Python nn.dropout方法代码示例

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import dropout [as 别名]
def dropout(x, level, noise_shape=None, seed=None):
  """Sets entries in `x` to zero at random, while scaling the entire tensor.

  Arguments:
      x: tensor
      level: fraction of the entries in the tensor
          that will be set to 0.
      noise_shape: shape for randomly generated keep/drop flags,
          must be broadcastable to the shape of `x`
      seed: random seed to ensure determinism.

  Returns:
      A tensor.
  """
  retain_prob = 1. - level
  if seed is None:
    seed = np.random.randint(10e6)
  # the dummy 1. works around a TF bug
  # (float32_ref vs. float32 incomptability)
  return nn.dropout(x * 1., retain_prob, noise_shape, seed=seed) 

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import dropout [as 别名]
def dropout(x, level, noise_shape=None, seed=None):
  """Sets entries in `x` to zero at random, while scaling the entire tensor.

  Arguments:
      x: tensor
      level: fraction of the entries in the tensor
          that will be set to 0.
      noise_shape: shape for randomly generated keep/drop flags,
          must be broadcastable to the shape of `x`
      seed: random seed to ensure determinism.

  Returns:
      A tensor.
  """
  retain_prob = 1. - level
  if seed is None:
    seed = np.random.randint(10e6)
  # the dummy 1. works around a TF bug
  # (float32_ref vs. float32 incompatibility)
  return nn.dropout(x * 1., retain_prob, noise_shape, seed=seed) 

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import dropout [as 别名]
def dropout(inputs,
            keep_prob=0.5,
            noise_shape=None,
            is_training=True,
            outputs_collections=None,
            scope=None,
            seed=None):
  """Returns a dropout op applied to the input.

  With probability `keep_prob`, outputs the input element scaled up by
  `1 / keep_prob`, otherwise outputs `0`.  The scaling is so that the expected
  sum is unchanged.

  Args:
    inputs: The tensor to pass to the nn.dropout op.
    keep_prob: A scalar `Tensor` with the same type as x. The probability that
      each element is kept.
    noise_shape: A 1-D `Tensor` of type `int32`, representing the shape for
      randomly generated keep/drop flags.
    is_training: A bool `Tensor` indicating whether or not the model is in
      training mode. If so, dropout is applied and values scaled. Otherwise,
      inputs is returned.
    outputs_collections: Collection to add the outputs.
    scope: Optional scope for name_scope.
    seed: A Python integer. Used to create random seeds. See
      `tf.compat.v1.set_random_seed` for behavior.

  Returns:
    A tensor representing the output of the operation.
  """
  with variable_scope.variable_scope(
      scope, 'Dropout', [inputs], custom_getter=_model_variable_getter) as sc:
    inputs = ops.convert_to_tensor(inputs)
    layer = core_layers.Dropout(
        rate=1 - keep_prob,
        noise_shape=noise_shape,
        seed=seed,
        name=sc.name,
        _scope=sc)
    outputs = layer.apply(inputs, training=is_training)
    return utils.collect_named_outputs(outputs_collections, sc.name, outputs) 

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import dropout [as 别名]
def call(self, inputs, training=False):
    def dropped_inputs():
      return nn.dropout(inputs, 1  - self.rate,
                        noise_shape=self._get_noise_shape(inputs),
                        seed=self.seed)
    return utils.smart_cond(training,
                            dropped_inputs,
                            lambda: array_ops.identity(inputs)) 

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import dropout [as 别名]
def dropout(inputs,
            rate=0.5,
            noise_shape=None,
            seed=None,
            training=False,
            name=None):
  """Applies Dropout to the input.

  Dropout consists in randomly setting a fraction `rate` of input units to 0
  at each update during training time, which helps prevent overfitting.
  The units that are kept are scaled by `1 / (1 - rate)`, so that their
  sum is unchanged at training time and inference time.

  Arguments:
    inputs: Tensor input.
    rate: The dropout rate, between 0 and 1. E.g. "rate=0.1" would drop out
      10% of input units.
    noise_shape: 1D tensor of type `int32` representing the shape of the
      binary dropout mask that will be multiplied with the input.
      For instance, if your inputs have shape
      `(batch_size, timesteps, features)`, and you want the dropout mask
      to be the same for all timesteps, you can use
      `noise_shape=[batch_size, 1, features]`.
    seed: A Python integer. Used to create random seeds. See
      @{tf.set_random_seed}
      for behavior.
    training: Either a Python boolean, or a TensorFlow boolean scalar tensor
      (e.g. a placeholder). Whether to return the output in training mode
      (apply dropout) or in inference mode (return the input untouched).
    name: The name of the layer (string).

