TensorFlow如何避免循环依赖

由 Carrie 创建, 最后一次修改 2017-10-11

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""由于依赖关系而不适合 math_ops 的算术运算.

为了避免循环依赖,一些 math_ops 应该在这里定义.文档

注解,例如“@@ my_op”应该在 math_ops 中.对用户而言,这些只是

正常的 math_ops

""

from __future__ import absolute_import from __future__ import division from __future__ import print_function import re from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.platform import tf_logging as logging # TODO(b/27419586) Change docstring for required dtype of x once int allowed def lbeta(x, name='lbeta'): r"""Computes \\(ln(|Beta(x)|)\\), reducing along the last dimension. Given one-dimensional `z = [z_0,...,z_{K-1}]`, we define $Beta(z) = \prod_j Gamma(z_j) / Gamma(\sum_j z_j)$ And for `n + 1` dimensional `x` with shape `[N1, ..., Nn, K]`, we define $lbeta(x)[i1, ..., in] = Log(|Beta(x[i1, ..., in, :])|)$. In other words, the last dimension is treated as the `z` vector. Note that if `z = [u, v]`, then \\(Beta(z) = int_0^1 t^{u-1} (1 - t)^{v-1} dt\\), which defines the traditional bivariate beta function. If the last dimension is empty, we follow the convention that the sum over the empty set is zero, and the product is one. Args: x: A rank `n + 1` `Tensor`, `n >= 0` with type `float`, or `double`. name: A name for the operation (optional). Returns: The logarithm of \\(|Beta(x)|\\) reducing along the last dimension. """ # In the event that the last dimension has zero entries, we return -inf. # This is consistent with a convention that the sum over the empty set 0, and # the product is 1. # This is standard. See https://en.wikipedia.org/wiki/Empty_set. with ops.name_scope(name, values=[x]): x = ops.convert_to_tensor(x, name='x') # Note reduce_sum([]) = 0. log_prod_gamma_x = math_ops.reduce_sum( math_ops.lgamma(x), reduction_indices=[-1]) # Note lgamma(0) = infinity, so if x = [] # log_gamma_sum_x = lgamma(0) = infinity, and # log_prod_gamma_x = lgamma(1) = 0, # so result = -infinity sum_x = math_ops.reduce_sum(x, axis=[-1]) log_gamma_sum_x = math_ops.lgamma(sum_x) result = log_prod_gamma_x - log_gamma_sum_x return result def einsum(equation, *inputs): """A generalized contraction between tensors of arbitrary dimension. This function returns a tensor whose elements are defined by `equation`, which is written in a shorthand form inspired by the Einstein summation convention. As an example, consider multiplying two matrices A and B to form a matrix C. The elements of C are given by: ``` C[i,k] = sum_j A[i,j] * B[j,k] ``` The corresponding `equation` is: ``` ij,jk->ik ``` In general, the `equation` is obtained from the more familiar element-wise equation by 1. removing variable names, brackets, and commas, 2. replacing "*" with ",", 3. dropping summation signs, and 4. moving the output to the right, and replacing "=" with "->". Many common operations can be expressed in this way. For example: ```python # Matrix multiplication >>> einsum('ij,jk->ik', m0, m1) # output[i,k] = sum_j m0[i,j] * m1[j, k] # Dot product >>> einsum('i,i->', u, v) # output = sum_i u[i]*v[i] # Outer product >>> einsum('i,j->ij', u, v) # output[i,j] = u[i]*v[j] # Transpose >>> einsum('ij->ji', m) # output[j,i] = m[i,j] # Batch matrix multiplication >>> einsum('aij,ajk->aik', s, t) # out[a,i,k] = sum_j s[a,i,j] * t[a, j, k] ``` This function behaves like `numpy.einsum`, but does not support: * Ellipses (subscripts like `ij...,jk...->ik...