def testInitializerDifferent(self):
   for dtype in [dtypes.float32, dtypes.float64]:
     init1 = init_ops.uniform_unit_scaling_initializer(seed=1, dtype=dtype)
     init2 = init_ops.uniform_unit_scaling_initializer(seed=2, dtype=dtype)
     init3 = init_ops.uniform_unit_scaling_initializer(
         1.5, seed=1, dtype=dtype)
     self.assertFalse(identicaltest(self, init1, init2))
     self.assertFalse(identicaltest(self, init1, init3))
     self.assertFalse(identicaltest(self, init2, init3))

 def testInitializerIdentical(self):
   for dtype in [dtypes.float32, dtypes.float64]:
     init1 = init_ops.uniform_unit_scaling_initializer(seed=1, dtype=dtype)
     init2 = init_ops.uniform_unit_scaling_initializer(seed=1, dtype=dtype)
     self.assertTrue(identicaltest(self, init1, init2))
     init3 = init_ops.uniform_unit_scaling_initializer(
         1.5, seed=1, dtype=dtype)
     init4 = init_ops.uniform_unit_scaling_initializer(
         1.5, seed=1, dtype=dtype)
     self.assertTrue(identicaltest(self, init3, init4))

 def testZeroSize(self):
   shape = [0, 2]
   with self.test_session():
     x = variable_scope.get_variable(
         "x",
         shape=shape,
         initializer=init_ops.uniform_unit_scaling_initializer())
     self.assertAllEqual(shape, x.eval().shape)

 def testZeroSize(self):
   shape = [0, 2]
   with self.cached_session():
     x = variable_scope.get_variable(
         "x",
         shape=shape,
         initializer=init_ops.uniform_unit_scaling_initializer())
     variables.global_variables_initializer().run()
     self.assertAllEqual(shape, self.evaluate(x).shape)

 def testDuplicatedInitializer(self):
   init = init_ops.uniform_unit_scaling_initializer()
   self.assertFalse(duplicated_initializer(self, init, 1))

  def _get_single_variable(self, name, shape=None, dtype=dtypes.float32,
                           initializer=None, regularizer=None, reuse=None,
                           trainable=True, collections=None,
                           caching_device=None, validate_shape=True):
    """Get or create a single Variable (e.g. a shard or entire variable).

    See the documentation of get_variable above (ignore partitioning components)
    for details.

    Args:
      name: see get_variable.
      shape: see get_variable.
      dtype: see get_variable.
      initializer: see get_variable.
      regularizer: see get_variable.
      reuse: see get_variable.
      trainable: see get_variable.
      collections: see get_variable.
      caching_device: see get_variable.
      validate_shape: see get_variable.

    Returns:
      A Variable.  See documentation of get_variable above.

    Raises:
      ValueError: See documentation of get_variable above.
    """

    # Set to true if initializer is a constant.
    initializing_from_value = False
    if initializer is not None and isinstance(initializer, ops.Tensor):
      initializing_from_value = True
    if shape is not None and initializing_from_value:
      raise ValueError("If initializer is a constant, do not specify shape.")

    should_check = reuse is not None
    dtype = dtypes.as_dtype(dtype)
    shape = tensor_shape.as_shape(shape)

    if name in self._vars:
      # Here we handle the case when returning an existing variable.
      if should_check and not reuse:
        tb = self._vars[name].op.traceback[::-1]
        # Throw away internal tf entries and only take a few lines.
        tb = [x for x in tb if "tensorflow/python" not in x[0]][:3]
        raise ValueError("Variable %s already exists, disallowed."
                         " Did you mean to set reuse=True in VarScope? "
                         "Originally defined at:\n\n%s" % (
                             name, "".join(traceback.format_list(tb))))
      found_var = self._vars[name]
      if not shape.is_compatible_with(found_var.get_shape()):
        raise ValueError("Trying to share variable %s, but specified shape %s"
                         " and found shape %s." % (name, shape,
                                                   found_var.get_shape()))
      if not dtype.is_compatible_with(found_var.dtype):
        dtype_str = dtype.name
        found_type_str = found_var.dtype.name
        raise ValueError("Trying to share variable %s, but specified dtype %s"
                         " and found dtype %s." % (name, dtype_str,
                                                   found_type_str))
      return found_var

