Python gen_math_ops.exp方法代碼示例

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import exp [as 別名]
def sigmoid(x, name=None):
  """Computes sigmoid of `x` element-wise.

  Specifically, `y = 1 / (1 + exp(-x))`.

  Args:
    x: A Tensor with type `float32`, `float64`, `int32`, `complex64`, `int64`,
      or `qint32`.
    name: A name for the operation (optional).

  Returns:
    A Tensor with the same type as `x` if `x.dtype != qint32`
      otherwise the return type is `quint8`.

  @compatibility(numpy)
  Equivalent to np.scipy.special.expit
  @end_compatibility
  """
  with ops.name_scope(name, "Sigmoid", [x]) as name:
    x = ops.convert_to_tensor(x, name="x")
    return gen_math_ops._sigmoid(x, name=name) 

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import exp [as 別名]
def log_sigmoid(x, name=None):
  """Computes log sigmoid of `x` element-wise.

  Specifically, `y = log(1 / (1 + exp(-x)))`.  For numerical stability,
  we use `y = -tf.nn.softplus(-x)`.

  Args:
    x: A Tensor with type `float32` or `float64`.
    name: A name for the operation (optional).

  Returns:
    A Tensor with the same type as `x`.
  """
  with ops.name_scope(name, "LogSigmoid", [x]) as name:
    x = ops.convert_to_tensor(x, name="x")
    return gen_math_ops._neg(gen_nn_ops.softplus(-x), name=name) 

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import exp [as 別名]
def sigmoid(x, name=None):
  """Computes sigmoid of `x` element-wise.

  Specifically, `y = 1 / (1 + exp(-x))`.

  Args:
    x: A Tensor with type `float32`, `float64`, `int32`, `complex64`, `int64`,
      or `qint32`.
    name: A name for the operation (optional).

  Returns:
    A Tensor with the same type as `x` if `x.dtype != qint32`
      otherwise the return type is `quint8`.
  """
  with ops.name_scope(name, "Sigmoid", [x]) as name:
    x = ops.convert_to_tensor(x, name="x")
    return gen_math_ops._sigmoid(x, name=name) 

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import exp [as 別名]
def _predicted_covariance_op(self, activations, num_values):
    activation, activation_size = activations[-1]
    if self.loss == ARModel.NORMAL_LIKELIHOOD_LOSS:
      log_sigma_square = model_utils.fully_connected(
          activation,
          activation_size,
          self.output_window_size * num_values,
          name="log_sigma_square",
          activation=None)
      predicted_covariance = gen_math_ops.exp(log_sigma_square)
      predicted_covariance = tf.reshape(
          predicted_covariance, [-1, self.output_window_size, num_values])
    else:
      shape = tf.stack([
          tf.compat.v1.shape(activation)[0],
          tf.constant(self.output_window_size),
          tf.constant(num_values)
      ])
      predicted_covariance = tf.ones(shape=shape, dtype=activation.dtype)
    return predicted_covariance 

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import exp [as 別名]
def sigmoid(x, name=None):
  """Computes sigmoid of `x` element-wise.

  Specifically, `y = 1 / (1 + exp(-x))`.

  Args:
    x: A Tensor with type `float16`, `float32`, `float64`, `complex64`,
      or `complex128`.
    name: A name for the operation (optional).

  Returns:
    A Tensor with the same type as `x`.

  @compatibility(numpy)
  Equivalent to np.scipy.special.expit
  @end_compatibility
  """
  with ops.name_scope(name, "Sigmoid", [x]) as name:
    x = ops.convert_to_tensor(x, name="x")
    return gen_math_ops._sigmoid(x, name=name) 

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import exp [as 別名]
def call(self, inputs):
        inputs = ops.convert_to_tensor(inputs, dtype=self.dtype)
        if common_shapes.rank(inputs) is not 2: 
            raise ValueError('`WalkerModel` only takes "rank 2" inputs.')
 
        sig = 1/(1+gen_math_ops.exp(self.kernel[0]*inputs[:,1]))
        gamma = sig*self.kernel[1]
        C = self.kernel[2]/((1-inputs[:,1])**(self.kernel[3]*(1-gamma)))
        output = C*(inputs[:,0]**self.kernel[3])
        output = array_ops.reshape(output,(array_ops.shape(output)[0],1))
        return output 

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import exp [as 別名]
def call(self, input_window_features, output_window_features):
    """Compute predictions from input and output windows."""
    _, state_h, state_c = self._encoder(input_window_features)
    encoder_states = [state_h, state_c]
    decoder_output = self._decoder(
        output_window_features, initial_state=encoder_states)
    predicted_mean = self._mean_transform(decoder_output)
    predicted_covariance = gen_math_ops.exp(
        self._covariance_transform(decoder_output))
    return {"mean": predicted_mean, "covariance": predicted_covariance} 

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import exp [as 別名]
def reduce_logsumexp(input_tensor,
                     axis=None,
                     keep_dims=False,
                     name=None,
                     reduction_indices=None):
  """Computes log(sum(exp(elements across dimensions of a tensor))).

