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未验证 提交 0ac8c74e 编写于 作者: Y Yu Yang 提交者： GitHub
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Unify fluid submodules to fluid module (#5924) 

Change books just use `import fluid`, not submodules
上级 e6546baa
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Showing with 381 addition and 425 deletion
python/paddle/v2/fluid/__init__.py
浏览文件 @ 0ac8c74e
import sys
import core
__all__ = ['proto']
argv = []
if core.is_compile_gpu():
argv = list(sys.argv) + [
"--tryfromenv=fraction_of_gpu_memory_to_use,use_pinned_memory"
]
else:
argv = list(sys.argv) + ["--tryfromenv=use_pinned_memory"]
core.init_gflags(argv)
# import all class inside framework into fluid module
import framework
from framework import *
# import all class inside executor into fluid module
import executor
from executor import *
import io
import evaluator
import initializer
import layers
import nets
import optimizer
import backward
import regularizer
from core import LoDTensor, CPUPlace, GPUPlace
Tensor = LoDTensor
__all__ = framework.__all__ + executor.__all__ + [
'io', 'initializer', 'layers', 'nets', 'optimizer', 'backward',
'regularizer', 'LoDTensor', 'CPUPlace', 'GPUPlace', 'Tensor'
]
def __read_gflags_from_env__():
"""
Enable reading gflags from environment variables.
Returns:
None
"""
import sys
import core
read_env_flags = ['use_pinned_memory']
if core.is_compile_gpu():
read_env_flags.append('fraction_of_gpu_memory_to_use')
core.init_gflags(sys.argv + ["--tryfromenv=" + ",".join(read_env_flags)])
__read_gflags_from_env__()
python/paddle/v2/fluid/evaluator.py
浏览文件 @ 0ac8c74e
import numpy as np
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.framework import Program, unique_name, \
Variable
from paddle.v2.fluid.layer_helper import LayerHelper
import layers
from framework import Program, unique_name, Variable
from layer_helper import LayerHelper
__all__ = ['Accuracy']
......
python/paddle/v2/fluid/executor.py
浏览文件 @ 0ac8c74e
import numpy as np
import paddle.v2.fluid.core as core
from paddle.v2.fluid.framework import Block, Program, g_main_program
from . import core
from framework import Program, g_main_program
__all__ = ['Executor', 'g_scope']
g_scope = core.Scope()
......
python/paddle/v2/fluid/framework.py
浏览文件 @ 0ac8c74e
import paddle.v2.fluid.core as core
import paddle.v2.fluid.proto.framework_pb2 as framework_pb2
import collections
import numpy as np
import copy
from . import core
import proto.framework_pb2 as framework_pb2
__all__ = [
'Block', 'Variable', 'Program', 'Operator', 'default_startup_program',
'default_main_program'
'default_main_program', 'g_startup_program', 'g_main_program'
]
......
python/paddle/v2/fluid/initializer.py
浏览文件 @ 0ac8c74e
import paddle.v2.fluid.framework as framework
import framework
import numpy as np
__all__ = [
'ConstantInitializer', 'UniformInitializer', 'NormalInitializer',
'XavierInitializer'
]
__all__ = ['Constant', 'Uniform', 'Normal', 'Xavier']
class Initializer(object):
......@@ -368,3 +365,19 @@ class MSRAInitializer(Initializer):
})
var.op = op
return op
# We short the class name, since users will use the initializer with the package
# name. The sample code:
#
# import paddle.fluid as fluid
#
# hidden = fluid.layers.fc(...,
# param_attr=ParamAttr(fluid.initializer.Xavier()))
#
# It is no need to add an `Initializer` as the class suffix
Constant = ConstantInitializer
Uniform = UniformInitializer
Normal = NormalInitializer
Xavier = XavierInitializer
MSRA = MSRAInitializer
python/paddle/v2/fluid/layer_helper.py
浏览文件 @ 0ac8c74e
import copy
import itertools
from paddle.v2.fluid.framework import Variable, g_main_program, \
g_startup_program, unique_name, Program, dtype_is_floating
from paddle.v2.fluid.initializer import ConstantInitializer, \
UniformInitializer, XavierInitializer
from framework import Variable, g_main_program, \
g_startup_program, unique_name, dtype_is_floating
from paddle.v2.fluid.initializer import Constant, Xavier
class LayerHelper(object):
......@@ -209,7 +208,7 @@ class LayerHelper(object):
def _get_default_initializer(self, dtype):
if dtype is None or dtype_is_floating(dtype) is True:
return XavierInitializer()
return Xavier()
else:
# For integer and boolean types, initialize with all zeros
return ConstantInitializer()
return Constant()
python/paddle/v2/fluid/layers.py
浏览文件 @ 0ac8c74e
import paddle.v2.fluid.core as core
import paddle.v2.fluid.proto.framework_pb2 as framework_pb2
from paddle.v2.fluid.framework import OpProtoHolder, Variable, Program, \
Operator
from paddle.v2.fluid.initializer import ConstantInitializer, \
NormalInitializer, XavierInitializer
from . import core
import proto.framework_pb2 as framework_pb2
from framework import OpProtoHolder, Variable, Program, Operator
from initializer import Constant, Normal, Xavier
from paddle.v2.fluid.layer_helper import LayerHelper, unique_name
import re
import cStringIO
......@@ -58,10 +56,10 @@ def fc(input,
"""
def _get_default_param_initializer():
return XavierInitializer()
return Xavier()
def _get_default_bias_initializer():
return ConstantInitializer()
return Constant()
helper = LayerHelper('fc', **locals())
......@@ -139,7 +137,7 @@ def embedding(input,
"""
def _get_default_param_initializer():
return XavierInitializer()
return Xavier()
helper = LayerHelper('embedding', **locals())
w = helper.create_parameter(
......@@ -477,7 +475,7 @@ def linear_chain_crf(input,
main_program=None,
startup_program=None):
def _get_default_param_initializer():
return XavierInitializer()
return Xavier()
helper = LayerHelper('linear_chain_crf', **locals())
size = input.shape[1]
......@@ -661,10 +659,10 @@ def sequence_conv(input,
"""
def _get_default_bias_initializer():
return ConstantInitializer()
return Constant()
def _get_default_param_initializer():
return XavierInitializer()
return Xavier()
