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Paddle/python/paddle/fluid/layers/nn.py

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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
All layers just related to the neural network.
"""
from ..layer_helper import LayerHelper
from ..initializer import Normal, Constant
from ..framework import Variable
from ..param_attr import ParamAttr
from layer_function_generator import autodoc
from tensor import concat
import utils
__all__ = [
'fc',
'embedding',
'dynamic_lstm',
'dynamic_lstmp',
'dynamic_gru',
'gru_unit',
'linear_chain_crf',
'crf_decoding',
'cos_sim',
'cross_entropy',
'square_error_cost',
'chunk_eval',
'sequence_conv',
'conv2d',
'sequence_pool',
'sequence_softmax',
'softmax',
'pool2d',
'batch_norm',
'beam_search_decode',
'conv2d_transpose',
'sequence_expand',
'lstm_unit',
'reduce_sum',
'reduce_mean',
'reduce_max',
'reduce_min',
'reduce_prod',
'sequence_first_step',
'sequence_last_step',
'dropout',
'split',
'ctc_greedy_decoder',
'edit_distance',
'l2_normalize',
'matmul',
'warpctc',
'sequence_reshape',
'transpose',
'im2sequence',
'nce',
'beam_search',
'row_conv',
'multiplex',
'layer_norm',
'softmax_with_cross_entropy',
'smooth_l1',
'one_hot',
'autoincreased_step_counter',
'lod_reset',
'lrn',
]
def fc(input,
size,
num_flatten_dims=1,
param_attr=None,
bias_attr=None,
use_mkldnn=False,
act=None,
name=None):
"""
**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 process can be formulated as follows:
.. math::
Out = Act({\sum_{i=0}^{N-1}X_iW_i + b})
In the above equation:
* :math:`N`: Number of the input.
* :math:`X_i`: The input tensor.
* :math:`W`: The weights created by this layer.
* :math:`b`: The bias parameter created by this layer (if needed).
* :math:`Act`: The activation function.
* :math:`Out`: The output tensor.
Args:
input (Variable|list of Variable): The input tensor(s) of this layer, and the dimension of
the input tensor(s) is at least 2.
size(int): The number of output units in this layer.
num_flatten_dims (int, default 1): The fc layer can accept an input tensor with more than
two dimensions. If this happens, the multidimensional tensor will first be flattened
into a 2-dimensional matrix. The parameter `num_flatten_dims` determines how the input
tensor is flattened: the first `num_flatten_dims` (inclusive, index starts from 1)
dimensions will be flatten to form the first dimension of the final matrix (height of
the matrix), and the rest `rank(X) - num_flatten_dims` dimensions are flattened to
form the second dimension of the final matrix (width of the matrix). For example, suppose
`X` is a 6-dimensional tensor with a shape [2, 3, 4, 5, 6], and `num_flatten_dims` = 3.
Then, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
param_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for learnable
parameters/weights of this layer.
bias_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for the bias
of this layer. If it is set to None, no bias will be added to the output units.
act (str, default None): Activation to be applied to the output of this layer.
name (str, default None): The name of this layer.
Returns:
A tensor variable storing the transformation result.
Raises:
ValueError: If rank of the input tensor is less than 2.
Examples:
.. code-block:: python
data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
fc = fluid.layers.fc(input=data, size=1000, act="tanh")
"""
helper = LayerHelper("fc", **locals())
dtype = helper.input_dtype()
mul_results = []
for input_var, param_attr in helper.iter_inputs_and_params():
input_shape = input_var.shape
param_shape = [
reduce(lambda a, b: a * b, input_shape[num_flatten_dims:], 1)
] + [size]
w = helper.create_parameter(
attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False)
tmp = helper.create_tmp_variable(dtype)
helper.append_op(
type="mul",
inputs={"X": input_var,
"Y": w},
outputs={"Out": tmp},
attrs={
"x_num_col_dims": num_flatten_dims,
"y_num_col_dims": 1,
'use_mkldnn': use_mkldnn
})
mul_results.append(tmp)
# sum
if len(mul_results) == 1:
pre_bias = mul_results[0]
else:
pre_bias = helper.create_tmp_variable(dtype)
helper.append_op(
type="sum", inputs={"X": mul_results}, outputs={"Out": pre_bias})
# add bias
pre_activation = helper.append_bias_op(pre_bias, dim_start=num_flatten_dims)
# add activation
return helper.append_activation(pre_activation)
def embedding(input,
size,
is_sparse=False,
padding_idx=None,
param_attr=None,
dtype='float32'):
"""
**Embedding Layer**
This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in
a lookup table. The result of this lookup is the embedding of each ID in the
:attr:`input`.
All the input variables are passed in as local variables to the LayerHelper
constructor.
Args:
input(Variable): The tensor variable containing the IDs.
size(tuple|list): The shape of the look up table parameter. It should
have two elements which indicate the size of the dictionary of
embeddings and the size of each embedding vector respectively.
is_sparse(bool): The flag indicating whether to use sparse update.
padding_idx(int|long|None): If :attr:`None`, it makes no effect to lookup.
Otherwise the given :attr:`padding_idx` indicates padding the output
with zeros whenever lookup encounters it in :attr:`input`. If
:math:`padding_idx < 0`, the padding_idx to use in lookup is
:math:`size[0] + dim`.
param_attr(ParamAttr): Parameters for this layer
dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc
Returns:
Variable: The tensor variable storing the embeddings of the \
supplied inputs.
Examples:
.. code-block:: python
dict_size = len(dataset.ids)
data = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32')
fc = fluid.layers.embedding(input=data, size=[dict_size, 16])
"""
helper = LayerHelper('embedding', **locals())
w = helper.create_parameter(
attr=helper.param_attr, shape=size, dtype=dtype, is_bias=False)
tmp = helper.create_tmp_variable(dtype)
padding_idx = -1 if padding_idx is None else padding_idx if padding_idx >= 0 else (
size[0] + padding_idx)
helper.append_op(
type='lookup_table',
inputs={'Ids': input,
'W': w},
outputs={'Out': tmp},
attrs={'is_sparse': is_sparse,
'padding_idx': padding_idx})
return tmp
# TODO(qijun): expose H0 and C0
def dynamic_lstm(input,
size,
param_attr=None,
bias_attr=None,
use_peepholes=True,
is_reverse=False,
gate_activation='sigmoid',
cell_activation='tanh',
candidate_activation='tanh',
dtype='float32',
name=None):
"""
**Dynamic LSTM Layer**
The defalut implementation is diagonal/peephole connection
(https://arxiv.org/pdf/1402.1128.pdf), the formula is as follows:
.. math::
i_t & = \sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + W_{ic}c_{t-1} + b_i)
f_t & = \sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + W_{fc}c_{t-1} + b_f)
\\tilde{c_t} & = act_g(W_{cx}x_t + W_{ch}h_{t-1} + b_c)
o_t & = \sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + W_{oc}c_t + b_o)
c_t & = f_t \odot c_{t-1} + i_t \odot \\tilde{c_t}
h_t & = o_t \odot act_h(c_t)
where the :math:`W` terms denote weight matrices (e.g. :math:`W_{xi}` is
the matrix of weights from the input gate to the input), :math:`W_{ic}, \
W_{fc}, W_{oc}` are diagonal weight matrices for peephole connections. In
our implementation, we use vectors to reprenset these diagonal weight
matrices. The :math:`b` terms denote bias vectors (:math:`b_i` is the input
gate bias vector), :math:`\sigma` is the non-linear activations, such as
logistic sigmoid function, and :math:`i, f, o` and :math:`c` are the input
gate, forget gate, output gate, and cell activation vectors, respectively,
all of which have the same size as the cell output activation vector :math:`h`.
The :math:`\odot` is the element-wise product of the vectors. :math:`act_g`
and :math:`act_h` are the cell input and cell output activation functions
and `tanh` is usually used for them. :math:`\\tilde{c_t}` is also called
candidate hidden state, which is computed based on the current input and
the previous hidden state.
Set `use_peepholes` to `False` to disable peephole connection. The formula
is omitted here, please refer to the paper
http://www.bioinf.jku.at/publications/older/2604.pdf for details.
Note that these :math:`W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}`
operations on the input :math:`x_{t}` are NOT included in this operator.
Users can choose to use fully-connect layer before LSTM layer.
Args:
input(Variable): The input of dynamic_lstm layer, which supports
variable-time length input sequence. The underlying
tensor in this Variable is a matrix with shape
(T X 4D), where T is the total time steps in this
mini-batch, D is the hidden size.
size(int): 4 * hidden size.
param_attr(ParamAttr|None): The parameter attribute for the learnable
hidden-hidden weights.
- Weights = {:math:`W_{ch}, W_{ih}, \
W_{fh}, W_{oh}`}
- The shape is (D x 4D), where D is the hidden
size.
bias_attr(ParamAttr|None): The bias attribute for the learnable bias
weights, which contains two parts, input-hidden
bias weights and peephole connections weights if
setting `use_peepholes` to `True`.
1. `use_peepholes = False`
- Biases = {:math:`b_c, b_i, b_f, b_o`}.
- The shape is (1 x 4D).
2. `use_peepholes = True`
- Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
W_{fc}, W_{oc}`}.
- The shape is (1 x 7D).
use_peepholes(bool): Whether to enable diagonal/peephole connections,
default `True`.
is_reverse(bool): Whether to compute reversed LSTM, default `False`.
gate_activation(str): The activation for input gate, forget gate and
output gate. Choices = ["sigmoid", "tanh", "relu",
"identity"], default "sigmoid".
cell_activation(str): The activation for cell output. Choices = ["sigmoid",
"tanh", "relu", "identity"], default "tanh".
candidate_activation(str): The activation for candidate hidden state.
Choices = ["sigmoid", "tanh", "relu", "identity"],
default "tanh".
dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
tuple: The hidden state, and cell state of LSTM. The shape of both \
is (T x D), and lod is the same with the `input`.
Examples:
.. code-block:: python
hidden_dim = 512
forward_proj = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
act=None, bias_attr=None)
forward, _ = fluid.layers.dynamic_lstm(
input=forward_proj, size=hidden_dim * 4, use_peepholes=False)
"""
helper = LayerHelper('lstm', **locals())
size = size / 4
weight = helper.create_parameter(
attr=helper.param_attr, shape=[size, 4 * size], dtype=dtype)
bias_size = [1, 7 * size]
if not use_peepholes:
bias_size[1] = 4 * size
bias = helper.create_parameter(
attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
hidden = helper.create_tmp_variable(dtype)
cell = helper.create_tmp_variable(dtype)
batch_gate = helper.create_tmp_variable(dtype)
batch_cell_pre_act = helper.create_tmp_variable(dtype)
helper.append_op(
type='lstm',
inputs={'Input': input,
'Weight': weight,
'Bias': bias},
outputs={
'Hidden': hidden,
'Cell': cell,
'BatchGate': batch_gate,
'BatchCellPreAct': batch_cell_pre_act
},
attrs={
'use_peepholes': use_peepholes,
'is_reverse': is_reverse,
'gate_activation': gate_activation,
'cell_activation': cell_activation,
'candidate_activation': candidate_activation
})
return hidden, cell
def dynamic_lstmp(input,
size,
proj_size,
param_attr=None,
bias_attr=None,
use_peepholes=True,
is_reverse=False,
gate_activation='sigmoid',
cell_activation='tanh',
candidate_activation='tanh',
proj_activation='tanh',
dtype='float32',
name=None):
"""
**Dynamic LSTMP Layer**
LSTMP (LSTM with recurrent projection) layer has a separate projection
layer after the LSTM layer, projecting the original hidden state to a
lower-dimensional one, which is proposed to reduce the number of total
parameters and furthermore computational complexity for the LSTM,
espeacially for the case that the size of output units is relative
large (https://research.google.com/pubs/archive/43905.pdf).
The formula is as follows:
.. math::
i_t & = \sigma(W_{ix}x_{t} + W_{ir}r_{t-1} + W_{ic}c_{t-1} + b_i)
f_t & = \sigma(W_{fx}x_{t} + W_{fr}r_{t-1} + W_{fc}c_{t-1} + b_f)
\\tilde{c_t} & = act_g(W_{cx}x_t + W_{cr}r_{t-1} + b_c)
o_t & = \sigma(W_{ox}x_{t} + W_{or}r_{t-1} + W_{oc}c_t + b_o)
c_t & = f_t \odot c_{t-1} + i_t \odot \\tilde{c_t}
h_t & = o_t \odot act_h(c_t)
r_t & = \overline{act_h}(W_{rh}h_t)
In the above formula:
* :math:`W`: Denotes weight matrices (e.g. :math:`W_{xi}` is \
the matrix of weights from the input gate to the input).