`) * Subscripts where an axis appears more than once for a single input (e.g. `ijj,k->ik`). * Subscripts that are summed across multiple inputs (e.g., `ij,ij,jk->ik`). Args: equation: a `str` describing the contraction, in the same format as `numpy.einsum`. *inputs: the inputs to contract (each one a `Tensor`), whose shapes should be consistent with `equation`. Returns: The contracted `Tensor`, with shape determined by `equation`. Raises: ValueError: If - the format of `equation` is incorrect, - the number of inputs implied by `equation` does not match `len(inputs)`, - an axis appears in the output subscripts but not in any of the inputs, - the number of dimensions of an input differs from the number of indices in its subscript, or - the input shapes are inconsistent along a particular axis. """ if '...' in equation: raise ValueError('Subscripts with ellipses are not yet supported.') match = re.match('([a-z,]+)(->[a-z]*)?', equation) if not match: raise ValueError( 'Indices have incorrect format: %s' % equation ) inputs = list(inputs) input_axis_labels = match.group(1).split(',') if len(inputs) != len(input_axis_labels): raise ValueError('Got %d arguments for equation "%s", expecting %d' % ( len(inputs), equation, len(input_axis_labels))) axis_labels = set(''.join(input_axis_labels)) if match.group(2): output_axis_labels = match.group(2)[2:] else: # infer the output subscripts if not given, assume alphabetical order indices = ''.join(sorted(axis_labels)) counts = {ax: 0 for ax in indices} for axes_ in input_axis_labels: for ax in axes_: counts[ax] += 1 output_axis_labels = ''.join(sorted( ax for ax in indices if counts[ax] == 1 )) for a in axis_labels: input_count = sum(1 for s in input_axis_labels if a in s) if input_count > 2 and a not in output_axis_labels: logging.warn( 'Falling back to exponential-space implementation of einsum() because' ' index "%s" is summed over more than two inputs.', a) return _exponential_space_einsum(equation, *inputs) temp = inputs[0] temp_axis_labels = input_axis_labels[0] for i in xrange(len(inputs)-1): axes_to_sum = (set(temp_axis_labels) & set(input_axis_labels[i+1]) - set(output_axis_labels)) temp, temp_axis_labels = _einsum_reduction(temp, temp_axis_labels, inputs[i+1], input_axis_labels[i+1], axes_to_sum) missing_indices = set(temp_axis_labels) - set(output_axis_labels) if missing_indices: reduction_indices = [i for i, a in enumerate(temp_axis_labels) if a not in output_axis_labels] temp = math_ops.reduce_sum(temp, reduction_indices=reduction_indices) temp_axis_labels = ''.join(a for a in temp_axis_labels if a in output_axis_labels) if sorted(temp_axis_labels) != sorted(output_axis_labels): raise ValueError('Invalid equation: %s' % equation) perm = [temp_axis_labels.index(a) for a in output_axis_labels] return _transpose_if_necessary(temp, perm) def _einsum_reduction(t0, t0_axis_labels, t1, t1_axis_labels, axes_to_sum): """Helper for einsum() that computes the result of a two-argument einsum(). Args: t0: a `Tensor` t0_axis_labels: a string of axis labels. This string's length must equal the rank of t0. t1: a `Tensor` t1_axis_labels: a string to axis labels. This string's length must equal the rank of t1. axes_to_sum: set of labels of axes to be summed over Returns: A `Tensor` whose elements are obtained by summing, over all axes in `axes_to_sum`, the corresponding elements of `t0` and `t1`. For example, if t0_axis_labels == 'abijk', t1_axis_labels == 'acjkl', and axes_to_sum == {j,k}, this will return a tensor x where out[a,b,c,i,l] = sum_j sum_k t0[a,b,i,j,k] * t1[a,c,j,k,l] Raises: ValueError: if the rank of `t0` does not match the length of `t0_axis_labels`, or that of `t1` does not match the length of `t1_axis_labels`. """ if len(t0_axis_labels) != len(t0.get_shape()): raise ValueError() if len(t1_axis_labels) != len(t1.get_shape()): raise ValueError() # This