提交 c6c9c657 编写于 作者: F fengjiayi

update doc

上级 8453740b
......@@ -748,16 +748,25 @@ def max_sequence_len(rank_table):
def lod_tensor_to_array(x, table):
""" Convert a LOD_TENSOR to an LOD_TENSOR_ARRAY.
"""
Convert a LoDTensor to a LoDTensorArray.
This function split a LoDTesnor to a LoDTensorArray according to its LoD
information. LoDTensorArray is an alias of C++ std::vector<LoDTensor> in
Paddle. The generated LoDTensorArray of this function can be further read
or written by 'read_from_array()' and 'write_to_array()' operators. However,
this function is generally an internal component of Paddle 'DynamicRNN'.
Users should not use it directly.
Args:
x (Variable|list): The LOD tensor to be converted to a LOD tensor array.
x (Variable|list): The LoDTensor to be converted to a LoDTensorArray.
table (ParamAttr|list): The variable that stores the level of lod
which is ordered by sequence length in
descending order.
descending order. It is generally generated
by 'layers.lod_rank_table()' API.
Returns:
Variable: The variable of type array that has been converted from a
Variable: The LoDTensorArray that has been converted from the input
tensor.
Examples:
......@@ -1047,6 +1056,13 @@ def array_length(array):
class ConditionalBlockGuard(BlockGuard):
"""
ConditionalBlockGuard is derived from BlockGuard. It is dedicated for
holding a ConditionalBlock, and helping users entering and exiting the
ConditionalBlock via Python's 'with' keyword. However, ConditionalBlockGuard
is generally an internal component of IfElse, users should not use it directly.
"""
def __init__(self, block):
if not isinstance(block, ConditionalBlock):
raise TypeError("block should be conditional block")
......@@ -1563,17 +1579,15 @@ def reorder_lod_tensor_by_rank(x, rank_table):
def is_empty(x, cond=None, **ignored):
"""
**Is Empty**
This layer returns the truth value of whether the variable is empty.
Test whether an Variable is empty.
Args:
x(Variable): Operand of *is_empty*
cond(Variable|None): Optional output variable to store the result
of *is_empty*
x (Variable): The Variable to be tested.
cond (Variable|None): Output parameter. Returns the test result
of given 'x'.
Returns:
Variable: The tensor variable storing the output of *is_empty*.
Variable: The tensor variable storing the test result of 'x'.
Raises:
TypeError: If input cond is not a variable, or cond's dtype is
......
......@@ -70,21 +70,40 @@ def noam_decay(d_model, warmup_steps):
def exponential_decay(learning_rate, decay_steps, decay_rate, staircase=False):
"""Applies exponential decay to the learning rate.
"""
Applies exponential decay to the learning rate.
When training a model, it is often recommended to lower the learning rate as the
training progresses. By using this function, the learning rate will be decayed by
'decay_rate' every 'decay_steps' steps.
>>> if staircase == True:
>>> decayed_learning_rate = learning_rate * decay_rate ^ floor(global_step / decay_steps)
>>> else:
>>> decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)
```python
decayed_learning_rate = learning_rate *
decay_rate ^ (global_step / decay_steps)
```
Args:
learning_rate: A scalar float32 value or a Variable. This
will be the initial learning rate during training
decay_steps: A Python `int32` number.
decay_rate: A Python `float` number.
staircase: Boolean. If set true, decay the learning rate every decay_steps.
learning_rate(Variable|float): The initial learning rate.
decay_steps(int): See the decay computation above.
decay_rate(float): The decay rate. See the decay computation above.
staircase(Boolean): If True, decay the learning rate at discrete intervals.
Default: False
Returns:
The decayed learning rate
Examples:
.. code-block:: python
base_lr = 0.1
sgd_optimizer = fluid.optimizer.SGD(
learning_rate=fluid.layers.exponential_decay(
learning_rate=base_lr,
decay_steps=10000,
decay_rate=0.5,
staircase=True))
sgd_optimizer.minimize(avg_cost)
"""
global_step = _decay_step_counter()
......@@ -128,22 +147,39 @@ def natural_exp_decay(learning_rate, decay_steps, decay_rate, staircase=False):
def inverse_time_decay(learning_rate, decay_steps, decay_rate, staircase=False):
"""Applies inverse time decay to the initial learning rate.
"""
Applies inverse time decay to the initial learning rate.
>>> if staircase:
When training a model, it is often recommended to lower the learning rate as the
training progresses. By using this function, an inverse decay function will be
applied to the initial learning rate.
>>> if staircase == True:
>>> decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step / decay_step))
>>> else:
>>> decayed_learning_rate = learning_rate / (1 + decay_rate * global_step / decay_step)
Args:
learning_rate: A scalar float32 value or a Variable. This
will be the initial learning rate during training.
decay_steps: A Python `int32` number.
decay_rate: A Python `float` number.
staircase: Boolean. If set true, decay the learning rate every decay_steps.
learning_rate(Variable|float): The initial learning rate.
decay_steps(int): See the decay computation above.
decay_rate(float): The decay rate. See the decay computation above.
staircase(Boolean): If True, decay the learning rate at discrete intervals.
Default: False
Returns:
The decayed learning rate
Examples:
.. code-block:: python
base_lr = 0.1
sgd_optimizer = fluid.optimizer.SGD(
learning_rate=fluid.layers.inverse_time_decay(
learning_rate=base_lr,
decay_steps=10000,
decay_rate=0.5,
staircase=True))
sgd_optimizer.minimize(avg_cost)
"""
global_step = _decay_step_counter()
......