# FIXME(dzh) : want to unify the argument of python layer
# function. So we ignore some unecessary attributes.
......@@ -725,11 +723,11 @@ def conv2d(input,
"""
def _get_default_bias_initializer():
return ConstantInitializer()
return Constant()
def _get_default_param_initializer(filter_size, num_channels):
std = (2.0 / (filter_size[0]**2 * num_channels))**0.5
return NormalInitializer(0.0, std, 0)
return Normal(0.0, std, 0)
helper = LayerHelper('conv2d', **locals())
dtype = helper.input_dtype()
......@@ -878,22 +876,20 @@ def batch_norm(input,
attr=helper.param_attr,
shape=param_shape,
dtype=dtype,
initializer=ConstantInitializer(1.0))
initializer=Constant(1.0))
bias = helper.create_parameter(
attr=helper.param_attr,
shape=param_shape,
dtype=dtype,
initializer=ConstantInitializer(0.0))
initializer=Constant(0.0))
mean = helper.create_global_variable(
dtype=input.dtype, shape=param_shape, persistable=True)
helper.set_variable_initializer(
var=mean, initializer=ConstantInitializer(0.0))
helper.set_variable_initializer(var=mean, initializer=Constant(0.0))
variance = helper.create_global_variable(
dtype=input.dtype, shape=param_shape, persistable=True)
helper.set_variable_initializer(
var=variance, initializer=ConstantInitializer(1.0))
helper.set_variable_initializer(var=variance, initializer=Constant(1.0))
# create output
# mean and mean_out share the same memory
......
python/paddle/v2/fluid/nets.py
浏览文件 @ 0ac8c74e
import paddle.v2.fluid.layers as layers
import layers
__all__ = ["simple_img_conv_pool", "sequence_conv_pool"]
......
python/paddle/v2/fluid/optimizer.py
浏览文件 @ 0ac8c74e
from collections import defaultdict
import paddle.v2.fluid.framework as framework
from paddle.v2.fluid.framework import unique_name, Program
from paddle.v2.fluid.backward import append_backward_ops
from paddle.v2.fluid.initializer import ConstantInitializer
from paddle.v2.fluid.regularizer import append_regularization_ops
from paddle.v2.fluid.layer_helper import LayerHelper
import framework
from backward import append_backward_ops
from framework import unique_name
from initializer import Constant
from layer_helper import LayerHelper
from regularizer import append_regularization_ops
__all__ = [
'SGDOptimizer', 'MomentumOptimizer', 'AdagradOptimizer', 'AdamOptimizer',
'AdamaxOptimizer', 'DecayedAdagradOptimizer'
]
__all__ = ['SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad']
class Optimizer(object):
......@@ -48,7 +45,7 @@ class Optimizer(object):
persistable=True)
param_lr = param_lr * self._learning_rate
self.helper.set_variable_initializer(
var=param_lr_var, initializer=ConstantInitializer(param_lr))
var=param_lr_var, initializer=Constant(param_lr))
return param_lr_var
def _create_accumulators(self, block, parameters):
......@@ -96,7 +93,7 @@ class Optimizer(object):
type=param.type,
shape=param.shape)
self.helper.set_variable_initializer(
var, initializer=ConstantInitializer(value=float(fill_value)))
var, initializer=Constant(value=float(fill_value)))
self._accumulators[name][param.name] = var
def _get_accumulator(self, name, param):
......@@ -360,7 +357,7 @@ class AdamOptimizer(Optimizer):
lod_level=0,
persistable=True)
self.helper.set_variable_initializer(
self._beta1_pow_acc, initializer=ConstantInitializer(self._beta1))
self._beta1_pow_acc, initializer=Constant(self._beta1))
self._beta2_pow_acc = self.helper.create_global_variable(
name=unique_name('beta2_pow_acc'),
......@@ -370,7 +367,7 @@ class AdamOptimizer(Optimizer):
persistable=True)
self.helper.set_variable_initializer(
self._beta2_pow_acc, initializer=ConstantInitializer(self._beta2))
self._beta2_pow_acc, initializer=Constant(self._beta2))
# Create accumulator tensors for first and second moments
for p in parameters:
......@@ -462,7 +459,7 @@ class AdamaxOptimizer(Optimizer):
lod_level=0,
persistable=True)
self.helper.set_variable_initializer(
self._beta1_pow_acc, initializer=ConstantInitializer(self._beta1))
self._beta1_pow_acc, initializer=Constant(self._beta1))
# Create accumulator tensors for first moment and infinity norm
for p in parameters:
......@@ -559,3 +556,19 @@ class DecayedAdagradOptimizer(Optimizer):
attrs={"epsilon": self._epsilon})
