tensorflow/tensorflow

File warm_starting_util_test.py has 978 lines of code (exceeds 250 allowed). Consider refactoring.

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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

WarmStartingUtilTest has 38 functions (exceeds 20 allowed). Consider refactoring.

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class WarmStartingUtilTest(test.TestCase):

  def _write_vocab(self, string_values, file_name):
    vocab_file = os.path.join(self.get_temp_dir(), file_name)
    with open(vocab_file, "w") as f:

Identical blocks of code found in 2 locations. Consider refactoring.

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        fruit_output_layer = variable_scope.get_variable(
            "fruit_output_layer",
            shape=[4, 3],

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Identical blocks of code found in 2 locations. Consider refactoring.

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        fruit_output_layer = variable_scope.get_variable(
            "fruit_output_layer",
            shape=[4, 3],

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Identical blocks of code found in 2 locations. Consider refactoring.

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        fruit_output_layer = variable_scope.get_variable(
            "fruit_output_layer",
            initializer=[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.],

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        fruit_output_layer = variable_scope.get_variable(
            "fruit_output_layer",
            initializer=[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.],

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        fruit_weights = variable_scope.get_variable(
            "fruit_weights", initializer=[[0.], [0.], [0.], [0.], [0.]])
        ws_util._warm_start_var_with_vocab(fruit_weights, new_vocab_path, 5,

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Identical blocks of code found in 2 locations. Consider refactoring.

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        fruit_weights = variable_scope.get_variable(
            "fruit_weights", initializer=[[0.], [0.], [0.], [0.], [0.]])
        ws_util._warm_start_var_with_vocab(fruit_weights, new_vocab_path, 5,

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Identical blocks of code found in 2 locations. Consider refactoring.

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        variable_scope.get_variable(
            "linear_model/sc_hash/weights", shape=[15, 1], initializer=norms())
        sc_keys_weights = variable_scope.get_variable(

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        variable_scope.get_variable(
            "linear_model/sc_hash/weights", shape=[15, 1], initializer=norms())
        sc_keys_weights = variable_scope.get_variable(

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Identical blocks of code found in 2 locations. Consider refactoring.

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        fruit_weights = variable_scope.get_variable(
            "fruit_weights", initializer=[[0.], [0.], [0.], [0.]])
        prev_tensor_name, var = ws_util._get_var_info(fruit_weights)

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Identical blocks of code found in 2 locations. Consider refactoring.

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        fruit_weights = variable_scope.get_variable(
            "fruit_weights", initializer=[[0.], [0.], [0.], [0.]])
        prev_tensor_name, var = ws_util._get_var_info(fruit_weights)

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        cols_to_vars = self._create_linear_model([sc_int], partitioner)
        ws_util.warm_start(self.get_temp_dir(), vars_to_warm_start=".*sc_int.*")
        self.evaluate(variables.global_variables_initializer())

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Similar blocks of code found in 4 locations. Consider refactoring.

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        cols_to_vars = self._create_linear_model([sc_vocab], partitioner)
        # Since old vocab is not explicitly set in WarmStartSettings, the old
        # vocab is assumed to be same as new vocab.

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        cols_to_vars = self._create_linear_model([sc_hash], partitioner)
        ws_util.warm_start(
            self.get_temp_dir(), vars_to_warm_start=".*sc_hash.*")

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Similar blocks of code found in 4 locations. Consider refactoring.

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        cols_to_vars = self._create_linear_model([real_bucket], partitioner)
        ws_util.warm_start(
            self.get_temp_dir(), vars_to_warm_start=".*real_bucketized.*")

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Similar blocks of code found in 6 locations. Consider refactoring.

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        cols_to_vars = self._create_linear_model([sc_hash], partitioner)
        self.evaluate(variables.global_variables_initializer())
        # Without warm-starting, the weights should be initialized using default

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Similar blocks of code found in 6 locations. Consider refactoring.