    # The code below handles only the case of creating a new variable.
    if should_check and reuse:
      raise ValueError("Variable %s does not exist, disallowed."
                       " Did you mean to set reuse=None in VarScope?" % name)
    if not shape.is_fully_defined() and not initializing_from_value:
      raise ValueError("Shape of a new variable (%s) must be fully defined, "
                       "but instead was %s." % (name, shape))

    # Create the tensor to initialize the variable.
    if initializer is None:
      initializer = init_ops.uniform_unit_scaling_initializer()
    # Clear control dependencies while creating the initializer.
    with ops.control_dependencies(None):
      if initializing_from_value:
        init_val = initializer
        variable_dtype = None
      else:
        init_val = lambda: initializer(shape.as_list(), dtype=dtype)
        variable_dtype = dtype.base_dtype

    # Create the variable.
    v = variables.Variable(initial_value=init_val,
                           name=name,
                           trainable=trainable,
                           collections=collections,
                           caching_device=caching_device,
                           dtype=variable_dtype,
                           validate_shape=validate_shape)
    self._vars[name] = v
    logging.info("Created variable %s with shape %s and init %s", v.name,
                 format(shape), initializer)

    # Run the regularizer if requested and save the resulting loss.
    if regularizer:
      with ops.name_scope(name + "/Regularizer/"):
        loss = regularizer(v)
      if loss is not None:
        logging.info("Applied regularizer to %s and added the result %s to "
#.........这里部分代码省略.........

#.........这里部分代码省略.........
    if name in self._vars:
      raise ValueError(
          "A partitioner was provided, but an unpartitioned version of the "
          "variable was found: %s.  Perhaps a variable of the same name was "
          "already created without partitioning?" % name)

    if initializing_from_value:
      shape = initializer.get_shape()

    if not shape.is_fully_defined():
      raise ValueError("Shape of a new partitioned variable (%s) must be "
                       "fully defined, but instead was %s." % (name, shape))

    if shape.ndims < 1:
      raise ValueError("A partitioned Variable must have rank at least 1, "
                       "shape: %s" % shape)

    partitions = partitioner(shape=shape, dtype=dtype)

    if not isinstance(partitions, collections_lib.Sequence):
      raise ValueError("Partitioner must return a sequence, but saw: %s"
                       % partitions)

    if len(partitions) != shape.ndims:
      raise ValueError(
          "Partitioner returned a partition list that does not match the "
          "Variable's rank: %s vs. %s" % (partitions, shape))

    if any([p < 1 for p in partitions]):
      raise ValueError(
          "Partitioner returned zero partitions for some axes: %s" % partitions)

    slice_dim, slice_shape = _compute_slice_dim_and_shape(
        shape.as_list(), partitions)

    vs = []
    num_slices = partitions[slice_dim]
    num_slices_with_excess = shape[slice_dim].value % num_slices

    slice_offset = [0] * shape.ndims

    if "%s_0" % name in self._vars:
      if "%s_%d" % (name, num_slices - 1) not in self._vars:
        raise ValueError(
            "Partitioner returned a different partitioning than what was "
            "already found.  Partitioner returned %d shards, and shard %s_0 "
            "was found, but %s_%d was not."
            % (num_slices, name, name, num_slices - 1))
      if "%s_%d" % (name, num_slices) in self._vars:
        raise ValueError(
            "Partitioner returned a different partitioning than what was "
            "already found.  Partitioner returned %d shards, and shard %s_0 "
            "was found, but so was the extra shard %s_%d."
            % (num_slices, name, name, num_slices))

    for i in xrange(num_slices):
      var_shape = slice_shape[:]
      var_offset = slice_offset[:]
      if i < num_slices_with_excess:
        var_shape[slice_dim] += 1
      slice_offset[slice_dim] += var_shape[slice_dim]

