目录
1 二值神经网络简介
1.1 优势
- 1 二值神经网络简介
- 1.1 优势
- 1.2 论文
- 2 二值神经网络的实现
- 2.1 如何二值化
- 2.2 如何求梯度
- 3 网络结构
- 4 实现
- mnist_train.py文件
- 5 结果分析
- 6 binary_layer.py文件源码附录
- 相关笔记
（1）内存占用 权重矩阵二值化（仅为-1和1），一个权重值只占一个比特，详单与单精度浮点型权重矩阵，网络模型的内存消耗理论上能减少32倍，因此二值神经网络在模型压缩上具有很大的优势 （2）计算速度 当权重值和激活函数值同时进行二值化之后，原来32位浮点型数的乘加运算，可以通过一次异或运算解决，在模型加速上具有很大的潜力。
1.2 论文《Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1》
2 二值神经网络的实现 2.1 如何二值化两种方式，决定式的二值化和随机式的二值化 （1）决定式的二值化 
一个简单的二值化例子：当权重是大于0的，二值化为1，小于0的二值化为-1 
（2）随机式的二值化 
问题：直接对决定式的二值化函数求导，求导厚点值都是0 解决办法：引入l= Htanh(x)函数来支持反向传播，假设损失函数为C，且已知C对q求导，那么C对r的求导如下： 表示【-1 1】区间，l=1，在其他区间l =0，则梯度根据以下公式去求 。 
输入像素是784。因为MNIST数据集的像素值是位于【0 1】之间，就需要做预处理把像素值抓换到【-1 1】区间去适应神经网络
4 实现完整源码下载
mnist_train.py文件加载mnist数据集像素值范围
# convert class vectors to binary class vectors
for i in range(mnist.train.images.shape[0]):
mnist.train.images[i] = mnist.train.images[i] * 2 - 1
for i in range(mnist.test.images.shape[0]):
mnist.test.images[i] = mnist.test.images[i] * 2 - 1
for i in range(mnist.train.labels.shape[0]):
mnist.train.labels[i] = mnist.train.labels[i] * 2 - 1 # -1 or 1 for hinge loss
for i in range(mnist.test.labels.shape[0]):
mnist.test.labels[i] = mnist.test.labels[i] * 2 - 1
神经网络结构
# 添加三层隐藏层，并做dropout
# 输入层dropout
layer0 = no_scale_dropout(x, drop_rate=0.2, training=training)
# 添加一层隐藏层
layer1 = fully_connect_bn(layer0, 4096, act=binary.binary_tanh_unit, use_bias=True, training=training)
layer1_dp = no_scale_dropout(layer1, drop_rate=0.5, training=training)
# 添加一层隐藏层
layer2 = fully_connect_bn(layer1_dp, 4096, act=binary.binary_tanh_unit, use_bias=True, training=training)
layer2_dp = no_scale_dropout(layer2, drop_rate=0.5, training=training)
# 添加一层隐藏层
layer3 = fully_connect_bn(layer2_dp, 4096, act=binary.binary_tanh_unit, use_bias=True, training=training)
layer3_dp = no_scale_dropout(layer3, drop_rate=0.5, training=training)
# 添加输出层
layer4 = fully_connect_bn(layer3_dp, 10, act=None, use_bias=True, training=training)
#out_act_training = tf.nn.softmax_cross_entropy_with_logits(logits=layer4, labels=target)
#out_act_testing = tf.nn.softmax(logits=layer4)
#out_act = tf.cond(training, lambda: out_act_training, lambda: out_act_testing)
# 损失函数（y_label*layer4即对应元素相乘再相加）
loss = tf.reduce_mean(tf.square(tf.maximum(0.,1.-target*layer4)))
Adam优化算法。使用两个优化器：因为将变量分为两部分更新，一部分（权重）使用binary.AdamOptimizer，一部分（偏置,归一化用到的参数等）用tf.train.AdamOptimizer
other_var = [var for var in tf.trainable_variables() if not var.name.endswith('kernel:0')]
opt = binary.AdamOptimizer(binary.get_all_LR_scale(), lr1)
opt2 = tf.train.AdamOptimizer(lr2)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops): # when training, the moving_mean and moving_variance in the BN need to be updated.