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        cols_to_vars = self._create_linear_model([sc_vocab], partitioner)
        self.evaluate(variables.global_variables_initializer())
        # Without warm-starting, the weights should be initialized using default

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Similar blocks of code found in 6 locations. Consider refactoring.

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        cols_to_vars = self._create_linear_model([sc_int], partitioner)
        self.evaluate(variables.global_variables_initializer())
        # Without warm-starting, the weights should be initialized using default

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Similar blocks of code found in 6 locations. Consider refactoring.

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        cols_to_vars = self._create_linear_model([real_bucket], partitioner)
        self.evaluate(variables.global_variables_initializer())
        # Without warm-starting, the weights should be initialized using default

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Similar blocks of code found in 6 locations. Consider refactoring.

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        cols_to_vars = self._create_linear_model([sc_vocab], partitioner)
        self.evaluate(variables.global_variables_initializer())
        # Without warm-starting, the weights should be initialized using default

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Similar blocks of code found in 6 locations. Consider refactoring.

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    with ops.Graph().as_default() as g:
      with self.session(graph=g) as sess:
        cols_to_vars = self._create_linear_model([sc_vocab], partitioner)
        self.evaluate(variables.global_variables_initializer())
        # Without warm-starting, the weights should be initialized using default

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        ws_util.warm_start(
            self.get_temp_dir(),
            vars_to_warm_start=".*(sc_keys|sc_vocab).*",
            var_name_to_vocab_info={
                ws_util._infer_var_name(cols_to_vars[sc_vocab]): vocab_info

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        ws_util.warm_start(
            self.get_temp_dir(),
            vars_to_warm_start=".*(sc_keys|sc_vocab).*",
            var_name_to_vocab_info={
                ws_util._infer_var_name(cols_to_vars[sc_vocab]): vocab_info

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Similar blocks of code found in 2 locations. Consider refactoring.

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  def _write_vocab(self, string_values, file_name):
    vocab_file = os.path.join(self.get_temp_dir(), file_name)
    with open(vocab_file, "w") as f:
      f.write("\n".join(string_values))
    return vocab_file

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    def _partitioner(shape, dtype):  # pylint:disable=unused-argument
      # Partition each var into 2 equal slices.
      partitions = [1] * len(shape)
      partitions[0] = min(2, shape.dims[0].value)
      return partitions

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Identical blocks of code found in 4 locations. Consider refactoring.

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    def _partitioner(shape, dtype):  # pylint:disable=unused-argument
      # Partition each var into 2 equal slices.
      partitions = [1] * len(shape)
      partitions[0] = min(2, shape.dims[0].value)
      return partitions

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Identical blocks of code found in 4 locations. Consider refactoring.

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    def _partitioner(shape, dtype):  # pylint:disable=unused-argument
      # Partition each var into 2 equal slices.
      partitions = [1] * len(shape)
      partitions[0] = min(2, shape.dims[0].value)
      return partitions

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Identical blocks of code found in 4 locations. Consider refactoring.

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    def _partitioner(shape, dtype):  # pylint:disable=unused-argument
      # Partition each var into 2 equal slices.
      partitions = [1] * len(shape)
      partitions[0] = min(2, shape.dims[0].value)
      return partitions

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        fruit_weights = variable_scope.get_variable(
            "fruit_weights",
            shape=[6, 1],
            initializer=[[0.], [0.], [0.], [0.], [0.], [0.]],
            partitioner=lambda shape, dtype: [2, 1])

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        fruit_weights = variable_scope.get_variable(
            "fruit_weights",
            shape=[6, 1],
            initializer=[[0.], [0.], [0.], [0.], [0.], [0.]],
            partitioner=lambda shape, dtype: [2, 1])

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    _, weights = self._create_prev_run_var(
        "fruit_weights",
        shape=[4, 1],
        initializer=[[0.5], [1.], [1.5], [2.]],
        partitioner=lambda shape, dtype: [2, 1])

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Similar blocks of code found in 2 locations. Consider refactoring.

