diff options
Diffstat (limited to 'tensorflow/python/ops/math_ops.py')
| -rw-r--r-- | tensorflow/python/ops/math_ops.py | 220 |
1 files changed, 158 insertions, 62 deletions
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py index 886b2048f9..81b3c21808 100644 --- a/tensorflow/python/ops/math_ops.py +++ b/tensorflow/python/ops/math_ops.py @@ -1265,16 +1265,19 @@ def _ReductionDims(x, axis, reduction_indices): return range(0, array_ops.rank(x)) +@deprecated_args(None, "keep_dims is deprecated, use keepdims instead", + "keep_dims") def reduce_sum(input_tensor, axis=None, - keep_dims=False, + keepdims=None, name=None, - reduction_indices=None): + reduction_indices=None, + keep_dims=None): """Computes the sum of elements across dimensions of a tensor. Reduces `input_tensor` along the dimensions given in `axis`. - Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each - entry in `axis`. If `keep_dims` is true, the reduced dimensions + Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each + entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. If `axis` has no entries, all dimensions are reduced, and a @@ -1287,7 +1290,7 @@ def reduce_sum(input_tensor, tf.reduce_sum(x) # 6 tf.reduce_sum(x, 0) # [2, 2, 2] tf.reduce_sum(x, 1) # [3, 3] - tf.reduce_sum(x, 1, keep_dims=True) # [[3], [3]] + tf.reduce_sum(x, 1, keepdims=True) # [[3], [3]] tf.reduce_sum(x, [0, 1]) # 6 ``` @@ -1296,7 +1299,7 @@ def reduce_sum(input_tensor, axis: The dimensions to reduce. If `None` (the default), reduces all dimensions. Must be in the range `[-rank(input_tensor), rank(input_tensor))`. - keep_dims: If true, retains reduced dimensions with length 1. + keepdims: If true, retains reduced dimensions with length 1. name: A name for the operation (optional). reduction_indices: The old (deprecated) name for axis. @@ -1307,24 +1310,35 @@ def reduce_sum(input_tensor, Equivalent to np.sum @end_compatibility """ + + if keep_dims is not None: + if keepdims is not None: + raise ValueError("Cannot specify both 'keep_dims' and 'keepdims'") + keepdims = keep_dims + if keepdims is None: + keepdims = False + return gen_math_ops._sum( input_tensor, _ReductionDims(input_tensor, axis, reduction_indices), - keep_dims, + keepdims, name=name) +@deprecated_args(None, "keep_dims is deprecated, use keepdims instead", + "keep_dims") def count_nonzero(input_tensor, axis=None, - keep_dims=False, + keepdims=None, dtype=dtypes.int64, name=None, - reduction_indices=None): + reduction_indices=None, + keep_dims=None): """Computes number of nonzero elements across dimensions of a tensor. Reduces `input_tensor` along the dimensions given in `axis`. - Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each - entry in `axis`. If `keep_dims` is true, the reduced dimensions + Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each + entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. If `axis` has no entries, all dimensions are reduced, and a @@ -1341,7 +1355,7 @@ def count_nonzero(input_tensor, tf.count_nonzero(x) # 3 tf.count_nonzero(x, 0) # [1, 2, 0] tf.count_nonzero(x, 1) # [1, 2] - tf.count_nonzero(x, 1, keep_dims=True) # [[1], [2]] + tf.count_nonzero(x, 1, keepdims=True) # [[1], [2]] tf.count_nonzero(x, [0, 1]) # 3 ``` @@ -1350,7 +1364,7 @@ def count_nonzero(input_tensor, axis: The dimensions to reduce. If `None` (the default), reduces all dimensions. Must be in the range `[-rank(input_tensor), rank(input_tensor))`. - keep_dims: If true, retains reduced dimensions with length 1. + keepdims: If true, retains reduced dimensions with length 1. dtype: The output dtype; defaults to `tf.int64`. name: A name for the operation (optional). reduction_indices: The old (deprecated) name for axis. @@ -1358,6 +1372,13 @@ def count_nonzero(input_tensor, Returns: The reduced tensor (number of nonzero values). """ + if keep_dims is not None: + if keepdims is not None: + raise ValueError("Cannot specify both 'keep_dims' and 'keepdims'") + keepdims = keep_dims + if keepdims is None: + keepdims = False + with ops.name_scope(name, "count_nonzero", [input_tensor]): input_tensor = ops.convert_to_tensor(input_tensor, name="input_tensor") zero = input_tensor.dtype.as_numpy_dtype() @@ -1366,21 +1387,24 @@ def count_nonzero(input_tensor, # int64 reduction happens on GPU