return decayed_adagrad_op
# We short the class name, since users will use the optimizer with the package
# name. The sample code:
#
# import paddle.fluid as fluid
#
# sgd = fluid.optimizer.SGD(...)
#
# It is no need to add an `Optimizer` as the class suffix
SGD = SGDOptimizer
Momentum = MomentumOptimizer
Adagrad = AdagradOptimizer
Adam = AdamOptimizer
Adamax = AdamaxOptimizer
DecayedAdagrad = DecayedAdagradOptimizer
python/paddle/v2/fluid/regularizer.py
浏览文件 @ 0ac8c74e
import paddle.v2.fluid.framework as framework
import framework
__all__ = [
'append_regularization_ops', 'L2DecayRegularizer', 'L1DecayRegularizer'
]
__all__ = ['append_regularization_ops', 'L1Decay', 'L2Decay']
def append_regularization_ops(parameters_and_grads):
......@@ -139,3 +137,16 @@ class L1DecayRegularizer(WeightDecayRegularizer):
attrs={"scale": self._regularization_coeff})
return decay
# We short the class name, since users will use the regulaizer with the package
# name. The sample code:
#
# import paddle.fluid as fluid
#
# hidden = fluid.layers.fc(...,
# param_attr=ParamAttr(fluid.regularizer.Xavier()))
#
# It is no need to add a `Regularizer` as the class suffix
L1Decay = L1DecayRegularizer
L2Decay = L2DecayRegularizer
python/paddle/v2/fluid/tests/book/test_fit_a_line.py
浏览文件 @ 0ac8c74e
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.io import save_persistables, load_persistables
from paddle.v2.fluid.optimizer import SGDOptimizer
import paddle.v2.fluid as fluid
x = layers.data(name='x', shape=[13], dtype='float32')
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = layers.fc(input=x, size=1, act=None)
y_predict = fluid.layers.fc(input=x, size=1, act=None)
y = layers.data(name='y', shape=[1], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = layers.square_error_cost(input=y_predict, label=y)
avg_cost = layers.mean(x=cost)
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(x=cost)
sgd_optimizer = SGDOptimizer(learning_rate=0.001)
opts = sgd_optimizer.minimize(avg_cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
BATCH_SIZE = 20
......@@ -26,32 +21,24 @@ train_reader = paddle.batch(
paddle.dataset.uci_housing.train(), buf_size=500),
batch_size=BATCH_SIZE)
place = core.CPUPlace()
exe = Executor(place)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(framework.default_startup_program())
exe.run(fluid.default_startup_program())
PASS_NUM = 100
for pass_id in range(PASS_NUM):
save_persistables(exe, "./fit_a_line.model/")
load_persistables(exe, "./fit_a_line.model/")
fluid.io.save_persistables(exe, "./fit_a_line.model/")
fluid.io.load_persistables(exe, "./fit_a_line.model/")
for data in train_reader():
x_data = np.array(map(lambda x: x[0], data)).astype("float32")
y_data = np.array(map(lambda x: x[1], data)).astype("float32")
tensor_x = core.LoDTensor()
tensor_x.set(x_data, place)
# print tensor_x.get_dims()
tensor_y = core.LoDTensor()
tensor_y.set(y_data, place)
# print tensor_y.get_dims()
outs = exe.run(framework.default_main_program(),
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost])
out = np.array(outs[0])
if out[0] < 10.0:
x_data = np.array(map(lambda _: _[0], data)).astype("float32")
y_data = np.array(map(lambda _: _[1], data)).astype("float32")
avg_loss_value, = exe.run(fluid.default_main_program(),
feed={'x': x_data,
'y': y_data},
fetch_list=[avg_cost])
if avg_loss_value[0] < 10.0:
exit(0) # if avg cost less than 10.0, we think our code is good.
exit(1)
python/paddle/v2/fluid/tests/book/test_image_classification_train.py
浏览文件 @ 0ac8c74e
from __future__ import print_function
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
import paddle.v2.fluid.evaluator as evaluator
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.initializer import XavierInitializer
from paddle.v2.fluid.optimizer import AdamOptimizer
import paddle.v2.fluid as fluid
def resnet_cifar10(input, depth=32):
def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
tmp = layers.conv2d(
tmp = fluid.layers.conv2d(
input=input,
filter_size=filter_size,
num_filters=ch_out,
......@@ -20,12 +14,11 @@ def resnet_cifar10(input, depth=32):
padding=padding,
act=None,
bias_attr=False)
return layers.batch_norm(input=tmp, act=act)
return fluid.layers.batch_norm(input=tmp, act=act)
def shortcut(input, ch_in, ch_out, stride, program, init_program):
def shortcut(input, ch_in, ch_out, stride):
if ch_in != ch_out:
return conv_bn_layer(input, ch_out, 1, stride, 0, None, program,
init_program)
return conv_bn_layer(input, ch_out, 1, stride, 0, None)
else:
return input
......@@ -33,7 +26,7 @@ def resnet_cifar10(input, depth=32):
tmp = conv_bn_layer(input, ch_out, 3, stride, 1)
tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, act=None)
short = shortcut(input, ch_in, ch_out, stride)
return layers.elementwise_add(x=tmp, y=short, act='relu')
return fluid.layers.elementwise_add(x=tmp, y=short, act='relu')
def layer_warp(block_func, input, ch_in, ch_out, count, stride):
tmp = block_func(input, ch_in, ch_out, stride)
......@@ -48,14 +41,14 @@ def resnet_cifar10(input, depth=32):
res1 = layer_warp(basicblock, conv1, 16, 16, n, 1)
res2 = layer_warp(basicblock, res1, 16, 32, n, 2)
res3 = layer_warp(basicblock, res2, 32, 64, n, 2)
pool = layers.pool2d(
pool = fluid.layers.pool2d(
input=res3, pool_size=8, pool_type='avg', pool_stride=1)
return pool
def vgg16_bn_drop(input):
def conv_block(input, num_filter, groups, dropouts):
return nets.img_conv_group(
return fluid.nets.img_conv_group(
input=input,
pool_size=2,
pool_stride=2,
......@@ -72,26 +65,20 @@ def vgg16_bn_drop(input):
conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
drop = layers.dropout(x=conv5, dropout_prob=0.5)
fc1 = layers.fc(input=drop,
size=512,
act=None,
param_attr={"initializer": XavierInitializer()})
reshape1 = layers.reshape(x=fc1, shape=list(fc1.shape + (1, 1)))
bn = layers.batch_norm(input=reshape1, act='relu')
drop2 = layers.dropout(x=bn, dropout_prob=0.5)
fc2 = layers.fc(input=drop2,
size=512,
act=None,
param_attr={"initializer": XavierInitializer()})
drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
fc1 = fluid.layers.fc(input=drop, size=512, act=None)
reshape1 = fluid.layers.reshape(x=fc1, shape=list(fc1.shape + (1, 1)))
bn = fluid.layers.batch_norm(input=reshape1, act='relu')
drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
fc2 = fluid.layers.fc(input=drop2, size=512, act=None)
return fc2
classdim = 10
data_shape = [3, 32, 32]
images = layers.data(name='pixel', shape=data_shape, dtype='float32')
label = layers.data(name='label', shape=[1], dtype='int64')
images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
# Add neural network config
# option 1. resnet
......@@ -99,17 +86,14 @@ label = layers.data(name='label', shape=[1], dtype='int64')
# option 2. vgg
net = vgg16_bn_drop(images)
# print(program)
predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
predict = layers.fc(input=net, size=classdim, act='softmax')
cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(x=cost)
# optimizer = SGDOptimizer(learning_rate=0.001)
optimizer = AdamOptimizer(learning_rate=0.001)
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
opts = optimizer.minimize(avg_cost)
accuracy = evaluator.Accuracy(input=predict, label=label)
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
BATCH_SIZE = 128
PASS_NUM = 1
......@@ -119,13 +103,12 @@ train_reader = paddle.batch(
paddle.dataset.cifar.train10(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
place = core.CPUPlace()
exe = Executor(place)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(framework.default_startup_program())
exe.run(fluid.default_startup_program())
for pass_id in range(PASS_NUM):
batch_id = 0
accuracy.reset(exe)
for data in train_reader():
img_data = np.array(map(lambda x: x[0].reshape(data_shape),
......@@ -136,25 +119,13 @@ for pass_id in range(PASS_NUM):
batch_size = batch_size * i
y_data = y_data.reshape([batch_size, 1])
tensor_img = core.LoDTensor()
tensor_y = core.LoDTensor()
tensor_img.set(img_data, place)
tensor_y.set(y_data, place)
outs = exe.run(framework.default_main_program(),
feed={"pixel": tensor_img,
"label": tensor_y},
fetch_list=[avg_cost] + accuracy.metrics)
loss = np.array(outs[0])
acc = np.array(outs[1])
loss, acc = exe.run(fluid.default_main_program(),
feed={"pixel": img_data,
"label": y_data},
fetch_list=[avg_cost] + accuracy.metrics)
pass_acc = accuracy.eval(exe)
print("pass_id:" + str(pass_id) + " batch_id:" + str(batch_id) +
" loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
pass_acc))
batch_id = batch_id + 1
if batch_id > 1:
# this model is slow, so if we can train two mini batch, we think it works properly.
exit(0)
print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
pass_acc))
# this model is slow, so if we can train two mini batch, we think it works properly.
exit(0)
exit(1)
python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py
浏览文件 @ 0ac8c74e
import numpy as np
import paddle.v2 as paddle
import paddle.v2.dataset.conll05 as conll05
import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.executor import Executor, g_scope
from paddle.v2.fluid.optimizer import SGDOptimizer
import paddle.v2.fluid as fluid
word_dict, verb_dict, label_dict = conll05.get_dict()
word_dict_len = len(word_dict)
......@@ -34,23 +30,23 @@ def load_parameter(file_name, h, w):
def db_lstm():
# 8 features
word = layers.data(name='word_data', shape=[1], dtype='int64')
predicate = layers.data(name='verb_data', shape=[1], dtype='int64')
ctx_n2 = layers.data(name='ctx_n2_data', shape=[1], dtype='int64')
ctx_n1 = layers.data(name='ctx_n1_data', shape=[1], dtype='int64')
ctx_0 = layers.data(name='ctx_0_data', shape=[1], dtype='int64')
ctx_p1 = layers.data(name='ctx_p1_data', shape=[1], dtype='int64')
ctx_p2 = layers.data(name='ctx_p2_data', shape=[1], dtype='int64')
mark = layers.data(name='mark_data', shape=[1], dtype='int64')
predicate_embedding = layers.embedding(
word = fluid.layers.data(name='word_data', shape=[1], dtype='int64')
predicate = fluid.layers.data(name='verb_data', shape=[1], dtype='int64')
ctx_n2 = fluid.layers.data(name='ctx_n2_data', shape=[1], dtype='int64')
ctx_n1 = fluid.layers.data(name='ctx_n1_data', shape=[1], dtype='int64')
ctx_0 = fluid.layers.data(name='ctx_0_data', shape=[1], dtype='int64')
ctx_p1 = fluid.layers.data(name='ctx_p1_data', shape=[1], dtype='int64')
ctx_p2 = fluid.layers.data(name='ctx_p2_data', shape=[1], dtype='int64')
mark = fluid.layers.data(name='mark_data', shape=[1], dtype='int64')
predicate_embedding = fluid.layers.embedding(
input=predicate,
size=[pred_len, word_dim],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr={'name': 'vemb'})
mark_embedding = layers.embedding(
mark_embedding = fluid.layers.embedding(
input=mark,
size=[mark_dict_len, mark_dim],
dtype='float32',
......@@ -58,7 +54,7 @@ def db_lstm():
word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
emb_layers = [
layers.embedding(
fluid.layers.embedding(
size=[word_dict_len, word_dim],
input=x,
param_attr={'name': embedding_name,
......@@ -68,12 +64,12 @@ def db_lstm():
