# Copyright 2015 The TensorFlow 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.
# ==============================================================================
"""Operations often used for initializing tensors.
All variable initializers returned by functions in this file should have the
following signature:
def _initializer(shape, dtype=dtypes.float32, partition_info=None):
Args:
shape: List of `int` representing the shape of the output `Tensor`. Some
initializers may also be able to accept a `Tensor`.
dtype: (Optional) Type of the output `Tensor`.
partition_info: (Optional) variable_scope._PartitionInfo object holding
additional information about how the variable is partitioned. May be
`None` if the variable is not partitioned.
Returns:
A `Tensor` of type `dtype` and `shape`.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import linalg_ops
from tensorflow.python.ops import random_ops
class Initializer(object):
"""Initializer base class: all initializers inherit from this class.
"""
def __call__(self, shape, dtype=None, partition_info=None):
raise NotImplementedError
class Zeros(Initializer):
"""Initializer that generates tensors initialized to 0."""
def __init__(self, dtype=dtypes.float32):
self.dtype = dtype
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
return constant_op.constant(False if dtype is dtypes.bool else 0,
dtype=dtype, shape=shape)
class Ones(Initializer):
"""Initializer that generates tensors initialized to 1."""
def __init__(self, dtype=dtypes.float32):
self.dtype = dtype
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
return constant_op.constant(1, dtype=dtype, shape=shape)
class Constant(Initializer):
"""Initializer that generates tensors with constant values.
The resulting tensor is populated with values of type `dtype`, as
specified by arguments `value` following the desired `shape` of the
new tensor (see examples below).
The argument `value` can be a constant value, or a list of values of type
`dtype`. If `value` is a list, then the length of the list must be less
than or equal to the number of elements implied by the desired shape of the
tensor. In the case where the total number of elements in `value` is less
than the number of elements required by the tensor shape, the last element
in `value` will be used to fill the remaining entries. If the total number of
elements in `value` is greater than the number of elements required by the
tensor shape, the initializer will raise a `ValueError`.
Args:
value: A Python scalar, list of values, or a N-dimensional numpy array. All
elements of the initialized variable will be set to the corresponding
value in the `value` argument.
dtype: The data type.
verify_shape: Boolean that enables verification of the shape of `value`. If
`True`, the initializer will throw an error if the shape of `value` is not
compatible with the shape of the initialized tensor.
Examples:
The following example can be rewritten using a numpy.ndarray instead
of the `value` list, even reshaped, as shown in the two commented lines
below the `value` list initialization.
```python
>>> import numpy as np
>>> import tensorflow as tf
>>> value = [0, 1, 2, 3, 4, 5, 6, 7]
>>> # value = np.array(value)
>>> # value = value.reshape([2, 4])
>>> init = tf.constant_initializer(value)
>>> print('fitting shape:')
>>> with tf.Session():
>>> x = tf.get_variable('x', shape=[2, 4], initializer=init)
>>> x.initializer.run()
>>> print(x.eval())
fitting shape:
[[ 0. 1. 2. 3.]
[ 4. 5. 6. 7.]]
>>> print('larger shape:')
>>> with tf.Session():
>>> x = tf.get_variable('x', shape=[3, 4], initializer=init)
>>> x.initializer.run()
>>> print(x.eval())
larger shape:
[[ 0. 1. 2. 3.]
[ 4. 5. 6. 7.]
[ 7. 7. 7. 7.]]
>>> print('smaller shape:')
>>> with tf.Session():
>>> x = tf.get_variable('x', shape=[2, 3], initializer=init)
ValueError: Too many elements provided. Needed at most 6, but received 8
>>> print('shape verification:')
>>> init_verify = tf.constant_initializer(value, verify_shape=True)
>>> with tf.Session():
>>> x = tf.get_variable('x', shape=[3, 4], initializer=init_verify)
TypeError: Expected Tensor's shape: (3, 4), got (8,).
```
"""
def __init__(self, value=0, dtype=dtypes.float32, verify_shape=False):
self.value = value
self.dtype = dtype
self.verify_shape = verify_shape
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
return constant_op.constant(self.value, dtype=dtype, shape=shape,
verify_shape=self.verify_shape)
class RandomUniform(Initializer):
"""Initializer that generates tensors with a uniform distribution.
