Python tensorflow.python.ops.gen_math_ops.maximum() Examples
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Example #1
| Source File: math_ops.py From deep_image_model with Apache License 2.0 | 6 votes |
|
def reduce_max(input_tensor, reduction_indices=None, keep_dims=False,
name=None):
"""Computes the maximum of elements across dimensions of a tensor.
Reduces `input_tensor` along the dimensions given in `reduction_indices`.
Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
are retained with length 1.
If `reduction_indices` has no entries, all dimensions are reduced, and a
tensor with a single element is returned.
Args:
input_tensor: The tensor to reduce. Should have numeric type.
reduction_indices: The dimensions to reduce. If `None` (the default),
reduces all dimensions.
keep_dims: If true, retains reduced dimensions with length 1.
name: A name for the operation (optional).
Returns:
The reduced tensor.
"""
return gen_math_ops._max(input_tensor, _ReductionDims(input_tensor,
reduction_indices),
keep_dims, name=name)
Example #2
| Source File: math_ops.py From lambda-packs with MIT License | 5 votes |
|
def saturate_cast(value, dtype, name=None):
"""Performs a safe saturating cast of `value` to `dtype`.
This function casts the input to `dtype` without applying any scaling. If
there is a danger that values would over or underflow in the cast, this op
applies the appropriate clamping before the cast.
Args:
value: A `Tensor`.
dtype: The desired output `DType`.
name: A name for the operation (optional).
Returns:
`value` safely cast to `dtype`.
"""
# When casting to a type with smaller representable range, clamp.
# Note that this covers casting to unsigned types as well.
with ops.name_scope(name, "saturate_cast", [value]) as name:
value = ops.convert_to_tensor(value, name="value")
dtype = dtypes.as_dtype(dtype).base_dtype
if value.dtype.min < dtype.min:
value = gen_math_ops.maximum(value,
ops.convert_to_tensor(
dtype.min, dtype=value.dtype,
name="min"))
if value.dtype.max > dtype.max:
value = gen_math_ops.minimum(value,
ops.convert_to_tensor(
dtype.max, dtype=value.dtype,
name="max"))
return cast(value, dtype, name=name)
Example #3
| Source File: math_ops.py From lambda-packs with MIT License | 5 votes |
|
def reduce_max(input_tensor,
axis=None,
keep_dims=False,
name=None,
reduction_indices=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
are retained with length 1.
If `axis` has no entries, all dimensions are reduced, and a
tensor with a single element is returned.
Args:
input_tensor: The tensor to reduce. Should have numeric type.
axis: The dimensions to reduce. If `None` (the default),
reduces all dimensions.
keep_dims: 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.
@compatibility(numpy)
Equivalent to np.max
@end_compatibility
"""
return gen_math_ops._max(
input_tensor,
_ReductionDims(input_tensor, axis, reduction_indices),
keep_dims,
name=name)
Example #4
| Source File: math_ops.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def saturate_cast(value, dtype, name=None):
"""Performs a safe saturating cast of `value` to `dtype`.
This function casts the input to `dtype` without applying any scaling. If
there is a danger that values would over or underflow in the cast, this op
applies the appropriate clamping before the cast.
Args:
value: A `Tensor`.
dtype: The desired output `DType`.
name: A name for the operation (optional).
Returns:
`value` safely cast to `dtype`.
"""
# When casting to a type with smaller representable range, clamp.
# Note that this covers casting to unsigned types as well.
with ops.name_scope(name, "saturate_cast", [value]) as name:
value = ops.convert_to_tensor(value, name="value")
dtype = dtypes.as_dtype(dtype).base_dtype
if value.dtype.min < dtype.min:
value = gen_math_ops.maximum(
value,
ops.convert_to_tensor(
dtype.min, dtype=value.dtype, name="min"))
if value.dtype.max > dtype.max:
value = gen_math_ops.minimum(
value,
ops.convert_to_tensor(
dtype.max, dtype=value.dtype, name="max"))
return cast(value, dtype, name=name)
Example #5
| Source File: math_ops.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def reduce_max(input_tensor,
axis=None,
keep_dims=False,
name=None,
reduction_indices=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
are retained with length 1.
If `axis` has no entries, all dimensions are reduced, and a
tensor with a single element is returned.
Args:
input_tensor: The tensor to reduce. Should have numeric type.
axis: The dimensions to reduce. If `None` (the default),
reduces all dimensions.
keep_dims: 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.
