# 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 __future__ import print_function
import numpy as np
from ..layer_helper import LayerHelper
from ..initializer import Normal, Constant
from ..framework import Variable, OpProtoHolder
from ..param_attr import ParamAttr
from .layer_function_generator import autodoc, templatedoc, _generate_doc_string_
from .tensor import concat
from . import utils
from .. import unique_name
from functools import reduce
__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',
'conv3d',
'sequence_pool',
'sequence_softmax',
'softmax',
'pool2d',
'pool3d',
'batch_norm',
'beam_search_decode',
'conv2d_transpose',
'conv3d_transpose',
'sequence_expand',
'sequence_expand_as',
'sequence_pad',
'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',
'topk',
'warpctc',
'sequence_reshape',
'transpose',
'im2sequence',
'nce',
'hsigmoid',
'beam_search',
'row_conv',
'multiplex',
'layer_norm',
'softmax_with_cross_entropy',
'smooth_l1',
'one_hot',
'autoincreased_step_counter',
'reshape',
'squeeze',
'unsqueeze',
'lod_reset',
'lrn',
'pad',
'pad_constant_like',
'label_smooth',
'roi_pool',
'dice_loss',
'image_resize',
'image_resize_short',
'resize_bilinear',
'gather',
'scatter',
'sequence_scatter',
'random_crop',
'mean_iou',
'relu',
'log',
'crop',
'rank_loss',
'elu',
'relu6',
'pow',
'stanh',
'hard_sigmoid',
'swish',
'prelu',
'brelu',
'leaky_relu',
'soft_relu',
'flatten',
'sequence_mask',
'stack',
'pad2d',
'unstack',
'sequence_enumerate',
'expand',
'sequence_concat',
'scale',
'elementwise_add',
'elementwise_div',
'elementwise_sub',
'elementwise_mul',
'elementwise_max',
'elementwise_min',
'elementwise_pow',
'logical_and',
'logical_or',
'logical_xor',
'logical_not',
]
def fc(input,
size,
num_flatten_dims=1,
param_attr=None,
bias_attr=None,
use_mkldnn=False,
act=None,
is_test=False,
name=None):
"""
**Fully Connected Layer**
This function creates a fully connected layer in the network. It can take
multiple tensors as its inputs. It creates a variable called weights for
each input tensor, which represents a fully connected weight matrix from
each input unit to each output unit. The fully connected layer multiplies
each input tensor with its coresponding weight to produce an output Tensor.
If multiple input tensors are given, the results of multiple multiplications
will be sumed up. If bias_attr is not None, a bias variable will be created
and added to the output. Finally, if activation is not None, it will be applied
to the output as well.
This process can be formulated as follows:
.. 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 False, no bias will be added to the output units.
If it is set to None, the bias is initialized zero. Default: None.
act (str, default None): Activation to be applied to the output of this layer.
is_test(bool): A flag indicating whether execution is in test phase.
use_mkldnn(bool): Use mkldnn kernel or not, it is valid only when the mkldnn
library is installed. Default: False
name (str, default None): The name of this layer.
Returns:
Variable: 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})
mul_results.append(tmp)
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},
attrs={"use_mkldnn": use_mkldnn})
# 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,
is_distributed=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.
is_distributed(bool): Whether to run lookup table from remote parameter server.
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 :attr:`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,
'is_distributed': is_distributed,
'padding_idx': padding_idx
})
return tmp
@templatedoc(op_type="lstm")
def dynamic_lstm(input,
size,
h_0=None,
c_0=None,
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):
"""
${comment}
Args:
input (Variable): ${input_comment}
size (int): 4 * hidden size.
h_0(Variable): The initial hidden state is an optional input, default is zero.
This is a tensor with shape (N x D), where N is the
batch size and D is the hidden size.
c_0(Variable): The initial cell state is an optional input, default is zero.
This is a tensor with shape (N x D), where N is the
batch size. `h_0` and `c_0` can be NULL but only at the same time.
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): ${use_peepholes_comment}
is_reverse (bool): ${is_reverse_comment}
gate_activation (str): ${gate_activation_comment}
cell_activation (str): ${cell_activation_comment}
candidate_activation (str): ${candidate_activation_comment}
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)
inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
batch_size = input.shape[0]
if h_0:
assert h_0.shape == (batch_size, size), \
'The shape of h0 should be (batch_size, %d)' % size
inputs['H0'] = h_0
if c_0:
assert c_0.shape == (batch_size, size), \
'The shape of c0 should be (batch_size, %d)' % size
inputs['C0'] = c_0
helper.append_op(
type='lstm',
inputs=inputs,
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: A tuple of two output variable: 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
dict_dim, emb_dim = 128, 64
data = fluid.layers.data(name='sequence', shape=[1],
dtype='int32', lod_level=1)
emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
hidden_dim, proj_dim = 512, 256
fc_out = fluid.layers.fc(input=emb, 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):
"""
**Gated Recurrent Unit (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".
candidate_activation(str): The activation for candidate hidden state.
Choices = ["sigmoid", "tanh", "relu", "identity"], default "tanh".
h_0 (Variable): This is initial hidden state. If not set, default is
zero. This is a tensor with shape (N x D), where N is the number of
total time steps of input mini-batch feature and D is the hidden
size.
Returns:
Variable: The hidden state of GRU. The shape is :math:`(T \\times D)`, \
and sequence length is the same with the input.
Examples:
.. code-block:: python
dict_dim, emb_dim = 128, 64
data = fluid.layers.data(name='sequence', shape=[1],
dtype='int32', lod_level=1)
emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
hidden_dim = 512
x = fluid.layers.fc(input=emb, 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)
batch_size = input.shape[0]
inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
if h_0 != None:
assert h_0.shape == (
batch_size, size
), 'The shape of h0 should be(batch_size, %d)' % 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,
param_attr=None,
bias_attr=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.
param_attr (ParamAttr): The weight parameters for gru unit. Default: None
bias_attr (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
weight = helper.create_parameter(
attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
gate = helper.create_tmp_variable(dtype)
reset_hidden_pre = helper.create_tmp_variable(dtype)
updated_hidden = helper.create_tmp_variable(dtype)
inputs = {'Input': input, 'HiddenPrev': hidden, 'Weight': weight}
# create bias
if helper.bias_attr:
bias_size = [1, 3 * size]
bias = helper.create_parameter(
attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
inputs['Bias'] = bias
helper.append_op(
type='gru_unit',
inputs=inputs,
outputs={
'Gate': gate,
'ResetHiddenPrev': reset_hidden_pre,
'Hidden': updated_hidden,
},
attrs={
'activation': 2, # tanh
'gate_activation': 1, # sigmoid
})
return updated_hidden, reset_hidden_pre, gate
@templatedoc()
def linear_chain_crf(input, label, param_attr=None):
"""
Linear Chain CRF.
${comment}
Args:
input(${emission_type}): ${emission_comment}
input(${transition_type}): ${transition_comment}
label(${label_type}): ${label_comment}
param_attr(ParamAttr): The attribute of the learnable parameter.
Returns:
output(${emission_exps_type}): ${emission_exps_comment} \n
output(${transition_exps_type}): ${transition_exps_comment} \n
output(${log_likelihood_type}): ${log_likelihood_comment}
"""
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
@templatedoc()
def crf_decoding(input, param_attr, label=None):
"""
${comment}
Args:
input(${emission_type}): ${emission_comment}
param_attr(ParamAttr): The parameter attribute for training.
label(${label_type}): ${label_comment}
Returns:
Variable: ${viterbi_path_comment}
Examples:
.. code-block:: python
crf_decode = layers.crf_decoding(
input=hidden, param_attr=ParamAttr(name="crfw"))
"""
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
@templatedoc()
def cos_sim(X, Y):
"""
${comment}
Args:
X (Variable): ${x_comment}.
Y (Variable): ${y_comment}.
Returns:
Variable: the output of cosine(X, Y).
"""
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, name=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 sets (according to the given dropout
probability) the outputs of some units to zero, while others are remain
unchanged.
Args:
x (Variable): The input tensor variable.
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.
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: A tensor variable is the shape with `x`.
Examples:
.. code-block:: python
x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
droped = fluid.layers.dropout(x, dropout_prob=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)
if (seed is None or seed == 0) and helper.main_program.random_seed != 0:
seed = helper.main_program.random_seed
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, ignore_index=-100):
"""
**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`.
ignore_index (int): Specifies a target value that is ignored and does
not contribute to the input gradient. Only valid
if soft_label is set to False. Default: -100
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,
"ignore_index": ignore_index})
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
@templatedoc()
def chunk_eval(input,
label,
chunk_scheme,
num_chunk_types,
excluded_chunk_types=None):
"""
**Chunk Evaluator**
This function computes and outputs the precision, recall and
F1-score of chunk detection.
For some basics of chunking, please refer to
'Chunking with Support Vector Machines <https://aclanthology.info/pdf/N/N01/N01-1025.pdf>'.
ChunkEvalOp computes the precision, recall, and F1-score of chunk detection,
and supports IOB, IOE, IOBES and IO (also known as plain) tagging schemes.
Here is a NER example of labeling for these tagging schemes:
.. code-block:: python
====== ====== ====== ===== == ============ ===== ===== ===== == =========
Li Ming works at Agricultural Bank of China in Beijing.
====== ====== ====== ===== == ============ ===== ===== ===== == =========
IO I-PER I-PER O O I-ORG I-ORG I-ORG I-ORG O I-LOC
IOB B-PER I-PER O O B-ORG I-ORG I-ORG I-ORG O B-LOC
IOE I-PER E-PER O O I-ORG I-ORG I-ORG E-ORG O E-LOC
IOBES B-PER E-PER O O I-ORG I-ORG I-ORG E-ORG O S-LOC
====== ====== ====== ===== == ============ ===== ===== ===== == =========
There are three chunk types(named entity types) including PER(person), ORG(organization)
and LOC(LOCATION), and we can see that the labels have the form <tag type>-<chunk type>.
Since the calculations actually use label ids rather than labels, extra attention
should be paid when mapping labels to ids to make CheckEvalOp work. The key point
is that the listed equations are satisfied by ids.
.. code-block:: python
tag_type = label % num_tag_type
chunk_type = label / num_tag_type
where `num_tag_type` is the num of tag types in the tagging scheme, `num_chunk_type`
is the num of chunk types, and `tag_type` get its value from the following table.
.. code-block:: python
Scheme Begin Inside End Single
plain 0 - - -
IOB 0 1 - -
IOE - 0 1 -
IOBES 0 1 2 3
Still use NER as example, assuming the tagging scheme is IOB while chunk types are ORG,
PER and LOC. To satisfy the above equations, the label map can be like this:
.. code-block:: python
B-ORG 0
I-ORG 1
B-PER 2
I-PER 3
B-LOC 4
I-LOC 5
O 6
It's not hard to verify the equations noting that the num of chunk types
is 3 and the num of tag types in IOB scheme is 2. For example, the label
id of I-LOC is 5, the tag type id of I-LOC is 1, and the chunk type id of
I-LOC is 2, which consistent with the results from the equations.
Args:
input (Variable): prediction output of the network.
label (Variable): label of the test data set.
chunk_scheme (str): ${chunk_scheme_comment}
num_chunk_types (int): ${num_chunk_types_comment}
excluded_chunk_types (list): ${excluded_chunk_types_comment}
Returns:
tuple: tuple containing: precision, recall, f1_score,
num_infer_chunks, num_label_chunks,
num_correct_chunks
Examples:
.. code-block:: python
crf = fluid.layers.linear_chain_crf(
input=hidden, label=label, param_attr=ParamAttr(name="crfw"))
crf_decode = fluid.layers.crf_decoding(
input=hidden, param_attr=ParamAttr(name="crfw"))
fluid.layers.chunk_eval(
input=crf_decode,
label=label,
chunk_scheme="IOB",
num_chunk_types=(label_dict_len - 1) / 2)
"""
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)
@templatedoc()
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.
Args:
input (Variable): ${x_comment}
num_filters (int): number of filters.
filter_size (int): the filter size (H and W).
filter_stride (int): stride of the filter.
padding (bool): if True, add paddings.
bias_attr (ParamAttr|None): attributes for bias
param_attr (ParamAttr|None): attributes for parameter
act (str): the activation type
Returns:
Variable: output of sequence_conv
"""
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=False):
"""
This function computes the softmax activation among all time-steps for each
sequence. The dimension of each time-step should be 1. Thus, the shape of
input Tensor can be either :math:`[N, 1]` or :math:`[N]`, where :math:`N`
is the sum of the length of all sequences.
