Python v1.__version__方法代碼示例

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import __version__ [as 別名]
def get_noised_result(self, sample_state, global_state):
    """See base class."""
    if LooseVersion(tf.__version__) < LooseVersion('2.0.0'):
      def add_noise(v):
        return v + tf.random.normal(
            tf.shape(input=v), stddev=global_state.stddev, dtype=v.dtype)
    else:
      random_normal = tf.random_normal_initializer(
          stddev=global_state.stddev)

      def add_noise(v):
        return v + tf.cast(random_normal(tf.shape(input=v)), dtype=v.dtype)

    if self._ledger:
      dependencies = [
          self._ledger.record_sum_query(
              global_state.l2_norm_clip, global_state.stddev)
      ]
    else:
      dependencies = []
    with tf.control_dependencies(dependencies):
      return tf.nest.map_structure(add_noise, sample_state), global_state 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import __version__ [as 別名]
def tensorflow_version_tuple():
  v = tf.__version__
  major, minor, patch = v.split('.')
  return (int(major), int(minor), patch) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import __version__ [as 別名]
def test_forward_concat_v2():
    if tf.__version__ < LooseVersion('1.4.1'):
        return

    _test_concat_v2([2, 3], [2, 3], 0)
    _test_concat_v2([10, 3, 5], [2, 3, 5], 0)
    _test_concat_v2([2, 3], [2, 3], 1)
    _test_concat_v2([5, 8], [5, 4], 1)
    _test_concat_v2([2, 8, 5], [2, 8, 6], -1)

#######################################################################
# Sigmoid
# ------- 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import __version__ [as 別名]
def test_forward_clip_by_value():
    '''test ClipByValue op'''
    if tf.__version__ < LooseVersion('1.9'):
        _test_forward_clip_by_value((4,), .1, 5., 'float32')
        _test_forward_clip_by_value((4, 4), 1, 5, 'int32')

#######################################################################
# Multi Input to graph
# -------------------- 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import __version__ [as 別名]
def test_forward_zeros_like():
    if tf.__version__ < LooseVersion('1.2'):
        _test_forward_zeros_like((2, 3), "int32")
        _test_forward_zeros_like((2, 3, 5), "int8")
        _test_forward_zeros_like((2, 3, 5, 7), "uint16")
        _test_forward_zeros_like((2, 3, 11), "float32")
        _test_forward_zeros_like((2, 3, 11), "float64") 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import __version__ [as 別名]
def li_regularizer(scale, scope=None):
  """li regularization removes the neurons of previous layer, `i` represents `inputs`.\n
  Returns a function that can be used to apply group li regularization to weights.\n
  The implementation follows `TensorFlow contrib <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/regularizers.py>`_.

  Parameters
  ----------
  scale : float
    A scalar multiplier `Tensor`. 0.0 disables the regularizer.
  scope: An optional scope name for TF12+.

  Returns
  --------
  A function with signature `li(weights, name=None)` that apply Li regularization.

  Raises
  ------
  ValueError : if scale is outside of the range [0.0, 1.0] or if scale is not a float.
  """
  import numbers
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import standard_ops
  # from tensorflow.python.platform import tf_logging as logging

  if isinstance(scale, numbers.Integral):
    raise ValueError('scale cannot be an integer: %s' % scale)
  if isinstance(scale, numbers.Real):
    if scale < 0.:
      raise ValueError('Setting a scale less than 0 on a regularizer: %g' %
                       scale)
    if scale >= 1.:
      raise ValueError('Setting a scale greater than 1 on a regularizer: %g' %
                       scale)
    if scale == 0.:
      logging.info('Scale of 0 disables regularizer.')
      return lambda _, name=None: None

