本文档为TensorFlow参考文档，本转载已得到TensorFlow中文社区授权。
Note: Functions taking Tensor arguments can also take anything accepted by tf.convert_to_tensor.
TensorFlow provides several operations that you can use to cast tensor data types in your graph.
Converts each string in the input Tensor to the specified numeric type.
(Note that int32 overflow results in an error while float overflow results in a rounded value.)
A Tensor of type out_type. A Tensor of the same shape as the input string_tensor.
Casts a tensor to type float64.
A Tensor or SparseTensor with same shape as x with type float64.
Casts a tensor to type float32.
A Tensor or SparseTensor with same shape as x with type float32.
Casts a tensor to type bfloat16.
A Tensor or SparseTensor with same shape as x with type bfloat16.
Casts a tensor to type int32.
A Tensor or SparseTensor with same shape as x with type int32.
Casts a tensor to type int64.
A Tensor or SparseTensor with same shape as x with type int64.
Casts a tensor to a new type.
The operation casts x (in case of Tensor) or x.values (in case of SparseTensor) to dtype.
For example:
# tensor `a` is [1.8, 2.2], dtype=tf.float tf.cast(a, tf.int32) ==> [1, 2] # dtype=tf.int32A Tensor or SparseTensor with same shape as x.
TensorFlow provides several operations that you can use to determine the shape of a tensor and change the shape of a tensor.
Returns the shape of a tensor.
This operation returns a 1-D integer tensor representing the shape of input.
For example:
# 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] shape(t) ==> [2, 2, 3]A Tensor of type int32.
Returns the size of a tensor.
This operation returns an integer representing the number of elements in input.
For example:
# 't' is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]] size(t) ==> 12A Tensor of type int32.
Returns the rank of a tensor.
This operation returns an integer representing the rank of input.
For example:
# 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] # shape of tensor 't' is [2, 2, 3] rank(t) ==> 3Note: The rank of a tensor is not the same as the rank of a matrix. The rank of a tensor is the number of indices required to uniquely select each element of the tensor. Rank is also known as "order", "degree", or "ndims."
A Tensor of type int32.
Reshapes a tensor.
Given tensor, this operation returns a tensor that has the same values as tensor with shape shape.
If shape is the special value [-1], then tensor is flattened and the operation outputs a 1-D tensor with all elements of tensor.
If shape is 1-D or higher, then the operation returns a tensor with shape shape filled with the values of tensor. In this case, the number of elements implied by shape must be the same as the number of elements in tensor.
For example:
# tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9] # tensor 't' has shape [9] reshape(t, [3, 3]) ==> [[1, 2, 3] [4, 5, 6] [7, 8, 9]] # tensor 't' is [[[1, 1], [2, 2]] # [[3, 3], [4, 4]]] # tensor 't' has shape [2, 2] reshape(t, [2, 4]) ==> [[1, 1, 2, 2] [3, 3, 4, 4]] # tensor 't' is [[[1, 1, 1], # [2, 2, 2]], # [[3, 3, 3], # [4, 4, 4]], # [[5, 5, 5], # [6, 6, 6]]] # tensor 't' has shape [3, 2, 3] # pass '[-1]' to flatten 't' reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]A Tensor. Has the same type as tensor.
Removes dimensions of size 1 from the shape of a tensor.
Given a tensor input, this operation returns a tensor of the same type with all dimensions of size 1 removed. If you don't want to remove all size 1 dimensions, you can remove specific size 1 dimensions by specifying squeeze_dims.
For example:
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1] shape(squeeze(t)) ==> [2, 3]Or, to remove specific size 1 dimensions:
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1] shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]A Tensor. Has the same type as input. Contains the same data as input, but has one or more dimensions of size 1 removed.
Inserts a dimension of 1 into a tensor's shape.
Given a tensor input, this operation inserts a dimension of 1 at the dimension index dim of input's shape. The dimension index dim starts at zero; if you specify a negative number for dim it is counted backward from the end.
This operation is useful if you want to add a batch dimension to a single element. For example, if you have a single image of shape [height, width, channels], you can make it a batch of 1 image with expand_dims(image, 0), which will make the shape [1, height, width, channels].
Other examples:
# 't' is a tensor of shape [2] shape(expand_dims(t, 0)) ==> [1, 2] shape(expand_dims(t, 1)) ==> [2, 1] shape(expand_dims(t, -1)) ==> [2, 1] # 't2' is a tensor of shape [2, 3, 5] shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5] shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5] shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]This operation requires that:
-1-input.dims() <= dim <= input.dims()
This operation is related to squeeze(), which removes dimensions of size 1.
A Tensor. Has the same type as input. Contains the same data as input, but its shape has an additional dimension of size 1 added.
TensorFlow provides several operations to slice or extract parts of a tensor, or join multiple tensors together.
Extracts a slice from a tensor.