For i-th sequence in a mini-batch:
.. math::
Out(X[lod[i]:lod[i+1]], :) = \\frac{\exp(X[lod[i]:lod[i+1], :])}{\sum(\exp(X[lod[i]:lod[i+1], :]))}
For example, for a mini-batch of 3 sequences with variable-length,
each containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7],
then softmax will be computed among :math:`X[0:2, :]`, :math:`X[2:5, :]`,
:math:`X[5:7, :]`, and :math:`N` turns out to be 7.
Args:
input (Variable): The input variable which is a LoDTensor.
bias_attr (ParamAttr|None): attributes for bias
param_attr (ParamAttr|None): attributes for parameter
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn \
library is installed. Default: False
Returns:
Variable: output of sequence_softmax
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
x_sequence_softmax = fluid.layers.sequence_softmax(input=x)
"""
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, name=None):
"""
The input of the softmax operator is a tensor of any rank. The output tensor
has the same shape as the input.
The input tensor will first be logically flattened to a 2-D matrix. The matrix's
second dimension(row length) is as same as the last dimension of the input
tensor, and the first dimension(column length) is the product of all other
dimensions of the input tensor. For each row of the matrix, the softmax operator
squashes the K-dimensional(K is the width of the matrix, which is also the size
of the input tensor's last dimension) vector of arbitrary real values to a
K-dimensional vector of real values in the range [0, 1] that add up to 1.
It computes the exponential of the given dimension and the sum of exponential
values of all the other dimensions in the K-dimensional vector input.
Then the ratio of the exponential of the given dimension and the sum of
exponential values of all the other dimensions is the output of the softmax
operator.
For each row :math:`i` and each column :math:`j` in the matrix, we have:
.. math::
Out[i, j] = \\frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])}
Args:
input (Variable): The input variable.
bias_attr (ParamAttr): attributes for bias
param_attr (ParamAttr): attributes for parameter
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn \
library is installed.
Returns:
Variable: output of softmax
Examples:
.. code-block:: python
fc = fluid.layers.fc(input=x, size=10)
softmax = fluid.layers.softmax(input=fc)
"""
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):
"""
The convolution2D layer calculates the output based on the input, filter
and strides, paddings, dilations, groups parameters. Input and
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.
Filter is in MCHW format, where M is the number of output image channels,
C is the number of input image channels, H is the height of the filter,
and W is the width of the filter. If the groups is greater than 1,
C will equal the number of input image channels divided by the groups.
Please refer to UFLDL's `convolution
<http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/>`_
for more detials.
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)
Where:
* :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: :math:`(N, C_{in}, H_{in}, W_{in})`
Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)`
- Output:
Output shape: :math:`(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
use_mkldnn (bool): Use mkldnn kernels or not, it is valid only when compiled
with mkldnn library. Default: False
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")
"""
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, int(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 conv3d(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):
"""
**Convlution3D Layer**
The convolution3D layer calculates the output based on the input, filter
and strides, paddings, dilations, groups parameters. Input(Input) and
Output(Output) are in NCDHW format. Where N is batch size C is the number of
channels, D is the depth of the feature, H is the height of the feature,
and W is the width of the feature. Convlution3D is similar with Convlution2D
but adds one dimension(depth). 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 NCDHW format.
* :math:`W`: Filter value, a tensor with MCDHW 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: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
Filter shape: :math:`(C_{out}, C_{in}, D_f, H_f, W_f)`
- Output:
Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`
Where
.. math::
D_{out}&= \\frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{strides[0]} + 1 \\\\
H_{out}&= \\frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{strides[1]} + 1 \\\\
W_{out}&= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{strides[2]} + 1
Args:
input (Variable): The input image with [N, C, D, 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 three integers, (filter_size_D, 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 three integers, (stride_D, stride_H, stride_W). Otherwise, the
stride_D = stride_H = stride_W = stride. Default: stride = 1.
padding (int|tuple): The padding size. If padding is a tuple, it must
contain three integers, (padding_D, padding_H, padding_W). Otherwise, the
padding_D = padding_H = padding_W = padding. Default: padding = 0.
dilation (int|tuple): The dilation size. If dilation is a tuple, it must
contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
dilation_D = dilation_H = dilation_W = dilation. Default: dilation = 1.
groups (int): The groups number of the Conv3d 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 Conv3d Layer. Default: None
bias_attr (ParamAttr): Bias parameter for the Conv3d layer. Default: None
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
use_mkldnn (bool): Use mkldnn kernels or not.
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, 12, 32, 32], dtype='float32')
conv3d = fluid.layers.conv3d(input=data, num_filters=2, filter_size=3, act="relu")
"""
l_type = 'conv3d'
helper = LayerHelper(l_type, **locals())
dtype = helper.input_dtype()
num_channels = input.shape[1]
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, 3, 'filter_size')
stride = utils.convert_to_list(stride, 3, 'stride')
padding = utils.convert_to_list(padding, 3, 'padding')
dilation = utils.convert_to_list(dilation, 3, '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]**3 * 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 = [[2, 3, 2]]
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]) == 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)
last : out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
first : 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.
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')
last_x = fluid.layers.sequence_pool(input=x, pool_type='last')
first_x = fluid.layers.sequence_pool(input=x, pool_type='first')
"""
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
@templatedoc()
def sequence_concat(input, name=None):
"""
${comment}
Args:
input(list): List of Variables to be concatenated.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: Output variable of the concatenation.
Examples:
.. code-block:: python
out = fluid.layers.sequence_concat(input=[seq1, seq2, seq3])
"""
helper = LayerHelper('sequence_concat', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(
type='sequence_concat', inputs={'X': input}, outputs={'Out': [out]})
return out
def sequence_first_step(input):
"""
This function gets the first step of sequence.
.. code-block:: text
x is a 1-level LoDTensor:
x.lod = [[2, 3, 2]]
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]) == 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 function gets the last step of sequence.
.. code-block:: text
x is a 1-level LoDTensor:
x.lod = [[2, 3, 2]]
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]) == 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")
@templatedoc()
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):
"""
${comment}
Args:
input (Variable): The input tensor of pooling operator. The format of
input tensor is NCHW, 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.
pool_size (int): The side length of pooling windows. All pooling
windows are squares with pool_size on a side.
pool_type: ${pooling_type_comment}
pool_stride (int): stride of the pooling layer.
pool_padding (int): padding size.
global_pooling: ${global_pooling_comment}
use_cudnn: ${use_cudnn_comment}
ceil_mode: ${ceil_mode_comment}
use_mkldnn: ${use_mkldnn_comment}
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
Returns:
Variable: The pooling result.
Raises:
ValueError: If 'pool_type' is not "max" nor "avg"
ValueError: If 'global_pooling' is False and 'pool_size' is -1
ValueError: If 'use_cudnn' is not a bool value.
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 32, 32], dtype='float32')
conv2d = fluid.layers.pool2d(
input=data,
pool_size=2,
pool_type='max',
pool_stride=1,
global_pooling=False)
"""
if pool_type not in ["max", "avg"]:
raise ValueError(
"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")
l_type = 'pool2d'
helper = LayerHelper(l_type, **locals())
dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype)
helper.append_op(
type=l_type,
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 pool3d(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 3-dimensions, using the
pooling configurations mentioned in input parameters.
Args:
input (Variable): ${input_comment}
pool_size (int): ${ksize_comment}
pool_type (str): ${pooling_type_comment}
pool_stride (int): stride of the pooling layer.
pool_padding (int): padding size.
global_pooling (bool): ${global_pooling_comment}
use_cudnn (bool): ${use_cudnn_comment}
ceil_mode (bool): ${ceil_mode_comment}
use_mkldnn (bool): ${use_mkldnn_comment}
name (str): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: output of pool3d layer.
"""
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, 3, 'pool_size')
pool_padding = utils.convert_to_list(pool_padding, 3, 'pool_padding')
pool_stride = utils.convert_to_list(pool_stride, 3, 'pool_stride')
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
l_type = "pool3d"
helper = LayerHelper(l_type, **locals())
dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype)
helper.append_op(
type=l_type,
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,
use_mkldnn=False,
name=None,
moving_mean_name=None,
moving_variance_name=None,
do_model_average_for_mean_and_var=False,
fuse_with_relu=False):
"""
**Batch Normalization Layer**
Can be used as a normalizer function for conv2d and fully_connected operations.
The required data format for this layer is one of the following:
1. NHWC `[batch, in_height, in_width, in_channels]`
2. NCHW `[batch, in_channels, in_height, in_width]`
Refer to `Batch Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift <https://arxiv.org/pdf/1502.03167.pdf>`_
for more details.
:math:`input` is the input features over a mini-batch.
.. math::
\\mu_{\\beta} &\\gets \\frac{1}{m} \\sum_{i=1}^{m} x_i \\qquad &//\\
\ mini-batch\ mean \\\\
\\sigma_{\\beta}^{2} &\\gets \\frac{1}{m} \\sum_{i=1}^{m}(x_i - \\
\\mu_{\\beta})^2 \\qquad &//\ mini-batch\ variance \\\\
\\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\
\\sigma_{\\beta}^{2} + \\epsilon}} \\qquad &//\ normalize \\\\
y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift
Args:
input(variable): The input variable which is a LoDTensor.
act(string, Default None): Activation type, linear|relu|prelu|...
is_test(bool, Default False): Used for training or training.
momentum(float, Default 0.9):
epsilon(float, Default 1e-05):
param_attr(ParamAttr): The parameter attribute for Parameter `scale`.
bias_attr(ParamAttr): The parameter attribute for Parameter `bias`.
data_layout(string, default NCHW): NCHW|NHWC
in_place(bool, Default False): Make the input and output of batch norm reuse memory.
use_mkldnn(bool, Default false): ${use_mkldnn_comment}
name(string, Default None): A name for this layer(optional). If set None, the layer
will be named automatically.
moving_mean_name(string, Default None): The name of moving_mean which store the global Mean.
moving_variance_name(string, Default None): The name of the moving_variance which store the global Variance.
do_model_average_for_mean_and_var(bool, Default False): Do model average for mean and variance or not.
fuse_with_relu (bool): if True, this OP performs relu after batch norm.
Returns:
Variable: A tensor variable which is the result after applying batch normalization on the input.
Examples:
.. code-block:: python
hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
hidden2 = fluid.layers.batch_norm(input=hidden1)
"""
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,
do_model_average=do_model_average_for_mean_and_var),
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,
do_model_average=do_model_average_for_mean_and_var),
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,
"use_mkldnn": use_mkldnn,
"fuse_with_relu": fuse_with_relu
})
return helper.append_activation(batch_norm_out)
@templatedoc()
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):
"""
${comment}
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)
* :math:`a`: the vector representation of the summed inputs to the neurons
in that layer.
* :math:`H`: the number of hidden units in a layers
* :math:`g`: the trainable scale parameter.
* :math:`b`: the trainable bias parameter.
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.
name (str): The name of this layer. It is optional.
Returns:
${y_comment}
Examples:
>>> 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 conv2d_transpose(input,
num_filters,
output_size=None,
filter_size=None,
padding=0,
stride=1,
dilation=1,
groups=None,
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>`_.
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)
Where:
* :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: :math:`(N, C_{in}, H_{in}, W_{in})`
Filter shape: :math:`(C_{in}, C_{out}, H_f, W_f)`
- Output:
Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`
Where
.. math::
H^\prime_{out} &= (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\\\
W^\prime_{out} &= (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1 \\\\
H_{out} \in [ H^\prime_{out}, H^\prime_{out} + strides[0] ) \\\\
W_{out} \in [ W^\prime_{out}, W^\prime_{out} + strides[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). None if use
filter_size, padding, and stride to calculate output_size.
if output_size and filter_size are specified at the same time, They
should follow the formula above.