* :math:`W_{ic}`, :math:`W_{fc}`, :math:`W_{oc}`: Diagonal weight \
matrices for peephole connections. In our implementation, \
we use vectors to reprenset these diagonal weight matrices.
* :math:`b`: Denotes bias vectors (e.g. :math:`b_i` is the input gate \
bias vector).
* :math:`\sigma`: The activation, such as logistic sigmoid function.
* :math:`i, f, o` and :math:`c`: The input gate, forget gate, output \
gate, and cell activation vectors, respectively, all of which have \
the same size as the cell output activation vector :math:`h`.
* :math:`h`: The hidden state.
* :math:`r`: The recurrent projection of the hidden state.
* :math:`\\tilde{c_t}`: The candidate hidden state, whose \
computation is based on the current input and previous hidden state.
* :math:`\odot`: The element-wise product of the vectors.
* :math:`act_g` and :math:`act_h`: The cell input and cell output \
activation functions and `tanh` is usually used for them.
* :math:`\overline{act_h}`: The activation function for the projection \
output, usually using `identity` or same as :math:`act_h`.
Set `use_peepholes` to `False` to disable peephole connection. The formula
is omitted here, please refer to the paper
http://www.bioinf.jku.at/publications/older/2604.pdf for details.
Note that these :math:`W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}`
operations on the input :math:`x_{t}` are NOT included in this operator.
Users can choose to use fully-connected layer before LSTMP layer.
Args:
input(Variable): The input of dynamic_lstmp layer, which supports
variable-time length input sequence. The underlying
tensor in this Variable is a matrix with shape
(T X 4D), where T is the total time steps in this
mini-batch, D is the hidden size.
size(int): 4 * hidden size.
proj_size(int): The size of projection output.
param_attr(ParamAttr|None): The parameter attribute for the learnable
hidden-hidden weight and projection weight.
- Hidden-hidden weight = {:math:`W_{ch}, W_{ih}, \
W_{fh}, W_{oh}`}.
- The shape of hidden-hidden weight is (P x 4D),
where P is the projection size and D the hidden
size.
- Projection weight = {:math:`W_{rh}`}.
- The shape of projection weight is (D x P).
bias_attr(ParamAttr|None): The bias attribute for the learnable bias
weights, which contains two parts, input-hidden
bias weights and peephole connections weights if
setting `use_peepholes` to `True`.
1. `use_peepholes = False`
- Biases = {:math:`b_c, b_i, b_f, b_o`}.
- The shape is (1 x 4D).
2. `use_peepholes = True`
- Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
W_{fc}, W_{oc}`}.
- The shape is (1 x 7D).
use_peepholes(bool): Whether to enable diagonal/peephole connections,
default `True`.
is_reverse(bool): Whether to compute reversed LSTM, default `False`.
gate_activation(str): The activation for input gate, forget gate and
output gate. Choices = ["sigmoid", "tanh", "relu",
"identity"], default "sigmoid".
cell_activation(str): The activation for cell output. Choices = ["sigmoid",
"tanh", "relu", "identity"], default "tanh".
candidate_activation(str): The activation for candidate hidden state.
Choices = ["sigmoid", "tanh", "relu", "identity"],
default "tanh".
proj_activation(str): The activation for projection output.
Choices = ["sigmoid", "tanh", "relu", "identity"],
default "tanh".
dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
tuple: The projection of hidden state, and cell state of LSTMP. The \
shape of projection is (T x P), for the cell state which is \
(T x D), and both LoD is the same with the `input`.
Examples:
.. code-block:: python
hidden_dim, proj_dim = 512, 256
fc_out = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
act=None, bias_attr=None)
proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out,
size=hidden_dim * 4,
proj_size=proj_dim,
use_peepholes=False,
is_reverse=True,
cell_activation="tanh",
proj_activation="tanh")
"""
helper = LayerHelper('lstmp', **locals())
size = size / 4
weight = helper.create_parameter(
attr=helper.param_attr, shape=[proj_size, 4 * size], dtype=dtype)
proj_weight = helper.create_parameter(
attr=helper.param_attr, shape=[size, proj_size], dtype=dtype)
bias_size = [1, 7 * size]
if not use_peepholes:
bias_size[1] = 4 * size
bias = helper.create_parameter(
attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
projection = helper.create_tmp_variable(dtype)
cell = helper.create_tmp_variable(dtype)
ordered_proj0 = helper.create_tmp_variable(dtype)
batch_hidden = helper.create_tmp_variable(dtype)
batch_gate = helper.create_tmp_variable(dtype)
batch_cell_pre_act = helper.create_tmp_variable(dtype)
helper.append_op(
type='lstmp',
inputs={
'Input': input,
'Weight': weight,
'ProjWeight': proj_weight,
'Bias': bias
},
outputs={
'Projection': projection,
'Cell': cell,
'OrderedP0': ordered_proj0,
'BatchHidden': batch_hidden,
'BatchGate': batch_gate,
'BatchCellPreAct': batch_cell_pre_act
},
attrs={
'use_peepholes': use_peepholes,
'is_reverse': is_reverse,
'gate_activation': gate_activation,
'cell_activation': cell_activation,
'candidate_activation': candidate_activation,
'proj_activation': proj_activation
})
return projection, cell
def dynamic_gru(input,
size,
param_attr=None,
bias_attr=None,
is_reverse=False,
gate_activation='sigmoid',
candidate_activation='tanh',
h_0=None):
"""
**Dynamic GRU Layer**
Refer to `Empirical Evaluation of Gated Recurrent Neural Networks on
Sequence Modeling <https://arxiv.org/abs/1412.3555>`_
The formula is as follows:
.. math::
u_t & = act_g(W_{ux}x_{t} + W_{uh}h_{t-1} + b_u)
r_t & = act_g(W_{rx}x_{t} + W_{rh}h_{t-1} + b_r)
\\tilde{h_t} & = act_c(W_{cx}x_{t} + W_{ch}(r_t \odot h_{t-1}) + b_c)
h_t & = (1-u_t) \odot h_{t-1} + u_t \odot \\tilde{h_t}
The :math:`\odot` is the element-wise product of the vectors. :math:`act_g`
is the update gate and reset gate activation function and :math:`sigmoid`
is usually used for it. :math:`act_c` is the activation function for
candidate hidden state and :math:`tanh` is usually used for it.
Note that these :math:`W_{ux}x_{t}, W_{rx}x_{t}, W_{cx}x_{t}` operations on
the input :math:`x_{t}` are NOT included in this operator. Users can choose
to use fully-connect layer before GRU layer.
Args:
input(Variable): The input of dynamic_gru layer, which supports
variable-time length input sequence. The underlying tensor in this
Variable is a matrix with shape :math:`(T \\times 3D)`, where
:math:`T` is the total time steps in this mini-batch, :math:`D`
is the hidden size.
size(int): The dimension of the gru cell.
param_attr(ParamAttr|None): The parameter attribute for the learnable
hidden-hidden weight matrix. Note:
- The shape of the weight matrix is :math:`(T \\times 3D)`, where
:math:`D` is the hidden size.
- All elements in the weight matrix can be divided into two parts.
The first part are weights of the update gate and reset gate with
shape :math:`(D \\times 2D)`, and the second part are weights for
candidate hidden state with shape :math:`(D \\times D)`.
bias_attr(ParamAttr): The parameter attribute for learnable the
hidden-hidden bias.
is_reverse(bool): Whether to compute reversed GRU, default
:attr:`False`.
gate_activation(str): The activation for update gate and reset gate.
Choices = ["sigmoid", "tanh", "relu", "identity"], default "sigmoid".
activation(str): The activation for candidate hidden state.
Choices = ["sigmoid", "tanh", "relu", "identity"], default "tanh".
Returns:
Variable: The hidden state of GRU. The shape is :math:`(T \\times D)`, \
and lod is the same with the input.
Examples:
.. code-block:: python
hidden_dim = 512
x = fluid.layers.fc(input=data, size=hidden_dim * 3)
hidden = fluid.layers.dynamic_gru(input=x, dim=hidden_dim)
"""
helper = LayerHelper('gru', **locals())
dtype = helper.input_dtype()
weight = helper.create_parameter(
attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
bias = helper.create_parameter(
attr=helper.bias_attr, shape=[1, 3 * size], dtype=dtype, is_bias=True)
inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
if h_0 != None:
assert h_0.shape == (
size, size), 'The shape of h0 should be(%d, %d)' % (size, size)
inputs['h0'] = h_0
hidden = helper.create_tmp_variable(dtype)
batch_gate = helper.create_tmp_variable(dtype)
batch_reset_hidden_prev = helper.create_tmp_variable(dtype)
batch_hidden = helper.create_tmp_variable(dtype)
helper.append_op(
type='gru',
inputs=inputs,
outputs={
'Hidden': hidden,
'BatchGate': batch_gate,
'BatchResetHiddenPrev': batch_reset_hidden_prev,
'BatchHidden': batch_hidden
},
attrs={
'is_reverse': is_reverse,
'gate_activation': gate_activation,
'activation': candidate_activation
})
return hidden
def gru_unit(input,
hidden,
size,
weight=None,
bias=None,
activation='tanh',
gate_activation='sigmoid'):
"""
GRU unit layer. The equation of a gru step is:
.. math::
u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)
r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)
m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m)
h_t & = dot((1-u_t), m_t) + dot(u_t, h_{t-1})
The inputs of gru unit includes :math:`z_t`, :math:`h_{t-1}`. In terms
of the equation above, the :math:`z_t` is split into 3 parts -
:math:`xu_t`, :math:`xr_t` and :math:`xm_t`. This means that in order to
implement a full GRU unit operator for an input, a fully
connected layer has to be applied, such that :math:`z_t = W_{fc}x_t`.
The terms :math:`u_t` and :math:`r_t` represent the update and reset gates
of the GRU cell. Unlike LSTM, GRU has one lesser gate. However, there is
an intermediate candidate hidden output, which is denoted by :math:`m_t`.
This layer has three outputs :math:`h_t`, :math:`dot(r_t, h_{t-1})`
and concatenation of :math:`u_t`, :math:`r_t` and :math:`m_t`.
Args:
input (Variable): The fc transformed input value of current step.
hidden (Variable): The hidden value of lstm unit from previous step.
size (integer): The input dimension value.
weight (ParamAttr): The weight parameters for gru unit. Default: None
bias (ParamAttr): The bias parameters for gru unit. Default: None
activation (string): The activation type for cell (actNode).
Default: 'tanh'
gate_activation (string): The activation type for gates (actGate).
Default: 'sigmoid'
Returns:
tuple: The hidden value, reset-hidden value and gate values.
Examples:
.. code-block:: python
# assuming we have x_t_data and prev_hidden of size=10
x_t = fluid.layers.fc(input=x_t_data, size=30)
hidden_val, r_h_val, gate_val = fluid.layers.gru_unit(input=x_t,
hidden = prev_hidden)
"""
activation_dict = dict(
identity=0,
sigmoid=1,
tanh=2,
relu=3, )
activation = activation_dict[activation]
gate_activation = activation_dict[gate_activation]
helper = LayerHelper('gru_unit', **locals())
dtype = helper.input_dtype()
size = size / 3
# create weight
if weight is None:
weight = helper.create_parameter(
attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
# create bias
if bias is None:
bias_size = [1, 3 * size]
bias = helper.create_parameter(
attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
gate = helper.create_tmp_variable(dtype)
reset_hidden_pre = helper.create_tmp_variable(dtype)
updated_hidden = helper.create_tmp_variable(dtype)
helper.append_op(
type='gru_unit',
inputs={'Input': input,
'HiddenPrev': hidden,
'Weight': weight},
outputs={
'Gate': gate,
'ResetHiddenPrev': reset_hidden_pre,
'Hidden': updated_hidden,
},
attrs={
'activation': 0,
'gate_activation': 1,
})
return updated_hidden, reset_hidden_pre, gate
def linear_chain_crf(input, label, param_attr=None):
helper = LayerHelper('linear_chain_crf', **locals())
size = input.shape[1]
transition = helper.create_parameter(
attr=helper.param_attr,
shape=[size + 2, size],
dtype=helper.input_dtype())
alpha = helper.create_tmp_variable(dtype=helper.input_dtype())
emission_exps = helper.create_tmp_variable(dtype=helper.input_dtype())
transition_exps = helper.create_tmp_variable(dtype=helper.input_dtype())
log_likelihood = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(
type='linear_chain_crf',
inputs={"Emission": [input],
"Transition": transition,
"Label": label},
outputs={
"Alpha": [alpha],
"EmissionExps": [emission_exps],
"TransitionExps": transition_exps,
"LogLikelihood": log_likelihood
})
return log_likelihood
def crf_decoding(input, param_attr, label=None):
helper = LayerHelper('crf_decoding', **locals())
transition = helper.get_parameter(param_attr.name)
viterbi_path = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(
type='crf_decoding',
inputs={"Emission": [input],
"Transition": transition,
"Label": label},
outputs={"ViterbiPath": [viterbi_path]})
return viterbi_path
def cos_sim(X, Y):
"""
This function performs the cosine similarity between two tensors
X and Y and returns that as the output.