  Returns:
    Output tensor.
  """
  layer = Dropout(rate, noise_shape=noise_shape, seed=seed, name=name)
  return layer.apply(inputs, training=training)


# Aliases 

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import dropout [as 别名]
def dropout(inputs,
            keep_prob=0.5,
            noise_shape=None,
            is_training=True,
            outputs_collections=None,
            scope=None):
  """Returns a dropout op applied to the input.

  With probability `keep_prob`, outputs the input element scaled up by
  `1 / keep_prob`, otherwise outputs `0`.  The scaling is so that the expected
  sum is unchanged.

  Args:
    inputs: The tensor to pass to the nn.dropout op.
    keep_prob: A scalar `Tensor` with the same type as x. The probability
      that each element is kept.
    noise_shape: A 1-D `Tensor` of type `int32`, representing the
      shape for randomly generated keep/drop flags.
    is_training: A bool `Tensor` indicating whether or not the model
      is in training mode. If so, dropout is applied and values scaled.
      Otherwise, inputs is returned.
    outputs_collections: Collection to add the outputs.
    scope: Optional scope for name_scope.

  Returns:
    A tensor representing the output of the operation.
  """
  with variable_scope.variable_scope(
      scope, 'Dropout', [inputs], custom_getter=_model_variable_getter) as sc:
    inputs = ops.convert_to_tensor(inputs)
    layer = core_layers.Dropout(rate=1 - keep_prob,
                                noise_shape=noise_shape,
                                name=sc.name,
                                _scope=sc)
    outputs = layer.apply(inputs, training=is_training)
    return utils.collect_named_outputs(
        outputs_collections, sc.original_name_scope, outputs) 

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import dropout [as 别名]
def call(self, inputs, training=False):
    def dropped_inputs():
      return nn.dropout(inputs, 1  - self.rate,
                        noise_shape=self.noise_shape,
                        seed=self.seed)
    return utils.smart_cond(training,
                            dropped_inputs,
                            lambda: array_ops.identity(inputs)) 

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import dropout [as 别名]
def dropout(inputs,
            rate=0.5,
            noise_shape=None,
            seed=None,
            training=False,
            name=None):
  """Applies Dropout to the input.

  Dropout consists in randomly setting a fraction `rate` of input units to 0
  at each update during training time, which helps prevent overfitting.
  The units that are kept are scaled by `1 / (1 - rate)`, so that their
  sum is unchanged at training time and inference time.

  Arguments:
    inputs: Tensor input.
    rate: The dropout rate, between 0 and 1. E.g. "rate=0.1" would drop out
      10% of input units.
    noise_shape: 1D tensor of type `int32` representing the shape of the
      binary dropout mask that will be multiplied with the input.
      For instance, if your inputs have shape
      `(batch_size, timesteps, features)`, and you want the dropout mask
      to be the same for all timesteps, you can use
      `noise_shape=[batch_size, 1, features]`.
    seed: A Python integer. Used to create random seeds. See
      [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
      for behavior.
    training: Either a Python boolean, or a TensorFlow boolean scalar tensor
      (e.g. a placeholder). Whether to return the output in training mode
      (apply dropout) or in inference mode (return the input untouched).
    name: The name of the layer (string).

  Returns:
    Output tensor.
  """
  layer = Dropout(rate, noise_shape=noise_shape, seed=seed, name=name)
  return layer.apply(inputs, training=training)


# Aliases 

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import dropout [as 别名]
def dropout(inputs,
            keep_prob=0.5,
            noise_shape=None,
            is_training=True,
            outputs_collections=None,
            scope=None):
  """Returns a dropout op applied to the input.

  With probability `keep_prob`, outputs the input element scaled up by
  `1 / keep_prob`, otherwise outputs `0`.  The scaling is so that the expected
  sum is unchanged.

  Args:
    inputs: the tensor to pass to the nn.dropout op.
    keep_prob: A scalar `Tensor` with the same type as x. The probability
      that each element is kept.
    noise_shape: A 1-D `Tensor` of type `int32`, representing the
      shape for randomly generated keep/drop flags.
    is_training: A bool `Tensor` indicating whether or not the model
      is in training mode. If so, dropout is applied and values scaled.
      Otherwise, inputs is returned.
    outputs_collections: collection to add the outputs.
    scope: Optional scope for name_scope.