#将变量分为两部分更新，一部分（权重）使用binary.AdamOptimizer，一部分（偏置,归一化用到的参数等）用tf.train.AdamOptimizer
train_kernel_op = opt.apply_gradients(binary.compute_grads(loss, opt), global_step=global_step1)
train_other_op = opt2.minimize(loss, var_list=other_var, global_step=global_step2)
根据右下角的图说明二值神经网络需要更多的迭代次数才可以达到全精度网络的准确率。但是根据左下角的图可以看到，在速度上二值神经网络有绝对的优势。 
项目总结：二值神经网络内存占用小很多，训练速度快很多，运行速度快很多，准确率偏低 
代码很长
# coding=UTF-8
import tensorflow as tf
from tensorflow.python.framework import tensor_shape, ops
from tensorflow.python.ops import standard_ops, nn, variable_scope, math_ops, control_flow_ops
from tensorflow.python.eager import context
from tensorflow.python.training import optimizer, training_ops
import numpy as np
# Warning: if you have a @property getter/setter function in a class, must inherit from object class
all_layers = []
def hard_sigmoid(x):
return tf.clip_by_value((x + 1.)/2., 0, 1)
def round_through(x):
'''Element-wise rounding to the closest integer with full gradient propagation.
A trick from [Sergey Ioffe](http://stackoverflow.com/a/36480182)
a op that behave as f(x) in forward mode,
but as g(x) in the backward mode.
'''
rounded = tf.round(x)
return x + tf.stop_gradient(rounded-x)
# The neurons' activations binarization function
# It behaves like the sign function during forward propagation
# And like:
# hard_tanh(x) = 2*hard_sigmoid(x)-1
# during back propagation
def binary_tanh_unit(x):
return 2.*round_through(hard_sigmoid(x))-1.
def binary_sigmoid_unit(x):
return round_through(hard_sigmoid(x))
# The weights' binarization function,
# taken directly from the BinaryConnect github repository
# (which was made available by his authors)
def binarization(W, H, binary=True, deterministic=False, stochastic=False, srng=None):
dim = W.get_shape().as_list()
# (deterministic == True) test-time inference-time
if not binary or (deterministic and stochastic):
# print("not binary")
Wb = W
else:
# [-1,1] -> [0,1]
#Wb = hard_sigmoid(W/H)
# Wb = T.clip(W/H,-1,1)
# Stochastic BinaryConnect
'''
if stochastic:
# print("stoch")
Wb = tf.cast(srng.binomial(n=1, p=Wb, size=tf.shape(Wb)), tf.float32)
'''
# Deterministic BinaryConnect (round to nearest)
#else:
# print("det")
#Wb = tf.round(Wb)
# 0 or 1 -> -1 or 1
#Wb = tf.where(tf.equal(Wb, 1.0), tf.ones_like(W), -tf.ones_like(W)) # cant differential
Wb = H * binary_tanh_unit(W / H)
return Wb
class Dense_BinaryLayer(tf.layers.Dense):
def __init__(self, output_dim,
activation = None,
use_bias = True,
binary = True, stochastic = True, H = 1., W_LR_scale="Glorot",
kernel_initializer = tf.glorot_normal_initializer(),
bias_initializer = tf.zeros_initializer(),
kernel_regularizer = None,
bias_regularizer = None,
activity_regularizer = None,
kernel_constraint = None,
bias_constraint = None,
trainable = True,
name = None,
**kwargs):
super(Dense_BinaryLayer, self).__init__(units = output_dim,
activation = activation,
use_bias = use_bias,
kernel_initializer = kernel_initializer,
bias_initializer = bias_initializer,
kernel_regularizer = kernel_regularizer,
bias_regularizer = bias_regularizer,
activity_regularizer = activity_regularizer,
kernel_constraint = kernel_constraint,