to_int64(gen_math_ops.not_equal(input_tensor, zero)), axis=axis, - keep_dims=keep_dims, + keepdims=keepdims, reduction_indices=reduction_indices), dtype=dtype) +@deprecated_args(None, "keep_dims is deprecated, use keepdims instead", + "keep_dims") def reduce_mean(input_tensor, axis=None, - keep_dims=False, + keepdims=None, name=None, - reduction_indices=None): + reduction_indices=None, + keep_dims=None): """Computes the mean of elements across dimensions of a tensor. Reduces `input_tensor` along the dimensions given in `axis`. - Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each - entry in `axis`. If `keep_dims` is true, the reduced dimensions + Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each + entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. If `axis` has no entries, all dimensions are reduced, and a @@ -1400,7 +1424,7 @@ def reduce_mean(input_tensor, axis: The dimensions to reduce. If `None` (the default), reduces all dimensions. Must be in the range `[-rank(input_tensor), rank(input_tensor))`. - keep_dims: If true, retains reduced dimensions with length 1. + keepdims: If true, retains reduced dimensions with length 1. name: A name for the operation (optional). reduction_indices: The old (deprecated) name for axis. @@ -1409,25 +1433,44 @@ def reduce_mean(input_tensor, @compatibility(numpy) Equivalent to np.mean + + Please note that `np.mean` has a `dtype` parameter that could be used to specify the output type. By default this is `dtype=float64`. On the other hand, `tf.reduce_mean` has an aggressive type inference from `input_tensor`, for example: + + ```python + x = tf.constant([1, 0, 1, 0]) + tf.reduce_mean(x) # 0 + y = tf.constant([1., 0., 1., 0.]) + tf.reduce_mean(y) # 0.5 + ``` + @end_compatibility """ + if keep_dims is not None: + if keepdims is not None: + raise ValueError("Cannot specify both 'keep_dims' and 'keepdims'") + keepdims = keep_dims + if keepdims is None: + keepdims = False return gen_math_ops._mean( input_tensor, _ReductionDims(input_tensor, axis, reduction_indices), - keep_dims, + keepdims, name=name) +@deprecated_args(None, "keep_dims is deprecated, use keepdims instead", + "keep_dims") def reduce_prod(input_tensor, axis=None, - keep_dims=False, + keepdims=None, name=None, - reduction_indices=None): + reduction_indices=None, + keep_dims=None): """Computes the product of elements across dimensions of a tensor. Reduces `input_tensor` along the dimensions given in `axis`. - Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each - entry in `axis`. If `keep_dims` is true, the reduced dimensions + Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each + entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. If `axis` has no entries, all dimensions are reduced, and a @@ -1438,7 +1481,7 @@ def reduce_prod(input_tensor, axis: The dimensions to reduce. If `None` (the default), reduces all dimensions. Must be in the range `[-rank(input_tensor), rank(input_tensor))`. - keep_dims: If true, retains reduced dimensions with length 1. + keepdims: If true, retains reduced dimensions with length 1. name: A name for the operation (optional). reduction_indices: The old (deprecated) name for axis. @@ -1449,23 +1492,32 @@ def reduce_prod(input_tensor, Equivalent to np.prod @end_compatibility """ + if keep_dims is not None: + if keepdims is not None: + raise ValueError("Cannot specify both 'keep_dims' and 'keepdims'") + keepdims = keep_dims + if keepdims is None: + keepdims = False return gen_math_ops._prod( input_tensor, _ReductionDims(input_tensor, axis, reduction_indices), - keep_dims, + keepdims, name=name) +@deprecated_args(None, "keep_dims is deprecated, use keepdims instead", + "keep_dims") def reduce_min(input_tensor, axis=None, - keep_dims=False, + keepdims=None, name=None, - reduction_indices=None): + reduction_indices=None, + keep_dims=None): """Computes the minimum of elements across dimensions of a tensor. Reduces `input_tensor` along the dimensions given in `axis`. - Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each - entry in `axis`. If `keep_dims` is true, the reduced dimensions + Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each + entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. If `axis` has no entries, all dimensions are reduced, and a @@ -1476,7 +1528,7 @@ def