47 lines
2.0 KiB
Python
47 lines
2.0 KiB
Python
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Config functions for TF NumPy."""
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from tensorflow.python.framework import ops
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from tensorflow.python.ops.numpy_ops import np_dtypes
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from tensorflow.python.ops.numpy_ops import np_export
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from tensorflow.python.ops.numpy_ops import np_math_ops
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@np_export.np_export("experimental_enable_numpy_behavior")
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def enable_numpy_behavior(prefer_float32=False):
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"""Enable NumPy behavior on Tensors.
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Enabling NumPy behavior has three effects:
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* It adds to `tf.Tensor` some common NumPy methods such as `T`,
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`reshape` and `ravel`.
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* It changes dtype promotion in `tf.Tensor` operators to be
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compatible with NumPy. For example,
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`tf.ones([], tf.int32) + tf.ones([], tf.float32)` used to throw a
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"dtype incompatible" error, but after this it will return a
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float64 tensor (obeying NumPy's promotion rules).
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* It enhances `tf.Tensor`'s indexing capability to be on par with
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[NumPy's](https://numpy.org/doc/stable/reference/arrays.indexing.html).
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Args:
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prefer_float32: Controls whether dtype inference will use float32
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for Python floats, or float64 (the default and the
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NumPy-compatible behavior).
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"""
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ops.enable_numpy_style_type_promotion()
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ops.enable_numpy_style_slicing()
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np_math_ops.enable_numpy_methods_on_tensor()
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np_dtypes.set_prefer_float32(prefer_float32)
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