV2 Initializes a table from a text file. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.InitializeTableFromTextFileV2
tf.raw_ops.InitializeTableFromTextFileV2(
table_handle, filename, key_index, value_index, vocab_size=-1,
delimiter='\t', name=None
)
It inserts one key-value pair into the table for each line of the file. The key and value is extracted from the whole line content, elements from the split line based on delimiter or the line number (starting from zero). Where to extract the key and value from a line is specified by key_index and value_index. A value of -1 means use the line number(starting from zero), expects int64. A value of -2 means use the whole line content, expects string. A value >= 0 means use the index (starting at zero) of the split line based on delimiter.
Args
table_handle A Tensor of type resource. Handle to a table which will be initialized.
filename A Tensor of type string. Filename of a vocabulary text file.
key_index An int that is >= -2. Column index in a line to get the table key values from.
value_index An int that is >= -2. Column index that represents information of a line to get the table value values from.
vocab_size An optional int that is >= -1. Defaults to -1. Number of elements of the file, use -1 if unknown.
delimiter An optional string. Defaults to "\t". Delimiter to separate fields in a line.
name A name for the operation (optional).
Returns The created Operation. | tensorflow.raw_ops.initializetablefromtextfilev2 |
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