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.
groups(int): The groups number of the Conv2d transpose layer. Inspired by
grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
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_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)
"""
input_channel = input.shape[1]
op_type = 'conv2d_transpose'
if (input_channel == groups and num_filters == input_channel and
not use_cudnn):
op_type = 'depthwise_conv2d_transpose'
helper = LayerHelper(op_type, **locals())
if not isinstance(input, Variable):
raise TypeError("Input of conv2d_transpose must be Variable")
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')
if output_size is None:
output_size = []
elif isinstance(output_size, list) or isinstance(output_size, int):
output_size = utils.convert_to_list(output_size, 2, 'output_size')
else:
raise ValueError("output_size should be list or int")
padding = utils.convert_to_list(padding, 2, 'padding')
groups = 1 if groups is None else groups
filter_shape = [input_channel, num_filters // groups] + 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=op_type,
inputs={'Input': [input],
'Filter': [img_filter]},
outputs={'Output': pre_bias},
attrs={
'output_size': output_size,
'strides': stride,
'paddings': padding,
'dilations': dilation,
'groups': groups,
'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 conv3d_transpose(input,
num_filters,
output_size=None,
filter_size=None,
padding=0,
stride=1,
dilation=1,
groups=None,
param_attr=None,
bias_attr=None,
use_cudnn=True,
act=None,
name=None):
"""
**Convlution3D transpose layer**
The convolution3D transpose layer calculates the output based on the input,
filter, and dilations, strides, paddings. Input(Input) and output(Output)
are in NCDHW format. Where N is batch size, C is the number of channels,
D is the depth of the feature, 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>`_.
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 NCDHW format.
* :math:`W`: Filter value, a tensor with MCDHW 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: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
Filter shape: :math:`(C_{in}, C_{out}, D_f, H_f, W_f)`
- Output:
Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`
Where
.. math::
D_{out} &= (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1 \\\\
H_{out} &= (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1 \\\\
W_{out} &= (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1
Args:
input(Variable): The input image with [N, C, D, 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 three integers, (image_D, 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 three integers, (filter_size_D, 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 three integers, (padding_D, padding_H, padding_W). Otherwise, the
padding_D = padding_H = padding_W = padding. Default: padding = 0.
stride(int|tuple): The stride size. If stride is a tuple, it must
contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
stride_D = stride_H = stride_W = stride. Default: stride = 1.
dilation(int|tuple): The dilation size. If dilation is a tuple, it must
contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
dilation_D = dilation_H = dilation_W = dilation. Default: dilation = 1.
groups(int): The groups number of the Conv3d transpose layer. Inspired by
grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
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 Conv3d_transpose Layer.
Default: None
bias_attr(ParamAttr): Bias parameter for the Conv3d 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, 12, 32, 32], dtype='float32')
conv3d_transpose = fluid.layers.conv3d_transpose(input=data, num_filters=2, filter_size=3)
"""
l_type = "conv3d_transpose"
helper = LayerHelper(l_type, **locals())
if not isinstance(input, Variable):
raise TypeError("Input of conv3d_transpose must be Variable")
input_channel = input.shape[1]
padding = utils.convert_to_list(padding, 3, 'padding')
stride = utils.convert_to_list(stride, 3, 'stride')
dilation = utils.convert_to_list(dilation, 3, '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]
d_in = input.shape[2]
h_in = input.shape[3]
w_in = input.shape[4]
filter_size_d = (output_size[0] - (d_in - 1) * stride[0] + 2 *
padding[0] - 1) // dilation[0] + 1
filter_size_h = (output_size[1] - (h_in - 1) * stride[1] + 2 *
padding[1] - 1) // dilation[1] + 1
filter_size_w = (output_size[2] - (w_in - 1) * stride[2] + 2 *
padding[2] - 1) // dilation[2] + 1
filter_size = [filter_size_d, filter_size_h, filter_size_w]
else:
filter_size = utils.convert_to_list(filter_size, 3,
'conv3d_transpose.filter_size')
groups = 1 if groups is None else groups
filter_shape = [input_channel, num_filters // groups] + 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=l_type,
inputs={'Input': [input],
'Filter': [img_filter]},
outputs={'Output': pre_bias},
attrs={
'strides': stride,
'paddings': padding,
'dilations': dilation,
'groups': groups,
'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 = [[2, 2]]
x.data = [[a], [b], [c], [d]]
x.dims = [4, 1]
y is a LoDTensor:
y.lod = [[2, 2],
[3, 3, 1, 1]]
ref_level: 0
then output is a 1-level LoDTensor:
out.lod = [[2, 2, 2, 2]]
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 = [[2, 0, 3]]
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 sequence_expand_as(x, y, name=None):
"""Sequence Expand As Layer. This layer will expand the input variable **x**
according to the zeroth level lod of **y**. Current implementation requires
the level number of Input(Y)'s lod must be 1, and the first dimension of
Input(X) should be equal to the size of Input(Y)'s zeroth level lod, and
lod of Input(X) is not considered.
Following examples will explain how sequence_expand_as works:
.. code-block:: text
* Case 1:
Given a 1-level LoDTensor input(X)
X.data = [[a], [b], [c], [d]]
X.dims = [4, 1]
and input(Y)
Y.lod = [[0, 3, 6, 7, 8]]
ref_level: 0
then we get 1-level LoDTensor
Out.lod = [[0, 3, 6, 7, 8]]
Out.data = [[a], [a], [a], [b], [b], [b], [c], [d]]
Out.dims = [8, 1]
* Case 2:
Given a common Tensor input(X)
X.data = [[a, b], [c, d], [e, f]]
X.dims = [3, 2]
and input(Y)
Y.lod = [[0, 2, 3, 6]]
ref_level: 0
then we get a common LoDTensor
Out.lod = [[0, 2, 3, 6]]
Out.data = [[a, b], [a, b] [c, d], [e, f], [e, f], [e, f]]
Out.dims = [6, 2]
Args:
x (Variable): The input variable which is a Tensor or LoDTensor.
y (Variable): The input variable which is a LoDTensor.
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_as(x=x, y=y)
"""
helper = LayerHelper('sequence_expand_as', input=x, **locals())
dtype = helper.input_dtype()
tmp = helper.create_tmp_variable(dtype)
helper.append_op(
type='sequence_expand_as',
inputs={'X': x,
'Y': y},
outputs={'Out': tmp})
return tmp
@templatedoc()
def sequence_pad(x, pad_value, maxlen=None):
"""
${comment}
Args:
x(Variable): Input variable which should contain lod information.
pad_value(Variable): The Variable that holds values that will be fill
into padded steps. It can be a scalar or a tensor whose shape
equals to time steps in sequences. If it's a scalar, it will be
automatically broadcasted to the shape of time step.
maxlen(int, default None): The length of padded sequences. It can be
None or any positive int. When it is None, all sequences will be
padded up to the length of the longest one among them; when it a
certain positive value, it must be greater than the length of the
longest original sequence."
Returns:
Variable: The padded sequence batch and the original lengths before
padding. All sequences has the same length.
Examples:
.. code-block:: python
import numpy
x = fluid.layers.data(name='y', shape=[10, 5],
dtype='float32', lod_level=1)
pad_value = fluid.layers.assign(input=numpy.array([0]))
out = fluid.layers.sequence_pad(x=x, pad_value=pad_value)
"""
helper = LayerHelper('sequence_pad', input=x, **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
length = helper.create_tmp_variable(dtype)
pad_value.stop_gradient = True
length.stop_gradient = True
if maxlen is None:
maxlen = -1
helper.append_op(
type='sequence_pad',
inputs={'X': x,
'PadValue': pad_value},
outputs={'Out': out,
'Length': length},
attrs={'padded_length': maxlen})
return out, length
def beam_search(pre_ids,
pre_scores,
ids,
scores,
beam_size,
end_id,
level=0,
name=None):
"""
Beam search is a classical algorithm for selecting candidate words in a
machine translation task.
Refer to `Beam search <https://en.wikipedia.org/wiki/Beam_search>`_
for more details.
This layer does the search in beams for one time step. Specifically, it
selects the top-K candidate word ids of current step from :attr:`ids`
according to their :attr:`scores` for all source sentences, where K is
:attr:`beam_size` and :attr:`ids, scores` are predicted results from the
computation cell. Additionally, :attr:`pre_ids` and :attr:`pre_scores` are
the output of beam_search at previous step, they are needed for special use
to handle ended candidate translations.
Note that the :attr:`scores` passed in should be accumulated scores, and
length penalty should be done with extra operators before calculating the
accumulated scores if needed, also suggest finding top-K before it and
using the top-K candidates following.
Please see the following demo for a fully beam search usage example:
fluid/tests/book/test_machine_translation.py
Args:
pre_ids(Variable): The LodTensor variable which is the output of
beam_search at previous step. It should be a LodTensor with shape
:math:`(batch_size, 1)` and lod
:math:`[[0, 1, ... , batch_size], [0, 1, ..., batch_size]]` at the
first step.
pre_scores(Variable): The LodTensor variable which is the output of
beam_search at previous step.
ids(Variable): The LodTensor variable containing the candidates ids.
Its shape should be :math:`(batch_size \\times beam_size, K)`,
where :math:`K` supposed to be :attr:`beam_size`.
scores(Variable): The LodTensor variable containing the accumulated
scores corresponding to :attr:`ids` and its shape is the same as
the shape of :attr:`ids`.
beam_size(int): The beam width used in beam search.
end_id(int): The id of end token.
level(int, default 0): It can be ignored and mustn't change currently.
It means the source level of lod, which is explained as following.
The lod level of :attr:`ids` should be 2. The first level is source
level which describes how many prefixes (branchs) for each source
sentece (beam), and the second level is sentence level which
describes how these candidates belong to the prefix. The paths
linking prefixes and selected candidates are organized and reserved
in lod.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The LodTensor pair containing the selected ids and the \
corresponding scores.
Examples:
.. code-block:: python
# Suppose `probs` contains predicted results from the computation
# cell and `pre_ids` and `pre_scores` is the output of beam_search
# at previous step.
topk_scores, topk_indices = layers.topk(probs, k=beam_size)
accu_scores = layers.elementwise_add(
x=layers.log(x=topk_scores)),
y=layers.reshape(
pre_scores, shape=[-1]),
axis=0)
selected_ids, selected_scores = layers.beam_search(
pre_ids=pre_ids,
pre_scores=pre_scores,
ids=topk_indices,
scores=accu_scores,
beam_size=beam_size,
end_id=end_id)
"""
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,
'pre_scores': pre_scores,
'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 beam_search_decode(ids, scores, beam_size, end_id, name=None):
"""
Beam Search Decode Layer. This layer constructs the full hypotheses for
each source sentence by walking back along the LoDTensorArray :attr:`ids`
whose lods can be used to restore the path in the beam search tree.
Please see the following demo for a fully beam search usage example:
fluid/tests/book/test_machine_translation.py
Args:
ids(Variable): The LodTensorArray variable containing the selected ids
of all steps.
scores(Variable): The LodTensorArray variable containing the selected
scores of all steps.
beam_size(int): The beam width used in beam search.
end_id(int): The id of end token.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The LodTensor pair containing the generated id sequences \
and the corresponding scores. The shapes and lods of the two \
LodTensor are same. The lod level is 2 and the two levels \
separately indicate how many hypotheses each source sentence has \
and how many ids each hypothesis has.
Examples:
.. code-block:: python
# Suppose `ids` and `scores` are LodTensorArray variables reserving
# the selected ids and scores of all steps
finished_ids, finished_scores = layers.beam_search_decode(
ids, scores, beam_size=5, end_id=0)
"""
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
},
attrs={"beam_size": beam_size,
"end_id": end_id})
return sentence_ids, sentence_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 (list|int|None): The dimensions 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[i] < 0`,
the dimension to reduce is :math:`rank + dim[i]`.
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 corresponding 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]]
# x is a Tensor variable with shape [2, 2, 2] and elements as below:
# [[[1, 2], [3, 4]],
# [[5, 6], [7, 8]]]
# Each example is followed by the corresponding output tensor.
fluid.layers.reduce_sum(x, dim=[1, 2]) # [10, 26]
fluid.layers.reduce_sum(x, dim=[0, 1]) # [16, 20]
"""
helper = LayerHelper('reduce_sum', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
if dim is not None and not isinstance(dim, list):
dim = [dim]
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 the input tensor's elements along the given dimension.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
dim (list|int|None): The dimension along which the mean is computed. If
`None`, compute the mean over all elements of :attr:`input`
and return a variable with a single element, otherwise it
must be in the range :math:`[-rank(input), rank(input))`. If
:math:`dim[i] < 0`, the dimension to reduce is
:math:`rank(input) + dim[i]`.