"""
helper = LayerHelper('cos_sim', **locals())
out = helper.create_tmp_variable(dtype=X.dtype)
xnorm = helper.create_tmp_variable(dtype=X.dtype)
ynorm = helper.create_tmp_variable(dtype=X.dtype)
helper.append_op(
type='cos_sim',
inputs={'X': [X],
'Y': [Y]},
outputs={'Out': [out],
'XNorm': [xnorm],
'YNorm': [ynorm]})
return out
def dropout(x, dropout_prob, is_test=False, seed=None):
"""
Computes dropout.
Drop or keep each element of `x` independently. Dropout is a regularization
technique for reducing overfitting by preventing neuron co-adaption during
training. The dropout operator randomly set (according to the given dropout
probability) the outputs of some units to zero, while others are remain
unchanged.
Args:
x(variable): The input tensor.
dropout_prob(float): Probability of setting units to zero.
is_test(bool): A flag indicating whether it is in test phrase or not.
seed(int): A Python integer used to create random seeds. If this
parameter is set to None, a random seed is used.
NOTE: If an integer seed is given, always the same output
units will be dropped. DO NOT use a fixed seed in training.
Returns:
Variable: A tensor variable.
Examples:
.. code-block:: python
x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
droped = fluid.layers.dropout(input=x, dropout_rate=0.5)
"""
helper = LayerHelper('dropout', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
mask = helper.create_tmp_variable(dtype=x.dtype, stop_gradient=True)
helper.append_op(
type='dropout',
inputs={'X': [x]},
outputs={'Out': [out],
'Mask': [mask]},
attrs={
'dropout_prob': dropout_prob,
'is_test': is_test,
'fix_seed': seed is not None,
'seed': seed if seed is not None else 0
})
return out
def cross_entropy(input, label, soft_label=False):
"""
**Cross Entropy Layer**
This layer computes the cross entropy between `input` and `label`. It
supports both standard cross-entropy and soft-label cross-entropy loss
computation.
1) One-hot cross-entropy:
`soft_label = False`, `Label[i, 0]` indicates the class index for sample i:
.. math::
Y[i] = -\log(X[i, Label[i]])
2) Soft-label cross-entropy:
`soft_label = True`, `Label[i, j]` indicates the soft label of class j
for sample i:
.. math::
Y[i] = \sum_j{-Label[i, j] * log(X[i, j])}
Please make sure that in this case the summation of each row of `label`
equals one.
3) One-hot cross-entropy with vecterized `label`:
As a special case of 2), when each row of 'label' has only one
non-zero element which is equal to 1, soft-label cross-entropy degenerates
to a one-hot cross-entropy with one-hot label representation.
Args:
input (Variable|list): a 2-D tensor with shape [N x D], where N is the
batch size and D is the number of classes. This
input is a probability computed by the previous
operator, which is almost always the result of
a softmax operator.
label (Variable|list): the ground truth which is a 2-D tensor. When
`soft_label` is set to `False`, `label` is a
tensor<int64> with shape [N x 1]. When
`soft_label` is set to `True`, `label` is a
tensor<float/double> with shape [N x D].
soft_label (bool): a flag indicating whether to
interpretate the given labels as soft
labels, default `False`.
Returns:
A 2-D tensor with shape [N x 1], the cross entropy loss.
Raises:
`ValueError`: 1) the 1st dimension of `input` and `label` are not equal.
2) when `soft_label == True`, and the 2nd dimension of
`input` and `label` are not equal.
3) when `soft_label == False`, and the 2nd dimension of
`label` is not 1.
Examples:
.. code-block:: python
predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
cost = fluid.layers.cross_entropy(input=predict, label=label)
"""
helper = LayerHelper('cross_entropy', **locals())
out = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type='cross_entropy',
inputs={'X': [input],
'Label': [label]},
outputs={'Y': [out]},
attrs={"soft_label": soft_label})
return out
def square_error_cost(input, label):
"""
**Square error cost layer**
This layer accepts input predictions and target label and returns the
squared error cost.
For predictions, :math:`X`, and target labels, :math:`Y`, the equation is:
.. math::
Out = (X - Y)^2
In the above equation:
* :math:`X`: Input predictions, a tensor.
* :math:`Y`: Input labels, a tensor.
* :math:`Out`: Output value, same shape with :math:`X`.
Args:
input(Variable): Input tensor, has predictions.
label(Variable): Label tensor, has target labels.
Returns:
Variable: The tensor variable storing the element-wise squared error \
difference of input and label.
Examples:
.. code-block:: python
y = layers.data(name='y', shape=[1], dtype='float32')
y_predict = layers.data(name='y_predict', shape=[1], dtype='float32')
cost = layers.square_error_cost(input=y_predict, label=y)
"""
helper = LayerHelper('square_error_cost', **locals())
minus_out = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type='elementwise_sub',
inputs={'X': [input],
'Y': [label]},
outputs={'Out': [minus_out]})
square_out = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type='square', inputs={'X': [minus_out]},
outputs={'Out': [square_out]})
return square_out
def chunk_eval(input,
label,
chunk_scheme,
num_chunk_types,
excluded_chunk_types=None):
"""
This function computes and outputs the precision, recall and
F1-score of chunk detection.
"""
helper = LayerHelper("chunk_eval", **locals())
# prepare output
precision = helper.create_tmp_variable(dtype="float32")
recall = helper.create_tmp_variable(dtype="float32")
f1_score = helper.create_tmp_variable(dtype="float32")
num_infer_chunks = helper.create_tmp_variable(dtype="int64")
num_label_chunks = helper.create_tmp_variable(dtype="int64")
num_correct_chunks = helper.create_tmp_variable(dtype="int64")
helper.append_op(
type="chunk_eval",
inputs={"Inference": [input],
"Label": [label]},
outputs={
"Precision": [precision],
"Recall": [recall],
"F1-Score": [f1_score],
"NumInferChunks": [num_infer_chunks],
"NumLabelChunks": [num_label_chunks],
"NumCorrectChunks": [num_correct_chunks]
},
attrs={
"num_chunk_types": num_chunk_types,
"chunk_scheme": chunk_scheme,
"excluded_chunk_types": excluded_chunk_types or []
})
return (precision, recall, f1_score, num_infer_chunks, num_label_chunks,
num_correct_chunks)
def sequence_conv(input,
num_filters,
filter_size=3,
filter_stride=1,
padding=None,
bias_attr=None,
param_attr=None,
act=None):
"""
This function creates the op for sequence_conv, using the inputs and
other convolutional configurations for the filters and stride as given
in the input parameters to the function.
"""
# FIXME(dzh) : want to unify the argument of python layer
# function. So we ignore some unecessary attributes.
# such as, padding_trainable, context_start.
helper = LayerHelper('sequence_conv', **locals())
dtype = helper.input_dtype()
filter_shape = [filter_size * input.shape[1], num_filters]
filter_param = helper.create_parameter(
attr=helper.param_attr, shape=filter_shape, dtype=dtype)
pre_bias = helper.create_tmp_variable(dtype)
helper.append_op(
type='sequence_conv',
inputs={
'X': [input],
'Filter': [filter_param],
},
outputs={"Out": pre_bias},
attrs={
'contextStride': filter_stride,
'contextStart': -int(filter_size / 2),
'contextLength': filter_size
})
pre_act = helper.append_bias_op(pre_bias)
return helper.append_activation(pre_act)
def sequence_softmax(input, param_attr=None, bias_attr=None, use_cudnn=True):
helper = LayerHelper('sequence_softmax', **locals())
dtype = helper.input_dtype()
softmax_out = helper.create_tmp_variable(dtype)
helper.append_op(
type="sequence_softmax",
inputs={"X": input},
outputs={"Out": softmax_out},
attrs={"use_cudnn": use_cudnn})
return softmax_out
def softmax(input, param_attr=None, bias_attr=None, use_cudnn=True):
helper = LayerHelper('softmax', **locals())
dtype = helper.input_dtype()
softmax_out = helper.create_tmp_variable(dtype)
helper.append_op(
type="softmax",
inputs={"X": input},
outputs={"Out": softmax_out},
attrs={"use_cudnn": use_cudnn})
return softmax_out
def conv2d(input,
num_filters,
filter_size,
stride=1,
padding=0,
dilation=1,
groups=None,
param_attr=None,
bias_attr=None,
use_cudnn=True,
use_mkldnn=False,
act=None,
name=None):
"""
**Convlution2D Layer**
The convolution2D layer calculates the output based on the input, filter
and strides, paddings, dilations, groups parameters. Input(Input) and
Output(Output) are in NCHW format. Where N is batch size, C is the number of
channels, H is the height of the feature, and W is the width of the feature.
The details of convolution layer, please refer UFLDL's `convolution,
<http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/>`_ .
If bias attribution and activation type are provided, bias is added to the
output of the convolution, and the corresponding activation function is
applied to the final result.
For each input :math:`X`, the equation is:
.. math::
Out = \sigma (W \\ast X + b)
In the above equation:
* :math:`X`: Input value, a tensor with NCHW format.
* :math:`W`: Filter value, a tensor with MCHW format.
* :math:`\\ast`: Convolution operation.
* :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
* :math:`\\sigma`: Activation function.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be
different.
Example:
- Input:
Input shape: $(N, C_{in}, H_{in}, W_{in})$
Filter shape: $(C_{out}, C_{in}, H_f, W_f)$
- Output:
Output shape: $(N, C_{out}, H_{out}, W_{out})$
Where
.. math::
H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\
W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1
Args:
input(Variable): The input image with [N, C, H, W] format.
num_filters(int): The number of filter. It is as same as the output
image channel.
filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square.
stride(int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1.
padding(int|tuple): The padding size. If padding is a tuple, it must
contain two integers, (padding_H, padding_W). Otherwise, the
padding_H = padding_W = padding. Default: padding = 0.
dilation(int|tuple): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_H, dilation_W). Otherwise, the
dilation_H = dilation_W = dilation. Default: dilation = 1.
groups(int): The groups number of the Conv2d Layer. According to grouped
convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
the first half of the filters is only connected to the first half
of the input channels, while the second half of the filters is only
connected to the second half of the input channels. Default: groups=1
param_attr(ParamAttr): The parameters to the Conv2d Layer. Default: None
bias_attr(ParamAttr): Bias parameter for the Conv2d layer. Default: None
use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
act(str): Activation type. Default: None
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The tensor variable storing the convolution and \
non-linearity activation result.
Raises:
ValueError: If the shapes of input, filter_size, stride, padding and
groups mismatch.
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 32, 32], dtype='float32')
conv2d = fluid.layers.conv2d(
input=data, num_filters=2, filter_size=3, act="relu")
"""
if stride is None:
stride = [1, 1]
num_channels = input.shape[1]
l_type = 'conv2d'
if (num_channels == groups and num_filters % num_channels == 0 and
not use_cudnn):
l_type = 'depthwise_conv2d'
helper = LayerHelper(l_type, **locals())
dtype = helper.input_dtype()
if groups is None:
num_filter_channels = num_channels
else:
if num_channels % groups != 0:
raise ValueError("num_channels must be divisible by groups.")
num_filter_channels = num_channels / groups
filter_size = utils.convert_to_list(filter_size, 2, 'filter_size')
stride = utils.convert_to_list(stride, 2, 'stride')
padding = utils.convert_to_list(padding, 2, 'padding')
dilation = utils.convert_to_list(dilation, 2, 'dilation')
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
input_shape = input.shape
filter_shape = [num_filters, num_filter_channels] + filter_size
def _get_default_param_initializer():
std = (2.0 / (filter_size[0]**2 * num_channels))**0.5
return Normal(0.0, std, 0)
filter_param = helper.create_parameter(
attr=helper.param_attr,
shape=filter_shape,
dtype=dtype,
default_initializer=_get_default_param_initializer())
pre_bias = helper.create_tmp_variable(dtype)
helper.append_op(
type=l_type,
inputs={
'Input': input,
'Filter': filter_param,
},
outputs={"Output": pre_bias},
attrs={
'strides': stride,
'paddings': padding,
'dilations': dilation,
'groups': groups,
'use_cudnn': use_cudnn,
'use_mkldnn': use_mkldnn
})
pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
return helper.append_activation(pre_act)
def sequence_pool(input, pool_type):
"""
This function add the operator for sequence pooling.