@compatibility(numpy)
Equivalent to np.max
@end_compatibility
"""
return gen_math_ops._max(
input_tensor,
_ReductionDims(input_tensor, axis, reduction_indices),
keep_dims,
name=name)
Example #6
| Source File: math_ops.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def saturate_cast(value, dtype, name=None):
"""Performs a safe saturating cast of `value` to `dtype`.
This function casts the input to `dtype` without applying any scaling. If
there is a danger that values would over or underflow in the cast, this op
applies the appropriate clamping before the cast.
Args:
value: A `Tensor`.
dtype: The desired output `DType`.
name: A name for the operation (optional).
Returns:
`value` safely cast to `dtype`.
"""
# When casting to a type with smaller representable range, clamp.
# Note that this covers casting to unsigned types as well.
with ops.name_scope(name, "saturate_cast", [value]) as name:
value = ops.convert_to_tensor(value, name="value")
dtype = dtypes.as_dtype(dtype).base_dtype
if value.dtype.min < dtype.min:
value = gen_math_ops.maximum(value, ops.convert_to_tensor(
dtype.min, dtype=value.dtype, name="min"))
if value.dtype.max > dtype.max:
value = gen_math_ops.minimum(value, ops.convert_to_tensor(
dtype.max, dtype=value.dtype, name="max"))
return cast(value, dtype, name=name)
Example #7
| Source File: utils.py From noisy-K-FAC with Apache License 2.0 | 5 votes |
|
def posdef_inv_eig(tensor, identity, damping):
"""Computes inverse(tensor + damping * identity) with eigendecomposition."""
eigenvalues, eigenvectors = linalg_ops.self_adjoint_eig(
tensor + damping * identity)
# TODO(GD): it's a little hacky
eigenvalues = gen_math_ops.maximum(eigenvalues, damping)
return math_ops.matmul(
eigenvectors / eigenvalues, eigenvectors, transpose_b=True)
Example #8
| Source File: math_ops.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
|
def saturate_cast(value, dtype, name=None):
"""Performs a safe saturating cast of `value` to `dtype`.
This function casts the input to `dtype` without applying any scaling. If
there is a danger that values would over or underflow in the cast, this op
applies the appropriate clamping before the cast.
Args:
value: A `Tensor`.
dtype: The desired output `DType`.
name: A name for the operation (optional).
Returns:
`value` safely cast to `dtype`.
"""
# When casting to a type with smaller representable range, clamp.
# Note that this covers casting to unsigned types as well.
with ops.name_scope(name, "saturate_cast", [value]) as name:
value = ops.convert_to_tensor(value, name="value")
dtype = dtypes.as_dtype(dtype).base_dtype
if value.dtype.min < dtype.min:
value = gen_math_ops.maximum(value,
ops.convert_to_tensor(
dtype.min, dtype=value.dtype,
name="min"))
if value.dtype.max > dtype.max:
value = gen_math_ops.minimum(value,
ops.convert_to_tensor(
dtype.max, dtype=value.dtype,
name="max"))
return cast(value, dtype, name=name)
Example #9
| Source File: math_ops.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
|
def reduce_max(input_tensor,
axis=None,
keep_dims=False,
name=None,
reduction_indices=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
are retained with length 1.
If `axis` has no entries, all dimensions are reduced, and a
tensor with a single element is returned.
Args:
input_tensor: The tensor to reduce. Should have numeric type.
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.
name: A name for the operation (optional).
reduction_indices: The old (deprecated) name for axis.
Returns:
The reduced tensor.
@compatibility(numpy)
Equivalent to np.max
@end_compatibility
"""
return gen_math_ops._max(
input_tensor,
_ReductionDims(input_tensor, axis, reduction_indices),
keep_dims,
name=name)
Example #10
| Source File: math_ops.py From keras-lambda with MIT License | 5 votes |
|
def saturate_cast(value, dtype, name=None):
"""Performs a safe saturating cast of `value` to `dtype`.
This function casts the input to `dtype` without applying any scaling. If
there is a danger that values would over or underflow in the cast, this op
applies the appropriate clamping before the cast.
Args:
value: A `Tensor`.
dtype: The desired output `DType`.
name: A name for the operation (optional).
Returns:
`value` safely cast to `dtype`.