      with ops.op_scope([], name + "/PartitionedVariableList"):
        if initializer is None:
          init = init_ops.uniform_unit_scaling_initializer(
              full_shape=shape.as_list())
          init_shape = var_shape
        elif callable(initializer):
          init = initializer
          init_shape = var_shape
        elif isinstance(initializer, ops.Tensor):
          init = array_ops.slice(initializer, var_offset, var_shape)
          # Use the dtype of the given tensor.
          dtype = init.dtype.base_dtype
          init_shape = None
        else:
          init = ops.convert_to_tensor(initializer, dtype=dtype)
          init = array_ops.slice(init, var_offset, var_shape)
          init_shape = None

      with ops.name_scope(None):
        var = self._get_single_variable(
            name="%s_%d" % (name, i),
            shape=init_shape,
            dtype=dtype,
            initializer=init,
            regularizer=regularizer,
            reuse=reuse,
            trainable=trainable,
            collections=collections,
            caching_device=caching_device,
            validate_shape=validate_shape)

      # pylint: disable=protected-access
      var._set_save_slice_info(variables.Variable.SaveSliceInfo(
          name, shape.as_list(), var_offset, var_shape))
      # pylint: enable=protected-access
      vs.append(var)

    return (vs, partitions)

  def get_variable(self, name, shape=None, dtype=types.float32,
                   initializer=None, reuse=None, trainable=True,
                   collections=None):
    """Gets an existing variable with these parameters or create a new one.

    If a variable with the given name is already stored, we return the stored
    variable. Otherwise, we create a new one.

    Set `reuse` to `True` when you only want to reuse existing Variables.
    Set `reuse` to `False` when you only want to create new Variables.
    If `reuse` is `None` (the default), both new and existing variables are
    returned.

    If initializer is `None` (the default), the default initializer passed in
    the constructor is used. If that one is `None` too, we use a new
    `UniformUnitScalingInitializer`.

    Args:
      name: the name of the new or existing variable.
      shape: shape of the new or existing variable.
      dtype: type of the new or existing variable (defaults to `DT_FLOAT`).
      initializer: initializer for the variable.
      reuse: a Boolean or `None`. Controls reuse or creation of variables.
      trainable: If `True` also add the variable to the graph collection
        `GraphKeys.TRAINABLE_VARIABLES` (see variables.Variable).
      collections: List of graph collections keys to add the Variable to.
        Defaults to `[GraphKeys.VARIABLES]` (see variables.Variable).

    Returns:
      The created or existing variable.

    Raises:
      ValueError: when creating a new variable and shape is not declared,
        when reusing a variable and specifying a conflicting shape,
        or when violating reuse during variable creation.
    """
    should_check = reuse is not None
    dtype = types.as_dtype(dtype)
    shape = tensor_shape.as_shape(shape)
    if name in self._vars:
      # Here we handle the case when returning an existing variable.
      if should_check and not reuse:
        raise ValueError("Over-sharing: Variable %s already exists, disallowed."
                         " Did you mean to set reuse=True in VarScope?" % name)
      found_var = self._vars[name]
      if not shape.is_compatible_with(found_var.get_shape()):
        raise ValueError("Trying to share variable %s, but specified shape %s"
                         " and found shape %s." % (name, shape,
                                                   found_var.get_shape()))
      if not dtype.is_compatible_with(found_var.dtype):
        dtype_str = dtype.name
        found_type_str = found_var.dtype.name
        raise ValueError("Trying to share variable %s, but specified dtype %s"
                         " and found dtype %s." % (name, dtype_str,
                                                   found_type_str))
      return found_var

    # The code below handles only the case of creating a new variable.
    if should_check and reuse:
      raise ValueError("Under-sharing: Variable %s does not exist, disallowed."
                       " Did you mean to set reuse=None in VarScope?" % name)
    if not shape.is_fully_defined():
      raise ValueError("Shape of a new variable (%s) must be fully defined, "
                       "but instead was %s." % (name, shape))
    if initializer is None:
      initializer = init_ops.uniform_unit_scaling_initializer()
    with ops.name_scope(name + "/Initializer/"):
      init_val = initializer(shape.as_list(), dtype=dtype)
    v = variables.Variable(init_val, name=name, trainable=trainable,
                           collections=collections)
    self._vars[name] = v
    logging.info("Created variable %s with shape %s and init %s", v.name,
                 format(shape), initializer)
    return v

  def get_variable(self, name, shape=None, dtype=dtypes.float32,
                   initializer=None, regularizer=None, reuse=None,
                   trainable=True, collections=None, caching_device=None):
    """Gets an existing variable with these parameters or create a new one.