bias_constraint = bias_constraint,
trainable = trainable,
name = name,
**kwargs)
self.binary = binary
self.stochastic = stochastic
self.H = H
self.W_LR_scale = W_LR_scale
all_layers.append(self)
def build(self, input_shape):
num_inputs = tensor_shape.TensorShape(input_shape).as_list()[-1]
num_units = self.units
print(num_units)
if self.H == "Glorot":
self.H = np.float32(np.sqrt(1.5 / (num_inputs + num_units))) # weight init method
self.W_LR_scale = np.float32(1. / np.sqrt(1.5 / (num_inputs + num_units))) # each layer learning rate
print("H = ", self.H)
print("LR scale = ", self.W_LR_scale)
self.kernel_initializer = tf.random_uniform_initializer(-self.H, self.H)
self.kernel_constraint = lambda w: tf.clip_by_value(w, -self.H, self.H)
'''
self.b_kernel = self.add_variable('binary_weight',
shape=[input_shape[-1], self.units],
initializer=self.kernel_initializer,
regularizer=None,
constraint=None,
dtype=self.dtype,
trainable=False) # add_variable must execute before call build()
'''
self.b_kernel = self.add_variable('binary_weight',
shape=[input_shape[-1], self.units],
initializer=tf.random_uniform_initializer(-self.H, self.H),
regularizer=None,
constraint=None,
dtype=self.dtype,
trainable=False)
super(Dense_BinaryLayer, self).build(input_shape)
#tf.add_to_collection('real', self.trainable_variables)
tf.add_to_collection(self.name + '_binary', self.kernel) # layer-wise group
tf.add_to_collection('binary', self.kernel) # global group
def call(self, inputs):
inputs = ops.convert_to_tensor(inputs, dtype=self.dtype)
shape = inputs.get_shape().as_list()
# binarization weight
self.b_kernel = binarization(self.kernel, self.H)
#r_kernel = self.kernel
#self.kernel = self.b_kernel
print("shape: ", len(shape))
if len(shape) > 2:
# Broadcasting is required for the inputs.
outputs = standard_ops.tensordot(inputs, self.b_kernel, [[len(shape) - 1], [0]])
# Reshape the output back to the original ndim of the input.
if context.in_graph_mode():
output_shape = shape[:-1] + [self.units]
outputs.set_shape(output_shape)
else:
outputs = standard_ops.matmul(inputs, self.b_kernel)
# restore weight
#self.kernel = r_kernel
if self.use_bias:
outputs = nn.bias_add(outputs, self.bias)
if self.activation is not None:
return self.activation(outputs)
return outputs
# Functional interface for the Dense_BinaryLayer class.
def dense_binary(
inputs, units,
activation=None,
use_bias=True,
binary = True, stochastic = True, H=1., W_LR_scale="Glorot",
kernel_initializer=tf.glorot_normal_initializer(),
bias_initializer=tf.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
reuse=None):
layer = Dense_BinaryLayer(units,
activation=activation,
use_bias=use_bias,
binary = binary, stochastic = stochastic, H = H, W_LR_scale = W_LR_scale,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name,
dtype=inputs.dtype.base_dtype,
_scope=name,
_reuse=reuse)
return layer.apply(inputs)
class Conv2D_BinaryLayer(tf.layers.Conv2D):
'''
__init__(): init variable
conv2d(): Functional interface for the 2D convolution layer.
This layer creates a convolution kernel that is convolved(actually cross-correlated)
with the layer input to produce a tensor of outputs.