This operation extracts a slice of size size from a tensor input starting at the location specified by begin. The slice size is represented as a tensor shape, where size[i] is the number of elements of the 'i'th dimension of input that you want to slice. The starting location (begin) for the slice is represented as an offset in each dimension of input. In other words, begin[i] is the offset into the 'i'th dimension of input that you want to slice from.
begin is zero-based; size is one-based. If size[i] is -1, all remaining elements in dimension i are included in the slice. In other words, this is equivalent to setting:
size[i] = input.dim_size(i) - begin[i]
This operation requires that:
0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n]
For example:
# 'input' is [[[1, 1, 1], [2, 2, 2]], # [[3, 3, 3], [4, 4, 4]], # [[5, 5, 5], [6, 6, 6]]] tf.slice(input, [1, 0, 0], [1, 1, 3]) ==> [[[3, 3, 3]]] tf.slice(input, [1, 0, 0], [1, 2, 3]) ==> [[[3, 3, 3], [4, 4, 4]]] tf.slice(input, [1, 0, 0], [2, 1, 3]) ==> [[[3, 3, 3]], [[5, 5, 5]]]A Tensor the same type as input.
Splits a tensor into num_split tensors along one dimension.
Splits value along dimension split_dim into num_split smaller tensors. Requires that num_splitevenly divide value.shape[split_dim].
For example:
# 'value' is a tensor with shape [5, 30] # Split 'value' into 3 tensors along dimension 1 split0, split1, split2 = tf.split(1, 3, value) tf.shape(split0) ==> [5, 10]num_split Tensor objects resulting from splitting value.
Constructs a tensor by tiling a given tensor.
This operation creates a new tensor by replicating input multiples times. The output tensor's i'th dimension has input.dims(i) * multiples[i] elements, and the values of input are replicated multiples[i] times along the 'i'th dimension. For example, tiling [a b c d] by [2] produces [a b c d a b c d].
A Tensor. Has the same type as input.
Pads a tensor with zeros.
This operation pads a input with zeros according to the paddings you specify. paddings is an integer tensor with shape [Dn, 2], where n is the rank of input. For each dimension D of input, paddings[D, 0] indicates how many zeros to add before the contents of input in that dimension, andpaddings[D, 1] indicates how many zeros to add after the contents of input in that dimension.
The padded size of each dimension D of the output is:
paddings(D, 0) + input.dim_size(D) + paddings(D, 1)
For example:
# 't' is [[1, 1], [2, 2]] # 'paddings' is [[1, 1]], [2, 2]] # rank of 't' is 2 pad(t, paddings) ==> [[0, 0, 0, 0, 0] [0, 0, 0, 0, 0] [0, 1, 1, 0, 0] [[0, 2, 2, 0, 0] [0, 0, 0, 0, 0]]A Tensor. Has the same type as input.
Concatenates tensors along one dimension.
Concatenates the list of tensors values along dimension concat_dim. If values[i].shape = [D0, D1, ... Dconcat_dim(i), ...Dn], the concatenated result has shape
[D0, D1, ... Rconcat_dim, ...Dn]where
Rconcat_dim = sum(Dconcat_dim(i))That is, the data from the input tensors is joined along the concat_dim dimension.
The number of dimensions of the input tensors must match, and all dimensions except concat_dim must be equal.
For example:
t1 = [[1, 2, 3], [4, 5, 6]] t2 = [[7, 8, 9], [10, 11, 12]] tf.concat(0, [t1, t2]) ==> [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]] tf.concat(1, [t1, t2]) ==> [[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]] # tensor t3 with shape [2, 3] # tensor t4 with shape [2, 3] tf.shape(tf.concat(0, [t3, t4])) ==> [4, 3] tf.shape(tf.concat(1, [t3, t4])) ==> [2, 6]A Tensor resulting from concatenation of the input tensors.
Packs a list of rank-R tensors into one rank-(R+1) tensor.
Packs tensors in values into a tensor with rank one higher than each tensor in values and shape [len(values)] + values[0].shape. The output satisfies output[i, ...] = values[i][...].
This is the opposite of unpack. The numpy equivalent is
tf.pack([x, y, z]) = np.asarray([x, y, z])Unpacks the outer dimension of a rank-R tensor into rank-(R-1) tensors.
Unpacks num tensors from value along the first dimension. If num is not specified (the default), it is inferred from value's shape. If value.shape[0] is not known, ValueError is raised.
The ith tensor in output is the slice value[i, ...]. Each tensor in output has shape value.shape[1:].
This is the opposite of pack. The numpy equivalent is
tf.unpack(x, n) = list(x)The list of Tensor objects unpacked from value.
Reverses variable length slices in dimension seq_dim.
This op first slices input along the first dimension, and for each slice i, reverses the first seq_lengths[i] elements along the dimension seq_dim.
The elements of seq_lengths must obey seq_lengths[i] < input.dims[seq_dim], and seq_lengthsmust be a vector of length input.dims(0).