"""
# When casting to a type with smaller representable range, clamp.
# Note that this covers casting to unsigned types as well.
with ops.name_scope(name, "saturate_cast", [value]) as name:
value = ops.convert_to_tensor(value, name="value")
dtype = dtypes.as_dtype(dtype).base_dtype
if value.dtype.min < dtype.min:
value = gen_math_ops.maximum(
value,
ops.convert_to_tensor(
dtype.min, dtype=value.dtype, name="min"))
if value.dtype.max > dtype.max:
value = gen_math_ops.minimum(
value,
ops.convert_to_tensor(
dtype.max, dtype=value.dtype, name="max"))
return cast(value, dtype, name=name)
Example #11
| Source File: math_ops.py From keras-lambda with MIT License | 5 votes |
|
def reduce_max(input_tensor,
axis=None,
keep_dims=False,
name=None,
reduction_indices=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
are retained with length 1.
If `axis` has no entries, all dimensions are reduced, and a
tensor with a single element is returned.
Args:
input_tensor: The tensor to reduce. Should have numeric type.
axis: The dimensions to reduce. If `None` (the default),
reduces all dimensions.
keep_dims: 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.
@compatibility(numpy)
Equivalent to np.max
@end_compatibility
"""
return gen_math_ops._max(
input_tensor,
_ReductionDims(input_tensor, axis, reduction_indices),
keep_dims,
name=name)
Example #12
| Source File: math_ops.py From lambda-packs with MIT License | 4 votes |
|
def bincount(arr,
weights=None,
minlength=None,
maxlength=None,
dtype=dtypes.int32):
"""Counts the number of occurrences of each value in an integer array.
If `minlength` and `maxlength` are not given, returns a vector with length
`tf.reduce_max(arr) + 1` if `arr` is non-empty, and length 0 otherwise.
If `weights` are non-None, then index `i` of the output stores the sum of the
value in `weights` at each index where the corresponding value in `arr` is
`i`.
Args:
arr: An int32 tensor of non-negative values.
weights: If non-None, must be the same shape as arr. For each value in
`arr`, the bin will be incremented by the corresponding weight instead
of 1.
minlength: If given, ensures the output has length at least `minlength`,
padding with zeros at the end if necessary.
maxlength: If given, skips values in `arr` that are equal or greater than
`maxlength`, ensuring that the output has length at most `maxlength`.
dtype: If `weights` is None, determines the type of the output bins.
Returns:
A vector with the same dtype as `weights` or the given `dtype`. The bin
values.
"""
arr = ops.convert_to_tensor(arr, name="arr", dtype=dtypes.int32)
array_is_nonempty = reduce_prod(array_ops.shape(arr)) > 0
output_size = cast(array_is_nonempty, dtypes.int32) * (reduce_max(arr) + 1)
if minlength is not None:
minlength = ops.convert_to_tensor(
minlength, name="minlength", dtype=dtypes.int32)
output_size = gen_math_ops.maximum(minlength, output_size)
if maxlength is not None:
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))
return gen_math_ops.bincount(arr, output_size, weights)
Example #13
| Source File: math_ops.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 4 votes |
|
def bincount(arr,
weights=None,
minlength=None,
maxlength=None,
dtype=dtypes.int32):
"""Counts the number of occurrences of each value in an integer array.
If `minlength` and `maxlength` are not given, returns a vector with length
`tf.reduce_max(arr) + 1` if `arr` is non-empty, and length 0 otherwise.
If `weights` are non-None, then index `i` of the output stores the sum of the
value in `weights` at each index where the corresponding value in `arr` is
`i`.
Args:
arr: An int32 tensor of non-negative values.
weights: If non-None, must be the same shape as arr. For each value in
`arr`, the bin will be incremented by the corresponding weight instead
of 1.
minlength: If given, ensures the output has length at least `minlength`,
padding with zeros at the end if necessary.
maxlength: If given, skips values in `arr` that are equal or greater than
`maxlength`, ensuring that the output has length at most `maxlength`.
dtype: If `weights` is None, determines the type of the output bins.
Returns:
A vector with the same dtype as `weights` or the given `dtype`. The bin
values.
"""
arr = ops.convert_to_tensor(arr, name="arr", dtype=dtypes.int32)
array_is_nonempty = reduce_prod(array_ops.shape(arr)) > 0
output_size = cast(array_is_nonempty, dtypes.int32) * (reduce_max(arr) + 1)
if minlength is not None:
minlength = ops.convert_to_tensor(
minlength, name="minlength", dtype=dtypes.int32)
output_size = gen_math_ops.maximum(minlength, output_size)
if maxlength is not None:
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))
return gen_math_ops.bincount(arr, output_size, weights)