apply(): Apply the layer on a input, This simply wraps `self.__call__`
__call__(): Wraps `call` and will be call build(), applying pre- and post-processing steps
call(): The logic of the layer lives here
'''
def __init__(self, kernel_num,
kernel_size,
strides=(1, 1),
padding='valid',
activation=None,
use_bias=True,
binary = True, stochastic = True, H = 1., W_LR_scale = "Glorot",
data_format='channels_last',
dilation_rate=(1, 1),
kernel_initializer=None,
bias_initializer=tf.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
**kwargs):
super(Conv2D_BinaryLayer, self).__init__(filters = kernel_num,
kernel_size = kernel_size,
strides = strides,
padding = padding,
data_format = data_format,
dilation_rate = dilation_rate,
activation = activation,
use_bias = use_bias,
kernel_initializer = kernel_initializer,
bias_initializer = bias_initializer,
kernel_regularizer = kernel_regularizer,
bias_regularizer = bias_regularizer,
activity_regularizer = activity_regularizer,
kernel_constraint = kernel_constraint,
bias_constraint = bias_constraint,
trainable = trainable,
name = name,
**kwargs)
self.binary = binary
self.stochastic = stochastic
self.H = H
self.W_LR_scale = W_LR_scale
all_layers.append(self)
def build(self, input_shape):
num_inputs = np.prod(self.kernel_size) * tensor_shape.TensorShape(input_shape).as_list()[3]
num_units = np.prod(self.kernel_size) * self.filters
if self.H == "Glorot":
self.H = np.float32(np.sqrt(1.5 / (num_inputs + num_units))) # weight init method
self.W_LR_scale = np.float32(1. / np.sqrt(1.5 / (num_inputs + num_units))) # each layer learning rate
print("H = ", self.H)
print("LR scale = ", self.W_LR_scale)
self.kernel_initializer = tf.random_uniform_initializer(-self.H, self.H)
self.kernel_constraint = lambda w: tf.clip_by_value(w, -self.H, self.H)
self.b_kernel = 0 # add_variable must execute before call build()
super(Conv2D_BinaryLayer, self).build(input_shape)
tf.add_to_collection(self.name + '_binary', self.kernel) # layer-wise group
tf.add_to_collection('binary', self.kernel)
def call(self, inputs):
# binarization weight
self.b_kernel = binarization(self.kernel, self.H)
outputs = self._convolution_op(inputs, self.b_kernel)
if self.use_bias:
if self.data_format == 'channels_first':
if self.rank == 1:
# nn.bias_add does not accept a 1D input tensor.
bias = array_ops.reshape(self.bias, (1, self.filters, 1))
outputs += bias
if self.rank == 2:
outputs = nn.bias_add(outputs, self.bias, data_format='NCHW')
if self.rank == 3:
# As of Mar 2017, direct addition is significantly slower than
# bias_add when computing gradients. To use bias_add, we collapse Z
# and Y into a single dimension to obtain a 4D input tensor.
outputs_shape = outputs.shape.as_list()
outputs_4d = array_ops.reshape(outputs,
[outputs_shape[0], outputs_shape[1],
outputs_shape[2] * outputs_shape[3],
outputs_shape[4]])
outputs_4d = nn.bias_add(outputs_4d, self.bias, data_format='NCHW')
outputs = array_ops.reshape(outputs_4d, outputs_shape)
else:
outputs = nn.bias_add(outputs, self.bias, data_format='NHWC')
if self.activation is not None:
return self.activation(outputs)