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 mean 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]]
# x is a Tensor variable with shape [2, 2, 2] and elements as below:
# [[[1.0, 2.0], [3.0, 4.0]],
# [[5.0, 6.0], [7.0, 8.0]]]
# Each example is followed by the correspending output tensor.
fluid.layers.reduce_mean(x, dim=[1, 2]) # [2.5, 6.5]
fluid.layers.reduce_mean(x, dim=[0, 1]) # [4.0, 5.0]
"""
helper = LayerHelper('reduce_mean', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
if dim is not None and not isinstance(dim, list):
dim = [dim]
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 (list|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[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
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]]
# x is a Tensor variable with shape [2, 2, 2] and elements as below:
# [[[1.0, 2.0], [3.0, 4.0]],
# [[5.0, 6.0], [7.0, 8.0]]]
# Each example is followed by the correspending output tensor.
fluid.layers.reduce_max(x, dim=[1, 2]) # [4.0, 8.0]
fluid.layers.reduce_max(x, dim=[0, 1]) # [7.0, 8.0]
"""
helper = LayerHelper('reduce_max', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
if dim is not None and not isinstance(dim, list):
dim = [dim]
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 (list|int|None): The dimensions 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[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
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]]
# x is a Tensor variable with shape [2, 2, 2] and elements as below:
# [[[1.0, 2.0], [3.0, 4.0]],
# [[5.0, 6.0], [7.0, 8.0]]]
# Each example is followed by the correspending output tensor.
fluid.layers.reduce_min(x, dim=[1, 2]) # [1.0, 5.0]
fluid.layers.reduce_min(x, dim=[0, 1]) # [1.0, 2.0]
"""
helper = LayerHelper('reduce_min', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
if dim is not None and not isinstance(dim, list):
dim = [dim]
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 (list|int|None): The dimensions 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[i] < 0`,
the dimension to reduce is :math:`rank + dim[i]`.
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]]
# x is a Tensor variable with shape [2, 2, 2] and elements as below:
# [[[1.0, 2.0], [3.0, 4.0]],
# [[5.0, 6.0], [7.0, 8.0]]]
# Each example is followed by the correspending output tensor.
fluid.layers.reduce_prod(x, dim=[1, 2]) # [24.0, 1680.0]
fluid.layers.reduce_prod(x, dim=[0, 1]) # [105.0, 384.0]
"""
helper = LayerHelper('reduce_prod', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
if dim is not None and not isinstance(dim, list):
dim = [dim]
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(Variable): 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
.. math::
y = \\frac{x}{ \sqrt{\sum {x^2} + epsion }}
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): The axis on which to apply normalization. If `axis < 0`, \
the dimension to normalization is rank(X) + axis. -1 is the
last dimension.
epsilon(float): The epsilon value is used to avoid division by zero, \
the defalut value is 1e-10.
name(str|None): A name for this layer(optional). If set None, the layer \
will be named automatically.
Returns:
Variable: The output tensor variable is the same shape with `x`.
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())
out = helper.create_tmp_variable(dtype=x.dtype)
norm = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type="norm",
inputs={"X": x},
outputs={"Out": out,
"Norm": norm},
attrs={
"axis": 1 if axis is None else axis,
"epsilon": epsilon,
})
return out
def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, 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.
alpha (float): The scale of output. Default 1.0.
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,
'alpha': float(alpha),
})
return out
def topk(input, k, name=None):
"""
This operator is used to find values and indices of the k largest entries
for the last dimension.
If the input is a vector (1-D Tensor), finds the k largest entries in the vector
and outputs their values and indices as vectors. Thus values[j] is the j-th
largest entry in input, and its index is indices[j].
If the input is a Tensor with higher rank, this operator computes the top k
entries along the last dimension.
For example:
.. code-block:: text
If:
input = [[5, 4, 2, 3],
[9, 7, 10, 25],
[6, 2, 10, 1]]
k = 2
Then:
The first output:
values = [[5, 4],
[10, 25],
[6, 10]]
The second output:
indices = [[0, 1],
[2, 3],
[0, 2]]
Args:
input(Variable): The input variable which can be a vector or Tensor with
higher rank.
k(int): The number of top elements to look for along the last dimension
of input.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Default: None
Returns:
Tuple[Variable]: A tuple with two elements. Each element is a Variable.
The first one is k largest elements along each last
dimensional slice. The second one is indices of values
within the last dimension of input.
Raises:
ValueError: If k < 1 or k is not less than the last dimension of input
Examples:
.. code-block:: python
top5_values, top5_indices = layers.topk(input, k=5)
"""
helper = LayerHelper("top_k", **locals())
values = helper.create_tmp_variable(dtype=input.dtype)
indices = helper.create_tmp_variable(dtype="int64")
helper.append_op(
type="top_k",
inputs={"X": [input]},
outputs={"Out": [values],
"Indices": [indices]},
attrs={"k": k})
values.stop_gradient = True
indices.stop_gradient = True
return values, indices
def edit_distance(input, label, normalized=True, ignored_tokens=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"
The input 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 input LoDTensor.
The output 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, default True): Indicated whether to normalize the edit distance by
the length of reference string.
ignored_tokens(list<int>, default None): Tokens that should be removed before
calculating edit distance.
name (str): The name of this layer. It is optional.
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": [erased_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 = [[4, 4]]
Then:
output.data = [[2],
[1],
[3]]
output.lod = [[2, 1]]
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).
name (str): The name of this layer. It is optional.
Returns:
Variable: CTC greedy decode result. If all the sequences in result were
empty, the result LoDTensor will be [-1] with LoD [[]] 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())
_, topk_indices = topk(input, 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): 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): 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
label = fluid.layers.data(shape=[11, 8], dtype='float32', lod_level=1)
predict = fluid.layers.data(shape=[11, 1], dtype='float32')
cost = fluid.layers.warpctc(input=predict, label=label)
"""
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): A 2-D LoDTensor with shape being [N, M] where M for dimension.
new_dim (int): New dimension that the input LoDTensor is reshaped to.
Returns:
Variable: Reshaped LoDTensor according to new dimension.
Examples:
.. code-block:: python
x = fluid.layers.data(shape=[5, 20], dtype='float32', lod_level=1)
x_reshaped = fluid.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
# FIXME(wuyi): let docstring_checker.py understand @autodoc.
# For now, the comments in c++ use types like Tensor, but in python side
# the type is often "Variable", and arguments may vary.
@templatedoc(op_type="nce")
def nce(input,
label,
num_total_classes,
sample_weight=None,
param_attr=None,
bias_attr=None,
num_neg_samples=None):
"""
${comment}
Args:
input (Variable): input variable.
label (Variable): label.
num_total_classes (int):${num_total_classes_comment}
sample_weight (Variable|None): A Variable of shape [batch_size, 1]
storing a weight for each sample. The default weight for each
sample is 1.0.
param_attr (ParamAttr|None): attributes for parameter
bias_attr (ParamAttr|None): attributes for bias
num_neg_samples (int): ${num_neg_samples_comment}
Returns:
Variable: The output nce loss.
Examples:
.. code-block:: python
window_size = 5
words = []
for i in xrange(window_size):
words.append(layers.data(
name='word_{0}'.format(i), shape=[1], dtype='int64'))
dict_size = 10000
label_word = int(window_size / 2) + 1
embs = []
for i in xrange(window_size):
if i == label_word:
continue
emb = layers.embedding(input=words[i], size=[dict_size, 32],
param_attr='emb.w', is_sparse=True)
embs.append(emb)
embs = layers.concat(input=embs, axis=1)
loss = layers.nce(input=embs, label=words[label_word],
num_total_classes=dict_size, param_attr='nce.w',
bias_attr='nce.b')
"""
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 hsigmoid(input, label, num_classes, param_attr=None, bias_attr=None):
"""
The hierarchical sigmoid operator is used to accelerate the training
process of language model. This operator organizes the classes into a
complete binary tree, each leaf node represents a class(a word) and each
internal node acts as a binary classifier. For each word there's a unique
path from root to it's leaf node, hsigmoid calculate the cost for each
internal node on the path, and sum them to get a total cost. hsigmoid can
achive a acceleration from :math:`O(N)` to :math:`O(logN)`, where :math:`N`
represents the size of word dict.
Refer to `Hierarchical Probabilistic Neural Network Language Model
<http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf>`_
Args:
input (Variable): The input tensor variable with shape
:math:`[N \\times D]`, where :math:`N` is the size of mini-batch,
and :math:`D` is the feature size.
label (Variable): The tensor variable contains labels of training data.
It's a tensor with shape is :math:`[N \\times 1]`.
num_classes: (int), The number of classes, must not be less than 2.
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 False, no
bias will be applied.
Returns:
Out: (Tensor) The cost of hierarchical sigmoid operator. the shape is [N, 1]
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[2], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='int64')
out = fluid.layers.hsigmoid(input=x, label=y, num_classes=6)
"""
helper = LayerHelper('hierarchical_sigmoid', **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
pre_out = helper.create_tmp_variable(dtype)
dim = input.shape[1]
if num_classes < 2:
raise ValueError("num_classes must not be less than 2.")
weights = helper.create_parameter(
attr=helper.param_attr,
shape=[num_classes - 1, dim],
is_bias=False,
dtype=input.dtype)
inputs = {"X": input, "W": weights, "Label": label}
if helper.bias_attr:
bias = helper.create_parameter(
attr=helper.bias_attr,
shape=[1, num_classes - 1],
is_bias=True,
dtype=input.dtype)
inputs['Bias'] = bias
helper.append_op(
type="hierarchical_sigmoid",
inputs=inputs,
outputs={"Out": out,
"PreOut": pre_out},
attrs={"num_classes": num_classes})
return out
def transpose(x, perm, name=None):
"""
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:
x (Variable): The input Tensor.
perm (list): A permutation of the dimensions of `input`.
name (str): The name of this layer. It is optional.
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)
x_shape = helper.create_tmp_variable(x.dtype)
helper.append_op(
type='transpose2',
inputs={'X': [x]},
outputs={'Out': [out],
'XShape': [x_shape]},
attrs={'axis': perm})
return out
def im2sequence(input,
filter_size=1,
stride=1,
padding=0,
input_image_size=None,
out_stride=1,
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.
input_image_size(Variable): the input contains image real size.It's dim
is [batchsize, 2]. It is dispensable.It is just for batch inference.
out_stride(int|tuple): The scaling of image through CNN. It is
dispensable. It is valid only when input_image_size is not null.
If out_stride is tuple, it must contain two intergers,
(out_stride_H, out_stride_W). Otherwise,
the out_stride_H = out_stride_W = out_stride.
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:
.. 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, 8}
output.lod = [[4, 4]]
Examples:
.. 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])
inputs = {"X": input}
attrs = {"kernels": filter_size, "strides": stride, "padding": padding}
if input_image_size:
if isinstance(out_stride, int):
out_stride = [out_stride, out_stride]
inputs["Y"] = input_image_size
attrs["out_stride"] = out_stride
helper = LayerHelper('im2sequence', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(
type='im2sequence', inputs=inputs, outputs={'Out': out}, attrs=attrs)
return out
@templatedoc()
def row_conv(input, future_context_size, param_attr=None, act=None):
"""
${comment}
Args:
input (${x_type}): ${x_comment}.
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:
${out_comment}.
Examples:
>>> import paddle.fluid as fluid
>>> 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)
@templatedoc()
def multiplex(inputs, index):
"""
${comment}
>>> import paddle.fluid as fluid
>>> 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)
Args:
inputs (list): ${x_comment}.
index (${ids_type}): ${ids_comment}.
Returns:
${out_comment}.
"""
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,
ignore_index=-100):
"""
**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.
ignore_index (int): Specifies a target value that is ignored and does
not contribute to the input gradient. Only valid
if soft_label is set to False. Default: -100
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,
'ignore_index': ignore_index})
return loss
def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
"""
This layer computes the smooth L1 loss for Variable :attr:`x` and :attr:`y`.
It takes the first dimension of :attr:`x` and :attr:`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 ouput Variable 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 :attr:`x`.
inside_weight (Variable|None): A tensor with rank at least 2. This
input is optional and should have same shape with :attr:`x`. If
provided, the result of (:attr:`x` - :attr:`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 :attr:`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 layer. A float
scalar with default value 1.0.
Returns:
Variable: 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='float32')
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):
"""
This layer creates the one-hot representations for input indices.
Args:
input(Variable): Input indices, last dimension must be 1.
depth(scalar): An interger defining the depth of the one-hot dimension.
Returns:
Variable: The one-hot representations of input.