  Reduces `input_tensor` along the dimensions given in `axis`.
  Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
  entry in `axis`. If `keep_dims` is true, the reduced dimensions
  are retained with length 1.

  If `axis` has no entries, all dimensions are reduced, and a
  tensor with a single element is returned.

  This function is more numerically stable than log(sum(exp(input))). It avoids
  overflows caused by taking the exp of large inputs and underflows caused by
  taking the log of small inputs.

  For example:

  ```python
  # 'x' is [[0, 0, 0]]
  #         [0, 0, 0]]
  tf.reduce_logsumexp(x) ==> log(6)
  tf.reduce_logsumexp(x, 0) ==> [log(2), log(2), log(2)]
  tf.reduce_logsumexp(x, 1) ==> [log(3), log(3)]
  tf.reduce_logsumexp(x, 1, keep_dims=True) ==> [[log(3)], [log(3)]]
  tf.reduce_logsumexp(x, [0, 1]) ==> log(6)
  ```

  Args:
    input_tensor: The tensor to reduce. Should have numeric type.
    axis: The dimensions to reduce. If `None` (the default),
      reduces all dimensions.
    keep_dims: If true, retains reduced dimensions with length 1.
    name: A name for the operation (optional).
    reduction_indices: The old (deprecated) name for axis.

  Returns:
    The reduced tensor.
  """
  with ops.name_scope(name, "ReduceLogSumExp", [input_tensor]) as name:
    my_max = array_ops.stop_gradient(
        reduce_max(
            input_tensor,
            axis=axis,
            reduction_indices=reduction_indices,
            keep_dims=True))
    result = gen_math_ops.log(
        reduce_sum(
            gen_math_ops.exp(input_tensor - my_max),
            axis,
            keep_dims=True,
            reduction_indices=reduction_indices)) + my_max
    if not keep_dims:
      if isinstance(axis, int):
        axis = [axis]
      result = array_ops.squeeze(result, axis)
    return result 

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import exp [as 別名]
def reduce_logsumexp(input_tensor, reduction_indices=None, keep_dims=False,
                     name=None):
  """Computes log(sum(exp(elements across dimensions of a tensor))).

  Reduces `input_tensor` along the dimensions given in `reduction_indices`.
  Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
  entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
  are retained with length 1.

  If `reduction_indices` has no entries, all dimensions are reduced, and a
  tensor with a single element is returned.

  This function is more numerically stable than log(sum(exp(input))). It avoids
  overflows caused by taking the exp of large inputs and underflows caused by
  taking the log of small inputs.

  For example:

  ```python
  # 'x' is [[0, 0, 0]]
  #         [0, 0, 0]]
  tf.reduce_logsumexp(x) ==> log(6)
  tf.reduce_logsumexp(x, 0) ==> [log(2), log(2), log(2)]
  tf.reduce_logsumexp(x, 1) ==> [log(3), log(3)]
  tf.reduce_logsumexp(x, 1, keep_dims=True) ==> [[log(3)], [log(3)]]
  tf.reduce_logsumexp(x, [0, 1]) ==> log(6)
  ```

  Args:
    input_tensor: The tensor to reduce. Should have numeric type.
    reduction_indices: The dimensions to reduce. If `None` (the default),
      reduces all dimensions.
    keep_dims: If true, retains reduced dimensions with length 1.
    name: A name for the operation (optional).

  Returns:
    The reduced tensor.
  """
  with ops.name_scope(name, "ReduceLogSumExp", [input_tensor]) as name:
    my_max = array_ops.stop_gradient(
        reduce_max(input_tensor, reduction_indices, keep_dims=True))
    result = gen_math_ops.log(reduce_sum(
        gen_math_ops.exp(input_tensor - my_max),
        reduction_indices,
        keep_dims=True)) + my_max
    if not keep_dims:
      if isinstance(reduction_indices, int):
        reduction_indices = [reduction_indices]
      result = array_ops.squeeze(result, reduction_indices)
    return result