The output slice i along dimension 0 is then given by input slice i, with the first seq_lengths[i]slices along dimension seq_dim reversed.
For example:
# Given this: seq_dim = 1 input.dims = (4, ...) seq_lengths = [7, 2, 3, 5] # then slices of input are reversed on seq_dim, but only up to seq_lengths: output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...] output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...] output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...] output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...] # while entries past seq_lens are copied through: output[0, 7:, :, ...] = input[0, 7:, :, ...] output[1, 2:, :, ...] = input[1, 2:, :, ...] output[2, 3:, :, ...] = input[2, 3:, :, ...] output[3, 2:, :, ...] = input[3, 2:, :, ...]A Tensor. Has the same type as input. The partially reversed input. It has the same shape as input.
Reverses specific dimensions of a tensor.
Given a tensor, and a bool tensor dims representing the dimensions of tensor, this operation reverses each dimension i of tensor where dims[i] is True.
tensor can have up to 8 dimensions. The number of dimensions of tensor must equal the number of elements in dims. In other words:
rank(tensor) = size(dims)
For example:
# tensor 't' is [[[[ 0, 1, 2, 3], # [ 4, 5, 6, 7], # [ 8, 9, 10, 11]], # [[12, 13, 14, 15], # [16, 17, 18, 19], # [20, 21, 22, 23]]]] # tensor 't' shape is [1, 2, 3, 4] # 'dims' is [False, False, False, True] reverse(t, dims) ==> [[[[ 3, 2, 1, 0], [ 7, 6, 5, 4], [ 11, 10, 9, 8]], [[15, 14, 13, 12], [19, 18, 17, 16], [23, 22, 21, 20]]]] # 'dims' is [False, True, False, False] reverse(t, dims) ==> [[[[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23] [[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]]] # 'dims' is [False, False, True, False] reverse(t, dims) ==> [[[[8, 9, 10, 11], [4, 5, 6, 7], [0, 1, 2, 3]] [[20, 21, 22, 23], [16, 17, 18, 19], [12, 13, 14, 15]]]]A Tensor. Has the same type as tensor. The same shape as tensor.
Transposes a. Permutes the dimensions according to perm.
The returned tensor's dimension i will correspond to the input dimension perm[i]. If perm is not given, it is set to (n-1...0), where n is the rank of the input tensor. Hence by default, this operation performs a regular matrix transpose on 2-D input Tensors.
For example:
# 'x' is [[1 2 3] # [4 5 6]] tf.transpose(x) ==> [[1 4] [2 5] [3 6]] # Equivalently tf.transpose(x perm=[0, 1]) ==> [[1 4] [2 5] [3 6]] # 'perm' is more useful for n-dimensional tensors, for n > 2 # 'x' is [[[1 2 3] # [4 5 6]] # [[7 8 9] # [10 11 12]]] # Take the transpose of the matrices in dimension-0 tf.transpose(b, perm=[0, 2, 1]) ==> [[[1 4] [2 5] [3 6]] [[7 10] [8 11] [9 12]]]A transposed Tensor.
Gather slices from params according to indices.
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.
A Tensor. Has the same type as params.
Partitions data into num_partitions tensors using indices from partitions.
For each index tuple js of size partitions.ndim, the slice data[js, ...] becomes part of outputs[partitions[js]]. The slices with partitions[js] = i are placed in outputs[i] in lexicographic order of js, and the first dimension of outputs[i] is the number of entries in partitions equal to i. In detail,
outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:] outputs[i] = pack([data[js, ...] for js if partitions[js] == i])data.shape must start with partitions.shape.
For example:
# Scalar partitions partitions = 1 num_partitions = 2 data = [10, 20] outputs[0] = [] # Empty with shape [0, 2] outputs[1] = [[10, 20]] # Vector partitions partitions = [0, 0, 1, 1, 0] num_partitions = 2 data = [10, 20, 30, 40, 50] outputs[0] = [10, 20, 50] outputs[1] = [30, 40]A list of num_partitions Tensor objects of the same type as data.
Interleave the values from the data tensors into a single tensor.
Builds a merged tensor such that
merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]For example, if each indices[m] is scalar or vector, we have
# Scalar indices merged[indices[m], ...] = data[m][...] # Vector indices merged[indices[m][i], ...] = data[m][i, ...]Each data[i].shape must start with the corresponding indices[i].shape, and the rest of data[i].shape must be constant w.r.t. i. That is, we must have data[i].shape = indices[i].shape + constant. In terms of this constant, the output shape is
merged.shape = [max(indices)] + constantValues are merged in order, so if an index appears in both indices[m][i] and indices[n][j] for (m,i) < (n,j) the slice data[n][j] will appear in the merged result.
For example:
indices[0] = 6 indices[1] = [4, 1] indices[2] = [[5, 2], [0, 3]] data[0] = [61, 62] data[1] = [[41, 42], [11, 12]] data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]] merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42], [51, 52], [61, 62]]A Tensor. Has the same type as data.