It pools features of all time-steps of each instance, and is applied
on top of the input using pool_type mentioned in the parameters.
It supports four pool_type:
- average: :math:`Out[i] = \\frac{\sum_i X_i}{N}`
- sum: :math:`Out[i] = \sum_jX_{ij}`
- sqrt: :math:`Out[i] = \\frac{\sum_jX_{ij}}{\sqrt{len(X_i)}}`
- max: :math:`Out[i] = max(X_i)`
.. code-block:: text
x is a 1-level LoDTensor:
x.lod = [[0, 2, 5, 7]]
x.data = [1, 3, 2, 4, 6, 5, 1]
x.dims = [7, 1]
then output is a Tensor:
out.dim = [3, 1]
with condition len(x.lod[-1]) - 1 == out.dims[0]
for different pool_type:
average: out.data = [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
sum : out.data = [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1
sqrt : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
max : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
Args:
input(variable): The input variable which is a LoDTensor.
pool_type (string): The pooling type of sequence_pool.
It supports average, sum, sqrt and max.
Returns:
The sequence pooling variable which is a Tensor.
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
avg_x = fluid.layers.sequence_pool(input=x, pool_type='average')
sum_x = fluid.layers.sequence_pool(input=x, pool_type='sum')
sqrt_x = fluid.layers.sequence_pool(input=x, pool_type='sqrt')
max_x = fluid.layers.sequence_pool(input=x, pool_type='max')
"""
helper = LayerHelper('sequence_pool', **locals())
dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype)
max_index = helper.create_tmp_variable(dtype)
helper.append_op(
type="sequence_pool",
inputs={"X": input},
outputs={"Out": pool_out,
"MaxIndex": max_index},
attrs={"pooltype": pool_type.upper()})
# when pool_type is max, variable max_index is initialized,
# so we stop the gradient explicitly here
if pool_type == 'max':
max_index.stop_gradient = True
return pool_out
def sequence_first_step(input):
"""
This funciton get the first step of sequence.
.. code-block:: text
x is a 1-level LoDTensor:
x.lod = [[0, 2, 5, 7]]
x.data = [1, 3, 2, 4, 6, 5, 1]
x.dims = [7, 1]
then output is a Tensor:
out.dim = [3, 1]
with condition len(x.lod[-1]) - 1 == out.dims[0]
out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
Args:
input(variable): The input variable which is a LoDTensor.
Returns:
The sequence's first step variable which is a Tensor.
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
x_first_step = fluid.layers.sequence_first_step(input=x)
"""
return sequence_pool(input=input, pool_type="first")
def sequence_last_step(input):
"""
This funciton get the last step of sequence.
.. code-block:: text
x is a 1-level LoDTensor:
x.lod = [[0, 2, 5, 7]]
x.data = [1, 3, 2, 4, 6, 5, 1]
x.dims = [7, 1]
then output is a Tensor:
out.dim = [3, 1]
with condition len(x.lod[-1]) - 1 == out.dims[0]
out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
Args:
input(variable): The input variable which is a LoDTensor.
Returns:
The sequence's last step variable which is a Tensor.
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
x_last_step = fluid.layers.sequence_last_step(input=x)
"""
return sequence_pool(input=input, pool_type="last")
def pool2d(input,
pool_size=-1,
pool_type="max",
pool_stride=1,
pool_padding=0,
global_pooling=False,
use_cudnn=True,
ceil_mode=False,
use_mkldnn=False,
name=None):
"""
This function adds the operator for pooling in 2 dimensions, using the
pooling configurations mentioned in input parameters.
"""
if pool_type not in ["max", "avg"]:
raise ValueError(
"Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",
str(pool_type))
if global_pooling is False and pool_size == -1:
raise ValueError(
"When the global_pooling is False, pool_size must be passed "
"and be a valid value. Received pool_size: " + str(pool_size))
pool_size = utils.convert_to_list(pool_size, 2, 'pool_size')
pool_padding = utils.convert_to_list(pool_padding, 2, 'pool_padding')
pool_stride = utils.convert_to_list(pool_stride, 2, 'pool_stride')
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
helper = LayerHelper('pool2d', **locals())
dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype)
helper.append_op(
type="pool2d",
inputs={"X": input},
outputs={"Out": pool_out},
attrs={
"pooling_type": pool_type,
"ksize": pool_size,
"global_pooling": global_pooling,
"strides": pool_stride,
"paddings": pool_padding,
"use_cudnn": use_cudnn,
"ceil_mode": ceil_mode,
"use_mkldnn": use_mkldnn
})
return pool_out
def batch_norm(input,
act=None,
is_test=False,
momentum=0.9,
epsilon=1e-05,
param_attr=None,
bias_attr=None,
data_layout='NCHW',
in_place=False,
name=None,
moving_mean_name=None,
moving_variance_name=None):
"""
This function helps create an operator to implement
the BatchNorm layer using the configurations from the input parameters.
"""
helper = LayerHelper('batch_norm', **locals())
dtype = helper.input_dtype()
input_shape = input.shape
if data_layout == 'NCHW':
channel_num = input_shape[1]
else:
if data_layout == 'NHWC':
channel_num = input_shape[-1]
else:
raise ValueError("unsupported data layout:" + data_layout)
param_shape = [channel_num]
# create parameter
scale = helper.create_parameter(
attr=helper.param_attr,
shape=param_shape,
dtype=dtype,
default_initializer=Constant(1.0))
bias = helper.create_parameter(
attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True)
mean = helper.create_parameter(
attr=ParamAttr(
name=moving_mean_name, initializer=Constant(0.0), trainable=False),
shape=param_shape,
dtype=input.dtype)
mean.stop_gradient = True
variance = helper.create_parameter(
attr=ParamAttr(
name=moving_variance_name,
initializer=Constant(1.0),
trainable=False),
shape=param_shape,
dtype=input.dtype)
variance.stop_gradient = True
# create output
# mean and mean_out share the same memory
mean_out = mean
# variance and variance out share the same memory
variance_out = variance
saved_mean = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
saved_variance = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
batch_norm_out = input if in_place else helper.create_tmp_variable(dtype)
helper.append_op(
type="batch_norm",
inputs={
"X": input,
"Scale": scale,
"Bias": bias,
"Mean": mean,
"Variance": variance
},
outputs={
"Y": batch_norm_out,
"MeanOut": mean_out,
"VarianceOut": variance_out,
"SavedMean": saved_mean,
"SavedVariance": saved_variance
},
attrs={"momentum": momentum,
"epsilon": epsilon,
"is_test": is_test})
return helper.append_activation(batch_norm_out)
def layer_norm(input,
scale=True,
shift=True,
begin_norm_axis=1,
epsilon=1e-05,
param_attr=None,
bias_attr=None,
act=None,
name=None):
"""
**Layer Normalization**
Assume feature vectors exist on dimensions
:attr:`begin_norm_axis ... rank(input)` and calculate the moment statistics
along these dimensions for each feature vector :math:`a` with size
:math:`H`, then normalize each feature vector using the corresponding
statistics. After that, apply learnable gain and bias on the normalized
tensor to scale and shift if :attr:`scale` and :attr:`shift` are set.
Refer to `Layer Normalization <https://arxiv.org/pdf/1607.06450v1.pdf>`_
The formula is as follows:
.. math::
\\mu & = \\frac{1}{H}\\sum_{i=1}^{H} a_i
\\sigma & = \\sqrt{\\frac{1}{H}\sum_{i=1}^{H}(a_i - \\mu)^2}
h & = f(\\frac{g}{\\sigma}(a - \\mu) + b)
Args:
input(Variable): The input tensor variable.
scale(bool): Whether to learn the adaptive gain :math:`g` after
normalization.
shift(bool): Whether to learn the adaptive bias :math:`b` after
normalization.
begin_norm_axis(bool): The normalization will be performed along
dimensions from :attr:`begin_norm_axis` to :attr:`rank(input)`.
epsilon(float): The small value added to the variance to prevent
division by zero.
param_attr(ParamAttr|None): The parameter attribute for the learnable
gain :math:`g`.
bias_attr(ParamAttr|None): The parameter attribute for the learnable
bias :math:`b`.
act(str): Activation to be applied to the output of layer normalizaiton.
Returns:
Variable: A tensor variable with the same shape as the input.
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 32, 32], dtype='float32')
x = fluid.layers.layer_norm(input=data, begin_norm_axis=1)
"""
helper = LayerHelper('layer_norm', **locals())
dtype = helper.input_dtype()
# create intput and parameters
inputs = {'X': input}
input_shape = input.shape
param_shape = [reduce(lambda x, y: x * y, input_shape[begin_norm_axis:])]
if scale:
scale = helper.create_parameter(
attr=helper.param_attr,
shape=param_shape,
dtype=dtype,
default_initializer=Constant(1.0))
inputs['Scale'] = scale
if shift:
assert bias_attr is not False
bias = helper.create_parameter(
attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True)
inputs['Bias'] = bias
# create output
mean_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
variance_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
layer_norm_out = helper.create_tmp_variable(dtype)
helper.append_op(
type="layer_norm",
inputs=inputs,
outputs={
"Y": layer_norm_out,
"Mean": mean_out,
"Variance": variance_out,
},
attrs={"epsilon": epsilon,
"begin_norm_axis": begin_norm_axis})
return helper.append_activation(layer_norm_out)
def beam_search_decode(ids, scores, name=None):
helper = LayerHelper('beam_search_decode', **locals())
sentence_ids = helper.create_tmp_variable(dtype=ids.dtype)
sentence_scores = helper.create_tmp_variable(dtype=ids.dtype)
helper.append_op(
type="beam_search_decode",
inputs={"Ids": ids,
"Scores": scores},
outputs={
"SentenceIds": sentence_ids,
"SentenceScores": sentence_scores
})
return sentence_ids, sentence_scores
def conv2d_transpose(input,
num_filters,
output_size=None,
filter_size=None,
padding=0,
stride=1,
dilation=1,
param_attr=None,
bias_attr=None,
use_cudnn=True,
act=None,
name=None):
"""
**Convlution2D transpose layer**
The convolution2D transpose layer calculates the output based on the input,
filter, and dilations, strides, paddings. Input(Input) and output(Output)
are in NCHW format. Where N is batch size, C is the number of channels,
H is the height of the feature, and W is the width of the feature.
Parameters(dilations, strides, paddings) are two elements. These two elements
represent height and width, respectively. The details of convolution transpose
layer, please refer to the following explanation and references
`therein <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_.
For each input :math:`X`, the equation is:
.. math::
Out = W \\ast X
In the above equation:
* :math:`X`: Input value, a tensor with NCHW format.
* :math:`W`: Filter value, a tensor with MCHW format.
* :math:`\\ast` : Convolution transpose operation.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be
different.
Example:
- Input:
Input shape: $(N, C_{in}, H_{in}, W_{in})$
Filter shape: $(C_{in}, C_{out}, H_f, W_f)$
- Output:
Output shape: $(N, C_{out}, H_{out}, W_{out})$
Where
.. math::
H_{out} &= (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\\\
W_{out} &= (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1
Args:
input(Variable): The input image with [N, C, H, W] format.
num_filters(int): The number of the filter. It is as same as the output
image channel.
output_size(int|tuple|None): The output image size. If output size is a
tuple, it must contain two integers, (image_H, image_W). This
parameter only works when filter_size is None.
filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square. None if use output size to
calculate filter_size.
padding(int|tuple): The padding size. If padding is a tuple, it must
contain two integers, (padding_H, padding_W). Otherwise, the
padding_H = padding_W = padding. Default: padding = 0.
stride(int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1.
dilation(int|tuple): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_H, dilation_W). Otherwise, the
dilation_H = dilation_W = dilation. Default: dilation = 1.
param_attr(ParamAttr): The parameters to the Conv2d_transpose Layer.
Default: None
bias_attr(ParamAttr): Bias parameter for the Conv2d layer. Default: None
use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
act(str): Activation type. Default: None
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The tensor variable storing the convolution transpose result.
Raises:
ValueError: If the shapes of input, filter_size, stride, padding and
groups mismatch.
