提交 16132c24 编写于 作者: D daming-lu

Merge branch 'develop' into fix_deploy_script

import paddle.v2.framework.framework as framework
import numpy as np
__all__ = ['ConstantInitializer', 'UniformInitializer']
__all__ = [
'ConstantInitializer', 'UniformInitializer', 'NormalInitializer',
'XavierInitializer'
]
class Initializer(object):
......@@ -20,6 +24,41 @@ class Initializer(object):
"""
raise NotImplementedError()
def _compute_fans(self, var):
"""Compute the fan_in and the fan_out for layers
This method computes the fan_in and the fan_out
for neural network layers, if not specified. It is
not possible to perfectly estimate fan_in and fan_out.
This method will estimate it correctly for matrix multiply and
convolutions.
Args:
var: variable for which fan_in and fan_out have to be computed
Returns:
tuple of two integers (fan_in, fan_out)
"""
shape = var.shape
if not shape or len(shape) == 0:
fan_in = fan_out = 1
elif len(shape) == 1:
fan_in = fan_out = shape[0]
elif len(shape) == 2:
# This is the case for simple matrix multiply
fan_in = shape[0]
fan_out = shape[1]
else:
# Assume this to be a convolutional kernel
# In PaddlePaddle, the shape of the kernel is like:
# [num_filters, num_filter_channels, ...] where the remaining
# dimensions are the filter_size
receptive_field_size = np.prod(shape[2:])
fan_in = shape[1] * receptive_field_size
fan_out = shape[0] * receptive_field_size
return (fan_in, fan_out)
class ConstantInitializer(Initializer):
"""Implements the constant initializer
......@@ -156,3 +195,93 @@ class NormalInitializer(Initializer):
})
var.op = op
return op
class XavierInitializer(Initializer):
"""Implements the Xavier initializer
This class implements the Xavier weight initializer from the paper
Understanding the difficulty of training deep feedforward neural
networks[1] by Xavier Glorot and Yoshua Bengio.
This initializer is designed to keep the scale of the gradients
approximately same in all the layers. In case of Uniform distribution,
the range is [-x, x], where x = sqrt(6 / (fan_in + fan_out)).
In case of Normal distribution, the mean is 0 and the standard deviation
is sqrt(2/ (fan_in + fan_out)).
References:
[1] Understanding the difficulty of training deep feedforward neural
networks. International conference on artificial intelligence and
statistics.
(http://proceedings.mlr.press/v9/glorot10a.html)
"""
def __init__(self, uniform=True, fan_in=None, fan_out=None, seed=0):
"""Constructor for XavierInitializer
Args:
uniform: whether to use uniform or normal distribution
fan_in: fan_in for Xavier initialization. If None, it is
inferred from the variable.
fan_out: fan_out for Xavier initialization. If None, it is
inferred from the variable.
seed: random seed
Note: It is recommended to set fan_in and fan_out to None for
most cases.
"""
assert uniform is not None
assert seed is not None
super(XavierInitializer, self).__init__()
self._uniform = uniform
self._fan_in = fan_in
self._fan_out = fan_out
self._seed = seed
def __call__(self, var, block):
"""Add xavier initialization ops for a variable
Args:
var: Variable that needs to be initialized
block: The block in which initialization ops
should be added
Returns:
the initialization op
"""
assert isinstance(var, framework.Variable)
assert isinstance(block, framework.Block)
f_in, f_out = self._compute_fans(var)
# If fan_in and fan_out are passed, use them
fan_in = f_in if self._fan_in is None else self._fan_in
fan_out = f_out if self._fan_out is None else self._fan_out
if self._uniform:
limit = np.sqrt(6.0 / float(fan_in + fan_out))
op = block.prepend_op(
type="uniform_random",
outputs={"Out": var},
attrs={
"shape": var.shape,
"data_type": int(var.data_type),
"min": -limit,
"max": limit,
"seed": self._seed
})
else:
std = np.sqrt(2.0 / float(fan_in + fan_out))
op = block.prepend_op(
type="gaussian_random",
outputs={"Out": var},
attrs={
"shape": var.shape,
"data_type": int(var.data_type),
"mean": 0.0,
"std": std,
"seed": self._seed
})
var.op = op
return op
import numpy as np
import unittest
import paddle.v2.framework.framework as framework
......@@ -116,5 +117,111 @@ class TestNormalInitializer(unittest.TestCase):
self.assertEqual(init_op.attr('seed'), 123)
class TestXavierInitializer(unittest.TestCase):
def test_uniform_xavier_initializer(self):
"""Test Xavier initializer with uniform distribution on
for matrix multiply.