reduce_min(input_tensor, axis: The dimensions to reduce. If `None` (the default), reduces all dimensions. Must be in the range `[-rank(input_tensor), rank(input_tensor))`. - keep_dims: If true, retains reduced dimensions with length 1. + keepdims: If true, retains reduced dimensions with length 1. name: A name for the operation (optional). reduction_indices: The old (deprecated) name for axis. @@ -1487,23 +1539,32 @@ def reduce_min(input_tensor, Equivalent to np.min @end_compatibility """ + if keep_dims is not None: + if keepdims is not None: + raise ValueError("Cannot specify both 'keep_dims' and 'keepdims'") + keepdims = keep_dims + if keepdims is None: + keepdims = False return gen_math_ops._min( input_tensor, _ReductionDims(input_tensor, axis, reduction_indices), - keep_dims, + keepdims, name=name) +@deprecated_args(None, "keep_dims is deprecated, use keepdims instead", + "keep_dims") def reduce_max(input_tensor, axis=None, - keep_dims=False, + keepdims=None, name=None, - reduction_indices=None): + reduction_indices=None, + keep_dims=None): """Computes the maximum of elements across dimensions of a tensor. Reduces `input_tensor` along the dimensions given in `axis`. - Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each - entry in `axis`. If `keep_dims` is true, the reduced dimensions + Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each + entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. If `axis` has no entries, all dimensions are reduced, and a @@ -1514,7 +1575,7 @@ def reduce_max(input_tensor, axis: The dimensions to reduce. If `None` (the default), reduces all dimensions. Must be in the range `[-rank(input_tensor), rank(input_tensor))`. - keep_dims: If true, retains reduced dimensions with length 1. + keepdims: If true, retains reduced dimensions with length 1. name: A name for the operation (optional). reduction_indices: The old (deprecated) name for axis. @@ -1525,23 +1586,32 @@ def reduce_max(input_tensor, Equivalent to np.max @end_compatibility """ + if keep_dims is not None: + if keepdims is not None: + raise ValueError("Cannot specify both 'keep_dims' and 'keepdims'") + keepdims = keep_dims + if keepdims is None: + keepdims = False return gen_math_ops._max( input_tensor, _ReductionDims(input_tensor, axis, reduction_indices), - keep_dims, + keepdims, name=name) +@deprecated_args(None, "keep_dims is deprecated, use keepdims instead", + "keep_dims") def reduce_all(input_tensor, axis=None, - keep_dims=False, + keepdims=None, name=None, - reduction_indices=None): + reduction_indices=None, + keep_dims=None): """Computes the "logical and" of elements across dimensions of a tensor. Reduces `input_tensor` along the dimensions given in `axis`. - Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each - entry in `axis`. If `keep_dims` is true, the reduced dimensions + Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each + entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. If `axis` has no entries, all dimensions are reduced, and a @@ -1561,7 +1631,7 @@ def reduce_all(input_tensor, axis: The dimensions to reduce. If `None` (the default), reduces all dimensions. Must be in the range `[-rank(input_tensor), rank(input_tensor))`. - keep_dims: If true, retains reduced dimensions with length 1. + keepdims: If true, retains reduced dimensions with length 1. name: A name for the operation (optional). reduction_indices: The old (deprecated) name for axis. @@ -1572,23 +1642,32 @@ def reduce_all(input_tensor, Equivalent to np.all @end_compatibility """ + if keep_dims is not None: + if keepdims is not None: + raise ValueError("Cannot specify both 'keep_dims' and 'keepdims'") + keepdims = keep_dims + if keepdims is None: + keepdims = False return gen_math_ops._all( input_tensor, _ReductionDims(input_tensor, axis, reduction_indices), - keep_dims, + keepdims, name=name) +@deprecated_args(None, "keep_dims is deprecated, use keepdims instead", + "keep_dims") def reduce_any(input_tensor, axis=None, - keep_dims=False, + keepdims=None, name=None, - reduction_indices=None): + reduction_indices=None, + keep_dims=None): """Computes the "logical or" of elements across dimensions of a tensor. Reduces `input_tensor` along the dimensions given in `axis`. - Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each - entry