......@@ -102,14 +102,15 @@ def fc(input,
"""
**Fully Connected Layer**
The fully connected layer can take multiple tensors as its inputs. It
creates a variable called weights for each input tensor, which represents
a fully connected weight matrix from each input unit to each output unit.
The fully connected layer multiplies each input tensor with its coresponding
weight to produce an output Tensor. If multiple input tensors are given,
the results of multiple multiplications will be sumed up. If bias_attr is
not None, a bias variable will be created and added to the output. Finally,
if activation is not None, it will be applied to the output as well.
This function creates a fully connected layer in the network. It can take
multiple tensors as its inputs. It creates a variable called weights for
each input tensor, which represents a fully connected weight matrix from
each input unit to each output unit. The fully connected layer multiplies
each input tensor with its coresponding weight to produce an output Tensor.
If multiple input tensors are given, the results of multiple multiplications
will be sumed up. If bias_attr is not None, a bias variable will be created
and added to the output. Finally, if activation is not None, it will be applied
to the output as well.
This process can be formulated as follows:
......@@ -1509,6 +1510,7 @@ def sequence_last_step(input):
return sequence_pool(input=input, pool_type="last")
@templatedoc()
def pool2d(input,
pool_size=-1,
pool_type="max",
......@@ -1520,12 +1522,12 @@ def pool2d(input,
use_mkldnn=False,
name=None):
"""
This function adds the operator for pooling in 2 dimensions, using the
pooling configurations mentioned in input parameters.
${comment}
Args:
input (Variable): ${input_comment}
pool_size (int): ${ksize_comment}
pool_size (int): The side length of pooling windows. All pooling
windows are squares with pool_size on a side.
pool_type (str): ${pooling_type_comment}
pool_stride (int): stride of the pooling layer.
pool_padding (int): padding size.
......@@ -1533,11 +1535,29 @@ def pool2d(input,
use_cudnn (bool): ${use_cudnn_comment}
ceil_mode (bool): ${ceil_mode_comment}
use_mkldnn (bool): ${use_mkldnn_comment}
name (str): A name for this layer(optional). If set None, the layer
will be named automatically.
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
Returns:
Variable: output of pool2d layer.
Raises:
ValueError: If 'pool_type' is not "max" nor "avg"
ValueError: If 'global_pooling' is False and 'pool_size' is -1
ValueError: If 'use_cudnn' is not a bool value.
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 32, 32], dtype='float32')
conv2d = fluid.layers.pool2d(
input=data,
pool_size=2,
pool_type='max',
pool_stride=1,
global_pooling=False)
"""
if pool_type not in ["max", "avg"]:
raise ValueError(
......@@ -2719,7 +2739,7 @@ def topk(input, k, name=None):
This operator is used to find values and indices of the k largest entries
for the last dimension.
If the input is a vector (rank=1), finds the k largest entries in the vector
If the input is a vector (1-D Tensor), finds the k largest entries in the vector
and outputs their values and indices as vectors. Thus values[j] is the j-th
largest entry in input, and its index is indices[j].
......@@ -2729,9 +2749,11 @@ def topk(input, k, name=None):
Args:
input(Variable): The input variable which can be a vector or Tensor with
higher rank.
k(int): An integer value to specify the top k largest elements.
k(int): The number of top elements to look for along the last dimension
of input.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Default: None
Returns:
values(Variable): The k largest elements along each last dimensional
......@@ -2739,13 +2761,16 @@ def topk(input, k, name=None):
indices(Variable): The indices of values within the last dimension of
input.
Raises:
ValueError: If k < 1 or k is not less than the last dimension of input
Examples:
.. code-block:: python
top5_values, top5_indices = layers.topk(input, k=5)
"""
shape = input.shape
if k < 1 and k >= shape[-1]:
if k < 1 or k >= shape[-1]:
raise ValueError("k must be greater than 0 and less than %d." %
(shape[-1]))
......
......@@ -79,20 +79,33 @@ def create_global_var(shape,
force_cpu=False,
name=None):
"""
Create a global variable. such as global_step
Create a new variable in the global block(block 0).
Args:
shape(list[int]): shape of the variable
value(float): the value of the variable
dtype(string): element type of the parameter
persistable(bool): if this variable is persistable
force_cpu(bool): force this variable to be on CPU
value(float): the value of the variable. The new created
variable will be filled with it.
dtype(string): data type of the variable
persistable(bool): if this variable is persistable.
Default: False
force_cpu(bool): force this variable to be on CPU.
Default: False
name(str|None): The name of the variable. If set to None the variable
name will be generated automatically.
Default: None
Returns:
Variable: the created Variable
Examples:
.. code-block:: python
var = fluid.create_global_var(shape=[2,3], value=1.0, dtype='float32',
persistable=True, force_cpu=True, name='new_var')
"""
helper = LayerHelper("global_var", **locals())
var = helper.create_global_variable(
dtype=dtype, shape=shape, persistable=persistable, name=name)
dtype=dtype, shape=shape, persistable=persistable)
helper.set_variable_initializer(
var, initializer=Constant(
value=float(value), force_cpu=force_cpu))
......@@ -152,10 +165,11 @@ def sums(input, out=None):
Args:
input (Variable|list): The input tensor that has the elements
that need to be summed up.
out (Variable|None): Output parameter. Returns the sum result.
Default: None
Returns:
Variable: The tensor type variable that has the sum of input
written to it.
Variable: the sum of input. The same as the argument 'out'
Examples:
.. code-block::python
......
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