Examples:
.. code-block:: python
label = layers.data(name="label", shape=[1], dtype="float32")
one_hot_label = layers.one_hot(input=label, depth=10)
"""
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):
"""
Create an auto-increase variable
which will be automatically increased by 1 every mini-batch
Return the run counter of the main program, default is started from 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.
Examples:
.. code-block:: python
global_step = fluid.layers.autoincreased_step_counter(
counter_name='@LR_DECAY_COUNTER@', begin=begin, step=1)
"""
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, force_cpu=True))
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 reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
"""
Gives a new shape to the input Tensor without changing its data.
The target shape can be given by :attr:`shape` or :attr:`actual_shape`.
:attr:`shape` is a list of integer while :attr:`actual_shape` is a tensor
variable. :attr:`actual_shape` has a higher priority than :attr:`shape`
if it is provided, while :attr:`shape` still should be set correctly to
gurantee shape inference in compile-time.
Some tricks exist when specifying the target shape.
1. -1 means the value of this dimension is inferred from the total element
number of x and remaining dimensions. Thus one and only one dimension can
be set -1.
2. 0 means the actual dimension value is going to be copied from the
corresponding dimension of x. The indice of 0s in shape can not exceed
Rank(X).
Here are some examples to explain it.
1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
is [6, 8], the reshape operator will transform x into a 2-D tensor with
shape [6, 8] and leaving x's data unchanged.
2. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
specified is [2, 3, -1, 2], the reshape operator will transform x into a
4-D tensor with shape [2, 3, 4, 2] and leaving x's data unchanged. In this
case, one dimension of the target shape is set to -1, the value of this
dimension is inferred from the total element number of x and remaining
dimensions.
3. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
is [-1, 0, 3, 2], the reshape operator will transform x into a 4-D tensor
with shape [2, 4, 3, 2] and leaving x's data unchanged. In this case,
besides -1, 0 means the actual dimension value is going to be copied from
the corresponding dimension of x.
Args:
x(variable): The input tensor.
shape(list): The new shape. At most one dimension of the new shape can
be -1.
actual_shape(variable): An optional input. If provided, reshape
according to this given shape rather than
:attr:`shape` specifying shape. That is to
say :attr:`actual_shape` has a higher priority
than :attr:`shape`.
act (str): The non-linear activation to be applied to output variable.
inplace(bool): If this flag is set true, the output
shares data with input without copying, otherwise
a new output tensor is created
whose data is copied from input x.
name (str): The name of this layer. It is optional.
Returns:
Variable: The output tensor.
Raises:
TypeError: if actual_shape is neither Variable nor None.
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[2, 4, 6], dtype='float32')
reshaped = fluid.layers.reshape(
x=data, shape=[-1, 0, 3, 2], act='tanh', inplace=True)
"""
if not (isinstance(shape, list) or isinstance(shape, tuple)):
raise ValueError("Input shape must be a python list or tuple.")
inputs = {"X": x}
if isinstance(actual_shape, Variable):
inputs["Shape"] = actual_shape
elif actual_shape is not None:
raise TypeError("actual_shape should either be Variable or None")
# Validate the shape
unk_dim_idx = -1
for dim_idx, dim_size in enumerate(shape):
if dim_size == -1:
assert unk_dim_idx == -1, (
"Only one dimension in shape can be unknown.")
unk_dim_idx = dim_idx
elif dim_size == 0:
assert dim_idx < len(x.shape), (
"The indice of 0s in shape can not exceed Rank(X).")
else:
assert dim_size > 0, (
"Each dimension size given in shape must not be negtive "
"except one unknown dimension.")
helper = LayerHelper("reshape2", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
x_shape = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type="reshape2",
inputs=inputs,
attrs={"shape": shape},
outputs={"Out": out,
"XShape": x_shape})
return helper.append_activation(out)
def squeeze(input, axes, name=None):
"""
Remove single-dimensional entries from the shape of a tensor. Takes a
parameter axes with a list of axes to squeeze. If axes is not provided, all
the single dimensions will be removed from the shape. If an axis is
selected with shape entry not equal to one, an error is raised.
Examples:
Case 1:
Given
X.shape = (1, 3, 1, 5)
and
axes = [0]
we get:
Out.shape = (3, 1, 5)
Case 2:
Given
X.shape = (1, 3, 1, 5)
and
axes = []
we get:
Out.shape = (3, 5)
Args:
input (Variable): The input variable to be squeezed.
axes (list): List of integers, indicating the dimensions to be squeezed.
name (str|None): Name for this layer.
Returns:
Variable: Output squeezed variable.
Examples:
.. code-block:: python
x = layers.data(name='x', shape=[5, 1, 10])
y = layers.sequeeze(input=x, axes=[1])
"""
helper = LayerHelper("squeeze", **locals())
out = helper.create_tmp_variable(dtype=input.dtype)
x_shape = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type="squeeze2",
inputs={"X": input},
attrs={"axes": axes},
outputs={"Out": out,
"XShape": x_shape})
return out
def unsqueeze(input, axes, name=None):
"""
Insert single-dimensional entries to the shape of a tensor. Takes one
required argument axes, a list of dimensions that will be inserted.
Dimension indices in axes are as seen in the output tensor.
For example:
Given a tensor such that tensor with shape [3, 4, 5],
then Unsqueezed tensor with axes=[0, 4] has shape [1, 3, 4, 5, 1].
Args:
input (Variable): The input variable to be unsqueezed.
axes (list): List of integers, indicating the dimensions to be inserted.
name (str|None): Name for this layer.
Returns:
Variable: Output unsqueezed variable.
Examples:
.. code-block:: python
x = layers.data(name='x', shape=[5, 10])
y = layers.unsequeeze(input=x, axes=[1])
"""
helper = LayerHelper("unsqueeze", **locals())
out = helper.create_tmp_variable(dtype=input.dtype)
x_shape = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type="unsqueeze2",
inputs={"X": input},
attrs={"axes": axes},
outputs={"Out": out,
"XShape": x_shape})
return out
def lod_reset(x, y=None, target_lod=None):
"""
Set LoD of :attr:`x` to a new one specified by :attr:`y` or
:attr:`target_lod`. When :attr:`y` provided, :attr:`y.lod` would be
considered as target LoD first, otherwise :attr:`y.data` would be
considered as target LoD. If :attr:`y` is not provided, target LoD should
be specified by :attr:`target_lod`. If target LoD is specified by
:attr:`Y.data` or :attr:`target_lod`, only one level LoD is supported.
.. code-block:: text
* Example 1:
Given a 1-level LoDTensor x:
x.lod = [[ 2, 3, 1 ]]
x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
x.dims = [6, 1]
target_lod: [4, 2]
then we get a 1-level LoDTensor:
out.lod = [[4, 2]]
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 = [[2, 3, 1]]
x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
x.dims = [6, 1]
y is a Tensor:
y.data = [[2, 4]]
y.dims = [1, 3]
then we get a 1-level LoDTensor:
out.lod = [[2, 4]]
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 = [[2, 3, 1]]
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 = [[2, 2], [2, 2, 1, 1]]
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 = [[2, 2], [2, 2, 1, 1]]
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 :attr:`y`.
target_lod (list|tuple|None): One level LoD which should be considered
as target LoD when :attr:`y` not provided.
Returns:
Variable: Output variable with LoD specified by this layer.
Raises:
ValueError: If :attr:`y` and :attr:`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
def pad(x, paddings, pad_value=0., name=None):
"""
Pads a tensor with a constant value given by :attr:`pad_value`, and the
padded width is specified by :attr:`paddings`.
Specifically, the number of values padded before the contents of :attr:`x`
in dimension :attr:`i` is indicated by :attr:`paddings[i]`, and the number
of values padded after the contents of :attr:`x` in dimension :attr:`i` is
indicated by :attr:`paddings[i+1]`.
See below for an example.
.. code-block:: text
Given:
x = [[1, 2], [3, 4]]
paddings = [0, 1, 1, 2]
pad_value = 0
Return:
out = [[0, 1, 2, 0, 0]
[0, 3, 4, 0, 0]
[0, 0, 0, 0, 0]]
Args:
x (Variable): The input tensor variable.
paddings (list): A list of integers. Its elements specify the padded
width before and after for each dimension in turn.
The length of :attr:paddings must be
:math:`rank(x) \\times 2`.
pad_value (float): The constant value used to pad.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The padded tensor variable.
Examples:
.. code-block:: python
# x is a rank 2 tensor variable.
out = fluid.layers.pad(
x=x, paddings=[0, 1, 1, 2], pad_value=0.)
"""
helper = LayerHelper('pad', input=x, **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
helper.append_op(
type='pad',
inputs={'X': x},
outputs={'Out': out},
attrs={'paddings': paddings,
'pad_value': float(pad_value)})
return out
def pad_constant_like(x, y, pad_value=0., name=None):
"""
Pad input(Y) with :attr:`pad_value`, the number of values padded to
the edges of each axis is specified by the difference of the shape
of X and Y. ((0, shape_x_0 - shape_y_0), ... (0, shape_x_n - shape_y_n))
unique pad widths for each axis. The input should be a k-D
tensor(k > 0 and k < 7).
See below for an example.
.. code-block:: text
Given:
X = [[[[ 0, 1, 2],
[ 3, 4, 5]],
[[ 6, 7, 8],
[ 9, 10, 11]],
[[12, 13, 14],
[15, 16, 17]]],
[[[18, 19, 20],
[21, 22, 23]],
[[24, 25, 26],
[27, 28, 29]],
[[30, 31, 32],
[33, 34, 35]]]]
X.shape = (2, 3, 2, 3)
Y = [[[[35, 36, 37]],
[[38, 39, 40]],
[[41, 42, 43]]]]
Y.shape = (1, 3, 1, 3)
And
pad_value = -1,
Return:
Out = [[[[35, 36, 37],
[-1, -1, -1]],
[[38, 39, 40],
[-1, -1, -1]],
[[41, 42, 43],
[-1, -1, -1]]],
[[[-1, -1, -1],
[-1, -1, -1]],
[[-1, -1, -1],
[-1, -1, -1]],
[[-1, -1, -1],
[-1, -1, -1]]]]
Out.shape = (2, 3, 2, 3)
Args:
x (Variable): The input tensor variable.
y (Variable): The input tensor variable.
pad_value (float): The constant value used to pad.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The padded tensor variable.
Examples:
.. code-block:: python
# x is a rank 4 tensor variable, x.shape = (2, 3, 2, 3)
# y is a rank 4 tensor variable, y.shape = (1, 3, 1, 3)
out = fluid.layers.pad_constant_like(x=x, y=y, pad_value=0.)
# out is a rank 4 tensor variable, and out.shape = [2, 3 ,2 , 3]
"""
helper = LayerHelper('pad_constant_like', input=x, **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
helper.append_op(
type='pad_constant_like',
inputs={'X': x,
'Y': y},
outputs={'Out': out},
attrs={'pad_value': float(pad_value)})
return out
def label_smooth(label,
prior_dist=None,
epsilon=0.1,
dtype="float32",
name=None):
"""
Label smoothing is a mechanism to regularize the classifier layer and is
called label-smoothing regularization (LSR).
Label smoothing is proposed to encourage the model to be less confident,
since optimizing the log-likelihood of the correct label directly may
cause overfitting and reduce the ability of the model to adapt. Label
smoothing replaces the ground-truth label :math:`y` with the weighted sum
of itself and some fixed distribution :math:`\mu`. For class :math:`k`,
i.e.
.. math::
\\tilde{y_k} = (1 - \epsilon) * y_k + \epsilon * \mu_k,
where :math:`1 - \epsilon` and :math:`\epsilon` are the weights
respectively, and :math:`\\tilde{y}_k` is the smoothed label. Usually
uniform distribution is used for :math:`\mu`.
See more details about label smoothing in https://arxiv.org/abs/1512.00567.
Args:
label(Variable): The input variable containing the label data. The
label data should use one-hot representation.
prior_dist(Variable): The prior distribution to be used to smooth
labels. If not provided, an uniform distribution
is used. The shape of :attr:`prior_dist` should
be :math:`(1, class\_num)`.
epsilon(float): The weight used to mix up the original ground-truth
distribution and the fixed distribution.
dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32,
float_64, int etc.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The tensor variable containing the smoothed labels.