emb_layers.append(mark_embedding)
hidden_0_layers = [
layers.fc(input=emb, size=hidden_dim) for emb in emb_layers
fluid.layers.fc(input=emb, size=hidden_dim) for emb in emb_layers
]
hidden_0 = layers.sums(input=hidden_0_layers)
hidden_0 = fluid.layers.sums(input=hidden_0_layers)
lstm_0 = layers.dynamic_lstm(
lstm_0 = fluid.layers.dynamic_lstm(
input=hidden_0,
size=hidden_dim,
candidate_activation='relu',
......@@ -84,12 +80,12 @@ def db_lstm():
input_tmp = [hidden_0, lstm_0]
for i in range(1, depth):
mix_hidden = layers.sums(input=[
layers.fc(input=input_tmp[0], size=hidden_dim),
layers.fc(input=input_tmp[1], size=hidden_dim)
mix_hidden = fluid.layers.sums(input=[
fluid.layers.fc(input=input_tmp[0], size=hidden_dim),
fluid.layers.fc(input=input_tmp[1], size=hidden_dim)
])
lstm = layers.dynamic_lstm(
lstm = fluid.layers.dynamic_lstm(
input=mix_hidden,
size=hidden_dim,
candidate_activation='relu',
......@@ -99,9 +95,9 @@ def db_lstm():
input_tmp = [mix_hidden, lstm]
feature_out = layers.sums(input=[
layers.fc(input=input_tmp[0], size=label_dict_len),
layers.fc(input=input_tmp[1], size=label_dict_len)
feature_out = fluid.layers.sums(input=[
fluid.layers.fc(input=input_tmp[0], size=label_dict_len),
fluid.layers.fc(input=input_tmp[1], size=label_dict_len)
])
return feature_out
......@@ -116,7 +112,7 @@ def to_lodtensor(data, place):
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = core.LoDTensor()
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
......@@ -125,29 +121,29 @@ def to_lodtensor(data, place):
def main():
# define network topology
feature_out = db_lstm()
target = layers.data(name='target', shape=[1], dtype='int64')
crf_cost = layers.linear_chain_crf(
target = fluid.layers.data(name='target', shape=[1], dtype='int64')
crf_cost = fluid.layers.linear_chain_crf(
input=feature_out,
label=target,
param_attr={"name": 'crfw',
"learning_rate": mix_hidden_lr})
avg_cost = layers.mean(x=crf_cost)
avg_cost = fluid.layers.mean(x=crf_cost)
# TODO(qiao)
# 1. add crf_decode_layer and evaluator
# 2. use other optimizer and check why out will be NAN
sgd_optimizer = SGDOptimizer(learning_rate=0.0001)
opts = sgd_optimizer.minimize(avg_cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.0001)
sgd_optimizer.minimize(avg_cost)
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.conll05.test(), buf_size=8192),
batch_size=BATCH_SIZE)
place = core.CPUPlace()
exe = Executor(place)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(framework.default_startup_program())
exe.run(fluid.default_startup_program())
embedding_param = g_scope.find_var(embedding_name).get_tensor()
embedding_param = fluid.g_scope.find_var(embedding_name).get_tensor()
embedding_param.set(
load_parameter(conll05.get_embedding(), word_dict_len, word_dim), place)
......@@ -164,7 +160,7 @@ def main():
mark_data = to_lodtensor(map(lambda x: x[7], data), place)
target = to_lodtensor(map(lambda x: x[8], data), place)
outs = exe.run(framework.default_main_program(),
outs = exe.run(fluid.default_main_program(),
feed={
'word_data': word_data,
'ctx_n2_data': ctx_n2_data,
......
python/paddle/v2/fluid/tests/book/test_recognize_digits_conv.py
浏览文件 @ 0ac8c74e
from __future__ import print_function
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.evaluator as evaluator
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.optimizer import AdamOptimizer
import paddle.v2.fluid as fluid
images = layers.data(name='pixel', shape=[1, 28, 28], dtype='float32')
label = layers.data(name='label', shape=[1], dtype='int64')
conv_pool_1 = nets.simple_img_conv_pool(
images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=images,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_2 = nets.simple_img_conv_pool(
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
......@@ -25,13 +20,13 @@ conv_pool_2 = nets.simple_img_conv_pool(
pool_stride=2,
act="relu")
predict = layers.fc(input=conv_pool_2, size=10, act="softmax")
cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(x=cost)
optimizer = AdamOptimizer(learning_rate=0.01, beta1=0.9, beta2=0.999)
opts = optimizer.minimize(avg_cost)
predict = fluid.layers.fc(input=conv_pool_2, size=10, act="softmax")
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
optimizer = fluid.optimizer.Adam(learning_rate=0.01)
optimizer.minimize(avg_cost)
accuracy = evaluator.Accuracy(input=predict, label=label)
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
BATCH_SIZE = 50
PASS_NUM = 3
......@@ -40,10 +35,10 @@ train_reader = paddle.batch(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=BATCH_SIZE)
place = core.CPUPlace()
exe = Executor(place)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(framework.default_startup_program())
exe.run(fluid.default_startup_program())
for pass_id in range(PASS_NUM):
accuracy.reset(exe)
......@@ -53,17 +48,10 @@ for pass_id in range(PASS_NUM):
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = y_data.reshape([BATCH_SIZE, 1])
tensor_img = core.LoDTensor()
tensor_y = core.LoDTensor()
tensor_img.set(img_data, place)
tensor_y.set(y_data, place)
outs = exe.run(framework.default_main_program(),
feed={"pixel": tensor_img,
"label": tensor_y},
fetch_list=[avg_cost] + accuracy.metrics)
loss = np.array(outs[0])
acc = np.array(outs[1])
loss, acc = exe.run(fluid.default_main_program(),
feed={"pixel": img_data,
"label": y_data},
fetch_list=[avg_cost] + accuracy.metrics)
pass_acc = accuracy.eval(exe)
print("pass_id=" + str(pass_id) + " acc=" + str(acc) + " pass_acc=" +
str(pass_acc))
......