return outputs
# Functional interface for the Conv2D_BinaryLayer.
def conv2d_binary(inputs,
kernel_num,
kernel_size,
strides = (1, 1),
padding = 'valid',
data_format = 'channels_last',
dilation_rate = (1, 1),
activation = None,
use_bias = True,
binary = True, stochastic = True, H=1., W_LR_scale="Glorot",
kernel_initializer = None,
bias_initializer = tf.zeros_initializer(),
kernel_regularizer = None,
bias_regularizer = None,
activity_regularizer = None,
kernel_constraint = None,
bias_constraint = None,
trainable = True,
name = None,
reuse = None):
layer = Conv2D_BinaryLayer(
kernel_num = kernel_num,
kernel_size = kernel_size,
strides = strides,
padding = padding,
data_format = data_format,
dilation_rate = dilation_rate,
activation = activation,
use_bias = use_bias,
binary = binary, stochastic = stochastic, H = H, W_LR_scale = W_LR_scale,
kernel_initializer = kernel_initializer,
bias_initializer = bias_initializer,
kernel_regularizer = kernel_regularizer,
bias_regularizer = bias_regularizer,
activity_regularizer = activity_regularizer,
kernel_constraint = kernel_constraint,
bias_constraint = bias_constraint,
trainable = trainable,
name = name,
dtype = inputs.dtype.base_dtype,
_reuse = reuse,
_scope = name)
return layer.apply(inputs)
# Not yet binarized
class BatchNormalization(tf.layers.BatchNormalization):
def __init__(self,
axis = -1,
momentum = 0.99,
epsilon = 1e-3,
center = True,
scale = True,
beta_initializer = tf.zeros_initializer(),
gamma_initializer = tf.ones_initializer(),
moving_mean_initializer = tf.zeros_initializer(),
moving_variance_initializer = tf.ones_initializer(),
beta_regularizer = None,
gamma_regularizer = None,
beta_constraint = None,
gamma_constraint = None,
renorm = False,
renorm_clipping = None,
renorm_momentum = 0.99,
fused = None,
trainable = True,
name = None,
**kwargs):
super(BatchNormalization, self).__init__(axis = axis,
momentum = momentum,
epsilon = epsilon,
center = center,
scale = scale,
beta_initializer = beta_initializer,
gamma_initializer = gamma_initializer,
moving_mean_initializer = moving_mean_initializer,
moving_variance_initializer = moving_variance_initializer,
beta_regularizer = beta_regularizer,
gamma_regularizer = gamma_regularizer,
beta_constraint = beta_constraint,
gamma_constraint = gamma_constraint,
renorm = renorm,
renorm_clipping = renorm_clipping,
renorm_momentum = renorm_momentum,
fused = fused,
trainable = trainable,
name = name,
**kwargs)
#all_layers.append(self)
def build(self, input_shape):
super(BatchNormalization, self).build(input_shape)
self.W_LR_scale = np.float32(1.)
# Functional interface for the batch normalization layer.
def batch_normalization(inputs,
axis=-1,
momentum=0.99,
epsilon=1e-3,
center=True,
scale=True,
beta_initializer=tf.zeros_initializer(),
gamma_initializer=tf.ones_initializer(),
moving_mean_initializer=tf.zeros_initializer(),
moving_variance_initializer=tf.ones_initializer(),
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
training=False,
trainable=True,
name=None,
reuse=None,
renorm=False,
renorm_clipping=None,
renorm_momentum=0.99,
fused=None):
layer = BatchNormalization(axis = axis,
momentum = momentum,
epsilon = epsilon,
center = center,
scale = scale,
beta_initializer = beta_initializer,
gamma_initializer = gamma_initializer,
moving_mean_initializer = moving_mean_initializer,
moving_variance_initializer = moving_variance_initializer,
beta_regularizer = beta_regularizer,
gamma_regularizer = gamma_regularizer,
beta_constraint = beta_constraint,
gamma_constraint = gamma_constraint,
renorm = renorm,
renorm_clipping = renorm_clipping,
renorm_momentum = renorm_momentum,
fused = fused,
trainable = trainable,
name = name,
dtype = inputs.dtype.base_dtype,
_reuse = reuse,
_scope = name)
return layer.apply(inputs, training = training)
class AdamOptimizer(optimizer.Optimizer):
"""Optimizer that implements the Adam algorithm.
See [Kingma et al., 2014](http://arxiv.org/abs/1412.6980)
([pdf](http://arxiv.org/pdf/1412.6980.pdf)).
"""
def __init__(self, weight_scale, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8,
use_locking=False, name="Adam"):
super(AdamOptimizer, self).__init__(use_locking, name)