  def li(weights, name=None):
    """Applies li regularization to weights."""
    with tf.name_scope('li_regularizer') as scope:
        my_scale = ops.convert_to_tensor(scale,
                                           dtype=weights.dtype.base_dtype,
                                           name='scale')
        if tf.__version__ <= '0.12':
            standard_ops_fn = standard_ops.mul
        else:
            standard_ops_fn = standard_ops.multiply
            return standard_ops_fn(
              my_scale,
              standard_ops.reduce_sum(standard_ops.sqrt(standard_ops.reduce_sum(tf.square(weights), 1))),
              name=scope)
  return li 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import __version__ [as 別名]
def lo_regularizer(scale, scope=None):
  """lo regularization removes the neurons of current layer, `o` represents `outputs`\n
  Returns a function that can be used to apply group lo regularization to weights.\n
  The implementation follows `TensorFlow contrib <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/regularizers.py>`_.

  Parameters
  ----------
  scale : float
    A scalar multiplier `Tensor`. 0.0 disables the regularizer.
  scope: An optional scope name for TF12+.

  Returns
  -------
  A function with signature `lo(weights, name=None)` that apply Lo regularization.

  Raises
  ------
  ValueError : If scale is outside of the range [0.0, 1.0] or if scale is not a float.
  """
  import numbers
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import standard_ops
  # from tensorflow.python.platform import tf_logging as logging

  if isinstance(scale, numbers.Integral):
    raise ValueError('scale cannot be an integer: %s' % scale)
  if isinstance(scale, numbers.Real):
    if scale < 0.:
      raise ValueError('Setting a scale less than 0 on a regularizer: %g' %
                       scale)
    if scale >= 1.:
      raise ValueError('Setting a scale greater than 1 on a regularizer: %g' %
                       scale)
    if scale == 0.:
      logging.info('Scale of 0 disables regularizer.')
      return lambda _, name=None: None

  def lo(weights, name='lo_regularizer'):
    """Applies group column regularization to weights."""
    with tf.name_scope(name) as scope:
        my_scale = ops.convert_to_tensor(scale,
                                       dtype=weights.dtype.base_dtype,
                                       name='scale')
        if tf.__version__ <= '0.12':
            standard_ops_fn = standard_ops.mul
        else:
            standard_ops_fn = standard_ops.multiply
        return standard_ops_fn(
          my_scale,
          standard_ops.reduce_sum(standard_ops.sqrt(standard_ops.reduce_sum(tf.square(weights), 0))),
          name=scope)
  return lo 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import __version__ [as 別名]
def maxnorm_regularizer(scale=1.0, scope=None):
  """Max-norm regularization returns a function that can be used
  to apply max-norm regularization to weights.
  About max-norm: `wiki <https://en.wikipedia.org/wiki/Matrix_norm#Max_norm>`_.\n
  The implementation follows `TensorFlow contrib <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/regularizers.py>`_.

  Parameters
  ----------
  scale : float
    A scalar multiplier `Tensor`. 0.0 disables the regularizer.
  scope: An optional scope name.

  Returns
  ---------
  A function with signature `mn(weights, name=None)` that apply Lo regularization.

  Raises
  --------
  ValueError : If scale is outside of the range [0.0, 1.0] or if scale is not a float.
  """
  import numbers
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import standard_ops

  if isinstance(scale, numbers.Integral):
    raise ValueError('scale cannot be an integer: %s' % scale)
  if isinstance(scale, numbers.Real):
    if scale < 0.:
      raise ValueError('Setting a scale less than 0 on a regularizer: %g' %
                       scale)
    # if scale >= 1.:
    #   raise ValueError('Setting a scale greater than 1 on a regularizer: %g' %
    #                    scale)
    if scale == 0.:
      logging.info('Scale of 0 disables regularizer.')
      return lambda _, name=None: None

  def mn(weights, name='max_regularizer'):
    """Applies max-norm regularization to weights."""
    with tf.name_scope(name) as scope:
          my_scale = ops.convert_to_tensor(scale,
                                           dtype=weights.dtype.base_dtype,
                                           name='scale')
          if tf.__version__ <= '0.12':
              standard_ops_fn = standard_ops.mul
          else:
              standard_ops_fn = standard_ops.multiply
          return standard_ops_fn(my_scale, standard_ops.reduce_max(standard_ops.abs(weights)), name=scope)
  return mn 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import __version__ [as 別名]
def maxnorm_o_regularizer(scale, scope):
  """Max-norm output regularization removes the neurons of current layer.\n
  Returns a function that can be used to apply max-norm regularization to each column of weight matrix.\n
  The implementation follows `TensorFlow contrib <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/regularizers.py>`_.