Args:
minval: A python scalar or a scalar tensor. Lower bound of the range
of random values to generate.
maxval: A python scalar or a scalar tensor. Upper bound of the range
of random values to generate. Defaults to 1 for float types.
seed: A Python integer. Used to create random seeds. See
@{tf.set_random_seed}
for behavior.
dtype: The data type.
"""
def __init__(self, minval=0, maxval=None, seed=None, dtype=dtypes.float32):
self.minval = minval
self.maxval = maxval
self.seed = seed
self.dtype = dtype
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
return random_ops.random_uniform(shape, self.minval, self.maxval,
dtype, seed=self.seed)
class RandomNormal(Initializer):
"""Initializer that generates tensors with a normal distribution.
Args:
mean: a python scalar or a scalar tensor. Mean of the random values
to generate.
stddev: a python scalar or a scalar tensor. Standard deviation of the
random values to generate.
seed: A Python integer. Used to create random seeds. See
@{tf.set_random_seed}
for behavior.
dtype: The data type. Only floating point types are supported.
"""
def __init__(self, mean=0.0, stddev=1.0, seed=None, dtype=dtypes.float32):
self.mean = mean
self.stddev = stddev
self.seed = seed
self.dtype = _assert_float_dtype(dtype)
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
return random_ops.random_normal(shape, self.mean, self.stddev,
dtype, seed=self.seed)
class TruncatedNormal(Initializer):
"""Initializer that generates a truncated normal distribution.
These values are similar to values from a `random_normal_initializer`
except that values more than two standard deviations from the mean
are discarded and re-drawn. This is the recommended initializer for
neural network weights and filters.
Args:
mean: a python scalar or a scalar tensor. Mean of the random values
to generate.
stddev: a python scalar or a scalar tensor. Standard deviation of the
random values to generate.
seed: A Python integer. Used to create random seeds. See
@{tf.set_random_seed}
for behavior.
dtype: The data type. Only floating point types are supported.
"""
def __init__(self, mean=0.0, stddev=1.0, seed=None, dtype=dtypes.float32):
self.mean = mean
self.stddev = stddev
self.seed = seed
self.dtype = _assert_float_dtype(dtype)
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
return random_ops.truncated_normal(shape, self.mean, self.stddev,
dtype, seed=self.seed)
class UniformUnitScaling(Initializer):
"""Initializer that generates tensors without scaling variance.
When initializing a deep network, it is in principle advantageous to keep
the scale of the input variance constant, so it does not explode or diminish
by reaching the final layer. If the input is `x` and the operation `x * W`,
and we want to initialize `W` uniformly at random, we need to pick `W` from
[-sqrt(3) / sqrt(dim), sqrt(3) / sqrt(dim)]
to keep the scale intact, where `dim = W.shape[0]` (the size of the input).
A similar calculation for convolutional networks gives an analogous result
with `dim` equal to the product of the first 3 dimensions. When
nonlinearities are present, we need to multiply this by a constant `factor`.
See [Sussillo et al., 2014](https://arxiv.org/abs/1412.6558)
([pdf](http://arxiv.org/pdf/1412.6558.pdf)) for deeper motivation, experiments
and the calculation of constants. In section 2.3 there, the constants were
numerically computed: for a linear layer it's 1.0, relu: ~1.43, tanh: ~1.15.
Args:
factor: Float. A multiplicative factor by which the values will be scaled.
seed: A Python integer. Used to create random seeds. See
@{tf.set_random_seed}
for behavior.
dtype: The data type. Only floating point types are supported.