function computes the result of a two-argument einsum() using batch # matrix multiplication. This involves # 1. transposing t0 and t1 so that axes are in the correct order for # batch matrix multiplication, and # 2. reshaping t0 and t1 so that they are both of rank 3. # First, we divide axes into three groups: # * "preserved" axes are present in both inputs and the output # * "summed" axes are present in both inputs but not the output # * "broadcast" axes are present in exactly one input and the output # # As an example, if the einsum is abijk,acjkl->abcil, then "a" is a # preserved axis, "b" and "c" are broadcast axes, and "j" and "k" are # summed axes. assert all(a in t0_axis_labels and a in t1_axis_labels for a in axes_to_sum) preserved_axes = (set(t0_axis_labels) & set(t1_axis_labels)) - axes_to_sum broadcast_axes = {} for i, sym_list in enumerate([t0_axis_labels, t1_axis_labels]): broadcast_axes[i] = set(sym_list) - preserved_axes - axes_to_sum # Reorder the axes so that: # 1. preserved axes come first in both inputs # 2. in input 0, broadcast axes come next, followed by summed axes # 3. in input 1, summed axes come next, followed by broadcast axes def sort_key(input_index, a): if a in preserved_axes: return (-1, a) elif ((input_index == 0 and a in broadcast_axes[0]) or (input_index == 1 and a in axes_to_sum)): return (0, a) else: return (1, a) axis_labels = [t0_axis_labels, t1_axis_labels] sorted_axes = [sorted(sym_list, key=lambda a: sort_key(i, a)) for i, sym_list in enumerate(axis_labels)] inputs = [t0, t1] for i, axes_str in enumerate(axis_labels): perm = [axes_str.find(a) for a in sorted_axes[i]] inputs[i] = _transpose_if_necessary(inputs[i], perm) t0, t1 = inputs if not axes_to_sum: # In the special case where there are no axes to sum over, reduce to mul() # rather than to batch matrix multiplication. for _ in broadcast_axes[1]: t0 = array_ops.expand_dims(t0, -1) for _ in broadcast_axes[0]: t1 = array_ops.expand_dims(t1, len(preserved_axes)) product = math_ops.multiply(t0, t1) product_axes = sorted_axes[0] + sorted_axes[1][len(preserved_axes):] return product, ''.join(product_axes) else: # Reduce to matmul(). # Reshape both inputs so as to combine multiple broadcast axes # into a single axis, and combine multiple summed axes into a # single axis. t0_shape = _get_shape(t0) num_broadcast_elements_t0 = _total_size( t0_shape[len(preserved_axes):-len(axes_to_sum)]) num_summed_elements = _total_size(t0_shape[-len(axes_to_sum):]) new_shape = (t0_shape[:len(preserved_axes)] + [num_broadcast_elements_t0, num_summed_elements]) t0 = _reshape_if_necessary(t0, new_shape) t1_shape = _get_shape(t1) num_broadcast_elements_t1 = _total_size( t1_shape[len(preserved_axes)+len(axes_to_sum):]) new_shape = (t1_shape[:len(preserved_axes)] + [num_summed_elements, num_broadcast_elements_t1]) t1 = _reshape_if_necessary(t1, new_shape) product = math_ops.matmul(t0, t1) # Undo compaction of broadcast axes uncompacted_shape = ( t0_shape[:len(preserved_axes)+len(broadcast_axes[0])] + t1_shape[len(t1_shape)-len(broadcast_axes[1]):] ) product = _reshape_if_necessary(product, uncompacted_shape) product_axes = ( sorted_axes[0][:len(preserved_axes)+len(broadcast_axes[0])] + sorted_axes[1][len(sorted_axes[1])-len(broadcast_axes[1]):] ) return product, ''.join(product_axes) def _transpose_if_necessary(tensor, perm): """Like transpose(), but avoids creating a new tensor if possible.""" if perm != range(len(perm)): return array_ops.transpose(tensor, perm=perm) else: return tensor def _reshape_if_necessary(tensor, new_shape): """Like reshape(), but avoids creating a new tensor if possible.""" # Accept