python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py
浏览文件 @ 0ac8c74e
from __future__ import print_function
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.evaluator as evaluator
from paddle.v2.fluid.io import get_inference_program
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.initializer import UniformInitializer
from paddle.v2.fluid.optimizer import MomentumOptimizer
from paddle.v2.fluid.regularizer import L2DecayRegularizer
import paddle.v2.fluid as fluid
BATCH_SIZE = 128
image = layers.data(name='x', shape=[784], dtype='float32')
image = fluid.layers.data(name='x', shape=[784], dtype='float32')
param_attr = {
'name': None,
'initializer': UniformInitializer(
low=-1.0, high=1.0),
'regularization': L2DecayRegularizer(0.0005 * BATCH_SIZE)
'regularization': fluid.regularizer.L2Decay(0.0005 * BATCH_SIZE)
}
hidden1 = layers.fc(input=image, size=128, act='relu', param_attr=param_attr)
hidden2 = layers.fc(input=hidden1, size=64, act='relu', param_attr=param_attr)
hidden1 = fluid.layers.fc(input=image,
size=128,
act='relu',
param_attr=param_attr)
hidden2 = fluid.layers.fc(input=hidden1,
size=64,
act='relu',
param_attr=param_attr)
predict = layers.fc(input=hidden2,
size=10,
act='softmax',
param_attr=param_attr)
predict = fluid.layers.fc(input=hidden2,
size=10,
act='softmax',
param_attr=param_attr)
label = layers.data(name='y', shape=[1], dtype='int64')
label = fluid.layers.data(name='y', shape=[1], dtype='int64')
cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(x=cost)
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
optimizer = fluid.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
opts = optimizer.minimize(avg_cost)
accuracy = evaluator.Accuracy(input=predict, label=label)
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
train_reader = paddle.batch(
paddle.reader.shuffle(
......@@ -45,10 +42,10 @@ train_reader = paddle.batch(
test_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128)
place = core.CPUPlace()
exe = Executor(place)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(framework.default_startup_program())
exe.run(fluid.default_startup_program())
PASS_NUM = 100
for pass_id in range(PASS_NUM):
......@@ -58,13 +55,13 @@ for pass_id in range(PASS_NUM):
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = np.expand_dims(y_data, axis=1)
tensor_x = core.LoDTensor()
tensor_x = fluid.LoDTensor()
tensor_x.set(x_data, place)
tensor_y = core.LoDTensor()
tensor_y = fluid.LoDTensor()
tensor_y.set(y_data, place)
outs = exe.run(framework.default_main_program(),
outs = exe.run(fluid.default_main_program(),
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost] + accuracy.metrics)
......@@ -72,10 +69,10 @@ for pass_id in range(PASS_NUM):
acc = np.array(outs[1])
pass_acc = accuracy.eval(exe)
test_accuracy = evaluator.Accuracy(input=predict, label=label)
test_accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
test_target = [avg_cost] + test_accuracy.metrics + test_accuracy.states
inference_program = get_inference_program(test_target)
inference_program = fluid.io.get_inference_program(test_target)
test_accuracy.reset(exe)
for data in test_reader():
......@@ -83,18 +80,10 @@ for pass_id in range(PASS_NUM):
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = np.expand_dims(y_data, axis=1)
tensor_x = core.LoDTensor()
tensor_x.set(x_data, place)
tensor_y = core.LoDTensor()
tensor_y.set(y_data, place)
outs = exe.run(inference_program,
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost] + test_accuracy.metrics)
out = np.array(outs[0])
acc = np.array(outs[1])
out, acc = exe.run(inference_program,
feed={'x': x_data,
'y': y_data},
fetch_list=[avg_cost] + test_accuracy.metrics)
test_pass_acc = test_accuracy.eval(exe)
print("pass_id=" + str(pass_id) + " train_cost=" + str(
......
python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py
浏览文件 @ 0ac8c74e
from __future__ import print_function
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.evaluator as evaluator
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.optimizer import AdamOptimizer
import paddle.v2.fluid as fluid
def convolution_net(input_dim, class_dim=2, emb_dim=32, hid_dim=32):
data = layers.data(name="words", shape=[1], dtype="int64")
label = layers.data(name="label", shape=[1], dtype="int64")
data = fluid.layers.data(name="words", shape=[1], dtype="int64")
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
emb = layers.embedding(input=data, size=[input_dim, emb_dim])
conv_3 = nets.sequence_conv_pool(
emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=3,
act="tanh",
pool_type="sqrt")
conv_4 = nets.sequence_conv_pool(
conv_4 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=4,
act="tanh",
pool_type="sqrt")
prediction = layers.fc(input=[conv_3, conv_4],
size=class_dim,
act="softmax")
cost = layers.cross_entropy(input=prediction, label=label)
avg_cost = layers.mean(x=cost)
adam_optimizer = AdamOptimizer(learning_rate=0.002)
prediction = fluid.layers.fc(input=[conv_3, conv_4],
size=class_dim,
act="softmax")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(x=cost)
adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
adam_optimizer.minimize(avg_cost)
accuracy = evaluator.Accuracy(input=prediction, label=label)
accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
return avg_cost, accuracy, accuracy.metrics[0]
......@@ -46,7 +41,7 @@ def to_lodtensor(data, place):
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = core.LoDTensor()
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
......@@ -67,10 +62,10 @@ def main():
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=1000),
batch_size=BATCH_SIZE)
place = core.CPUPlace()
exe = Executor(place)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(framework.default_startup_program())
exe.run(fluid.default_startup_program())
for pass_id in xrange(PASS_NUM):
accuracy.reset(exe)
......@@ -80,15 +75,14 @@ def main():
label = np.array(map(lambda x: x[1], data)).astype("int64")
label = label.reshape([BATCH_SIZE, 1])
tensor_label = core.LoDTensor()
tensor_label = fluid.LoDTensor()
tensor_label.set(label, place)
outs = exe.run(framework.default_main_program(),
feed={"words": tensor_words,
"label": tensor_label},
fetch_list=[cost, acc_out])
cost_val = np.array(outs[0])
acc_val = np.array(outs[1])
cost_val, acc_val = exe.run(
fluid.default_main_program(),
feed={"words": tensor_words,
"label": tensor_label},
fetch_list=[cost, acc_out])
pass_acc = accuracy.eval(exe)
print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
" pass_acc=" + str(pass_acc))
......