"""
def __init__(self, factor=1.0, seed=None, dtype=dtypes.float32):
self.factor = factor
self.seed = seed
self.dtype = _assert_float_dtype(dtype)
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
scale_shape = shape
if partition_info is not None:
scale_shape = partition_info.full_shape
input_size = 1.0
# Estimating input size is not possible to do perfectly, but we try.
# The estimate, obtained by multiplying all dimensions but the last one,
# is the right thing for matrix multiply and convolutions (see above).
for dim in scale_shape[:-1]:
input_size *= float(dim)
# Avoid errors when initializing zero-size tensors.
input_size = max(input_size, 1.0)
max_val = math.sqrt(3 / input_size) * self.factor
return random_ops.random_uniform(shape, -max_val, max_val,
dtype, seed=self.seed)
class VarianceScaling(Initializer):
"""Initializer capable of adapting its scale to the shape of weights tensors.
With `distribution="normal"`, samples are drawn from a truncated normal
distribution centered on zero, with `stddev = sqrt(scale / n)`
where n is:
- number of input units in the weight tensor, if mode = "fan_in"
- number of output units, if mode = "fan_out"
- average of the numbers of input and output units, if mode = "fan_avg"
With `distribution="uniform"`, samples are drawn from a uniform distribution
within [-limit, limit], with `limit = sqrt(3 * scale / n)`.
Arguments:
scale: Scaling factor (positive float).
mode: One of "fan_in", "fan_out", "fan_avg".
distribution: Random distribution to use. One of "normal", "uniform".
seed: A Python integer. Used to create random seeds. See
@{tf.set_random_seed}
for behavior.
dtype: The data type. Only floating point types are supported.
Raises:
ValueError: In case of an invalid value for the "scale", mode" or
"distribution" arguments.
"""
def __init__(self, scale=1.0,
mode="fan_in",
distribution="normal",
seed=None,
dtype=dtypes.float32):
if scale <= 0.:
raise ValueError("`scale` must be positive float.")
if mode not in {"fan_in", "fan_out", "fan_avg"}:
raise ValueError("Invalid `mode` argument:", mode)
distribution = distribution.lower()
if distribution not in {"normal", "uniform"}:
raise ValueError("Invalid `distribution` argument:", distribution)
self.scale = scale
self.mode = mode
self.distribution = distribution
self.seed = seed
self.dtype = _assert_float_dtype(dtype)
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
scale = self.scale
scale_shape = shape
if partition_info is not None:
scale_shape = partition_info.full_shape
fan_in, fan_out = _compute_fans(scale_shape)
if self.mode == "fan_in":
scale /= max(1., fan_in)
elif self.mode == "fan_out":
scale /= max(1., fan_out)
else:
scale /= max(1., (fan_in + fan_out) / 2.)
if self.distribution == "normal":
stddev = math.sqrt(scale)
return random_ops.truncated_normal(shape, 0.0, stddev,
dtype, seed=self.seed)
else:
limit = math.sqrt(3.0 * scale)
return random_ops.random_uniform(shape, -limit, limit,
dtype, seed=self.seed)
class Orthogonal(Initializer):
"""Initializer that generates an orthogonal matrix.
If the shape of the tensor to initialize is two-dimensional, i is initialized
with an orthogonal matrix obtained from the singular value decomposition of a
matrix of uniform random numbers.
If the shape of the tensor to initialize is more than two-dimensional,
a matrix of shape `(shape[0] * ... * shape[n - 2], shape[n - 1])`
is initialized, where `n` is the length of the shape vector.
The matrix is subsequently reshaped to give a tensor of the desired shape.
Args:
gain: multiplicative factor to apply to the orthogonal matrix
dtype: The type of the output.
seed: A Python integer. Used to create random seeds. See
@{tf.set_random_seed}
for behavior.
"""
def __init__(self, gain=1.0, dtype=dtypes.float32, seed=None):
self.gain = gain
self.dtype = _assert_float_dtype(dtype)
self.seed = seed
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
# Check the shape
if len(shape) < 2:
raise ValueError("The tensor to initialize must be "
"at least two-dimensional")
# Flatten the input shape with the last dimension remaining
# its original shape so it works for conv2d
num_rows = 1
for dim in shape[:-1]:
num_rows *= dim
num_cols = shape[-1]
flat_shape = (num_rows, num_cols)
# Generate a random matrix
a = random_ops.random_uniform(flat_shape, dtype=dtype, seed=self.seed)
# Compute the svd
_, u, v = linalg_ops.svd(a, full_matrices=False)
# Pick the appropriate singular value decomposition
if num_rows > num_cols:
q = u
else:
# Tensorflow departs from numpy conventions
# such that we need to transpose axes here
q = array_ops.transpose(v)
return self.gain * array_ops.reshape(q, shape)