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 32, 32], dtype='float32')
conv2d_transpose = fluid.layers.conv2d_transpose(
input=data, num_filters=2, filter_size=3)
"""
helper = LayerHelper("conv2d_transpose", **locals())
if not isinstance(input, Variable):
raise TypeError("Input of conv2d_transpose must be Variable")
input_channel = input.shape[1]
padding = utils.convert_to_list(padding, 2, 'padding')
stride = utils.convert_to_list(stride, 2, 'stride')
dilation = utils.convert_to_list(dilation, 2, 'dilation')
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
if filter_size is None:
if output_size is None:
raise ValueError("output_size must be set when filter_size is None")
if isinstance(output_size, int):
output_size = [output_size, output_size]
h_in = input.shape[2]
w_in = input.shape[3]
filter_size_h = (output_size[0] - (h_in - 1) * stride[0] + 2 *
padding[0] - 1) / dilation[0] + 1
filter_size_w = (output_size[1] - (w_in - 1) * stride[1] + 2 *
padding[1] - 1) / dilation[1] + 1
filter_size = [filter_size_h, filter_size_w]
else:
filter_size = utils.convert_to_list(filter_size, 2,
'conv2d_transpose.filter_size')
filter_shape = [input_channel, num_filters] + filter_size
img_filter = helper.create_parameter(
dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)
pre_bias = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type='conv2d_transpose',
inputs={'Input': [input],
'Filter': [img_filter]},
outputs={'Output': pre_bias},
attrs={
'strides': stride,
'paddings': padding,
'dilations': dilation,
'use_cudnn': use_cudnn
})
pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
out = helper.append_activation(pre_act)
return out
def sequence_expand(x, y, ref_level=-1, name=None):
"""Sequence Expand Layer. This layer will expand the input variable **x**
according to specified level lod of **y**. Please note that lod level of
**x** is at most 1 and rank of **x** is at least 2. When rank of **x**
is greater than 2, then it would be viewed as a 2-D tensor.
Following examples will explain how sequence_expand works:
.. code-block:: text
* Case 1
x is a LoDTensor:
x.lod = [[0, 2, 4]]
x.data = [[a], [b], [c], [d]]
x.dims = [4, 1]
y is a LoDTensor:
y.lod = [[0, 2, 4],
[0, 3, 6, 7, 8]]
ref_level: 0
then output is a 1-level LoDTensor:
out.lod = [[0, 2, 4, 6, 8]]
out.data = [[a], [b], [a], [b], [c], [d], [c], [d]]
out.dims = [8, 1]
* Case 2
x is a Tensor:
x.data = [[a], [b], [c]]
x.dims = [3, 1]
y is a LoDTensor:
y.lod = [[0, 2, 2, 5]]
ref_level: -1
then output is a Tensor:
out.data = [[a], [a], [c], [c], [c]]
out.dims = [5, 1]
Args:
x (Variable): The input variable which is a Tensor or LoDTensor.
y (Variable): The input variable which is a LoDTensor.
ref_level (int): Lod level of `y` to be referred by `x`. If set to -1,
refer the last level of lod.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The expanded variable which is a LoDTensor.
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[10], dtype='float32')
y = fluid.layers.data(name='y', shape=[10, 20],
dtype='float32', lod_level=1)
out = layers.sequence_expand(x=x, y=y, ref_level=0)
"""
helper = LayerHelper('sequence_expand', input=x, **locals())
dtype = helper.input_dtype()
tmp = helper.create_tmp_variable(dtype)
helper.append_op(
type='sequence_expand',
inputs={'X': x,
'Y': y},
outputs={'Out': tmp},
attrs={'ref_level': ref_level})
return tmp
def beam_search(pre_ids, ids, scores, beam_size, end_id, level=0):
'''
This function implements the beam search algorithm.
'''
helper = LayerHelper('beam_search', **locals())
score_type = scores.dtype
id_type = ids.dtype
selected_scores = helper.create_tmp_variable(dtype=score_type)
selected_ids = helper.create_tmp_variable(dtype=id_type)
helper.append_op(
type='beam_search',
inputs={
'pre_ids': pre_ids,
'ids': ids,
'scores': scores,
},
outputs={
'selected_ids': selected_ids,
'selected_scores': selected_scores,
},
attrs={
# TODO(ChunweiYan) to assure other value support
'level': level,
'beam_size': beam_size,
'end_id': end_id,
})
return selected_ids, selected_scores
def lstm_unit(x_t,
hidden_t_prev,
cell_t_prev,
forget_bias=0.0,
param_attr=None,
bias_attr=None,
name=None):
"""Lstm unit layer. The equation of a lstm step is:
.. math::
i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i)
f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + b_f)
c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t + W_{h_c}h_{t-1} + b_c)
o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + b_o)
h_t & = o_t tanh(c_t)
The inputs of lstm unit include :math:`x_t`, :math:`h_{t-1}` and
:math:`c_{t-1}`. The 2nd dimensions of :math:`h_{t-1}` and :math:`c_{t-1}`
should be same. The implementation separates the linear transformation and
non-linear transformation apart. Here, we take :math:`i_t` as an example.
The linear transformation is applied by calling a `fc` layer and the
equation is:
.. math::
L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i
The non-linear transformation is applied by calling `lstm_unit_op` and the
equation is:
.. math::
i_t = \sigma(L_{i_t})
This layer has two outputs including :math:`h_t` and :math:`o_t`.
Args:
x_t (Variable): The input value of current step, a 2-D tensor with shape
M x N, M for batch size and N for input size.
hidden_t_prev (Variable): The hidden value of lstm unit, a 2-D tensor
with shape M x S, M for batch size and S for size of lstm unit.
cell_t_prev (Variable): The cell value of lstm unit, a 2-D tensor with
shape M x S, M for batch size and S for size of lstm unit.
forget_bias (float): The forget bias of lstm unit.
param_attr (ParamAttr): The attributes of parameter weights, used to set
initializer, name etc.
bias_attr (ParamAttr): The attributes of bias weights, if not False,
bias weights will be created and be set to default value.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
tuple: The hidden value and cell value of lstm unit.
Raises:
ValueError: The ranks of **x_t**, **hidden_t_prev** and **cell_t_prev**
not be 2 or the 1st dimensions of **x_t**, **hidden_t_prev**
and **cell_t_prev** not be the same or the 2nd dimensions of
**hidden_t_prev** and **cell_t_prev** not be the same.
Examples:
.. code-block:: python
x_t = fluid.layers.fc(input=x_t_data, size=10)
prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=30)
prev_cell = fluid.layers.fc(input=prev_cell_data, size=30)
hidden_value, cell_value = fluid.layers.lstm_unit(x_t=x_t,
hidden_t_prev=prev_hidden,
cell_t_prev=prev_cell)
"""
helper = LayerHelper('lstm_unit', **locals())
if len(x_t.shape) != 2:
raise ValueError("Rank of x_t must be 2.")
if len(hidden_t_prev.shape) != 2:
raise ValueError("Rank of hidden_t_prev must be 2.")
if len(cell_t_prev.shape) != 2:
raise ValueError("Rank of cell_t_prev must be 2.")
if x_t.shape[0] != hidden_t_prev.shape[0] or x_t.shape[
0] != cell_t_prev.shape[0]:
raise ValueError("The 1st dimensions of x_t, hidden_t_prev and "
"cell_t_prev must be the same.")
if hidden_t_prev.shape[1] != cell_t_prev.shape[1]:
raise ValueError("The 2nd dimensions of hidden_t_prev and "
"cell_t_prev must be the same.")
if bias_attr is None:
bias_attr = ParamAttr()
size = cell_t_prev.shape[1]
concat_out = concat(input=[x_t, hidden_t_prev], axis=1)
fc_out = fc(input=concat_out,
size=4 * size,
param_attr=param_attr,
bias_attr=bias_attr)
dtype = x_t.dtype
c = helper.create_tmp_variable(dtype)
h = helper.create_tmp_variable(dtype)
helper.append_op(
type='lstm_unit',
inputs={"X": fc_out,
"C_prev": cell_t_prev},
outputs={"C": c,
"H": h},
attrs={"forget_bias": forget_bias})
return h, c
def reduce_sum(input, dim=None, keep_dim=False, name=None):
"""
Computes the sum of tensor elements over the given dimension.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
dim (int|None): The dimension along which the sum is performed. If
:attr:`None`, sum all elements of :attr:`input` and return a
Tensor variable with a single element, otherwise must be in the
range :math:`[-rank(input), rank(input))`. If :math:`dim < 0`,
the dimension to reduce is :math:`rank + dim`.
keep_dim (bool|False): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The reduced Tensor variable.
Examples:
.. code-block:: python
# x is a Tensor variable with following elements:
# [[0.2, 0.3, 0.5, 0.9]
# [0.1, 0.2, 0.6, 0.7]]
# Each example is followed by the correspending output tensor.
fluid.layers.reduce_sum(x) # [3.5]
fluid.layers.reduce_sum(x, dim=0) # [0.3, 0.5, 1.1, 1.6]
fluid.layers.reduce_sum(x, dim=-1) # [1.9, 1.6]
fluid.layers.reduce_sum(x, dim=1, keep_dim=True) # [[1.9], [1.6]]
"""
helper = LayerHelper('reduce_sum', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(
type='reduce_sum',
inputs={'X': input},
outputs={'Out': out},
attrs={
'dim': dim if dim != None else 0,
'keep_dim': keep_dim,
'reduce_all': True if dim == None else False
})
return out
def reduce_mean(input, dim=None, keep_dim=False, name=None):
"""
Computes the mean of tensor elements over the given dimension.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
dim (int|None): The dimension along which the mean is computed. If
:attr:`None`, compute the mean over all elements of :attr:`input`
and return a Tensor variable with a single element, otherwise
must be in the range :math:`[-rank(input), rank(input))`. If
:math:`dim < 0`, the dimension to reduce is :math:`rank + dim`.
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The reduced Tensor variable.
Examples:
.. code-block:: python
# x is a Tensor variable with following elements:
# [[0.2, 0.3, 0.5, 0.9]
# [0.1, 0.2, 0.6, 0.7]]
# Each example is followed by the correspending output tensor.
fluid.layers.reduce_mean(x) # [0.4375]
fluid.layers.reduce_mean(x, dim=0) # [0.15, 0.25, 0.55, 0.8]
fluid.layers.reduce_mean(x, dim=-1) # [0.475, 0.4]
fluid.layers.reduce_mean(x, dim=1, keep_dim=True) # [[0.475], [0.4]]
"""
helper = LayerHelper('reduce_mean', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(
type='reduce_mean',
inputs={'X': input},
outputs={'Out': out},
attrs={
'dim': dim if dim != None else 0,
'keep_dim': keep_dim,
'reduce_all': True if dim == None else False
})
return out
def reduce_max(input, dim=None, keep_dim=False, name=None):
"""
Computes the maximum of tensor elements over the given dimension.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
dim (int|None): The dimension along which the maximum is computed.
If :attr:`None`, compute the maximum over all elements of
:attr:`input` and return a Tensor variable with a single element,
otherwise must be in the range :math:`[-rank(input), rank(input))`.
If :math:`dim < 0`, the dimension to reduce is :math:`rank + dim`.
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The reduced Tensor variable.
Examples:
.. code-block:: python
# x is a Tensor variable with following elements:
# [[0.2, 0.3, 0.5, 0.9]
# [0.1, 0.2, 0.6, 0.7]]
# Each example is followed by the correspending output tensor.
fluid.layers.reduce_max(x) # [0.9]
fluid.layers.reduce_max(x, dim=0) # [0.2, 0.3, 0.6, 0.9]
fluid.layers.reduce_max(x, dim=-1) # [0.9, 0.7]
fluid.layers.reduce_max(x, dim=1, keep_dim=True) # [[0.9], [0.7]]
"""
helper = LayerHelper('reduce_max', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(
type='reduce_max',
inputs={'X': input},
outputs={'Out': out},
attrs={
'dim': dim if dim != None else 0,
'keep_dim': keep_dim,
'reduce_all': True if dim == None else False
})
return out
def reduce_min(input, dim=None, keep_dim=False, name=None):
"""
Computes the minimum of tensor elements over the given dimension.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
dim (int|None): The dimension along which the minimum is computed.
If :attr:`None`, compute the minimum over all elements of
:attr:`input` and return a Tensor variable with a single element,
otherwise must be in the range :math:`[-rank(input), rank(input))`.
If :math:`dim < 0`, the dimension to reduce is :math:`rank + dim`.
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The reduced Tensor variable.