  Parameters
  ----------
  scale : float
    A scalar multiplier `Tensor`. 0.0 disables the regularizer.
  scope: An optional scope name.

  Returns
  ---------
  A function with signature `mn_o(weights, name=None)` that apply Lo regularization.

  Raises
  ---------
  ValueError : If scale is outside of the range [0.0, 1.0] or if scale is not a float.
  """
  import numbers
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import standard_ops

  if isinstance(scale, numbers.Integral):
    raise ValueError('scale cannot be an integer: %s' % scale)
  if isinstance(scale, numbers.Real):
    if scale < 0.:
      raise ValueError('Setting a scale less than 0 on a regularizer: %g' %
                       scale)
    # if scale >= 1.:
    #   raise ValueError('Setting a scale greater than 1 on a regularizer: %g' %
    #                    scale)
    if scale == 0.:
      logging.info('Scale of 0 disables regularizer.')
      return lambda _, name=None: None

  def mn_o(weights, name='maxnorm_o_regularizer'):
     """Applies max-norm regularization to weights."""
     with tf.name_scope(name) as scope:
          my_scale = ops.convert_to_tensor(scale,
                                           dtype=weights.dtype.base_dtype,
                                                   name='scale')
          if tf.__version__ <= '0.12':
             standard_ops_fn = standard_ops.mul
          else:
             standard_ops_fn = standard_ops.multiply
          return standard_ops_fn(my_scale, standard_ops.reduce_sum(standard_ops.reduce_max(standard_ops.abs(weights), 0)), name=scope)
  return mn_o 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import __version__ [as 別名]
def maxnorm_i_regularizer(scale, scope=None):
  """Max-norm input regularization removes the neurons of previous layer.\n
  Returns a function that can be used to apply max-norm regularization to each row of weight matrix.\n
  The implementation follows `TensorFlow contrib <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/regularizers.py>`_.

  Parameters
  ----------
  scale : float
    A scalar multiplier `Tensor`. 0.0 disables the regularizer.
  scope: An optional scope name.

  Returns
  ---------
  A function with signature `mn_i(weights, name=None)` that apply Lo regularization.

  Raises
  ---------
  ValueError : If scale is outside of the range [0.0, 1.0] or if scale is not a float.
  """
  import numbers
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import standard_ops

  if isinstance(scale, numbers.Integral):
    raise ValueError('scale cannot be an integer: %s' % scale)
  if isinstance(scale, numbers.Real):
    if scale < 0.:
      raise ValueError('Setting a scale less than 0 on a regularizer: %g' %
                       scale)
    # if scale >= 1.:
    #   raise ValueError('Setting a scale greater than 1 on a regularizer: %g' %
    #                    scale)
    if scale == 0.:
      logging.info('Scale of 0 disables regularizer.')
      return lambda _, name=None: None

  def mn_i(weights, name='maxnorm_i_regularizer'):
     """Applies max-norm regularization to weights."""
     with tf.name_scope(name) as scope:
          my_scale = ops.convert_to_tensor(scale,
                                           dtype=weights.dtype.base_dtype,
                                                   name='scale')
          if tf.__version__ <= '0.12':
             standard_ops_fn = standard_ops.mul
          else:
             standard_ops_fn = standard_ops.multiply
          return standard_ops_fn(my_scale, standard_ops.reduce_sum(standard_ops.reduce_max(standard_ops.abs(weights), 1)), name=scope)
  return mn_i





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