# Aliases.
# pylint: disable=invalid-name
zeros_initializer = Zeros
ones_initializer = Ones
constant_initializer = Constant
random_uniform_initializer = RandomUniform
random_normal_initializer = RandomNormal
truncated_normal_initializer = TruncatedNormal
uniform_unit_scaling_initializer = UniformUnitScaling
variance_scaling_initializer = VarianceScaling
orthogonal_initializer = Orthogonal
# pylint: enable=invalid-name
def glorot_uniform_initializer(seed=None, dtype=dtypes.float32):
"""The Glorot uniform initializer, also called Xavier uniform initializer.
It draws samples from a uniform distribution within [-limit, limit]
where `limit` is `sqrt(6 / (fan_in + fan_out))`
where `fan_in` is the number of input units in the weight tensor
and `fan_out` is the number of output units in the weight tensor.
Reference: http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf
Arguments:
seed: A Python integer. Used to create random seeds. See
@{tf.set_random_seed}
for behavior.
dtype: The data type. Only floating point types are supported.
Returns:
An initializer.
"""
return variance_scaling_initializer(scale=1.0,
mode="fan_avg",
distribution="uniform",
seed=seed,
dtype=dtype)
def glorot_normal_initializer(seed=None, dtype=dtypes.float32):
"""The Glorot normal initializer, also called Xavier normal initializer.
It draws samples from a truncated normal distribution centered on 0
with `stddev = sqrt(2 / (fan_in + fan_out))`
where `fan_in` is the number of input units in the weight tensor
and `fan_out` is the number of output units in the weight tensor.
Reference: http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf
Arguments:
seed: A Python integer. Used to create random seeds. See
@{tf.set_random_seed}
for behavior.
dtype: The data type. Only floating point types are supported.
Returns:
An initializer.
"""
return variance_scaling_initializer(scale=1.0,
mode="fan_avg",
distribution="normal",
seed=seed,
dtype=dtype)
# Utility functions.
def _compute_fans(shape):
"""Computes the number of input and output units for a weight shape.
Arguments:
shape: Integer shape tuple or TF tensor shape.
Returns:
A tuple of scalars (fan_in, fan_out).
"""
if len(shape) < 1: # Just to avoid errors for constants.
fan_in = fan_out = 1
elif len(shape) == 1:
fan_in = fan_out = shape[0]
elif len(shape) == 2:
fan_in = shape[0]
fan_out = shape[1]
else:
# Assuming convolution kernels (2D, 3D, or more).
# kernel shape: (..., input_depth, depth)
receptive_field_size = 1.
for dim in shape[:-2]:
receptive_field_size *= dim
fan_in = shape[-2] * receptive_field_size
fan_out = shape[-1] * receptive_field_size
return fan_in, fan_out
def _assert_float_dtype(dtype):
"""Validate and return floating point type based on `dtype`.
`dtype` must be a floating point type.
Args:
dtype: The data type to validate.
Returns:
Validated type.
Raises:
ValueError: if `dtype` is not a floating point type.
"""
if not dtype.is_floating:
raise ValueError("Expected floating point type, got %s." % dtype)
return dtype는 tf.Constant()
로 축약될 수도 있습니다. 이는 매우 유용하며 오프셋 항은 일반적으로 이를 사용하여 초기화됩니다.