self._lr = learning_rate
self._beta1 = beta1
self._beta2 = beta2
self._epsilon = epsilon
# BNN weight scale factor
self._weight_scale = weight_scale
# Tensor versions of the constructor arguments, created in _prepare().
self._lr_t = None
self._beta1_t = None
self._beta2_t = None
self._epsilon_t = None
# Variables to accumulate the powers of the beta parameters.
# Created in _create_slots when we know the variables to optimize.
self._beta1_power = None
self._beta2_power = None
# Created in SparseApply if needed.
self._updated_lr = None
def _get_beta_accumulators(self):
return self._beta1_power, self._beta2_power
def _non_slot_variables(self):
return self._get_beta_accumulators()
def _create_slots(self, var_list):
first_var = min(var_list, key=lambda x: x.name)
create_new = self._beta1_power is None
if not create_new and context.in_graph_mode():
create_new = (self._beta1_power.graph is not first_var.graph)
if create_new:
with ops.colocate_with(first_var):
self._beta1_power = variable_scope.variable(self._beta1,
name="beta1_power",
trainable=False)
self._beta2_power = variable_scope.variable(self._beta2,
name="beta2_power",
trainable=False)
# Create slots for the first and second moments.
for v in var_list:
self._zeros_slot(v, "m", self._name)
self._zeros_slot(v, "v", self._name)
def _prepare(self):
self._lr_t = ops.convert_to_tensor(self._lr, name="learning_rate")
self._beta1_t = ops.convert_to_tensor(self._beta1, name="beta1")
self._beta2_t = ops.convert_to_tensor(self._beta2, name="beta2")
self._epsilon_t = ops.convert_to_tensor(self._epsilon, name="epsilon")
def _apply_dense(self, grad, var):
m = self.get_slot(var, "m")
v = self.get_slot(var, "v")
# for BNN kernel
# origin version clipping weight method is new_w = old_w + scale*(new_w - old_w)
# and adam update function is new_w = old_w - lr_t * m_t / (sqrt(v_t) + epsilon)
# so subtitute adam function into weight clipping
# new_w = old_w - (scale * lr_t * m_t) / (sqrt(v_t) + epsilon)
scale = self._weight_scale[ var.name ] / 4
return training_ops.apply_adam(
var, m, v,
math_ops.cast(self._beta1_power, var.dtype.base_dtype),
math_ops.cast(self._beta2_power, var.dtype.base_dtype),
math_ops.cast(self._lr_t * scale, var.dtype.base_dtype),
math_ops.cast(self._beta1_t, var.dtype.base_dtype),
math_ops.cast(self._beta2_t, var.dtype.base_dtype),
math_ops.cast(self._epsilon_t, var.dtype.base_dtype),
grad, use_locking=self._use_locking).op
def _resource_apply_dense(self, grad, var):
m = self.get_slot(var, "m")
v = self.get_slot(var, "v")
return training_ops.resource_apply_adam(
var.handle, m.handle, v.handle,
math_ops.cast(self._beta1_power, grad.dtype.base_dtype),
math_ops.cast(self._beta2_power, grad.dtype.base_dtype),
math_ops.cast(self._lr_t, grad.dtype.base_dtype),
math_ops.cast(self._beta1_t, grad.dtype.base_dtype),
math_ops.cast(self._beta2_t, grad.dtype.base_dtype),
math_ops.cast(self._epsilon_t, grad.dtype.base_dtype),
grad, use_locking=self._use_locking)
def _apply_sparse_shared(self, grad, var, indices, scatter_add):
beta1_power = math_ops.cast(self._beta1_power, var.dtype.base_dtype)
beta2_power = math_ops.cast(self._beta2_power, var.dtype.base_dtype)
lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype)
beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype)
beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype)
epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype)
lr = (lr_t * math_ops.sqrt(1 - beta2_power) / (1 - beta1_power))
# m_t = beta1 * m + (1 - beta1) * g_t
m = self.get_slot(var, "m")
m_scaled_g_values = grad * (1 - beta1_t)
m_t = state_ops.assign(m, m * beta1_t,
use_locking=self._use_locking)
with ops.control_dependencies([m_t]):
m_t = scatter_add(m, indices, m_scaled_g_values)
# v_t = beta2 * v + (1 - beta2) * (g_t * g_t)
v = self.get_slot(var, "v")
v_scaled_g_values = (grad * grad) * (1 - beta2_t)
v_t = state_ops.assign(v, v * beta2_t, use_locking=self._use_locking)
with ops.control_dependencies([v_t]):
v_t = scatter_add(v, indices, v_scaled_g_values)
v_sqrt = math_ops.sqrt(v_t)
var_update = state_ops.assign_sub(var,
lr * m_t / (v_sqrt + epsilon_t),
use_locking=self._use_locking)
return control_flow_ops.group(*[var_update, m_t, v_t])
def _apply_sparse(self, grad, var):
return self._apply_sparse_shared(
grad.values, var, grad.indices,
lambda x, i, v: state_ops.scatter_add( # pylint: disable=g-long-lambda
x, i, v, use_locking=self._use_locking))
def _resource_scatter_add(self, x, i, v):
with ops.control_dependencies(
[resource_variable_ops.resource_scatter_add(
x.handle, i, v)]):
return x.value()
def _resource_apply_sparse(self, grad, var, indices):
return self._apply_sparse_shared(
grad, var, indices, self._resource_scatter_add)
def _finish(self, update_ops, name_scope):