None as an alias for -1 in new_shape. new_shape = tuple(-1 if x is None else x for x in new_shape) cur_shape = tuple(x.value for x in tensor.get_shape()) if (len(new_shape) == len(cur_shape) and all(d0 == d1 or d1 == -1 for d0, d1 in zip(cur_shape, new_shape))): return tensor else: return array_ops.reshape(tensor, new_shape) def _get_shape(tensor): """Like get_shape().as_list(), but explicitly queries the shape of a tensor if necessary to ensure that the returned value contains no unknown value.""" shape = tensor.get_shape().as_list() none_indices = [i for i, d in enumerate(shape) if d is None] if none_indices: # Query the shape if shape contains None values shape_tensor = array_ops.shape(tensor) for i in none_indices: shape[i] = shape_tensor[i] return shape def _total_size(shape_values): """Given list of tensor shape values, returns total size. If shape_values contains tensor values (which are results of array_ops.shape), then it returns a scalar tensor. If not, it returns an integer.""" result = 1 for val in shape_values: result *= val return result def _exponential_space_einsum(equation, *inputs): """Fallback implementation that supports summing an index over > 2 inputs.""" if '...' in equation: raise ValueError("Subscripts with ellipses are not yet supported.") match = re.match('([a-z,]+)(->[a-z]*)?', equation) if not match: raise ValueError( 'Indices have incorrect format: %s' % equation ) inputs = list(inputs) idx_in = match.group(1).split(',') idx_all = set(''.join(idx_in)) indices = ''.join(sorted(idx_all)) if match.group(2): idx_out = match.group(2)[2:] else: # infer the output subscripts if not given, assume alphabetical order counts = {ax: 0 for ax in indices} for axes_ in idx_in: for ax in axes_: counts[ax] += 1 idx_out = ''.join(sorted( ax for ax in indices if counts[ax] == 1 )) if len(idx_in) != len(inputs): raise ValueError( 'Expected %d inputs but got %d' % (len(idx_in), len(inputs)) ) missing_idx = set(idx_out).difference(idx_all) if missing_idx: raise ValueError( 'Unknown output axes: %s' % missing_idx ) axis_order = {} for ax in indices: if ax not in idx_out: axis_order[ax] = len(axis_order) for ax in idx_out: axis_order[ax] = len(axis_order) # transpose inputs so axes are in order for i, (input_, axes_) in enumerate(zip(inputs, idx_in)): if input_.get_shape().ndims != len(axes_): raise ValueError( 'Input %d with axes %s has incorrect' \ ' number of dimensions (expected %d, got %d)' % ( i, axes_, len(axes_), input_.get_shape().ndims ) ) sorted_idx = sorted(axes_, key=axis_order.get) if len(set(axes_)) != len(axes_): raise ValueError( 'Subscript not supported: an axis appears more than once: %s' % axes_ ) if list(axes_) != sorted_idx: permuted = [axes_.find(ax) for ax in sorted_idx] inputs[i] = array_ops.transpose(input_, permuted) idx_in[i] = sorted_idx reduction_idx = [] shapes = [[dim if dim else -1 for dim in tensor.get_shape().as_list()] for tensor in inputs] # validate shapes for broadcasting for j, ax in enumerate(sorted(idx_all, key=axis_order.get)): dims = [] for i, idx in enumerate(idx_in): if ax not in idx: shapes[i].insert(j, 1) else: dim = shapes[i][j] if isinstance(dim, int) and dim > 1: dims.append(dim) if len(set(dims)) > 1: raise ValueError( 'Dimension mismatch on axis: %s' % ax ) if ax not in idx_out: reduction_idx.append(j) # reshape, multiply expanded_inputs = [array_ops.reshape(input_, shape) for input_, shape in zip(inputs, shapes)] expanded_output = 1 for input_ in expanded_inputs: expanded_output *= input_ # contract return math_ops.reduce_sum(expanded_output, reduction_idx)
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