Examples:
.. code-block:: python
# x is a Tensor variable with following elements:
# [[0.2, 0.3, 0.5, 0.9]
# [0.1, 0.2, 0.6, 0.7]]
# Each example is followed by the correspending output tensor.
fluid.layers.reduce_min(x) # [0.1]
fluid.layers.reduce_min(x, dim=0) # [0.1, 0.2, 0.5, 0.7]
fluid.layers.reduce_min(x, dim=-1) # [0.2, 0.1]
fluid.layers.reduce_min(x, dim=1, keep_dim=True) # [[0.2], [0.1]]
"""
helper = LayerHelper('reduce_min', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(
type='reduce_min',
inputs={'X': input},
outputs={'Out': out},
attrs={
'dim': dim if dim != None else 0,
'keep_dim': keep_dim,
'reduce_all': True if dim == None else False
})
return out
def reduce_prod(input, dim=None, keep_dim=False, name=None):
"""
Computes the product of tensor elements over the given dimension.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
dim (int|None): The dimension along which the product is performed. If
:attr:`None`, multipy all elements of :attr:`input` and return a
Tensor variable with a single element, otherwise must be in the
range :math:`[-rank(input), rank(input))`. If :math:`dim < 0`,
the dimension to reduce is :math:`rank + dim`.
keep_dim (bool|False): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true.
name(str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
Returns:
Variable: The reduced Tensor variable.
Examples:
.. code-block:: python
# x is a Tensor variable with following elements:
# [[0.2, 0.3, 0.5, 0.9]
# [0.1, 0.2, 0.6, 0.7]]
# Each example is followed by the correspending output tensor.
fluid.layers.reduce_prod(x) # [0.0002268]
fluid.layers.reduce_prod(x, dim=0) # [0.02, 0.06, 0.3, 0.63]
fluid.layers.reduce_prod(x, dim=-1) # [0.027, 0.0084]
fluid.layers.reduce_prod(x, dim=1,
keep_dim=True) # [[0.027], [0.0084]]
"""
helper = LayerHelper('reduce_prod', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(
type='reduce_prod',
inputs={'X': input},
outputs={'Out': out},
attrs={
'dim': dim if dim != None else 0,
'keep_dim': keep_dim,
'reduce_all': True if dim == None else False
})
return out
def split(input, num_or_sections, dim=-1, name=None):
"""
Split the input tensor into multiple sub-tensors.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
num_or_sections (int|list): If :attr:`num_or_sections` is an integer,
then the integer indicates the number of equal sized sub-tensors
that the tensor will be divided into. If :attr:`num_or_sections`
is a list of integers, the length of list indicates the number of
sub-tensors and the integers indicate the sizes of sub-tensors'
:attr:`dim` dimension orderly.
dim (int): The dimension along which to split. If :math:`dim < 0`, the
dimension to split along is :math:`rank(input) + dim`.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
List: The list of segmented tensor variables.
Examples:
.. code-block:: python
# x is a Tensor variable with shape [3, 9, 5]:
x0, x1, x2 = fluid.layers.split(x, num_or_sections=3, dim=1)
x0.shape # [3, 3, 5]
x1.shape # [3, 3, 5]
x2.shape # [3, 3, 5]
x0, x1, x2 = fluid.layers.split(x, num_or_sections=[2, 3, 4], dim=1)
x0.shape # [3, 2, 5]
x1.shape # [3, 3, 5]
x2.shape # [3, 4, 5]
"""
helper = LayerHelper('split', **locals())
input_shape = input.shape
dim = (len(input_shape) + dim) if dim < 0 else dim
if isinstance(num_or_sections, int):
assert num_or_sections > 1, 'num_or_sections must be more than 1.'
num = num_or_sections
else:
assert len(num_or_sections) < input_shape[
dim], 'len(num_or_sections) must not be more than input.shape[dim].'
num = len(num_or_sections)
outs = [
helper.create_tmp_variable(dtype=helper.input_dtype())
for i in range(num)
]
helper.append_op(
type='split',
inputs={'X': input},
outputs={'Out': outs},
attrs={
'num': num_or_sections if isinstance(num_or_sections, int) else 0,
'sections': num_or_sections
if isinstance(num_or_sections, list) else [],
'axis': dim
})
return outs
def l2_normalize(x, axis, epsilon=1e-12, name=None):
"""
**L2 normalize Layer**
The l2 normalize layer normalizes `x` along dimension `axis` using an L2
norm. For a 1-D tensor (`dim` is fixed to 0), this layer computes
output = x / sqrt(max(sum(x**2), epsilon))
For `x` with more dimensions, this layer independently normalizes each 1-D
slice along dimension `axis`.
Args:
x(Variable|list): The input tensor to l2_normalize layer.
axis(int): Dimension along which to normalize the input.
epsilon(float): A lower bound value for `x`'s l2 norm. sqrt(epsilon) will
be used as the divisor if the l2 norm of `x` is less than
sqrt(epsilon).
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The output tensor variable.
Examples:
.. code-block:: python
data = fluid.layers.data(name="data",
shape=(3, 17, 13),
dtype="float32")
normed = fluid.layers.l2_normalize(x=data, axis=1)
"""
if len(x.shape) == 1:
axis = 0
helper = LayerHelper("l2_normalize", **locals())
square = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(type="square", inputs={"X": x}, outputs={"Out": square})
reduced_sum = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type="reduce_sum",
inputs={"X": square},
outputs={"Out": reduced_sum},
attrs={
"dim": 1 if axis is None else axis,
"keep_dim": True,
"reduce_all": False
})
# TODO(caoying) A lower bound value epsilon for the norm is needed to
# imporve the numeric stability of reciprocal. This requires a maximum_op.
rsquare = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type="reciprocal", inputs={"X": reduced_sum}, outputs={"Out": rsquare})
# TODO(caoying) the current elementwise_mul operator does not support a
# general broadcast rule which broadcasts input(Y) to have the same
# dimension with Input(X) starting from a specified dimension. So this
# exanpsion is requred. Once a general broadcast rule is spported, this
# expanding canbe removed.
rsquare_expanded = helper.create_tmp_variable(dtype=x.dtype)
expand_times = [1] * len(x.shape)
expand_times[axis] = int(x.shape[axis])
helper.append_op(
type="expand",
inputs={"X": rsquare},
outputs={"Out": rsquare_expanded},
attrs={"expand_times": expand_times})
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type="elementwise_mul",
inputs={"X": x,
"Y": rsquare_expanded},
outputs={"Out": out})
return out
def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
"""
Applies matrix multiplication to two tensors.
Currently, the input tensors' rank can be any, but when the rank of any
inputs is bigger than 3, this two inputs' rank should be equal.
The actual behavior depends on the shapes of :math:`x`, :math:`y` and the
flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:
- If a transpose flag is specified, the last two dimensions of the tensor
are transposed. If the tensor is rank-1 of shape :math:`[D]`, then for
:math:`x` it is treated as :math:`[1, D]` in nontransposed form and as
:math:`[D, 1]` in transposed form, whereas for :math:`y` it is the
opposite: It is treated as :math:`[D, 1]` in nontransposed form and as
:math:`[1, D]` in transposed form.
- After transpose, the two tensors are 2-D or n-D and matrix multiplication
performs in the following way.
- If both are 2-D, they are multiplied like conventional matrices.
- If either is n-D, it is treated as a stack of matrices residing in the
last two dimensions and a batched matrix multiply supporting broadcast
applies on the two tensors.
Also note that if the raw tensor :math:`x` or :math:`y` is rank-1 and
nontransposed, the prepended or appended dimension :math:`1` will be
removed after matrix multiplication.
Args:
x (Variable): The input variable which is a Tensor or LoDTensor.
y (Variable): The input variable which is a Tensor or LoDTensor.
transpose_x (bool): Whether to transpose :math:`x` before multiplication.
transpose_y (bool): Whether to transpose :math:`y` before multiplication.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The product Tensor variable.
Examples:
.. code-block:: python
# Examples to clarify shapes of the inputs and output
# x: [B, ..., M, K], y: [B, ..., K, N]
fluid.layers.matmul(x, y) # out: [B, ..., M, N]
# x: [B, M, K], y: [B, K, N]
fluid.layers.matmul(x, y) # out: [B, M, N]
# x: [B, M, K], y: [K, N]
fluid.layers.matmul(x, y) # out: [B, M, N]
# x: [M, K], y: [K, N]
fluid.layers.matmul(x, y) # out: [M, N]
# x: [B, M, K], y: [K]
fluid.layers.matmul(x, y) # out: [B, M]
# x: [K], y: [K]
fluid.layers.matmul(x, y) # out: [1]
# x: [M], y: [N]
fluid.layers.matmul(x, y, True, True) # out: [M, N]
"""
def __check_input(x, y):
if len(y.shape) > len(x.shape):
raise ValueError(
"Invalid inputs for matmul. "
"x's rank should be always greater than or equal to y'rank.")
x_shape = list(x.shape)
y_shape = list(y.shape)
if len(x_shape) == 1:
x_shape = [1] + x_shape
if len(y_shape) == 1:
y_shape = y_shape + [1]
# check the inner 2 dimensions
if transpose_x:
x_shape[-2], x_shape[-1] = x_shape[-1], x_shape[-2]
if transpose_y:
y_shape[-2], y_shape[-1] = y_shape[-1], y_shape[-2]
if x_shape[-1] != y_shape[-2]:
raise ValueError("Invalid inputs for matmul.")
if len(y_shape) > 2:
for i, dim_x in enumerate(x_shape[:-2]):
if dim_x != y_shape[i]:
raise ValueError("Invalid inputs for matmul.")
__check_input(x, y)
helper = LayerHelper('matmul', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type='matmul',
inputs={'X': x,
'Y': y},
outputs={'Out': out},
attrs={'transpose_X': transpose_x,
'transpose_Y': transpose_y})
return out
def edit_distance(input, label, normalized=True, ignored_tokens=None,
name=None):
"""
EditDistance operator computes the edit distances between a batch of
hypothesis strings and their references. Edit distance, also called
Levenshtein distance, measures how dissimilar two strings are by counting
the minimum number of operations to transform one string into anthor.
Here the operations include insertion, deletion, and substitution.
For example, given hypothesis string A = "kitten" and reference
B = "sitting", the edit distance is 3 for A will be transformed into B
at least after two substitutions and one insertion:
"kitten" -> "sitten" -> "sittin" -> "sitting"
Input(Hyps) is a LoDTensor consisting of all the hypothesis strings with
the total number denoted by `batch_size`, and the separation is specified
by the LoD information. And the `batch_size` reference strings are arranged
in order in the same way in the LoDTensor Input(Refs).
Output(Out) contains the `batch_size` results and each stands for the edit
distance for a pair of strings respectively. If Attr(normalized) is true,
the edit distance will be divided by the length of reference string.
Args:
input(Variable): The indices for hypothesis strings.
label(Variable): The indices for reference strings.
normalized(bool): Indicated whether to normalize the edit distance by
the length of reference string.
ignored_tokens(list of int): Tokens that should be removed before
calculating edit distance.
Returns:
Variable: sequence-to-sequence edit distance in shape [batch_size, 1].
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[8], dtype='float32')
y = fluid.layers.data(name='y', shape=[7], dtype='float32')
cost = fluid.layers.edit_distance(input=x,label=y)
"""
helper = LayerHelper("edit_distance", **locals())
# remove some tokens from input and labels
if ignored_tokens is not None and len(ignored_tokens) > 0:
erased_input = helper.create_tmp_variable(dtype="int64")
erased_label = helper.create_tmp_variable(dtype="int64")
helper.append_op(
type="sequence_erase",
inputs={"X": [input]},
outputs={"Out": [erased_input]},
attrs={"tokens": ignored_tokens})
input = erased_input
helper.append_op(
type="sequence_erase",
inputs={"X": [label]},
outputs={"Out": [erase_label]},
attrs={"tokens": ignored_tokens})
label = erased_label
# edit distance op
edit_distance_out = helper.create_tmp_variable(dtype="int64")
sequence_num = helper.create_tmp_variable(dtype="int64")
helper.append_op(
type="edit_distance",
inputs={"Hyps": [input],
"Refs": [label]},
outputs={"Out": [edit_distance_out],
"SequenceNum": [sequence_num]},
attrs={"normalized": normalized})
return edit_distance_out, sequence_num
def ctc_greedy_decoder(input, blank, name=None):
"""
This op is used to decode sequences by greedy policy by below steps:
1. Get the indexes of max value for each row in input. a.k.a.
numpy.argmax(input, axis=0).
2. For each sequence in result of step1, merge repeated tokens between two
blanks and delete all blanks.
A simple example as below:
.. code-block:: text
Given:
input.data = [[0.6, 0.1, 0.3, 0.1],
[0.3, 0.2, 0.4, 0.1],
[0.1, 0.5, 0.1, 0.3],
[0.5, 0.1, 0.3, 0.1],
[0.5, 0.1, 0.3, 0.1],
[0.2, 0.2, 0.2, 0.4],
[0.2, 0.2, 0.1, 0.5],
[0.5, 0.1, 0.3, 0.1]]
input.lod = [[0, 4, 8]]
Then:
output.data = [[2],
[1],
[3]]
output.lod = [[0, 2, 3]]
Args:
input(Variable): (LoDTensor<float>), the probabilities of
variable-length sequences, which is a 2-D Tensor with
LoD information. It's shape is [Lp, num_classes + 1],
where Lp is the sum of all input sequences' length and
num_classes is the true number of classes. (not
including the blank label).
blank(int): the blank label index of Connectionist Temporal
Classification (CTC) loss, which is in thehalf-opened
interval [0, num_classes + 1).