# Update the power accumulators.
with ops.control_dependencies(update_ops):
with ops.colocate_with(self._beta1_power):
update_beta1 = self._beta1_power.assign(
self._beta1_power * self._beta1_t,
use_locking=self._use_locking)
update_beta2 = self._beta2_power.assign(
self._beta2_power * self._beta2_t,
use_locking=self._use_locking)
return control_flow_ops.group(*update_ops + [update_beta1, update_beta2],
name=name_scope)
def get_all_layers():
return all_layers;
def get_all_LR_scale():
return {layer.kernel.name: layer.W_LR_scale for layer in get_all_layers()}
# This function computes the gradient of the binary weights
def compute_grads(loss, opt):
layers = get_all_layers()
grads_list = []
update_weights = []
for layer in layers:
# refer to self.params[self.W]=set(['binary'])
# The list can optionally be filtered by specifying tags as keyword arguments.
# For example,
#``trainable=True`` will only return trainable parameters, and
#``regularizable=True`` will only return parameters that can be regularized
# function return, e.g. [W, b] for dense layer
params = tf.get_collection(layer.name + "_binary")
if params:
# print(params[0].name)
# theano.grad(cost, wrt) -> d(cost)/d(wrt)
# wrt – with respect to which we want gradients
# http://blog.csdn.net/shouhuxianjian/article/details/46517143
# http://blog.csdn.net/qq_33232071/article/details/52806630
#grad = opt.compute_gradients(loss, layer.b_kernel) # origin version
grad = opt.compute_gradients(loss, params[0]) # modify
print("grad: ", grad)
grads_list.append( grad[0][0] )
update_weights.extend( params )
print(grads_list)
print(update_weights)
return zip(grads_list, update_weights)
以下所有源码以及更详细PDF笔记请在github下载 TensorFolwNotebook-from-Peking-University
- 【北京大学】1 TensorFlow1.x中Python基础知识
- 【北京大学】2 TensorFlow1.x的张量、计算图、会话
- 【北京大学】3 TensorFlow1.x的前向传播推导与实现
- 【北京大学】4 TensorFlow1.x的反向传播推导与实现
- 【北京大学】5 TensorFlow1.x的损失函数和交叉熵举例讲解及实现
- 【北京大学】6 TensorFlow1.x的学习率、滑动平均和正则化实例及实现
- 【北京大学】7 TensorFlow1.x的神经网络模块设计思想举例及实现
- 【北京大学】8 TensorFlow1.x的Mnist数据集实例实现
- 【北京大学】9 TensorFlow1.x的实现自定义Mnist数据集
- 【北京大学】10 TensorFlow1.x的卷积神经网络(CNN)相关基础知识
- 【北京大学】11 TensorFlow1.x的卷积神经网络模型Lenet5实现
- 【北京大学】12 TensorFlow1.x的卷积神经网络模型VGGNet实现
- 【北京大学】13 TensorFlow1.x的项目实战之手写英文体识别OCR技术
- 【北京大学】14 TensorFlow1.x的二值神经网络实现MNIST数据集手写数字识别
- 【北京大学】15 TensorFlow1.x的项目实战之人脸表情识别
- 【北京大学】16 TensorFlow1.x的项目实战之图像风格融合与快速迁移