python/paddle/v2/fluid/tests/book/test_understand_sentiment_dynamic_lstm.py
浏览文件 @ 0ac8c74e
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.evaluator as evaluator
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.optimizer import AdamOptimizer
import paddle.v2.fluid as fluid
def stacked_lstm_net(input_dim,
......@@ -14,35 +9,35 @@ def stacked_lstm_net(input_dim,
hid_dim=512,
stacked_num=3):
assert stacked_num % 2 == 1
data = layers.data(name="words", shape=[1], dtype="int64")
label = layers.data(name="label", shape=[1], dtype="int64")
data = fluid.layers.data(name="words", shape=[1], dtype="int64")
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
emb = layers.embedding(input=data, size=[input_dim, emb_dim])
emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
# add bias attr
# TODO(qijun) linear act
fc1 = layers.fc(input=emb, size=hid_dim)
lstm1, cell1 = layers.dynamic_lstm(input=fc1, size=hid_dim)
fc1 = fluid.layers.fc(input=emb, size=hid_dim)
lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)
inputs = [fc1, lstm1]
for i in range(2, stacked_num + 1):
fc = layers.fc(input=inputs, size=hid_dim)
lstm, cell = layers.dynamic_lstm(
fc = fluid.layers.fc(input=inputs, size=hid_dim)
lstm, cell = fluid.layers.dynamic_lstm(
input=fc, size=hid_dim, is_reverse=(i % 2) == 0)
inputs = [fc, lstm]
fc_last = layers.sequence_pool(input=inputs[0], pool_type='max')
lstm_last = layers.sequence_pool(input=inputs[1], pool_type='max')
fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max')
prediction = layers.fc(input=[fc_last, lstm_last],
size=class_dim,
act='softmax')
cost = layers.cross_entropy(input=prediction, label=label)
avg_cost = layers.mean(x=cost)
adam_optimizer = AdamOptimizer(learning_rate=0.002)
prediction = fluid.layers.fc(input=[fc_last, lstm_last],
size=class_dim,
act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(x=cost)
adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
adam_optimizer.minimize(avg_cost)
accuracy = evaluator.Accuracy(input=prediction, label=label)
accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
return avg_cost, accuracy, accuracy.metrics[0]
......@@ -55,7 +50,7 @@ def to_lodtensor(data, place):
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = core.LoDTensor()
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
......@@ -77,10 +72,10 @@ def main():
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=1000),
batch_size=BATCH_SIZE)
place = core.CPUPlace()
exe = Executor(place)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(framework.default_startup_program())
exe.run(fluid.default_startup_program())
for pass_id in xrange(PASS_NUM):
accuracy.reset(exe)
......@@ -90,15 +85,14 @@ def main():
label = np.array(map(lambda x: x[1], data)).astype("int64")
label = label.reshape([BATCH_SIZE, 1])
tensor_label = core.LoDTensor()
tensor_label = fluid.LoDTensor()
tensor_label.set(label, place)
outs = exe.run(framework.default_main_program(),
feed={"words": tensor_words,
"label": tensor_label},
fetch_list=[cost, acc_out])
cost_val = np.array(outs[0])
acc_val = np.array(outs[1])
cost_val, acc_val = exe.run(
fluid.default_main_program(),
feed={"words": tensor_words,
"label": tensor_label},
fetch_list=[cost, acc_out])
pass_acc = accuracy.eval(exe)
print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
" pass_acc=" + str(pass_acc))
......
python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py
浏览文件 @ 0ac8c74e
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.optimizer import AdamOptimizer
import paddle.v2.fluid as fluid
def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50):
data = layers.data(
data = fluid.layers.data(
name="words",
shape=[seq_len * batch_size, 1],
append_batch_size=False,
dtype="int64")
label = layers.data(
label = fluid.layers.data(
name="label",
shape=[batch_size, 1],
append_batch_size=False,
dtype="int64")
emb = layers.embedding(input=data, size=[dict_dim, emb_dim])
emb = layers.reshape(x=emb, shape=[batch_size, seq_len, emb_dim])
emb = layers.transpose(x=emb, axis=[1, 0, 2])
emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
emb = fluid.layers.reshape(x=emb, shape=[batch_size, seq_len, emb_dim])
emb = fluid.layers.transpose(x=emb, axis=[1, 0, 2])
c_pre_init = layers.fill_constant(
c_pre_init = fluid.layers.fill_constant(
dtype=emb.dtype, shape=[batch_size, emb_dim], value=0.0)
layer_1_out = layers.lstm(emb, c_pre_init=c_pre_init, hidden_dim=emb_dim)
layer_1_out = layers.transpose(x=layer_1_out, axis=[1, 0, 2])
layer_1_out = fluid.layers.lstm(
emb, c_pre_init=c_pre_init, hidden_dim=emb_dim)
layer_1_out = fluid.layers.transpose(x=layer_1_out, axis=[1, 0, 2])
prediction = layers.fc(input=layer_1_out, size=class_dim, act="softmax")
cost = layers.cross_entropy(input=prediction, label=label)
prediction = fluid.layers.fc(input=layer_1_out,
size=class_dim,
act="softmax")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = layers.mean(x=cost)
adam_optimizer = AdamOptimizer(learning_rate=0.002)
opts = adam_optimizer.minimize(avg_cost)
acc = layers.accuracy(input=prediction, label=label)
avg_cost = fluid.layers.mean(x=cost)
adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
adam_optimizer.minimize(avg_cost)
acc = fluid.layers.accuracy(input=prediction, label=label)
return avg_cost, acc
......@@ -48,7 +47,7 @@ def to_lodtensor(data, place):
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = core.LoDTensor()
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
......@@ -65,7 +64,7 @@ def prepare_feed_data(data, place):
label = np.array(map(lambda x: x[1], data)).astype("int64")
label = label.reshape([len(label), 1])
tensor_label = core.LoDTensor()
tensor_label = fluid.LoDTensor()
tensor_label.set(label, place)
return tensor_words, tensor_label
......@@ -86,17 +85,17 @@ def main():
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=BATCH_SIZE * 10),
batch_size=BATCH_SIZE)
place = core.CPUPlace()
exe = Executor(place)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(framework.default_startup_program())
exe.run(fluid.default_startup_program())
for pass_id in xrange(PASS_NUM):
for data in train_data():
chopped_data = chop_data(data)
tensor_words, tensor_label = prepare_feed_data(chopped_data, place)
outs = exe.run(framework.default_main_program(),
outs = exe.run(fluid.default_main_program(),
feed={"words": tensor_words,
"label": tensor_label},
fetch_list=[cost, acc])
......
