Python math_ops.divide方法代碼示例

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import divide [as 別名]
def get_drop_fraction(self, global_step, is_mask_update_iter_op):
    """Returns a constant or annealing drop_fraction op."""
    if self._drop_fraction_anneal == 'constant':
      drop_frac = self._drop_fraction_initial_value
    elif self._drop_fraction_anneal == 'cosine':
      decay_steps = self._end_step - self._begin_step
      drop_frac = learning_rate_decay.cosine_decay(
          self._drop_fraction_initial_value, global_step, decay_steps,
          name='cosine_drop_fraction')
    elif self._drop_fraction_anneal.startswith('exponential'):
      exponent = extract_number(self._drop_fraction_anneal)
      div_dtype = self._drop_fraction_initial_value.dtype
      power = math_ops.divide(
          math_ops.cast(global_step - self._begin_step, div_dtype),
          math_ops.cast(self._end_step - self._begin_step, div_dtype),
          )
      drop_frac = math_ops.multiply(
          self._drop_fraction_initial_value,
          math_ops.pow(1 - power, exponent),
          name='%s_drop_fraction' % self._drop_fraction_anneal)
    else:
      raise ValueError('drop_fraction_anneal: %s is not valid' %
                       self._drop_fraction_anneal)
    return array_ops.where(is_mask_update_iter_op, drop_frac,
                           array_ops.zeros_like(drop_frac)) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import divide [as 別名]
def __init__(self, embedding, start_tokens, end_token,
               softmax_temperature=None, seed=None):
    """Initializer.
    Args:
      embedding: A callable that takes a vector tensor of `ids` (argmax ids),
        or the `params` argument for `embedding_lookup`. The returned tensor
        will be passed to the decoder input.
      start_tokens: `int32` vector shaped `[batch_size]`, the start tokens.
      end_token: `int32` scalar, the token that marks end of decoding.
      softmax_temperature: (Optional) `float32` scalar, value to divide the
        logits by before computing the softmax. Larger values (above 1.0) result
        in more random samples, while smaller values push the sampling
        distribution towards the argmax. Must be strictly greater than 0.
        Defaults to 1.0.
      seed: (Optional) The sampling seed.
    Raises:
      ValueError: if `start_tokens` is not a 1D tensor or `end_token` is not a
        scalar.
    """
    super(MySampleEmbeddingHelper, self).__init__(
        embedding, start_tokens, end_token)
    self._softmax_temperature = softmax_temperature
    self._seed = seed 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import divide [as 別名]
def sample(self, time, outputs, state, name=None):
    """sample for SampleEmbeddingHelper."""
    del time, state  # unused by sample_fn
    # Outputs are logits, we sample instead of argmax (greedy).
    if not isinstance(outputs, ops.Tensor):
      raise TypeError("Expected outputs to be a single Tensor, got: %s" %
                      type(outputs))
    if self._softmax_temperature is None:
      logits = outputs
    else:
      #logits = outputs / self._softmax_temperature
      logits = math_ops.divide(outputs, self._softmax_temperature)

    sample_id_sampler = categorical.Categorical(logits=logits)
    sample_ids = sample_id_sampler.sample(seed=self._seed)

    return sample_ids 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import divide [as 別名]
def testDebugNumericSummaryMuteOnHealthyAndCustomBoundsWork(self):
    with session.Session() as sess:
      a = variables.Variable([10.0, 10.0], name="a")
      b = variables.Variable([10.0, 2.0], name="b")

      x = math_ops.add(a, b, name="x")  # [20.0, 12.0]
      y = math_ops.divide(x, b, name="y")  # [2.0, 6.0]

      sess.run(variables.global_variables_initializer())

      # Here, validate=False is necessary to avoid causality check error.
      # TODO(cais): Maybe let DebugDumpDir constructor automatically ignore
      #   debug ops with mute_if_healthy=false attribute during validation.
      _, dump = self._debug_run_and_get_dump(
          sess, y, debug_ops=[
              "DebugNumericSummary(mute_if_healthy=true; upper_bound=11.0)"],
          validate=False)

      self.assertEqual(1, dump.size)
      self.assertAllClose([[
          1.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 12.0, 20.0, 16.0, 16.0, 1.0,
          1.0, 2.0]], dump.get_tensors("x", 0, "DebugNumericSummary")) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import divide [as 別名]
def _safe_div(numerator, denominator, name):
    """Divides two values, returning 0 if the denominator is <= 0.
    Args:
      numerator: A real `Tensor`.
      denominator: A real `Tensor`, with dtype matching `numerator`.
      name: Name for the returned op.
    Returns:
      0 if `denominator` <= 0, else `numerator` / `denominator`
    """
    return tf.where(
        math_ops.greater(denominator, 0),
        math_ops.divide(numerator, denominator),
        tf.zeros_like(numerator),
        name=name)