Examples:
.. code-block:: python
label = layers.data(name="label", shape=[1], dtype="float32")
one_hot_label = layers.one_hot(input=label, depth=10)
smooth_label = layers.label_smooth(
label=one_hot_label, epsilon=0.1, dtype="float32")
"""
if epsilon > 1. or epsilon < 0.:
raise ValueError("The value of epsilon must be between 0 and 1.")
helper = LayerHelper("label_smooth", **locals())
label.stop_gradient = True
smooth_label = helper.create_tmp_variable(dtype)
helper.append_op(
type="label_smooth",
inputs={"X": label,
"PriorDist": prior_dist} if prior_dist else {"X": label},
outputs={"Out": smooth_label},
attrs={"epsilon": float(epsilon)})
return smooth_label
@templatedoc()
def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
"""
${comment}
Args:
input (Variable): ${x_comment}
rois (Variable): ROIs (Regions of Interest) to pool over.
pooled_height (integer): ${pooled_height_comment} Default: 1
pooled_width (integer): ${pooled_width_comment} Default: 1
spatial_scale (float): ${spatial_scale_comment} Default: 1.0
Returns:
Variable: ${out_comment}.
Examples:
.. code-block:: python
pool_out = fluid.layers.roi_pool(input=x, rois=rois, 7, 7, 1.0)
"""
helper = LayerHelper('roi_pool', **locals())
dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype)
argmaxes = helper.create_tmp_variable(dtype='int32')
helper.append_op(
type="roi_pool",
inputs={"X": input,
"ROIs": rois},
outputs={"Out": pool_out,
"Argmax": argmaxes},
attrs={
"pooled_height": pooled_height,
"pooled_width": pooled_width,
"spatial_scale": spatial_scale
})
return pool_out
def dice_loss(input, label, epsilon=0.00001):
"""
Dice loss for comparing the similarity of two batch of data,
usually is used for binary image segmentation i.e. labels are binary.
The dice loss can be defined as below equation:
.. math::
dice\_loss &= 1 - \\frac{2 * intersection\_area}{total\_area} \\\\
&= \\frac{(total\_area - intersection\_area) - intersection\_area}{total\_area} \\\\
&= \\frac{(union\_area - intersection\_area)}{total\_area}
Args:
input (Variable): The predictions with rank>=2. The first dimension is batch size,
and the last dimension is class number.
label (Variable): The groud truth with the same rank with input. The first dimension
is batch size, and the last dimension is 1.
epsilon (float): The epsilon will be added to the numerator and denominator.
If both input and label are empty, it makes sure dice is 1.
Default: 0.00001
Returns:
dice_loss (Variable): The dice loss with shape [1].
Examples:
.. code-block:: python
predictions = fluid.layers.softmax(x)
loss = fluid.layers.dice_loss(input=predictions, label=label, 2)
"""
label = one_hot(label, depth=input.shape[-1])
reduce_dim = list(range(1, len(input.shape)))
inse = reduce_sum(input * label, dim=reduce_dim)
dice_denominator = reduce_sum(
input, dim=reduce_dim) + reduce_sum(
label, dim=reduce_dim)
dice_score = 1 - inse * 2 / (dice_denominator + epsilon)
return reduce_mean(dice_score)
def image_resize(input,
out_shape=None,
scale=None,
name=None,
resample='BILINEAR'):
"""
**Resize a Batch of Images**
The input must be a tensor of the shape (num_batches, channels, in_h, in_w),
and the resizing only applies on the last two dimensions(hight and width).
Supporting resample methods:
'BILINEAR' : Bilinear interpolation
Args:
input (Variable): The input tensor of image resize layer,
This is a 4-D tensor of the shape
(num_batches, channels, in_h, in_w).
out_shape(list|tuple|Variable|None): Output shape of image resize
layer, the shape is (out_h, out_w).
Default: None
scale(float|None): The multiplier for the input height or width.
At least one of out_shape or scale must be set.
And out_shape has a higher priority than scale.
Default: None
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
resample(str): The resample method. It can only be 'BILINEAR' currently.
Default: 'BILINEAR'
Returns:
Variable: The output is a 4-D tensor of the shape
(num_batches, channls, out_h, out_w).
Examples:
.. code-block:: python
out = fluid.layers.image_resize(input, out_shape=[12, 12])
"""
resample_methods = {'BILINEAR': 'bilinear_interp'}
if resample not in resample_methods:
raise ValueError(
"The 'resample' of image_resize can only be 'BILINEAR' currently.")
if out_shape is None and scale is None:
raise ValueError("One of out_shape and scale must not be None")
helper = LayerHelper('bilinear_interp', **locals())
dtype = helper.input_dtype()
def _is_list_or_turple_(data):
return (isinstance(data, list) or isinstance(data, tuple))
out_h = 0
out_w = 0
inputs = {"X": input}
if out_shape is not None:
if not (_is_list_or_turple_(out_shape) and
len(out_shape) == 2) and not isinstance(out_shape, Variable):
raise ValueError('out_shape should be a list or tuple or variable')
if _is_list_or_turple_(out_shape):
out_shape = list(map(int, out_shape))
out_h = out_shape[0]
out_w = out_shape[1]
else:
inputs['OutSize'] = out_shape
else:
out_h = int(input.shape[2] * scale)
out_w = int(input.shape[3] * scale)
out = helper.create_tmp_variable(dtype)
helper.append_op(
type=resample_methods[resample],
inputs=inputs,
outputs={"Out": out},
attrs={"out_h": out_h,
"out_w": out_w})
return out
@templatedoc(op_type="bilinear_interp")
def resize_bilinear(input, out_shape=None, scale=None, name=None):
"""
${comment}
Args:
input(${x_type}): ${x_comment}.
out_shape(${out_size_type}): ${out_size_comment}.
scale(float|None): The multiplier for the input height or width. At
least one of out_shape or scale must be set. And out_shape has
a higher priority than scale. Default: None.
name(str|None): The output variable name.
Returns:
${out_comment}.
"""
return image_resize(input, out_shape, scale, name, 'BILINEAR')
def image_resize_short(input, out_short_len, resample='BILINEAR'):
"""
Resize a batch of images. The short edge of input images will be
resized to the given 'out_short_len'. The long edge of input images
will be resized proportionately to make images' length-width ratio
constant.
Args:
input (Variable): The input tensor of image resize layer,
This is a 4-D tensor of the shape
(num_batches, channels, in_h, in_w).
out_short_len(int): The length of output images' short edge.
resample (str): resample method, default: BILINEAR.
Returns:
Variable: The output is a 4-D tensor of the shape
(num_batches, channls, out_h, out_w).
"""
in_shape = input.shape
if len(in_shape) != 4:
raise ValueError(
"The rank of input must be 4 (num_batches, channels, in_h, in_w).")
hw = in_shape[2:4]
short_idx = hw.index(min(hw))
long_idx = 1 - short_idx
out_shape = list(hw)
out_shape[short_idx] = out_short_len
out_shape[long_idx] = int(
float(out_shape[long_idx]) * (float(out_short_len) / float(hw[
short_idx])) + 0.5)
return image_resize(input=input, out_shape=out_shape, resample=resample)
def gather(input, index):
"""
**Gather Layer**
Output is obtained by gathering entries of the outer-most dimension
of X indexed by `index` and concatenate them together.
.. math::
Out = X[Index]
.. code-block:: text
Given:
X = [[1, 2],
[3, 4],
[5, 6]]
Index = [1, 2]
Then:
Out = [[3, 4],
[5, 6]]
Args:
input (Variable): The source input with rank>=1.
index (Variable): The index input with rank=1.
Returns:
output (Variable): The output is a tensor with the same rank as input.
Examples:
.. code-block:: python
output = fluid.layers.gather(x, index)
"""
helper = LayerHelper('gather', **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
helper.append_op(
type="gather",
inputs={"X": input,
"Index": index},
outputs={"Out": out})
return out
def scatter(input, index, updates, name=None):
"""
**Scatter Layer**
Output is obtained by updating the input on selected indices on the first
axis.
.. math::
Out = X
Out[Ids] = Updates
Args:
input (Variable): The source input with rank>=1.
index (Variable): The index input with rank=1. Its dtype should be
int32 or int64 as it is used as indexes.
updates (Variable): The updated value of scatter op.
name (str|None): The output variable name. Default None.
Returns:
output (Variable): The output is a tensor with the same shape as input.
Examples:
.. code-block:: python
output = fluid.layers.scatter(input, index, updates)
"""
helper = LayerHelper('scatter', **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
helper.append_op(
type="scatter",
inputs={"X": input,
"Ids": index,
"Updates": updates},
outputs={"Out": out})
return out
def sequence_scatter(input, index, updates, name=None):
"""
**Sequence Scatter Layer**
This operator scatters the Updates tensor to the input X. It uses the LoD
information of Ids to select the rows to update, and use the values in Ids as
the columns to update in each row of X.
Here is an example:
Given the following input:
.. code-block:: text
input.data = [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0]]
input.dims = [3, 6]
index.data = [[0], [1], [2], [5], [4], [3], [2], [1], [3], [2], [5], [4]]
index.lod = [[0, 3, 8, 12]]
updates.data = [[0.3], [0.3], [0.4], [0.1], [0.2], [0.3], [0.4], [0.0], [0.2], [0.3], [0.1], [0.4]]
updates.lod = [[ 0, 3, 8, 12]]
Then we have the output:
.. code-block:: text
out.data = [[1.3, 1.3, 1.4, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.4, 1.3, 1.2, 1.1],
[1.0, 1.0, 1.3, 1.2, 1.4, 1.1]]
out.dims = X.dims = [3, 6]
Args:
input (Variable): The source input with rank>=1.
index (Variable): A LoD Tensor. The index input of sequence scatter op
where input will be updated. The index input with rank=1. Its dtype
should be int32 or int64 as it is used as indexes.
updates (Variable): A LoD Tensor. The values to scatter to the input
tensor X, must be a LoDTensor with the same LoD information as index.
name (str|None): The output variable name. Default None.
Returns:
output (Variable): The output is a tensor with the same shape as input.
Examples:
.. code-block:: python
output = fluid.layers.sequence_scatter(input, index, updates)
"""
helper = LayerHelper('sequence_scatter', **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
helper.append_op(
type="sequence_scatter",
inputs={"X": input,
"Ids": index,
"Updates": updates},
outputs={"Out": out})
return out
@templatedoc()
def random_crop(x, shape, seed=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
shape(${shape_type}): ${shape_comment}
seed(int|${seed_type}|None): ${seed_comment} By default, the seed will
get from `random.randint(-65536, 65535)`.
Returns:
${out_comment}
Examples:
>>> img = fluid.layers.data("img", [3, 256, 256])
>>> cropped_img = fluid.layers.random_crop(img, shape=[3, 224, 224])
"""
helper = LayerHelper("random_crop", **locals())
dtype = x.dtype
out = helper.create_tmp_variable(dtype)
if seed is None:
seed = np.random.randint(-65536, 65536)
op_attrs = {"shape": shape}
if isinstance(seed, int):
op_attrs["startup_seed"] = seed
seed = helper.create_variable(
name=unique_name.generate("random_crop_seed"),
dtype="int64",
persistable=True)
elif not isinstance(seed, Variable):
raise ValueError("'seed' must be a Variable or an int.")
helper.append_op(
type="random_crop",
inputs={"X": x,
"Seed": seed},
outputs={"Out": out,
"SeedOut": seed},
attrs=op_attrs)
return out
def log(x, name=None):
"""
Calculates the natural log of the given input tensor, element-wise.
.. math::
Out = \\ln(x)
Args:
x (Variable): Input tensor.
name (str|None, default None): A name for this layer If set None,
the layer will be named automatically.
Returns:
Variable: The natural log of the input tensor computed element-wise.
Examples:
.. code-block:: python
output = fluid.layers.log(x)
"""
helper = LayerHelper('log', **locals())
dtype = helper.input_dtype(input_param_name='x')
out = helper.create_tmp_variable(dtype)
helper.append_op(type="log", inputs={"X": x}, outputs={"Out": out})
return out
def relu(x, name=None):
"""
Relu takes one input data (Tensor) and produces one output data (Tensor)
where the rectified linear function, y = max(0, x), is applied to
the tensor elementwise.
.. math::
Out = \\max(0, x)
Args:
x (Variable): The input tensor.
name (str|None, default None): A name for this layer If set None,
the layer will be named automatically.
Returns:
Variable: The output tensor with the same shape as input.