Returns:
Variable: CTC greedy decode result. If all the sequences in result were
empty, the result LoDTensor will be [-1] with LoD [[0]] and dims [1, 1].
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[8], dtype='float32')
cost = fluid.layers.ctc_greedy_decoder(input=x, blank=0)
"""
helper = LayerHelper("ctc_greedy_decoder", **locals())
# top 1 op
topk_out = helper.create_tmp_variable(dtype=input.dtype)
topk_indices = helper.create_tmp_variable(dtype="int64")
helper.append_op(
type="top_k",
inputs={"X": [input]},
outputs={"Out": [topk_out],
"Indices": [topk_indices]},
attrs={"k": 1})
# ctc align op
ctc_out = helper.create_tmp_variable(dtype="int64")
helper.append_op(
type="ctc_align",
inputs={"Input": [topk_indices]},
outputs={"Output": [ctc_out]},
attrs={"merge_repeated": True,
"blank": blank})
return ctc_out
def warpctc(input, label, blank=0, norm_by_times=False):
"""
An operator integrating the open source Warp-CTC library
(https://github.com/baidu-research/warp-ctc)
to compute Connectionist Temporal Classification (CTC) loss.
It can be aliased as softmax with CTC, since a native softmax activation is
interated to the Warp-CTC library, to to normlize values for each row of the
input tensor.
Args:
input(Variable): (LodTensor, default: LoDTensor<float>),
the unscaled probabilities of variable-length sequences,
which is a 2-D Tensor with LoD information.
It's shape is [Lp, num_classes + 1], where Lp is the sum of all input
sequences' length and num_classes is the true number of classes.
(not including the blank label).
label(Variable): (LodTensor, default: LoDTensor<int>), the ground truth
of variable-length sequence, which is a 2-D Tensor with LoD
information. It is of the shape [Lg, 1], where Lg is th sum of
all labels' length.
blank: (int, default: 0), the blank label index of Connectionist
Temporal Classification (CTC) loss, which is in the
half-opened interval [0, num_classes + 1).
norm_by_times: (bool, default: false), whether to normalize
the gradients by the number of time-step, which is also the
sequence's length. There is no need to normalize the gradients
if warpctc layer was follewed by a mean_op.
Returns:
Variable: The Connectionist Temporal Classification (CTC) loss,
which is a 2-D Tensor of the shape [batch_size, 1].
Examples:
.. code-block:: python
y = layers.data(
name='y', shape=[11, 8], dtype='float32', lod_level=1)
y_predict = layers.data(
name='y_predict', shape=[11, 1], dtype='float32')
cost = layers.warpctc(input=y_predict, label=y)
"""
helper = LayerHelper('warpctc', **locals())
loss_out = helper.create_tmp_variable(dtype=input.dtype)
grad_out = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type='warpctc',
inputs={'Logits': [input],
'Label': [label]},
outputs={'WarpCTCGrad': [grad_out],
'Loss': [loss_out]},
attrs={'blank': blank,
'norm_by_times': norm_by_times})
return loss_out
def sequence_reshape(input, new_dim):
"""
**Sequence Reshape Layer**
This layer will rearrange the input sequences. The new dimension is set by
user. Length of each sequence is computed according to original length,
original dimension and new dimension. The following example will help to
illustrate the function of this layer:
.. code-block:: text
x is a LoDTensor:
x.lod = [[0, 2, 6]]
x.data = [[1, 2], [3, 4],
[5, 6], [7, 8], [9, 10], [11, 12]]
x.dims = [6, 2]
set new_dim = 4
then out is a LoDTensor:
out.lod = [[0, 1, 3]]
out.data = [[1, 2, 3, 4],
[5, 6, 7, 8], [9, 10, 11, 12]]
out.dims = [3, 4]
Currently, only 1-level LoDTensor is supported and please make sure
(original length * original dimension) can be divided by new dimension with
no remainder for each sequence.
Args:
input (Variable): (LodTensor, default: LoDTensor<float>), a 2-D LoDTensor
with shape being [N, M] where M for dimension.
new_dim (int): New dimension which the input LoDTensor is reshaped to.
Returns:
Variable: Reshaped LoDTensor according to new dimension.
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[5, 20],
dtype='float32', lod_level=1)
x_reshaped = layers.sequence_reshape(input=x, new_dim=10)
"""
helper = LayerHelper('sequence_reshape', **locals())
out = helper.create_tmp_variable(helper.input_dtype())
helper.append_op(
type='sequence_reshape',
inputs={'X': [input]},
outputs={'Out': [out]},
attrs={'new_dim': new_dim})
return out
@autodoc()
def nce(input,
label,
num_total_classes,
sample_weight=None,
param_attr=None,
bias_attr=None,
num_neg_samples=None):
helper = LayerHelper('nce', **locals())
assert isinstance(input, Variable)
dim = input.shape[1]
assert isinstance(label, Variable)
num_true_class = label.shape[1]
w = helper.create_parameter(
attr=helper.param_attr,
shape=[num_total_classes, dim],
is_bias=False,
dtype=input.dtype)
b = helper.create_parameter(
attr=helper.bias_attr,
shape=[num_total_classes, 1],
is_bias=True,
dtype=input.dtype)
cost = helper.create_tmp_variable(dtype=input.dtype)
sample_logits = helper.create_tmp_variable(dtype=input.dtype)
sample_labels = helper.create_tmp_variable(dtype=label.dtype)
if num_neg_samples is None:
num_neg_samples = 10
else:
num_neg_samples = int(num_neg_samples)
attrs = {
'num_total_classes': int(num_total_classes),
'num_neg_samples': num_neg_samples
}
helper.append_op(
type='nce',
inputs={
'Input': input,
'Label': label,
'Weight': w,
'Bias': b,
'SampleWeight': sample_weight if sample_weight is not None else []
},
outputs={
'Cost': cost,
'SampleLogits': sample_logits,
'SampleLabels': sample_labels
},
attrs=attrs)
return cost / (num_neg_samples + 1)
def transpose(x, perm, name=None):
"""
**transpose Layer**
Permute the dimensions of `input` according to `perm`.
The `i`-th dimension of the returned tensor will correspond to the
perm[i]-th dimension of `input`.
Args:
input (Variable): (Tensor), A Tensor.
perm (list): A permutation of the dimensions of `input`.
Returns:
Variable: A transposed Tensor.
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[5, 10, 15], dtype='float32')
x_transposed = layers.transpose(x, perm=[1, 0, 2])
"""
if len(perm) != len(x.shape):
raise ValueError(
"Input(perm) is the permutation of dimensions of Input(input). "
"It's length shoud be equal to Input(input)'s rank.")
for idx, dim in enumerate(perm):
if dim >= len(x.shape):
raise ValueError(
"Each element in perm should be less than x's rank. "
"%d-th element in perm is %d which accesses x's rank %d." %
(idx, perm[idx], len(x.shape)))
helper = LayerHelper('transpose', **locals())
out = helper.create_tmp_variable(x.dtype)
helper.append_op(
type='transpose',
inputs={'X': [x]},
outputs={'Out': [out]},
attrs={'axis': perm})
return out
def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
"""
Extracts image patches from the input tensor to form a tensor of shape
{input.batch_size * output_height * output_width, filter_size_H *
filter_size_W * input.channels} which is similar with im2col.
This op use filter / kernel to scan images and convert these images to
sequences. After expanding, the number of time step are
output_height * output_width for an image, in which output_height and
output_width are calculated by below equation:
.. math::
output\_size = 1 + \
(2 * padding + img\_size - block\_size + stride - 1) / stride
And the dimension of each time step is block_y * block_x * input.channels.
Args:
input (Variable): The input should be a tensor in NCHW format.
filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square.
stride(int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1.
padding(int|tuple): The padding size. If padding is a tuple, it can
contain two integers like (padding_H, padding_W) which means
padding_up = padding_down = padding_H and
padding_left = padding_right = padding_W. Or it can use
(padding_up, padding_left, padding_down, padding_right) to indicate
paddings of four direction. Otherwise, a scalar padding means
padding_up = padding_down = padding_left = padding_right = padding
Default: padding = 0.
name (int): The name of this layer. It is optional.
Returns:
output: The output is a LoDTensor with shape
{input.batch_size * output_height * output_width,
filter_size_H * filter_size_W * input.channels}.
If we regard output as a matrix, each row of this matrix is
a step of a sequence.
Examples:
As an example:
.. code-block:: text
Given:
x = [[[[ 6. 2. 1.]
[ 8. 3. 5.]
[ 0. 2. 6.]]
[[ 2. 4. 4.]
[ 6. 3. 0.]
[ 6. 4. 7.]]]
[[[ 6. 7. 1.]
[ 5. 7. 9.]
[ 2. 4. 8.]]
[[ 1. 2. 1.]
[ 1. 3. 5.]
[ 9. 0. 8.]]]]
x.dims = {2, 2, 3, 3}
And:
filter = [2, 2]
stride = [1, 1]
padding = [0, 0]
Then:
output.data = [[ 6. 2. 8. 3. 2. 4. 6. 3.]
[ 2. 1. 3. 5. 4. 4. 3. 0.]
[ 8. 3. 0. 2. 6. 3. 6. 4.]
[ 3. 5. 2. 6. 3. 0. 4. 7.]
[ 6. 7. 5. 7. 1. 2. 1. 3.]
[ 7. 1. 7. 9. 2. 1. 3. 5.]
[ 5. 7. 2. 4. 1. 3. 9. 0.]
[ 7. 9. 4. 8. 3. 5. 0. 8.]]
output.dims = {8, 9}
output.lod = [[0, 4, 8]]
The simple usage is:
.. code-block:: python
output = fluid.layers.im2sequence(
input=layer, stride=[1, 1], filter_size=[2, 2])
"""
if isinstance(filter_size, int):
filter_size = [filter_size, filter_size]
if isinstance(stride, int):
stride = [stride, stride]
if isinstance(padding, int):
padding = [padding, padding]
if len(padding) == 2:
padding.append(padding[0])
padding.append(padding[1])
helper = LayerHelper('im2sequence', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(
type='im2sequence',
inputs={'X': input},
outputs={'Out': out},
attrs={
'kernels': filter_size,
'strides': stride,
'paddings': padding,
})
return out
def row_conv(input, future_context_size, param_attr=None, act=None):
"""Row Conv Operator. This layer will apply lookahead convolution to
**input**. The input variable should be a 2D LoDTensor with shape [T, D].
Parameters with shape [future_context_size + 1, D] will be created. The math
equation of row convolution is as follows:
.. math::
Out_{i} = \sum_{j = i} ^ {i + \\tau} X_{j} \odot W_{i - j}
In the above equation:
* :math:`Out_{i}`: The i-th row of output variable with shape [1, D].
* :math:`\\tau`: Future context size.
* :math:`X_{j}`: The j-th row of input variable with shape [1, D].
* :math:`W_{i-j}`: The (i-j)-th row of parameters with shape [1, D].
More details about row_conv please refer to the paper \
(http://www.cs.cmu.edu/~dyogatam/papers/wang+etal.iclrworkshop2016.pdf) and
the design document \
(https://github.com/PaddlePaddle/Paddle/issues/2228#issuecomment-303903645).
Args:
input (Variable): Input variable, a 2D LoDTensor with shape [T, D].
future_context_size (int): Future context size. Please note, the shape
of convolution kernel is [future_context_size + 1, D].
param_attr (ParamAttr): Attributes of parameters, including
name, initializer etc.
act (str): Non-linear activation to be applied to output variable.
Returns:
Variable: The output tensor with same shape as input tensor.
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[16],
dtype='float32', lod_level=1)
out = fluid.layers.row_conv(input=x, future_context_size=2)
"""
helper = LayerHelper('row_conv', **locals())
dtype = helper.input_dtype()
filter_shape = [future_context_size + 1, input.shape[1]]
filter_param = helper.create_parameter(
attr=helper.param_attr, shape=filter_shape, dtype=dtype)
out = helper.create_tmp_variable(dtype)
helper.append_op(
type='row_conv',
inputs={'X': [input],
'Filter': [filter_param]},
outputs={'Out': [out]})
return helper.append_activation(out)
def multiplex(inputs, index):
"""
**Multiplex Layer**
Referring to the given index variable, this layer selects rows from the
input variables to construct a multiplex variable. Assuming that there are
:math:`m` input variables and :math:`I_i` represents the i-th input
variable and :math:`i` is in [0, :math:`m`). All input variables are
tensors with same shape [:math:`d_0`, :math:`d_1`, ..., :math:`d_R`].