# =========================================================================== #
# TF Extended metrics: TP and FP arrays.
# =========================================================================== # 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import divide [as 別名]
def safe_divide(numerator, denominator, name):
    """Divides two values, returning 0 if the denominator is <= 0.
    Args:
      numerator: A real `Tensor`.
      denominator: A real `Tensor`, with dtype matching `numerator`.
      name: Name for the returned op.
    Returns:
      0 if `denominator` <= 0, else `numerator` / `denominator`
    """
    return tf.where(
        math_ops.greater(denominator, 0),
        math_ops.divide(numerator, denominator),
        tf.zeros_like(numerator),
        name=name) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import divide [as 別名]
def testDebugNumericSummaryMuteOnHealthyAndCustomBoundsWork(self):
    with session.Session() as sess:
      a = variables.Variable([10.0, 10.0], name="a")
      b = variables.Variable([10.0, 2.0], name="b")

      x = math_ops.add(a, b, name="x")  # [20.0, 12.0]
      y = math_ops.divide(x, b, name="y")  # [2.0, 6.0]

      sess.run(variables.global_variables_initializer())

      run_metadata = config_pb2.RunMetadata()
      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=[
              "DebugNumericSummary(mute_if_healthy=true; upper_bound=11.0)"],
          debug_urls=self._debug_urls())
      sess.run(y, options=run_options, run_metadata=run_metadata)

      dump = debug_data.DebugDumpDir(
          self._dump_root, partition_graphs=run_metadata.partition_graphs,
          validate=False)
      # Here, validate=False is necessary to avoid causality check error.
      # TODO(cais): Maybe let DebugDumpDir constructor automatically ignore
      #   debug ops with mute_if_healthy=false attribute during validation.

      self.assertEqual(1, dump.size)
      self.assertAllClose(
          [[1.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 12.0, 20.0, 16.0, 16.0]],
          dump.get_tensors("x", 0, "DebugNumericSummary")) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import divide [as 別名]
def _safe_div(numerator, denominator, name):
    """Divides two values, returning 0 if the denominator is <= 0.
    Args:
      numerator: A real `Tensor`.
      denominator: A real `Tensor`, with dtype matching `numerator`.
      name: Name for the returned op.
    Returns:
      0 if `denominator` <= 0, else `numerator` / `denominator`
    """
    return tf.where(
        math_ops.greater(denominator, 0),
        math_ops.divide(numerator, denominator),
        tf.zeros_like(numerator),
        name=name) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import divide [as 別名]
def _test_div(data, fused_activation_function=None):
    """ One iteration of divide """
    return _test_elemwise(math_ops.divide, data, fused_activation_function)
#######################################################################
# Power
# ----- 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import divide [as 別名]
def testDebugNumericSummaryMuteOnHealthyMutesOnlyHealthyTensorDumps(self):
    with session.Session(config=no_rewrite_session_config()) as sess:
      a = variables.Variable(10.0, name="a")
      b = variables.Variable(0.0, name="b")
      c = variables.Variable(0.0, name="c")

      x = math_ops.divide(a, b, name="x")
      y = math_ops.multiply(x, c, name="y")

      sess.run(variables.global_variables_initializer())

      # Here, validate=False is necessary to avoid causality check error.
      # TODO(cais): Maybe let DebugDumpDir constructor automatically ignore
      #   debug ops with mute_if_healthy=false attribute during validation.
      _, dump = self._debug_run_and_get_dump(
          sess, y, debug_ops=["DebugNumericSummary(mute_if_healthy=true)"],
          validate=False)

      self.assertEqual(2, dump.size)
      self.assertAllClose([[
          1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, np.inf, -np.inf, np.nan,
          np.nan, 1.0, 0.0
      ]], dump.get_tensors("x", 0, "DebugNumericSummary"))
      self.assertAllClose([[
          1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, np.inf, -np.inf, np.nan,
          np.nan, 1.0, 0.0
      ]], dump.get_tensors("y", 0, "DebugNumericSummary"))

      # Another run with the default mute_if_healthy (false) value should
      # dump all the tensors.
      shutil.rmtree(self._dump_root)
      _, dump = self._debug_run_and_get_dump(
          sess, y, debug_ops=["DebugNumericSummary()"])
      self.assertEqual(8, dump.size) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import divide [as 別名]
def testDebugNumericSummaryInvalidAttributesStringAreCaught(self):
    with session.Session() as sess:
      a = variables.Variable(10.0, name="a")
      b = variables.Variable(0.0, name="b")
      c = variables.Variable(0.0, name="c")

      x = math_ops.divide(a, b, name="x")
      y = math_ops.multiply(x, c, name="y")

      sess.run(variables.global_variables_initializer())

      run_metadata = config_pb2.RunMetadata()
      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=["DebugNumericSummary(foo=1.0)"],
          debug_urls=self._debug_urls())
      with self.assertRaisesRegexp(
          errors.FailedPreconditionError,
          r"1 attribute key\(s\) were not valid for debug node "
          r"__dbg_a:0_0_DebugNumericSummary: foo"):
        sess.run(y, options=run_options, run_metadata=run_metadata)