Examples:
.. code-block:: python
output = fluid.layers.relu(x)
"""
helper = LayerHelper('relu', **locals())
dtype = helper.input_dtype(input_param_name='x')
out = helper.create_tmp_variable(dtype)
helper.append_op(type="relu", inputs={"X": x}, outputs={"Out": out})
return out
def mean_iou(input, label, num_classes):
"""
Mean Intersection-Over-Union is a common evaluation metric for
semantic image segmentation, which first computes the IOU for each
semantic class and then computes the average over classes.
IOU is defined as follows:
.. math::
IOU = \\frac{true\_positiv}{(true\_positive + false\_positive + false\_negative)}.
The predictions are accumulated in a confusion matrix and mean-IOU
is then calculated from it.
Args:
input (Variable): A Tensor of prediction results for semantic labels with type int32 or int64.
label (Variable): A Tensor of ground truth labels with type int32 or int64.
Its shape should be the same as input.
num_classes (int): The possible number of labels.
Returns:
mean_iou (Variable): A Tensor representing the mean intersection-over-union with shape [1].
out_wrong(Variable): A Tensor with shape [num_classes]. The wrong numbers of each class.
out_correct(Variable): A Tensor with shape [num_classes]. The correct numbers of each class.
Examples:
.. code-block:: python
iou, wrongs, corrects = fluid.layers.mean_iou(predict, label, num_classes)
"""
helper = LayerHelper('mean_iou', **locals())
dtype = helper.input_dtype()
out_mean_iou = helper.create_tmp_variable(dtype='float32')
out_wrong = helper.create_tmp_variable(dtype='int32')
out_correct = helper.create_tmp_variable(dtype='int32')
helper.append_op(
type="mean_iou",
inputs={"Predictions": input,
"Labels": label},
outputs={
"OutMeanIou": out_mean_iou,
"OutWrong": out_wrong,
"OutCorrect": out_correct
},
attrs={"num_classes": num_classes})
return out_mean_iou, out_wrong, out_correct
def crop(x, shape=None, offsets=None, name=None):
"""
Crop input into output, as specified by offsets and shape.
.. code-block:: text
* Case 1:
Given
X = [[0, 1, 2, 0, 0]
[0, 3, 4, 0, 0]
[0, 0, 0, 0, 0]],
and
shape = [2, 2],
offsets = [0, 1],
output is:
Out = [[1, 2],
[3, 4]].
* Case 2:
Given
X = [[0, 1, 2, 5, 0]
[0, 3, 4, 6, 0]
[0, 0, 0, 0, 0]],
and shape is tensor
shape = [[0, 0, 0]
[0, 0, 0]]
and
offsets = [0, 1],
output is:
Out = [[1, 2, 5],
[3, 4, 6]].
Args:
x (Variable): The input tensor variable.
shape (Variable|list/tuple of integer): The output shape is specified
by `shape`, which can a Variable or a list/tupe of integer.
If a tensor Variable, it's rank must be the same as `x`. This way
is suitable for the case that the output shape may be changed each
iteration. If a list/tupe of integer, it's length must be the same
as the rank of `x`
offsets (Variable|list/tuple of integer|None): Specifies the copping
offsets at each dimension. It can be a Variable or or a list/tupe
of integer. If a tensor Variable, it's rank must be the same as `x`.
This way is suitable for the case that the offsets may be changed
each iteration. If a list/tupe of integer, it's length must be the
same as the rank of `x`. If None, the offsets are 0 at each
dimension.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The cropped tensor variable.
Raises:
ValueError: If shape is not a list, tuple or Variable.
Examples:
.. code-block:: python
x = fluid.layers.data(name="x", shape=[3, 5], dtype="float32")
y = fluid.layers.data(name="y", shape=[2, 3], dtype="float32")
crop = fluid.layers.crop(x, shape=y)
# or
z = fluid.layers.data(name="z", shape=[3, 5], dtype="float32")
crop = fluid.layers.crop(z, shape=[2, 3])
"""
helper = LayerHelper('crop', **locals())
if not (isinstance(shape, list) or isinstance(shape, tuple) or \
isinstance(shape, Variable)):
raise ValueError("The shape should be a list, tuple or Variable.")
if offsets is None:
offsets = [0] * len(x.shape)
out = helper.create_tmp_variable(x.dtype)
ipts = {'X': x}
attrs = {}
if isinstance(shape, Variable):
ipts['Y'] = shape
else:
attrs['shape'] = shape
if isinstance(offsets, Variable):
ipts['Offsets'] = offsets
else:
attrs['offsets'] = offsets
helper.append_op(
type='crop',
inputs=ipts,
outputs={'Out': out},
attrs=None if len(attrs) == 0 else attrs)
return out
def rank_loss(label, left, right, name=None):
"""
**Rank loss layer for RankNet**
RankNet(http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf)
is a pairwise ranking model with a training sample consisting of a pair
of documents, A and B. Label P indicates whether A is ranked higher than B
or not:
P = {0, 1} or {0, 0.5, 1}, where 0.5 means that there is no information
about the rank of the input pair.
Rank loss layer takes three inputs: left (o_i), right (o_j) and
label (P_{i,j}). The inputs respectively represent RankNet's output scores
for documents A and B and the value of label P. The following equation
computes rank loss C_{i,j} from the inputs:
$$
C_{i,j} = -\tilde{P_{ij}} * o_{i,j} + \log(1 + e^{o_{i,j}}) \\
o_{i,j} = o_i - o_j \\
\tilde{P_{i,j}} = \left \{0, 0.5, 1 \right \} \ or \ \left \{0, 1 \right \}
$$
Rank loss layer takes batch inputs with size batch_size (batch_size >= 1).
Args:
label (Variable): Indicats whether A ranked higher than B or not.
left (Variable): RankNet's output score for doc A.
right (Variable): RankNet's output score for doc B.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
list: The value of rank loss.
Raises:
ValueError: Any of label, left, and right is not a variable.
Examples:
.. code-block:: python
label = fluid.layers.data(name="label", shape=[4, 1], dtype="float32")
left = fluid.layers.data(name="left", shape=[4, 1], dtype="float32")
right = fluid.layers.data(name="right", shape=[4, 1], dtype="float32")
out = fluid.layers.rank_loss(label, left, right)
"""
helper = LayerHelper('rank_loss', **locals())
if not (isinstance(label, Variable)):
raise ValueError("The label should be a Variable")
if not (isinstance(left, Variable)):
raise ValueError("The left should be a Variable")
if not (isinstance(right, Variable)):
raise ValueError("The right should be a Variable")
out = helper.create_tmp_variable("float32")
helper.append_op(
type='rank_loss',
inputs={"Label": label,
"Left": left,
"Right": right},
outputs={'Out': out})
return out
def pad2d(input,
paddings=[0, 0, 0, 0],
mode='constant',
pad_value=0.0,
data_format="NCHW",
name=None):
"""
Pad 2-d images accordding to 'paddings' and 'mode'.
If mode is 'reflect', paddings[0] and paddings[1] must be no greater
than height-1. And the width dimension has the same condition.
Example:
Given that X is a channel of image from input:
X = [[1, 2, 3],
[4, 5, 6]]
Case 0:
paddings = [0, 1, 2, 3],
mode = 'constant'
pad_value = 0
Out = [[0, 0, 1, 2, 3, 0, 0, 0]
[0, 0, 4, 5, 6, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0]]
Case 1:
paddings = [0, 1, 2, 1],
mode = 'reflect'
Out = [[3, 2, 1, 2, 3, 2]
[6, 5, 4, 5, 6, 5]
[3, 2, 1, 2, 3, 2]]
Case 2:
paddings = [0, 1, 2, 1],
mode = 'edge'
Out = [[1, 1, 1, 2, 3, 3]
[4, 4, 4, 5, 6, 6]
[4, 4, 4, 5, 6, 6]]
Args:
input (Variable): The input image with [N, C, H, W] format or [N, H, W, C] format.
paddings (tuple|list): The padding size. If padding is a tuple, it must
contain four integers, (padding_top, padding_bottom, padding_left, padding_right).
Default: padding = [0, 0, 0, 0].
mode (str): Three modes: constant(default), reflect, edge. Default: constant
pad_value (float32): The value to fill the padded areas in constant mode. Default: 0
data_format (str): An optional string from: "NHWC", "NCHW". Specify the data format of
the input data.
Default: "NCHW"
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The tensor variable padded accordding to paddings and mode.
Examples:
.. code-block:: python
data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32')
result = fluid.layers.pad2d(input=data, padding=[1,2,3,4], mode='reflect')
"""
helper = LayerHelper('pad2d', **locals())
dtype = helper.input_dtype(input_param_name='input')
out = helper.create_tmp_variable(dtype)
helper.append_op(
type='pad2d',
inputs={'X': input},
outputs={"Out": out},
attrs={
'paddings': paddings,
'mode': mode,
'pad_value': pad_value,
'data_frmat': data_format
})
return out
@templatedoc()
def elu(x, alpha=1.0, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
alpha(${alpha_type}|1.0): ${alpha_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('elu', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type='elu',
inputs={'X': x},
outputs={'Out': out},
attrs={'alpha': alpha})
return out
@templatedoc()
def relu6(x, threshold=6.0, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
threshold(${threshold_type}|6.0): ${threshold_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('relu6', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type='relu6',
inputs={'X': x},
outputs={'Out': out},
attrs={'threshold': threshold})
return out
@templatedoc()
def pow(x, factor=1.0, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
factor(${factor_type}|1.0): ${factor_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('pow', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type='pow',
inputs={'X': x},
outputs={'Out': out},
attrs={'factor': factor})
return out
@templatedoc()
def stanh(x, scale_a=2.0 / 3.0, scale_b=1.7159, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
scale_a(${scale_a_type}|2.0 / 3.0): ${scale_a_comment}
scale_b(${scale_b_type}|1.7159): ${scale_b_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('stanh', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type='stanh',
inputs={'X': x},
outputs={'Out': out},
attrs={'scale_a': scale_a,
'scale_b': scale_b})
return out
@templatedoc()
def hard_sigmoid(x, slope=0.2, offset=0.5, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
slope(${slope_type}|0.2): ${slope_comment}
offset(${offset_type}|0.5): ${offset_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('hard_sigmoid', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type='hard_sigmoid',
inputs={'X': x},
outputs={'Out': out},
attrs={'slope': slope,
'offset': offset})
return out
@templatedoc()
def swish(x, beta=1.0, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
beta(${beta_type}|1.0): ${beta_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('swish', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type='swish',
inputs={'X': x},
outputs={'Out': out},
attrs={'slope': beta})
return out
def prelu(x, mode, param_attr=None, name=None):
"""
Equation:
y = \max(0, x) + alpha \min(0, x)
Args:
x (Variable): The input tensor.
param_attr(ParamAttr|None): The parameter attribute for the learnable
weight (alpha).
mode (string): The mode for weight sharing
all: all elements share same weight
channel:elements in a channel share same weight
element:each element has a weight
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The output tensor with the same shape as input.
Examples:
.. code-block:: python
x = fluid.layers.data(name="x", shape=[10,10], dtype="float32")
mode = 'channel'
output = fluid.layers.prelu(x,mode)
"""
helper = LayerHelper('prelu', **locals())
if mode not in ['all', 'channel', 'element']:
raise ValueError('mode should be one of all, channel, element.')
alpha_shape = [1]
if mode == 'channel':
alpha_shape = [1, x.shape[1], 1, 1]
elif mode == 'element':
alpha_shape = x.shape
dtype = helper.input_dtype(input_param_name='x')
alpha = helper.create_parameter(
attr=param_attr,
shape=alpha_shape,
dtype='float32',
is_bias=False,
default_initializer=Constant(1.0))
out = helper.create_tmp_variable(dtype)
helper.append_op(
type="prelu",
inputs={"X": x,
'Alpha': alpha},
attrs={"mode": mode},
outputs={"Out": out})
return out
@templatedoc()
def brelu(x, t_min=0.0, t_max=24.0, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
t_min(${t_min_type}|0.0): ${t_min_comment}
t_max(${t_max_type}|24.0): ${t_max_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('brelu', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type='brelu',
inputs={'X': x},
outputs={'Out': out},
attrs={'t_min': t_min,
't_max': t_max})
return out
@templatedoc()
def leaky_relu(x, alpha=0.02, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
alpha(${alpha_type}|0.02): ${alpha_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('leaky_relu', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type='leaky_relu',
inputs={'X': x},
outputs={'Out': out},
attrs={'alpha': alpha})
return out
@templatedoc()
def soft_relu(x, threshold=40.0, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
threshold(${threshold_type}|40.0): ${threshold_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('soft_relu', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type='soft_relu',
inputs={'X': x},
outputs={'Out': out},
attrs={'threshold': threshold})
return out
def flatten(x, axis=1, name=None):
"""
**Flatten layer**
Flattens the input tensor into a 2D matrix.