Please note that rank of the input tensor should be at least 2. Each input
variable will be treated as a 2-D matrix with shape [:math:`M`, :math:`N`]
where :math:`M` for :math:`d_0` and :math:`N` for :math:`d_1` * :math:`d_2`
* ... * :math:`d_R`. Let :math:`I_i[j]` be the j-th row of the i-th input
variable. The given index variable should be a 2-D tensor with shape
[:math:`M`, 1]. Let `ID[i]` be the i-th index value of the index variable.
Then the output variable will be a tensor with shape [:math:`d_0`,
:math:`d_1`, ..., :math:`d_R`]. If we treat the output tensor as a 2-D
matrix with shape [:math:`M`, :math:`N`] and let :math:`O[i]` be the i-th
row of the matrix, then `O[i]` is equal to :math:`I_{ID[i]}[i]`.
Args:
inputs (list): A list of variables to gather from. All variables have the
same shape and the rank is at least 2.
index (Variable): Tensor<int32>, index variable which is a 2-D tensor
with shape [M, 1] where M is the batch size.
Returns:
Variable: Multiplex variable gathered from input variables.
Examples:
.. code-block:: python
x1 = fluid.layers.data(name='x1', shape=[4], dtype='float32')
x2 = fluid.layers.data(name='x2', shape=[4], dtype='float32')
index = fluid.layers.data(name='index', shape=[1], dtype='int32')
out = fluid.layers.multiplex(inputs=[x1, x2], index=index)
"""
helper = LayerHelper('multiplex', **locals())
if not isinstance(inputs, list) and len(inputs) < 2:
raise ValueError("inputs should be a list object and contains at least "
"2 elements.")
out = helper.create_tmp_variable(inputs[0].dtype)
helper.append_op(
type='multiplex',
inputs={'X': inputs,
'Ids': index},
outputs={'Out': [out]})
return out
def softmax_with_cross_entropy(logits, label, soft_label=False):
"""
**Softmax With Cross Entropy Operator.**
Cross entropy loss with softmax is used as the output layer extensively. This
operator computes the softmax normalized values for each row of the input
tensor, after which cross-entropy loss is computed. This provides a more
numerically stable gradient.
Because this operator performs a softmax on logits internally, it expects
unscaled logits. This operator should not be used with the output of
softmax operator since that would produce incorrect results.
When the attribute soft_label is set false, this operators expects mutually
exclusive hard labels, each sample in a batch is in exactly one class with a
probability of 1.0. Each sample in the batch will have a single label.
The equation is as follows:
1) Hard label (one-hot label, so every sample has exactly one class)
.. math::
loss_j = -\\text{logit}_{label_j} +
\\log\\left(\\sum_{i=0}^{K}\\exp(\\text{logit}_i)\\right), j = 1,..., K
2) Soft label (each sample can have a distribution over all classes)
.. math::
loss_j = -\\sum_{i=0}^{K}\\text{label}_i
\\left(\\text{logit}_i - \\log\\left(\\sum_{i=0}^{K}
\\exp(\\text{logit}_i)\\right)\\right), j = 1,...,K
Args:
logits (Variable): The unscaled log probabilities, which is a 2-D tensor
with shape [N x K]. N is the batch_size, and K is the class number.
label (Variable): The ground truth which is a 2-D tensor. If soft_label
is set to false, Label is a Tensor<int64> with shape [N x 1]. If
soft_label is set to true, Label is a Tensor<float/double> with
soft_label (bool): A flag to indicate whether to interpretate the given
labels as soft labels. By default, `soft_label` is set to False.
Returns:
Variable: The cross entropy loss is a 2-D tensor with shape [N x 1].
Examples:
.. code-block:: python
data = fluid.layers.data(name='data', shape=[128], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
fc = fluid.layers.fc(input=data, size=100)
out = fluid.layers.softmax_with_cross_entropy(logits=fc, label=label)
"""
helper = LayerHelper('softmax_with_cross_entropy', **locals())
softmax = helper.create_tmp_variable(dtype=logits.dtype)
loss = helper.create_tmp_variable(dtype=logits.dtype)
helper.append_op(
type='softmax_with_cross_entropy',
inputs={'Logits': logits,
'Label': label},
outputs={'Softmax': softmax,
'Loss': loss},
attrs={'soft_label': soft_label})
return loss
def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
"""
**Smooth L1 Loss Operator. **
This operator computes the smooth l1 loss for X and Y.
The operator takes the first dimension of X and Y as batch size.
For each instance, it computes the smooth l1 loss element by element first
and then sums all the losses. So the shape of Out is [batch_size, 1].
Args:
x (Variable): A tensor with rank at least 2. The input value of smooth
l1 loss op with shape [batch_size, dim1, ..., dimN].
y (Variable): A tensor with rank at least 2. The target value of smooth
l1 loss op with same shape as x.
inside_weight (Variable|None): A tensor with rank at least 2. This
input is optional and should have same shape with x. If provided,
the result of (x - y) will be multiplied by this tensor element by
element.
outside_weight (Variable|None): A tensor with rank at least 2. This
input is optional and should have same shape with x. If provided,
the out smooth l1 loss will be multiplied by this tensor element
by element.
sigma (float|None): Hyper parameter of smooth l1 loss op. A float scalar
with default value 1.0.
Returns:
Variable: A tensor with rank be 2. The output smooth l1 loss with
shape [batch_size, 1].
Examples:
.. code-block:: python
data = fluid.layers.data(name='data', shape=[128], dtype='float32')
label = fluid.layers.data(name='label', shape=[100], dtype='int64')
fc = fluid.layers.fc(input=data, size=100)
out = fluid.layers.smooth_l1(x=fc, y=label)
"""
helper = LayerHelper('smooth_l1_loss', **locals())
diff = helper.create_tmp_variable(dtype=x.dtype)
loss = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type='smooth_l1_loss',
inputs={
'X': x,
'Y': y,
'InsideWeight': inside_weight,
'OutsideWeight': outside_weight
},
outputs={'Diff': diff,
'Out': loss},
attrs={'sigma': sigma})
return loss
def one_hot(input, depth):
"""
One Hot Operator. This operator creates the one-hot representations for input
index values. The following example will help to explain the function of this
operator.
Args:
input(variable): A Tensor/LodTensor of indices, last dimension must be 1.
depth(scalar): an interger defining the depth of the one hot dimension.
Returns:
The one-hot tensor or LodTensor, same as input.
Examples:
X is a LoDTensor:
X.lod = [[0, 1, 4]]
X.shape = [4, 1]
X.data = [[1], [1], [3], [0]]
set depth = 4
Out is a LoDTensor:
Out.lod = [[0, 1, 4]]
Out.shape = [4, 4]
Out.data = [[0., 1., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 0., 1.],
[1., 0., 0., 0.]]
"""
helper = LayerHelper("one_hot", **locals())
one_hot_out = helper.create_tmp_variable(dtype='float32')
helper.append_op(
type="one_hot",
inputs={'X': input},
attrs={'depth': depth},
outputs={'Out': one_hot_out})
return one_hot_out
def autoincreased_step_counter(counter_name=None, begin=1, step=1):
"""
NOTE: The counter will be automatically increased by 1 every mini-batch
Return the run counter of the main program, which is started with 1.
Args:
counter_name(str): The counter name, default is '@STEP_COUNTER@'.
begin(int): The first value of this counter.
step(int): The increment step between each execution.
Returns(Variable): The global run counter.
"""
helper = LayerHelper('global_step_counter')
if counter_name is None:
counter_name = '@STEP_COUNTER@'
counter, is_new_var = helper.create_or_get_global_variable(
name=counter_name, dtype='int64', shape=[1], persistable=True)
if is_new_var:
helper.set_variable_initializer(
counter, initializer=Constant(value=begin - 1))
helper.main_program.global_block().prepend_op(
type='increment',
inputs={'X': [counter]},
outputs={'Out': [counter]},
attrs={'step': float(step)})
counter.stop_gradient = True
return counter
def lod_reset(x, y=None, target_lod=None):
"""
LoD Reset Operator. Set LoD of **x** to a new one specified by **y** or
**target_lod**. When **y** provided, **y.lod** would be considered as target
LoD first, otherwise **y.data** would be considered as target LoD. If **y**
is not provided, target LoD should be specified by **target_lod**.
If target LoD is specified by **Y.data** or **target_lod**, only one level
LoD is supported.
.. code-block:: text
* Example 1:
Given a 1-level LoDTensor x:
x.lod = [[ 0, 2, 5 6 ]]
x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
x.dims = [6, 1]
target_lod: [0, 4, 6]
then we get a 1-level LoDTensor:
out.lod = [[ 0, 4, 6 ]]
out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
out.dims = [6, 1]
* Example 2:
Given a 1-level LoDTensor x:
x.lod = [[ 0, 2, 5 6 ]]
x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
x.dims = [6, 1]
y is a Tensor:
y.data = [[0, 2, 6]]
y.dims = [1, 3]
then we get a 1-level LoDTensor:
out.lod = [[ 0, 2, 6 ]]
out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
out.dims = [6, 1]
* Example 3:
Given a 1-level LoDTensor x:
x.lod = [[ 0, 2, 5 6 ]]
x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
x.dims = [6, 1]
y is a 2-level LoDTensor:
y.lod = [[0, 2, 4], [0, 2, 5, 6]]
y.data = [[1.1], [2.1], [3.1], [4.1], [5.1], [6.1]]
y.dims = [6, 1]
then we get a 2-level LoDTensor:
out.lod = [[0, 2, 4], [0, 2, 5, 6]]
out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
out.dims = [6, 1]
Args:
x (Variable): Input variable which could be a Tensor or LodTensor.
y (Variable|None): If provided, output's LoD would be derived from y.
target_lod (list|tuple|None): One level LoD which should be considered
as target LoD when y not provided.
Returns:
Variable: Output variable with LoD specified by this operator.
Raises:
ValueError: If y and target_lod are both None.
Examples:
.. code-block:: python
x = layers.data(name='x', shape=[10])
y = layers.data(name='y', shape=[10, 20], lod_level=2)
out = layers.lod_reset(x=x, y=y)
"""
helper = LayerHelper("lod_reset", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
if y is not None:
helper.append_op(
type="lod_reset", inputs={'X': x,
'Y': y}, outputs={'Out': out})
elif target_lod is not None:
helper.append_op(
type="lod_reset",
inputs={'X': x},
attrs={'target_lod': target_lod},
outputs={'Out': out})
else:
raise ValueError("y and target_lod should not be both None.")
return out
def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None):
"""
Local Response Normalization Layer. This layer performs a type of
"lateral inhibition" by normalizing over local input regions.
The formula is as follows:
.. math::
Output(i, x, y) = Input(i, x, y) / \left(
k + \alpha \sum\limits^{\min(C, c + n/2)}_{j = \max(0, c - n/2)}
(Input(j, x, y))^2 \right)^{\beta}
In the above equation:
* :math:`n`: The number of channels to sum over.
* :math:`k`: The offset (avoid being divided by 0).
* :math:`alpha`: The scaling parameter.
* :math:`beta`: The exponent parameter.
Refer to `ImageNet Classification with Deep Convolutional Neural Networks
<https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf>`_
Args:
input (Variable): The input tensor of this layer, and the dimension of input tensor must be 4.
n (int, default 5): The number of channels to sum over.
k (float, default 1.0): An offset (usually positive to avoid dividing by 0).
alpha (float, default 1e-4): The scaling parameter.
beta (float, default 0.75): The exponent.
name (str, default None): A name for this operation.
Raises:
ValueError: If rank of the input tensor is not 4.
Returns:
A tensor variable storing the transformation result.
Examples:
.. code-block:: python
data = fluid.layers.data(name="data", shape=[3, 112, 112], dtype="float32")
lrn = fluid.layers.lrn(input=data)
"""
helper = LayerHelper('lrn', **locals())
dtype = helper.input_dtype()
input_shape = input.shape
dims = len(input_shape)
if dims != 4:
raise ValueError(
"dims of input must be 4(not %d), and it's order must be NCHW" %
(dims))
mid_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
lrn_out = helper.create_tmp_variable(dtype)
helper.append_op(
type="lrn",
inputs={"X": input},
outputs={
"Out": lrn_out,
"MidOut": mid_out,
},
attrs={"n": n,
"k": k,
"alpha": alpha,
"beta": beta})
return lrn_out
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