in `axis`. If `keep_dims` is true, the reduced dimensions + Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each + entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. If `axis` has no entries, all dimensions are reduced, and a @@ -1608,7 +1687,7 @@ def reduce_any(input_tensor, axis: The dimensions to reduce. If `None` (the default), reduces all dimensions. Must be in the range `[-rank(input_tensor), rank(input_tensor))`. - keep_dims: If true, retains reduced dimensions with length 1. + keepdims: If true, retains reduced dimensions with length 1. name: A name for the operation (optional). reduction_indices: The old (deprecated) name for axis. @@ -1619,23 +1698,32 @@ def reduce_any(input_tensor, Equivalent to np.any @end_compatibility """ + if keep_dims is not None: + if keepdims is not None: + raise ValueError("Cannot specify both 'keep_dims' and 'keepdims'") + keepdims = keep_dims + if keepdims is None: + keepdims = False return gen_math_ops._any( input_tensor, _ReductionDims(input_tensor, axis, reduction_indices), - keep_dims, + keepdims, name=name) +@deprecated_args(None, "keep_dims is deprecated, use keepdims instead", + "keep_dims") def reduce_logsumexp(input_tensor, axis=None, - keep_dims=False, + keepdims=None, name=None, - reduction_indices=None): + reduction_indices=None, + keep_dims=None): """Computes log(sum(exp(elements across dimensions of a tensor))). Reduces `input_tensor` along the dimensions given in `axis`. - Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each - entry in `axis`. If `keep_dims` is true, the reduced dimensions + Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each + entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. If `axis` has no entries, all dimensions are reduced, and a @@ -1652,7 +1740,7 @@ def reduce_logsumexp(input_tensor, tf.reduce_logsumexp(x) # log(6) tf.reduce_logsumexp(x, 0) # [log(2), log(2), log(2)] tf.reduce_logsumexp(x, 1) # [log(3), log(3)] - tf.reduce_logsumexp(x, 1, keep_dims=True) # [[log(3)], [log(3)]] + tf.reduce_logsumexp(x, 1, keepdims=True) # [[log(3)], [log(3)]] tf.reduce_logsumexp(x, [0, 1]) # log(6) ``` @@ -1661,19 +1749,25 @@ def reduce_logsumexp(input_tensor, axis: The dimensions to reduce. If `None` (the default), reduces all dimensions. Must be in the range `[-rank(input_tensor), rank(input_tensor))`. - keep_dims: If true, retains reduced dimensions with length 1. + keepdims: If true, retains reduced dimensions with length 1. name: A name for the operation (optional). reduction_indices: The old (deprecated) name for axis. Returns: The reduced tensor. """ + if keep_dims is not None: + if keepdims is not None: + raise ValueError("Cannot specify both 'keep_dims' and 'keepdims'") + keepdims = keep_dims + if keepdims is None: + keepdims = False with ops.name_scope(name, "ReduceLogSumExp", [input_tensor]) as name: raw_max = reduce_max( input_tensor, axis=axis, reduction_indices=reduction_indices, - keep_dims=True) + keepdims=True) my_max = array_ops.stop_gradient( array_ops.where( gen_math_ops.is_finite(raw_max), @@ -1683,9 +1777,9 @@ def reduce_logsumexp(input_tensor, reduce_sum( gen_math_ops.exp(input_tensor - my_max), axis, - keep_dims=True, + keepdims=True, reduction_indices=reduction_indices)) + my_max - if not keep_dims: + if not keepdims: if isinstance(axis, int): axis = [axis] result = array_ops.squeeze(result, axis) @@ -2191,8 +2285,10 @@ def bincount(arr, maxlength = ops.convert_to_tensor( maxlength, name="maxlength", dtype=dtypes.int32) output_size = gen_math_ops.minimum(maxlength, output_size) - weights = (ops.convert_to_tensor(weights, name="weights") - if weights is not None else constant_op.constant([], dtype)) + if weights is not None: + weights = ops.convert_to_tensor(weights, name="weights") + return gen_math_ops.unsorted_segment_sum(weights, arr, output_size) + weights = constant_op.constant([], dtype) return gen_math_ops.bincount(arr, output_size, weights) @@ -2355,7 +2451,7 @@ def reduced_shape(input_shape, axes): input_shape: 1-D Tensor, the shape of the Tensor being reduced. axes: 1-D Tensor, the reduction axes. Returns: - A 1-D Tensor, the output shape as if keep_dims were set to True. + A 1-D Tensor, the output shape as if keepdims were set to True. """ # Example: # cast needed for SparseTensor reductions |