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@ -65,15 +65,15 @@ __all__ = [
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# 'sums',
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'tanh',
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'elementwise_sum',
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# 'max',
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# 'min',
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'max',
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'min',
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'mm',
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'div',
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'add',
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# 'atan',
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'logsumexp',
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# 'inverse',
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# 'log1p',
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'log1p',
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# 'erf',
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# 'addcmul',
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'addmm'
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@ -1062,3 +1062,196 @@ Examples:
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return out
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return layers.log(sum_out, name)
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def max(input, dim=None, keep_dim=False, out=None, name=None):
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"""
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Computes the maximum of tensor elements over the given dimension.
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Args:
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input (Variable): The input variable which is a Tensor, the data type is float32,
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float64, int32, int64.
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dim (list|int, optional): The dimension along which the maximum is computed.
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If :attr:`None`, compute the maximum over all elements of
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:attr:`input` and return a Tensor variable with a single element,
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otherwise must be in the range :math:`[-rank(input), rank(input))`.
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If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
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keep_dim (bool, optional): Whether to reserve the reduced dimension in the
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output Tensor. The result tensor will have one fewer dimension
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than the :attr:`input` unless :attr:`keep_dim` is true, default
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value is False.
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out(Variable, optional): Optional output which can be any created
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Variable that meets the requirements to store the result of operation.
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if out is None, a new Varibale will be create to store the result.
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name(str, optional): The default value is None. Normally there is no need for
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user to set this property. For more information, please refer to :ref:`api_guide_Name`
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Returns:
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Variable: Tensor, results of maximum on the specified dim of input tensor,
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it's data type is the same as input's Tensor.
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Examples:
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.. code-block:: python
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import paddle
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import paddle.fluid as fluid
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# x is a Tensor variable with following elements:
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# [[0.2, 0.3, 0.5, 0.9]
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# [0.1, 0.2, 0.6, 0.7]]
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# Each example is followed by the corresponding output tensor.
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x = fluid.data(name='x', shape=[2, 4], dtype='float32')
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paddle.max(x) # [0.9]
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paddle.max(x, dim=0) # [0.2, 0.3, 0.6, 0.9]
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paddle.max(x, dim=-1) # [0.9, 0.7]
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paddle.max(x, dim=1, keep_dim=True) # [[0.9], [0.7]]
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# y is a Tensor variable with shape [2, 2, 2] and elements as below:
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# [[[1.0, 2.0], [3.0, 4.0]],
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# [[5.0, 6.0], [7.0, 8.0]]]
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# Each example is followed by the corresponding output tensor.
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y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
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paddle.max(y, dim=[1, 2]) # [4.0, 8.0]
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paddle.max(y, dim=[0, 1]) # [7.0, 8.0]
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"""
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helper = LayerHelper('max', **locals())
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if out is None:
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out = helper.create_variable_for_type_inference(
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dtype=helper.input_dtype())
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if dim is not None and not isinstance(dim, list):
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dim = [dim]
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check_variable_and_dtype(
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input, 'input', ['float32', 'float64', 'int32', 'int64'], 'max')
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reduce_all = True if dim == None or dim == [] else False
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dim = dim if dim != None and dim != [] else [0]
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if in_dygraph_mode():
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return core.ops.reduce_max(input, 'dim', dim, 'keep_dim', keep_dim,
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'reduce_all', reduce_all)
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helper.append_op(
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type='reduce_max',
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inputs={'X': input},
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outputs={'Out': out},
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attrs={
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'dim': dim,
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'keep_dim': keep_dim,
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'reduce_all': reduce_all
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})
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return out
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def min(input, dim=None, keep_dim=False, out=None, name=None):
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"""
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Computes the minimum of tensor elements over the given dimension.
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Args:
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input (Variable): The input variable which is a Tensor, the data type is float32,
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float64, int32, int64.
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dim (list|int, optional): The dimensions along which the minimum is computed.
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If :attr:`None`, compute the minimum over all elements of
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:attr:`input` and return a Tensor variable with a single element,
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otherwise must be in the range :math:`[-rank(input), rank(input))`.
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If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
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keep_dim (bool, optional): Whether to reserve the reduced dimension in the
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output Tensor. The result tensor will have one fewer dimension
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than the :attr:`input` unless :attr:`keep_dim` is true, default
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value is False.
