wehub-resource-sync
67 lines
2.5 KiB
Python
67 lines
2.5 KiB
Python
# Copyright 2021 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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"""Test configs for multinomial."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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from tensorflow.lite.testing.zip_test_utils import create_tensor_data
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from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
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from tensorflow.lite.testing.zip_test_utils import register_make_test_function
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@register_make_test_function()
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def make_multinomial_tests(options):
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"""Make a set of tests to do multinomial."""
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test_parameters = [{
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"logits_shape": [[1, 2], [2, 5]],
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"dtype": [tf.int64, tf.int32],
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"seed": [None, 1234],
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"seed2": [5678],
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}, {
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"logits_shape": [[1, 2]],
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"dtype": [tf.int64, tf.int32],
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"seed": [1234],
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"seed2": [None]
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}]
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def build_graph(parameters):
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"""Build the op testing graph."""
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tf.compat.v1.set_random_seed(seed=parameters["seed"])
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logits_tf = tf.compat.v1.placeholder(
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name="logits", dtype=tf.float32, shape=parameters["logits_shape"])
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num_samples_tf = tf.compat.v1.placeholder(
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name="num_samples", dtype=tf.int32, shape=None)
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out = tf.random.categorical(
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logits=logits_tf,
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num_samples=num_samples_tf,
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dtype=parameters["dtype"],
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seed=parameters["seed2"])
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return [logits_tf, num_samples_tf], [out]
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def build_inputs(parameters, sess, inputs, outputs):
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input_values = [
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create_tensor_data(
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dtype=tf.float32, shape=parameters["logits_shape"], min_value=-2,
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max_value=-1),
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create_tensor_data(
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dtype=tf.int32, shape=None, min_value=10, max_value=100)
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]
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return input_values, sess.run(
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outputs, feed_dict=dict(zip(inputs, input_values)))
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make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
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