正文
所有初始化方法定義
1、tf.constant_initializer()
2、tf.truncated_normal_initializer()
3、tf.random_normal_initializer()
4、random_uniform_initializer = RandomUniform()
5、tf.uniform_unit_scaling_initializer()
6、tf.variance_scaling_initializer()
7、tf.orthogonal_initializer()
8、tf.glorot_uniform_initializer()
9、glorot_normal_initializer（）
正文CNN中最重要的就是參數了，包括W,b。 我們訓練CNN的最終目的就是得到最好的參數，使得目標函數取得最小值。參數的初始化也同樣重要，因此微調受到很多人的重視，那麼tf提供了哪些初始化參數的方法呢，我們能不能自己進行初始化呢？
所有初始化方法定義
# 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 thefollowing 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_importfrom __future__ import divisionfrom __future__ import print_functionimport mathfrom tensorflow.python.framework import constant_opfrom tensorflow.python.framework import dtypesfrom tensorflow.python.ops import array_opsfrom tensorflow.python.ops import linalg_opsfrom tensorflow.python.ops import random_opsclass Initializer(object): """Initializer base class: all initializers inherit from this class. """ def __call__(self, shape, dtype=None, partition_info=None): raise NotImplementedErrorclass 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-namezeros_initializer = Zerosones_initializer = Onesconstant_initializer = Constantrandom_uniform_initializer = RandomUniformrandom_normal_initializer = RandomNormaltruncated_normal_initializer = TruncatedNormaluniform_unit_scaling_initializer = UniformUnitScalingvariance_scaling_initializer = VarianceScalingorthogonal_initializer = Orthogonal# pylint: enable=invalid-namedef 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_outdef _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 dtype1、tf.constant_initializer()也可以簡寫為tf.Constant()
初始化為常數，這個非常有用，通常偏置項就是用它初始化的。
由它衍生出的兩個初始化方法：
a、 tf.zeros_initializer()， 也可以簡寫為tf.Zeros()
b、tf.ones_initializer(), 也可以簡寫為tf.Ones()
例：在卷積層中，將偏置項b初始化為0，則有多種寫法：
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), )或者：
bias_initializer=tf.constant_initializer(0)或者：
bias_initializer=tf.zeros_initializer()或者：
bias_initializer=tf.Zeros()例：如何將W初始化成拉普拉斯算子？
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)2、tf.truncated_normal_initializer()或者簡寫為tf.TruncatedNormal()
生成截斷正態分布的隨機數，這個初始化方法好像在tf中用得比較多。
它有四個參數（mean=0.0, stddev=1.0, seed=None, dtype=dtypes.float32)，分別用於指定均值、標准差、隨機數種子和隨機數的數據類型，一般只需要設置stddev這一個參數就可以了。
例：
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), )或者：
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(), )3、tf.random_normal_initializer()可簡寫為 tf.RandomNormal()
生成標准正態分布的隨機數，參數和truncated_normal_initializer一樣。
4、random_uniform_initializer = RandomUniform()可簡寫為tf.RandomUniform()
生成均勻分布的隨機數，參數有四個（minval=0, maxval=None, seed=None, dtype=dtypes.float32)，分別用於指定最小值，最大值，隨機數種子和類型。
5、tf.uniform_unit_scaling_initializer()可簡寫為tf.UniformUnitScaling()
和均勻分布差不多，只是這個初始化方法不需要指定最小最大值，是通過計算出來的。參數為（factor=1.0, seed=None, dtype=dtypes.float32)
max_val = math.sqrt(3 / input_size) * factor這裡的input_size是指輸入數據的維數，假設輸入為x, 運算為x * W，則input_size= W.shape[0]
它的分布區間為[ -max_val, max_val]
6、tf.variance_scaling_initializer()可簡寫為tf.VarianceScaling()
參數為（scale=1.0,mode="fan_in",distribution="normal",seed=None，dtype=dtypes.float32)
scale: 縮放尺度（正浮點數）
mode: "fan_in", "fan_out", "fan_avg"中的一個，用於計算標准差stddev的值。
distribution：分布類型，"normal"或“uniform"中的一個。
當 distribution="normal" 的時候，生成truncated normal distribution（截斷正態分布） 的隨機數，其中stddev = sqrt(scale / n) ，n的計算與mode參數有關。
如果mode = "fan_in"， n為輸入單元的結點數；
如果mode = "fan_out"，n為輸出單元的結點數；
如果mode = "fan_avg",n為輸入和輸出單元結點數的平均值。
當distribution="uniform”的時候 ，生成均勻分布的隨機數，假設分布區間為[-limit, limit]，則
7、tf.orthogonal_initializer()limit = sqrt(3 * scale / n)
簡寫為tf.Orthogonal()
生成正交矩陣的隨機數。
當需要生成的參數是2維時，這個正交矩陣是由均勻分布的隨機數矩陣經過SVD分解而來。
8、tf.glorot_uniform_initializer()也稱之為Xavier uniform initializer，由一個均勻分布（uniform distribution)來初始化數據。
假設均勻分布的區間是[-limit, limit],則
limit=sqrt(6 / (fan_in + fan_out))
其中的fan_in和fan_out分別表示輸入單元的結點數和輸出單元的結點數。
9、glorot_normal_initializer（）也稱之為 Xavier normal initializer. 由一個 truncated normal distribution來初始化數據.
stddev = sqrt(2 / (fan_in + fan_out))
其中的fan_in和fan_out分別表示輸入單元的結點數和輸出單元的結點數。
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