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out(Variable, optional): Optional output which can be any created
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Variable that meets the requirements to store the result of operation.
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if out is None, a new Varibale will be create to store the result.
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name(str, optional): The default value is None. Normally there is no need for
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user to set this property. For more information, please refer to :ref:`api_guide_Name`
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Returns:
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Variable: Tensor, result of minimum on the specified dim of input tensor,
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it's data type is the same as input's Tensor.
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Examples:
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.. code-block:: python
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import paddle
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import paddle.fluid as fluid
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# x is a Tensor variable with following elements:
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# [[0.2, 0.3, 0.5, 0.9]
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# [0.1, 0.2, 0.6, 0.7]]
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# Each example is followed by the corresponding output tensor.
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x = fluid.data(name='x', shape=[2, 4], dtype='float32')
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paddle.min(x) # [0.1]
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paddle.min(x, dim=0) # [0.1, 0.2, 0.5, 0.7]
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paddle.min(x, dim=-1) # [0.2, 0.1]
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paddle.min(x, dim=1, keep_dim=True) # [[0.2], [0.1]]
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# y is a Tensor variable with shape [2, 2, 2] and elements as below:
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# [[[1.0, 2.0], [3.0, 4.0]],
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# [[5.0, 6.0], [7.0, 8.0]]]
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# Each example is followed by the corresponding output tensor.
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y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
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paddle.min(y, dim=[1, 2]) # [1.0, 5.0]
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paddle.min(y, dim=[0, 1]) # [1.0, 2.0]
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"""
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helper = LayerHelper('min', **locals())
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if out is None:
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out = helper.create_variable_for_type_inference(
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dtype=helper.input_dtype())
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if dim is not None and not isinstance(dim, list):
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dim = [dim]
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check_variable_and_dtype(
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input, 'input', ['float32', 'float64', 'int32', 'int64'], 'max')
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reduce_all = True if dim == None or dim == [] else False
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dim = dim if dim != None and dim != [] else [0]
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if in_dygraph_mode():
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return core.ops.reduce_min(input, 'dim', dim, 'keep_dim', keep_dim,
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'reduce_all', reduce_all)
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helper.append_op(
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type='reduce_min',
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inputs={'X': input},
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outputs={'Out': out},
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attrs={
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'dim': dim,
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'keep_dim': keep_dim,
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'reduce_all': reduce_all
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})
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return out
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def log1p(x, out=None, name=None):
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"""
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Calculates the natural log of the given input tensor, element-wise.
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.. math::
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Out = \\ln(x+1)
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Args:
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x (Variable): Input LoDTensor or Tensor. Must be one of the following types: float32, float64.
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out(Variable, optional): Optional output which can be any created
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Variable that meets the requirements to store the result of operation.
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if out is None, a new Varibale will be create to store the result.
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name(str, optional): The default value is None. Normally there is no need for
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user to set this property. For more information, please refer to :ref:`api_guide_Name`
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Returns:
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Variable: The natural log of the input LoDTensor or Tensor computed element-wise.
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Examples:
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.. code-block:: python
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import paddle
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import paddle.fluid as fluid
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import numpy as np
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# Graph Organizing
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x = fluid.data(name="x", shape=[2,1], dtype="float32")
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res = paddle.log1p(x)
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# Create an executor using CPU as an example
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exe = fluid.Executor(fluid.CPUPlace())
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# Execute
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x_i = np.array([[0], [1]]).astype(np.float32)
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res_val, = exe.run(fluid.default_main_program(), feed={'x':x_i}, fetch_list=[res])
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print(res_val) # [[0.], [0.6931472]]
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"""
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if in_dygraph_mode():
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return core.ops.log1p(x)
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check_variable_and_dtype(x, 'x', ['float32', 'float64'], "log1p")
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inputs = {'X': [x]}
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helper = LayerHelper('log1p', **locals())
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dtype = helper.input_dtype(input_param_name='x')
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if out is None:
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out = helper.create_variable_for_type_inference(dtype)
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helper.append_op(type="log1p", inputs={"X": x}, outputs={"Out": out})
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return out
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danleifeng
5 years ago
committed by
GitHub