      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=["DebugNumericSummary(foo=1.0; bar=false)"],
          debug_urls=self._debug_urls())
      with self.assertRaisesRegexp(
          errors.FailedPreconditionError,
          r"2 attribute key\(s\) were not valid for debug node "
          r"__dbg_a:0_0_DebugNumericSummary:"):
        sess.run(y, options=run_options, run_metadata=run_metadata)

      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=["DebugNumericSummary(foo=1.0; mute_if_healthy=true)"],
          debug_urls=self._debug_urls())
      with self.assertRaisesRegexp(
          errors.FailedPreconditionError,
          r"1 attribute key\(s\) were not valid for debug node "
          r"__dbg_a:0_0_DebugNumericSummary: foo"):
        sess.run(y, options=run_options, run_metadata=run_metadata) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import divide [as 別名]
def testDebugNumericSummaryMuteOnHealthyMutesOnlyHealthyTensorDumps(self):
    with session.Session() as sess:
      a = variables.Variable(10.0, name="a")
      b = variables.Variable(0.0, name="b")
      c = variables.Variable(0.0, name="c")

      x = math_ops.divide(a, b, name="x")
      y = math_ops.multiply(x, c, name="y")

      sess.run(variables.global_variables_initializer())

      run_metadata = config_pb2.RunMetadata()
      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=["DebugNumericSummary(mute_if_healthy=true)"],
          debug_urls=self._debug_urls())
      sess.run(y, options=run_options, run_metadata=run_metadata)

      dump = debug_data.DebugDumpDir(
          self._dump_root, partition_graphs=run_metadata.partition_graphs,
          validate=False)
      # Here, validate=False is necessary to avoid causality check error.
      # TODO(cais): Maybe let DebugDumpDir constructor automatically ignore
      #   debug ops with mute_if_healthy=false attribute during validation.

      self.assertEqual(2, dump.size)
      self.assertAllClose(
          [[1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, np.inf, -np.inf, np.nan,
            np.nan]],
          dump.get_tensors("x", 0, "DebugNumericSummary"))
      self.assertAllClose(
          [[1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, np.inf, -np.inf, np.nan,
            np.nan]],
          dump.get_tensors("y", 0, "DebugNumericSummary"))

      # Another run with the default mute_if_healthy (false) value should
      # dump all the tensors.
      shutil.rmtree(self._dump_root)
      run_metadata = config_pb2.RunMetadata()
      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=["DebugNumericSummary()"],
          debug_urls=self._debug_urls())
      sess.run(y, options=run_options, run_metadata=run_metadata)

      dump = debug_data.DebugDumpDir(
          self._dump_root, partition_graphs=run_metadata.partition_graphs)
      self.assertEqual(8, dump.size) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import divide [as 別名]
def testDebugNumericSummaryInvalidAttributesStringAreCaught(self):
    with session.Session(config=no_rewrite_session_config()) as sess:
      a = variables.Variable(10.0, name="a")
      b = variables.Variable(0.0, name="b")
      c = variables.Variable(0.0, name="c")

      x = math_ops.divide(a, b, name="x")
      y = math_ops.multiply(x, c, name="y")

      sess.run(variables.global_variables_initializer())

      run_metadata = config_pb2.RunMetadata()
      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=["DebugNumericSummary(foo=1.0)"],
          debug_urls=self._debug_urls())
      with self.assertRaisesRegexp(
          errors.FailedPreconditionError,
          r"1 attribute key\(s\) were not valid for debug node "
          r"__dbg_.:0_0_DebugNumericSummary: foo"):
        sess.run(y, options=run_options, run_metadata=run_metadata)

      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=["DebugNumericSummary(foo=1.0; bar=false)"],
          debug_urls=self._debug_urls())
      with self.assertRaisesRegexp(
          errors.FailedPreconditionError,
          r"2 attribute key\(s\) were not valid for debug node "
          r"__dbg_.:0_0_DebugNumericSummary:"):
        sess.run(y, options=run_options, run_metadata=run_metadata)

      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=["DebugNumericSummary(foo=1.0; mute_if_healthy=true)"],
          debug_urls=self._debug_urls())
      with self.assertRaisesRegexp(
          errors.FailedPreconditionError,
          r"1 attribute key\(s\) were not valid for debug node "
          r"__dbg_.:0_0_DebugNumericSummary: foo"):
        sess.run(y, options=run_options, run_metadata=run_metadata)