python/paddle/v2/fluid/tests/book/test_word2vec.py
浏览文件 @ 0ac8c74e
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.optimizer import SGDOptimizer
import paddle.v2.fluid as fluid
PASS_NUM = 100
EMBED_SIZE = 32
......@@ -16,57 +12,57 @@ IS_SPARSE = True
word_dict = paddle.dataset.imikolov.build_dict()
dict_size = len(word_dict)
first_word = layers.data(name='firstw', shape=[1], dtype='int64')
second_word = layers.data(name='secondw', shape=[1], dtype='int64')
third_word = layers.data(name='thirdw', shape=[1], dtype='int64')
forth_word = layers.data(name='forthw', shape=[1], dtype='int64')
next_word = layers.data(name='nextw', shape=[1], dtype='int64')
first_word = fluid.layers.data(name='firstw', shape=[1], dtype='int64')
second_word = fluid.layers.data(name='secondw', shape=[1], dtype='int64')
third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64')
forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64')
next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
embed_first = layers.embedding(
embed_first = fluid.layers.embedding(
input=first_word,
size=[dict_size, EMBED_SIZE],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr={'name': 'shared_w'})
embed_second = layers.embedding(
embed_second = fluid.layers.embedding(
input=second_word,
size=[dict_size, EMBED_SIZE],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr={'name': 'shared_w'})
embed_third = layers.embedding(
embed_third = fluid.layers.embedding(
input=third_word,
size=[dict_size, EMBED_SIZE],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr={'name': 'shared_w'})
embed_forth = layers.embedding(
embed_forth = fluid.layers.embedding(
input=forth_word,
size=[dict_size, EMBED_SIZE],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr={'name': 'shared_w'})
concat_embed = layers.concat(
concat_embed = fluid.layers.concat(
input=[embed_first, embed_second, embed_third, embed_forth], axis=1)
hidden1 = layers.fc(input=concat_embed, size=HIDDEN_SIZE, act='sigmoid')
predict_word = layers.fc(input=hidden1, size=dict_size, act='softmax')
cost = layers.cross_entropy(input=predict_word, label=next_word)
avg_cost = layers.mean(x=cost)
sgd_optimizer = SGDOptimizer(learning_rate=0.001)
opts = sgd_optimizer.minimize(avg_cost)
hidden1 = fluid.layers.fc(input=concat_embed, size=HIDDEN_SIZE, act='sigmoid')
predict_word = fluid.layers.fc(input=hidden1, size=dict_size, act='softmax')
cost = fluid.layers.cross_entropy(input=predict_word, label=next_word)
avg_cost = fluid.layers.mean(x=cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
train_reader = paddle.batch(
paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
place = core.CPUPlace()
exe = Executor(place)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
# fix https://github.com/PaddlePaddle/Paddle/issues/5434 then remove
# below exit line.
exit(0)
exe.run(framework.default_startup_program())
exe.run(fluid.default_startup_program())
for pass_id in range(PASS_NUM):
for data in train_reader():
......@@ -74,36 +70,15 @@ for pass_id in range(PASS_NUM):
input_data = map(lambda x: np.array(x).astype("int64"), input_data)
input_data = map(lambda x: np.expand_dims(x, axis=1), input_data)
first_data = input_data[0]
first_tensor = core.LoDTensor()
first_tensor.set(first_data, place)
second_data = input_data[1]
second_tensor = core.LoDTensor()
second_tensor.set(second_data, place)
third_data = input_data[2]
third_tensor = core.LoDTensor()
third_tensor.set(third_data, place)
forth_data = input_data[3]
forth_tensor = core.LoDTensor()
forth_tensor.set(forth_data, place)
next_data = input_data[4]
next_tensor = core.LoDTensor()
next_tensor.set(next_data, place)
outs = exe.run(framework.default_main_program(),
feed={
'firstw': first_tensor,
'secondw': second_tensor,
'thirdw': third_tensor,
'forthw': forth_tensor,
'nextw': next_tensor
},
fetch_list=[avg_cost])
out = np.array(outs[0])
if out[0] < 10.0:
avg_cost_np = exe.run(fluid.default_main_program(),
feed={
'firstw': input_data[0],
'secondw': input_data[1],
'thirdw': input_data[2],
'forthw': input_data[3],
'nextw': input_data[4]
},
fetch_list=[avg_cost])
if avg_cost_np[0] < 10.0:
exit(0) # if avg cost less than 10.0, we think our code is good.
exit(1)
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