    If a variable with the given name is already stored, we return the stored
    variable. Otherwise, we create a new one.

    Set `reuse` to `True` when you only want to reuse existing Variables.
    Set `reuse` to `False` when you only want to create new Variables.
    If `reuse` is `None` (the default), both new and existing variables are
    returned.

    If initializer is `None` (the default), the default initializer passed in
    the constructor is used. If that one is `None` too, we use a new
    `UniformUnitScalingInitializer`. If initializer is a Tensor, we use
    it as a value and derive the shape from the initializer.

    Args:
      name: the name of the new or existing variable.
      shape: shape of the new or existing variable.
      dtype: type of the new or existing variable (defaults to `DT_FLOAT`).
      initializer: initializer for the variable.
      regularizer: a (Tensor -> Tensor or None) function; the result of
        applying it on a newly created variable will be added to the collection
        GraphKeys.REGULARIZATION_LOSSES and can be used for regularization.
      reuse: a Boolean or `None`. Controls reuse or creation of variables.
      trainable: If `True` also add the variable to the graph collection
        `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
      collections: List of graph collections keys to add the Variable to.
        Defaults to `[GraphKeys.VARIABLES]` (see tf.Variable).
      caching_device: Optional device string or function describing where the
        Variable should be cached for reading.  Defaults to the Variable's
        device.  If not `None`, caches on another device.  Typical use is to
        cache on the device where the Ops using the Variable reside, to
        deduplicate copying through `Switch` and other conditional statements.

    Returns:
      The created or existing variable.

    Raises:
      ValueError: when creating a new variable and shape is not declared,
        when reusing a variable and specifying a conflicting shape,
        or when violating reuse during variable creation.
    """
    # Set to true if initializer is a constant.
    initializing_from_value = False
    if initializer is not None and isinstance(initializer, ops.Tensor):
      initializing_from_value = True
    if shape is not None and initializing_from_value:
      raise ValueError("If initializer is a constant, do not specify shape.")

    should_check = reuse is not None
    dtype = dtypes.as_dtype(dtype)
    shape = tensor_shape.as_shape(shape)

    if name in self._vars:
      # Here we handle the case when returning an existing variable.
      if should_check and not reuse:
        tb = self._vars[name].op.traceback[::-1]
        # Throw away internal tf entries and only take a few lines.
        tb = [x for x in tb if "tensorflow/python" not in x[0]][:3]
        raise ValueError("Variable %s already exists, disallowed."
                         " Did you mean to set reuse=True in VarScope? "
                         "Originally defined at:\n\n%s" % (
                             name, "".join(traceback.format_list(tb))))
      found_var = self._vars[name]
      if not shape.is_compatible_with(found_var.get_shape()):
        raise ValueError("Trying to share variable %s, but specified shape %s"
                         " and found shape %s." % (name, shape,
                                                   found_var.get_shape()))
      if not dtype.is_compatible_with(found_var.dtype):
        dtype_str = dtype.name
        found_type_str = found_var.dtype.name
        raise ValueError("Trying to share variable %s, but specified dtype %s"
                         " and found dtype %s." % (name, dtype_str,
                                                   found_type_str))
      return found_var

    # The code below handles only the case of creating a new variable.
    if should_check and reuse:
      raise ValueError("Variable %s does not exist, disallowed."
                       " Did you mean to set reuse=None in VarScope?" % name)
    if not shape.is_fully_defined() and not initializing_from_value:
      raise ValueError("Shape of a new variable (%s) must be fully defined, "
                       "but instead was %s." % (name, shape))

    # Create the tensor to initialize the variable.
    if initializer is None:
      initializer = init_ops.uniform_unit_scaling_initializer()
    # Clear control dependencies while creating the initializer.
    with ops.control_dependencies(None):
      if initializing_from_value:
        init_val = initializer
      else:
        with ops.name_scope(name + "/Initializer/"):
          init_val = initializer(shape.as_list(), dtype=dtype)

    # Create the variable.
    v = variables.Variable(init_val, name=name, trainable=trainable,
#.........这里部分代码省略.........