Examples:
Case 1:
Given
X.shape = (3, 100, 100, 4)
and
axis = 2
We get:
Out.shape = (3 * 100, 4 * 100)
Case 2:
Given
X.shape = (3, 100, 100, 4)
and
axis = 0
We get:
Out.shape = (1, 3 * 100 * 100 * 4)
Args:
x (Variable): A tensor of rank >= axis.
axis (int): Indicate up to which input dimensions (exclusive) should
be flattened to the outer dimension of the output.
The value for axis must be in the range [0, R], where R
is the rank of the input tensor. When axis = 0, the shape
of the output tensor is (1, (d_0 X d_1 ... d_n), where the
shape of the input tensor is (d_0, d_1, ... d_n).
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: A 2D tensor with the contents of the input tensor, with input
dimensions up to axis flattened to the outer dimension of
the output and remaining input dimensions flattened into the
inner dimension of the output.
Raises:
ValueError: If x is not a variable.
ValueError: If axis is not in range [0, rank(x)].
Examples:
.. code-block:: python
x = fluid.layers.data(name="x", shape=[4, 4, 3], dtype="float32")
out = fluid.layers.flatten(x=x, axis=2)
"""
helper = LayerHelper('flatten', **locals())
if not (isinstance(x, Variable)):
raise ValueError("The input x should be a Variable")
if not (isinstance(axis, int)) or axis > len(x.shape) or axis < 0:
raise ValueError("The axis should be a int, and in range [0, rank(x)]")
out = helper.create_tmp_variable(x.dtype)
x_shape = helper.create_tmp_variable(x.dtype)
helper.append_op(
type='flatten2',
inputs={"X": x},
outputs={'Out': out,
'XShape': x_shape},
attrs={"axis": axis})
return out
def sequence_enumerate(input, win_size, pad_value=0, name=None):
"""
Generate a new sequence for the input index sequence, which enumerates all the
sub-sequences with length `win_size` of the input.
The enumerated sequence has the same 1st dimension with variable `input`, and
the 2nd dimension is `win_size`, padded by `pad_value` if necessary in generation.
Examples:
Case 1:
Input:
X.lod = [[0, 3, 5]]
X.data = [[1], [2], [3], [4], [5]]
X.dims = [5, 1]
Attrs:
win_size = 2
pad_value = 0
Output:
Out.lod = [[0, 3, 5]]
Out.data = [[1, 2], [2, 3], [3, 0], [4, 5], [5, 0]]
Out.dims = [5, 2]
Args:
input (Variable): The input variable which is a index sequence.
win_size (int): The window size for enumerating all sub-sequences.
pad_value (int): The padding value, default 0.
Returns:
Variable: The enumerate sequence variable which is a LoDTensor.
Examples:
.. code-block:: python
x = fluid.layers.data(shape[30, 1], dtype='int32', lod_level=1)
out = fluid.layers.sequence_enumerate(input=x, win_size=3, pad_value=0)
"""
helper = LayerHelper('sequence_enumerate', **locals())
out = helper.create_tmp_variable(helper.input_dtype(), stop_gradient=True)
helper.append_op(
type='sequence_enumerate',
inputs={'X': input},
outputs={'Out': out},
attrs={'win_size': win_size,
'pad_value': pad_value})
def sequence_mask(x, maxlen=None, dtype='int64', name=None):
"""
**SequenceMask Layer**
This layer outputs a mask according to the input :code:`x` and
:code:`maxlen` with data type of :code:`dtype`.
Supposing :code:`x` is a Tensor with shape [d_1, d_2, ..., d_n], the
:code:`y` is a mask with shape [d_1, d_2, ..., d_n, maxlen], where:
.. math::
y(i_1, i_2,..., i_n, j) = (j < x(i_1, i_2,..., i_n))
Args:
x (Variable): Input tensor of sequence_mask layer,
whose elements are integers less than :code:`maxlen`.
maxlen (int|None): Maximum length of the sequence. If :code:`maxlen`
is None, it would be replace with :math:`max(x)`.
dtype (np.dtype|core.VarDesc.VarType|str): Data type of the output.
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
Returns:
Variable: The output sequence mask.
"""
helper = LayerHelper('sequence_mask', **locals())
if name is None:
out = helper.create_tmp_variable(dtype=dtype)
else:
out = helper.create_tmp_variable(dtype=dtype, name=name)
helper.append_op(
type='sequence_mask',
inputs={'X': [x]},
outputs={'Y': out},
attrs={
'maxlen': maxlen if maxlen is not None else -1,
'out_dtype': out.dtype
})
return out
def stack(x, axis=0):
"""
**Stack Layer**
This layer stacks all of the input :code:`x` along axis.
Input :code:`x` can be a single variable, a :code:`list` of variables,
or a :code:`tuple` of variables. If :code:`x` is a :code:`list` or
:code:`tuple`, the shapes of all these variables must be the same.
Supposing the shape of each input is :math:`[d_0, d_1, ..., d_{n-1}]`,
the shape of the output variable would be
:math:`[d_0, d_1, ..., d_{axis}=len(x), ..., d_{n-1}]`.
If :code:`axis` < 0, it would be replaced with :code:`axis+rank(x[0])+1`.
If :code:`axis` is None, it would be replaced with 0.
Args:
x (Variable|list(Variable)|tuple(Variable)): Input variables.
axis (int|None): The axis along which all inputs are stacked.
Returns:
Variable: The stacked variable.
"""
helper = LayerHelper('stack', **locals())
axis = 0 if axis is None else axis
if not isinstance(x, list) and not isinstance(x, tuple):
x = [x]
out = helper.create_tmp_variable(x[0].dtype)
helper.append_op(
type='stack', inputs={'X': x}, outputs={'Y': out},
attrs={'axis': axis})
return out
def unstack(x, axis=0, num=None):
"""
**UnStack Layer**
This layer unstacks input :code:`x` into several tensors along axis.
If :code:`axis` < 0, it would be replaced with :code:`axis+rank(x)`.
If :code:`num` is None, it would be inferred from :code:`x.shape[axis]`,
and if :code:`x.shape[axis]` <= 0 or is unknown, :code:`ValueError` is
raised.
Args:
x (Variable): Input variable.
axis (int): The axis along which the input is unstacked.
num (int|None): The number of output variables.
Returns:
list(Variable): The unstacked variables.
"""
helper = LayerHelper('unstack', **locals())
if num is None:
if axis is None or x.shape[axis] <= 0:
raise ValueError('unknown unstack number')
else:
num = x.shape[axis]
outs = []
for _ in num:
outs.append(helper.create_tmp_variable(x.dtype))
helper.append_op(
type='unstack',
inputs={'X': [x]},
outputs={'Y': outs},
attrs={'axis': axis,
'num': num})
return outs
def expand(x, expand_times, name=None):
"""Expand operator tiles the input by given times number. You should set times
number for each dimension by providing attribute 'expand_times'. The rank of X
should be in [1, 6]. Please note that size of 'expand_times' must be the same
with X's rank. Following is a using case:
.. code-block:: text
Input(X) is a 3-D tensor with shape [2, 3, 1]:
[
[[1], [2], [3]],
[[4], [5], [6]]
]
Attr(expand_times): [1, 2, 2]
Output(Out) is a 3-D tensor with shape [2, 6, 2]:
[
[[1, 1], [2, 2], [3, 3], [1, 1], [2, 2], [3, 3]],
[[4, 4], [5, 5], [6, 6], [4, 4], [5, 5], [6, 6]]
]
Args:
x (Variable): A tensor with rank in [1, 6].
expand_times (list|tuple): Expand times number for each dimension.
Returns:
Variable: The expanded variable which is a LoDTensor. After expanding, size of each dimension of Output(Out) is equal to ithe size of the corresponding dimension of Input(X) multiplying the corresponding value given by expand_times.
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[10], dtype='float32')
out = fluid.layers.expand(x=x, expand_times=[1, 2, 2])
"""
helper = LayerHelper('expand', input=x, **locals())
dtype = helper.input_dtype(input_param_name='x')
out = helper.create_tmp_variable(dtype)
helper.append_op(
type='expand',
inputs={'X': x},
outputs={'Out': out},
attrs={'expand_times': expand_times})
return out
def _elementwise_op(helper):
op_type = helper.layer_type
x = helper.kwargs.get('x', None)
y = helper.kwargs.get('y', None)
assert x is not None, 'x cannot be None in {}'.format(op_type)
assert y is not None, 'y cannot be None in {}'.format(op_type)
axis = helper.kwargs.get('axis', -1)
use_mkldnn = helper.kwargs.get('use_mkldnn', False)
out = helper.kwargs.get('out', None)
if out is None:
name = helper.kwargs.get('name', None)
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
helper.append_op(
type=op_type,
inputs={'X': x,
'Y': y},
outputs={'Out': out},
attrs={'axis': axis,
'use_mkldnn': use_mkldnn})
return helper.append_activation(out)
@templatedoc()
def scale(x,
scale=1.0,
bias=0.0,
bias_after_scale=True,
out=None,
act=None,
name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
scale(${scale_type}): ${scale_comment}
bias(${bias_type}): ${bias_comment}
bias_after_scale(${bias_after_scale_type}): ${bias_after_scale_comment}
out(Tensor): Output tensor.
act(basestring|None): Activation applied to the output.
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
helper = LayerHelper('scale', **locals())
if out is None:
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
helper.append_op(
type='scale',
inputs={'X': x},
outputs={'Out': out},
attrs={
'scale': float(scale),
'bias': float(bias),
'bias_after_scale': bias_after_scale
})
return helper.append_activation(out)
def elementwise_add(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
return _elementwise_op(LayerHelper('elementwise_add', **locals()))
def elementwise_div(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
return _elementwise_op(LayerHelper('elementwise_div', **locals()))
def elementwise_sub(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
return _elementwise_op(LayerHelper('elementwise_sub', **locals()))
def elementwise_mul(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
return _elementwise_op(LayerHelper('elementwise_mul', **locals()))
def elementwise_max(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
return _elementwise_op(LayerHelper('elementwise_max', **locals()))
def elementwise_min(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
return _elementwise_op(LayerHelper('elementwise_min', **locals()))
def elementwise_pow(x,
y,
out=None,
axis=-1,
use_mkldnn=False,
act=None,
name=None):
return _elementwise_op(LayerHelper('elementwise_pow', **locals()))
for func in [
elementwise_add, elementwise_div, elementwise_sub, elementwise_mul,
elementwise_max, elementwise_min, elementwise_pow
]:
op_proto = OpProtoHolder.instance().get_op_proto(func.__name__)
func.__doc__ = _generate_doc_string_(
op_proto,
additional_args_lines=[
"out (Tensor): The output tensor of elementwise op.",
"act (basestring|None): Activation applied to the output.",
"name (basestring|None): Name of the output."
])
def _logical_op(op_name, x, y, name=None, out=None, binary_op=True):
helper = LayerHelper(op_name, **locals())
assert x.dtype == y.dtype
if out is None:
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
if binary_op:
helper.append_op(
type=op_name, inputs={"X": x,
"Y": y}, outputs={"Out": out})
else:
helper.append_op(type=op_name, inputs={"X": x}, outputs={"Out": out})
return out
@templatedoc()
def logical_and(x, y, name=None, out=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
y(${y_type}): ${y_comment}
out(Tensor): Output tensor of logical operation.
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
return _logical_op(
op_name="logical_and", x=x, y=y, name=name, out=out, binary_op=True)
@templatedoc()
def logical_or(x, y, name=None, out=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
y(${y_type}): ${y_comment}
out(Tensor): Output tensor of logical operation.
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
return _logical_op(
op_name="logical_or", x=x, y=y, name=name, out=out, binary_op=True)
@templatedoc()
def logical_xor(x, y, name=None, out=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
y(${y_type}): ${y_comment}
out(Tensor): Output tensor of logical operation.
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
return _logical_op(
op_name="logical_xor", x=x, y=y, name=name, out=out, binary_op=True)
@templatedoc()
def logical_not(x, name=None, out=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
out(Tensor): Output tensor of logical operation.
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
return _logical_op(
op_name="logical_not", x=x, y=None, name=name, out=out, binary_op=False)