"""
program = framework.Program()
block = program.global_block()
param = block.create_parameter(
dtype="float32",
shape=[5, 10],
lod_level=0,
name="param",
initializer=initializer.XavierInitializer())
self.assertEqual(len(block.ops), 1)
init_op = block.ops[0]
self.assertEqual(init_op.type, 'uniform_random')
limit = np.sqrt(6.0 / (param.shape[0] + param.shape[1]))
self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
self.assertEqual(init_op.attr('seed'), 0)
def test_uniform_xavier_initializer_conv(self):
"""Test Xavier initializer with uniform distribution on
for convolutions.
"""
program = framework.Program()
block = program.global_block()
param = block.create_parameter(
dtype="float32",
shape=[5, 10, 15, 20],
lod_level=0,
name="param",
initializer=initializer.XavierInitializer())
self.assertEqual(len(block.ops), 1)
init_op = block.ops[0]
self.assertEqual(init_op.type, 'uniform_random')
receptive_field_size = float(15 * 20)
limit = np.sqrt(6.0 / (
(param.shape[0] + param.shape[1]) * receptive_field_size))
self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
self.assertEqual(init_op.attr('seed'), 0)
def test_normal_xavier_initializer(self):
"""Test Xavier initializer with normal distribution on
for matrix multiply.
"""
program = framework.Program()
block = program.global_block()
param = block.create_parameter(
dtype="float32",
shape=[5, 10],
lod_level=0,
name="param",
initializer=initializer.XavierInitializer(uniform=False))
self.assertEqual(len(block.ops), 1)
init_op = block.ops[0]
self.assertEqual(init_op.type, 'gaussian_random')
std = np.sqrt(2.0 / (param.shape[0] + param.shape[1]))
self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
self.assertAlmostEqual(init_op.attr('std'), std, delta=DELTA)
self.assertEqual(init_op.attr('seed'), 0)
def test_normal_xavier_initializer_conv(self):
"""Test Xavier initializer with normal distribution on
for convolutions.
"""
program = framework.Program()
block = program.global_block()
param = block.create_parameter(
dtype="float32",
shape=[5, 10, 15, 20],
lod_level=0,
name="param",
initializer=initializer.XavierInitializer(uniform=False))
self.assertEqual(len(block.ops), 1)
init_op = block.ops[0]
self.assertEqual(init_op.type, 'gaussian_random')
receptive_field_size = float(15 * 20)
std = np.sqrt(2.0 / (
(param.shape[0] + param.shape[1]) * receptive_field_size))
self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
self.assertAlmostEqual(init_op.attr('std'), std, delta=DELTA)
self.assertEqual(init_op.attr('seed'), 0)
def test_xavier_initializer_supplied_arguments(self):
"""Test the Xavier initializer with supplied arguments
"""
program = framework.Program()
block = program.global_block()
block.create_parameter(
dtype="float32",
shape=[5, 10],
lod_level=0,
name="param",
initializer=initializer.XavierInitializer(
fan_in=12, fan_out=23, seed=134))
self.assertEqual(len(block.ops), 1)
init_op = block.ops[0]
self.assertEqual(init_op.type, 'uniform_random')
limit = np.sqrt(6.0 / (12 + 23))
self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
self.assertEqual(init_op.attr('seed'), 134)
if __name__ == '__main__':
unittest.main()
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