  Returns:
    a tensor representing the output of the operation.
  """
  with variable_scope.variable_scope(
      scope, 'Dropout', [inputs], custom_getter=_model_variable_getter) as sc:
    inputs = ops.convert_to_tensor(inputs)
    layer = core_layers.Dropout(rate=1 - keep_prob,
                                noise_shape=noise_shape,
                                name=sc.name,
                                _scope=sc)
    outputs = layer.apply(inputs, training=is_training)
    return utils.collect_named_outputs(
        outputs_collections, sc.original_name_scope, outputs) 

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import dropout [as 别名]
def dropout(inputs,
            keep_prob=0.5,
            noise_shape=None,
            is_training=True,
            outputs_collections=None,
            scope=None):
  """Returns a dropout op applied to the input.

  With probability `keep_prob`, outputs the input element scaled up by
  `1 / keep_prob`, otherwise outputs `0`.  The scaling is so that the expected
  sum is unchanged.

  Args:
    inputs: the tensor to pass to the nn.dropout op.
    keep_prob: A scalar `Tensor` with the same type as x. The probability
      that each element is kept.
    noise_shape: A 1-D `Tensor` of type `int32`, representing the
      shape for randomly generated keep/drop flags.
    is_training: A bool `Tensor` indicating whether or not the model
      is in training mode. If so, dropout is applied and values scaled.
      Otherwise, inputs is returned.
    outputs_collections: collection to add the outputs.
    scope: Optional scope for name_scope.

  Returns:
    a tensor representing the output of the operation.
  """
  with ops.name_scope(scope, 'Dropout', [inputs]) as sc:
    inputs = ops.convert_to_tensor(inputs)
    dropout_fn = lambda: nn.dropout(inputs, keep_prob, noise_shape)
    id_fn = lambda: array_ops.identity(inputs)
    outputs = utils.smart_cond(is_training, dropout_fn, id_fn)
    return utils.collect_named_outputs(outputs_collections, sc, outputs) 

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import dropout [as 别名]
def vs_multilayer(input_batch,name,middle_layer_dim=1000,reuse=False):
    with tf.variable_scope(name):
        if reuse==True:
            print name+" reuse variables"
            tf.get_variable_scope().reuse_variables()
        else:
            print name+" doesn't reuse variables"
        
        layer1 = fc_relu('layer1', input_batch, output_dim=middle_layer_dim)
        layer1=drop(layer1,0.5)
        outputs = fc('layer2', layer1,output_dim=2)
    return outputs 

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import dropout [as 别名]
def call(self, inputs, training=False):

    def dropped_inputs():
      return nn.dropout(inputs, 1  - self.rate,
                        noise_shape=self._get_noise_shape(inputs),
                        seed=self.seed)
    return utils.smart_cond(training,
                            dropped_inputs,
                            lambda: array_ops.identity(inputs)) 

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import dropout [as 别名]
def dropout(inputs,
            rate=0.5,
            noise_shape=None,
            seed=None,
            training=False,
            name=None):
  """Applies Dropout to the input.

  Dropout consists in randomly setting a fraction `rate` of input units to 0
  at each update during training time, which helps prevent overfitting.
  The units that are kept are scaled by `1 / (1 - rate)`, so that their
  sum is unchanged at training time and inference time.

  Arguments:
    inputs: Tensor input.
    rate: The dropout rate, between 0 and 1. E.g. "rate=0.1" would drop out
      10% of input units.
    noise_shape: 1D tensor of type `int32` representing the shape of the
      binary dropout mask that will be multiplied with the input.
      For instance, if your inputs have shape
      `(batch_size, timesteps, features)`, and you want the dropout mask
      to be the same for all timesteps, you can use
      `noise_shape=[batch_size, 1, features]`.
    seed: A Python integer. Used to create random seeds. See
      @{tf.set_random_seed}
      for behavior.
    training: Either a Python boolean, or a TensorFlow boolean scalar tensor
      (e.g. a placeholder). Whether to return the output in training mode
      (apply dropout) or in inference mode (return the input untouched).
    name: The name of the layer (string).

  Returns:
    Output tensor.
  """
  layer = Dropout(rate, noise_shape=noise_shape, seed=seed, name=name)
  return layer.apply(inputs, training=training)