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    _, weights = self._create_prev_run_var(
        "old_scope/fruit_weights",
        shape=[4, 1],
        initializer=[[0.5], [1.], [1.5], [2.]],
        partitioner=lambda shape, dtype: [2, 1])

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    self._create_prev_run_var(
        "fruit_output_layer",
        shape=[4, 2],
        initializer=[[0.5, 0.3], [1., 0.8], [1.5, 1.2], [2., 2.3]],
        partitioner=lambda shape, dtype: [2, 1])

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    self._create_prev_run_var(
        "fruit_output_layer",
        shape=[4, 2],
        initializer=[[0.5, 0.3], [1., 0.8], [1.5, 1.2], [2., 2.3]],
        partitioner=lambda shape, dtype: [2, 1])

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        fruit_weights = variable_scope.get_variable(
            "fruit_weights",
            shape=[4, 1],
            initializer=[[0.], [0.], [0.], [0.]],
            partitioner=lambda shape, dtype: [2, 1])

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        fruit_weights = variable_scope.get_variable(
            "new_scope/fruit_weights",
            shape=[4, 1],
            initializer=[[0.], [0.], [0.], [0.]],
            partitioner=lambda shape, dtype: [2, 1])

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Identical blocks of code found in 2 locations. Consider refactoring.

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    self._create_prev_run_var(
        "fruit_weights",
        shape=[4, 1],
        initializer=[[0.5], [1.], [1.5], [2.]],
        partitioner=lambda shape, dtype: [2, 1])

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Identical blocks of code found in 2 locations. Consider refactoring.

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    self._create_prev_run_var(
        "fruit_weights",
        shape=[4, 1],
        initializer=[[0.5], [1.], [1.5], [2.]],
        partitioner=lambda shape, dtype: [2, 1])

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        vocab_info = ws_util.VocabInfo(
            new_vocab=sc_vocab.vocabulary_file,
            new_vocab_size=sc_vocab.vocabulary_size,
            num_oov_buckets=sc_vocab.num_oov_buckets,
            old_vocab=prev_vocab_path,

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        new_val = np.concatenate(
            [fruit_weights[0].eval(sess), fruit_weights[1].eval(sess)], axis=0)

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        new_val = np.concatenate(
            [fruit_weights[0].eval(sess), fruit_weights[1].eval(sess)], axis=0)

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        vocab_info = ws_util.VocabInfo(
            new_vocab=sc_vocab.vocabulary_file,
            new_vocab_size=sc_vocab.vocabulary_size,
            num_oov_buckets=sc_vocab.num_oov_buckets,
            old_vocab=prev_vocab_path,

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        x = variable_scope.get_variable(
            "x",
            shape=[4, 1],
            initializer=ones(),
            partitioner=lambda shape, dtype: [2, 1])

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    x = variable_scope.get_variable(
        "x",
        shape=[4, 1],
        initializer=ones(),
        partitioner=lambda shape, dtype: [2, 1])

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            sc_vocab: [
                np.array([[3.], [2.], [1.]]),
                np.array([[0.5], [0.], [0.]])

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            sc_vocab: [
                np.array([[3.], [2.], [1.]]),
                np.array([[0.5], [0.], [0.]])

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        with variable_scope.variable_scope("", partitioner=_partitioner):
          # Create the variables.
          fc.input_layer(
              features=self._create_dummy_inputs(),

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        with variable_scope.variable_scope("", partitioner=_partitioner):
          # Create the variables.
          fc.linear_model(
              features=self._create_dummy_inputs(),

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        self.assertAllClose([[2.], [1.5], [1.]],
                            fruit_weights_vars[0].eval(sess))

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        self.assertAllClose([[0.5], [0.], [0.]],
                            fruit_weights_vars[1].eval(sess))

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        self.assertAllClose([[2.], [1.5], [1.]],
                            fruit_weights_vars[0].eval(sess))

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        self.assertAllClose([[0.5], [0.], [0.]],
                            fruit_weights_vars[1].eval(sess))

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