두 가지 초기화 방법이 파생되었습니다:
a, tf.zeros_initializer()(tf.Zeros()로 축약될 수도 있음)
b, tf.ones_initializer()(tf.Ones로 축약할 수도 있음) ( )
예: 컨벌루션 레이어에서 바이어스 항 b를 0으로 초기화합니다. 이를 작성하는 방법은 다양합니다:
conv1 = tf.layers.conv2d(batch_images,
filters=64,
kernel_size=7,
strides=2,
activation=tf.nn.relu,
kernel_initializer=tf.TruncatedNormal(stddev=0.01)
bias_initializer=tf.Constant(0),
)또는:
bias_initializer=tf.constant_initializer(0)
또는:
bias_initializer=tf.zeros_initializer()
또는:
bias_initializer=tf.Zeros()
예: 방법 W 초기화를 Laplacian으로 작성하시겠습니까?
value = [1, 1, 1, 1, -8, 1, 1, 1，1] init = tf.constant_initializer(value) W= tf.get_variable('W', shape=[3, 3], initializer=init)
또는 tf.TruncatedNormal()
로 줄여서 이 초기화 방법은 tf에서 더 많이 사용되는 것 같습니다.
평균, 표준 편차, 난수 시드 및 난수 데이터 유형을 각각 지정하는 데 사용되는 4개의 매개변수(평균=0.0, stddev=1.0, 시드=None, dtype=dtypes.float32)가 있습니다. stddev 매개변수만 설정하면 충분합니다.
예:
conv1 = tf.layers.conv2d(batch_images,
filters=64,
kernel_size=7,
strides=2,
activation=tf.nn.relu,
kernel_initializer=tf.TruncatedNormal(stddev=0.01)
bias_initializer=tf.Constant(0),
)또는:
conv1 = tf.layers.conv2d(batch_images,
filters=64,
kernel_size=7,
strides=2,
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01)
bias_initializer=tf.zero_initializer(),
)는 tf.RandomNormal()
으로 축약될 수 있습니다. 표준 정규 분포에서 난수를 생성합니다. 매개변수는 truncated_normal_initializer와 동일합니다.
은 tf.RandomUniform()
으로 축약될 수 있습니다. 4개의 매개변수(minval=0, maxval=None, seed=None, dtype=dtypes.float32)를 사용하여 균일하게 분포된 난수를 생성합니다. 최소값, 최대값, 난수 시드 및 유형을 각각 지정하는 데 사용됩니다.
는 tf.UniformUnitScaling()
으로 축약될 수 있습니다. 이 초기화 방법은 최소값과 최대값을 지정할 필요가 없으며 계산된다는 점만 다릅니다. 매개변수는 (factor=1.0, Seed=None, dtype=dtypes.float32)
max_val = math.sqrt(3 / input_size) * factor
여기서 input_size는 입력 데이터의 차원을 나타냅니다. 입력이 x이고 연산이 x * W라고 가정하면 input_size=입니다. W.shape[0 ]
분포 간격은 [-max_val, max_val]
는 tf.VarianceScaling()
으로 축약될 수 있습니다. 매개변수는 (scale=1.0, mode) ="fan_in", distribution ="normal",seed=None, dtype=dtypes.float32)
scale: 스케일링 스케일(양의 부동 소수점 수)scale: 缩放尺度（正浮点数）
mode: "fan_in", "fan_out", "fan_avg"中的一个，用于计算标准差stddev的值。
distribution
모드: "fan_in", " "fan_out"과 "fan_avg" 중 하나이며 표준편차 stddev 값을 계산하는 데 사용됩니다.
배포: 배포 유형은 "일반" 또는 "균일" 중 하나입니다. 은 직교 행렬의 난수를 생성합니다. 생성할 매개변수가 2차원인 경우 이 직교 행렬은 균일하게 분포된 난수 행렬에서 SVD로 분해됩니다. 8. tf.glorot_uniform_initializer()는 균일한 분포로 데이터를 초기화하는 Xavier 균일 초기화라고도 합니다. 등분포 간격을 [-limit,limit]라고 가정하면약어로 tf.Orthogonal()
limit=sqrt(6/(fan_in + fan_out))
여기서 fan_in과 fan_out은 입력 장치의 노드 수를 나타내고 각각의 출력 단위 수 노드 수입니다.라고도 합니다. 입력 장치의 노드 수와 출력 장치의 노드 수를 나타냅니다.9.glorot_normal_initializer()
위 내용은 Python 딥 러닝 텐서플로우 매개변수 초기화 초기화 방법의 상세 내용입니다. 자세한 내용은 PHP 중국어 웹사이트의 기타 관련 기사를 참조하세요!
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