我已经加载了经过训练的检查点文件以进行推理。我已经从模型中提取了贝塔值、移动均值和移动方差以及所有权重。在批量标准化中，当我手动计算 batch_normalization 的输出时，我得到了错误的结果。
[已更新]
在这里，我分享我的代码，它加载检查点，打印批量归一化的输入，打印 beta，移动均值和移动方差，并在控制台上打印批量归一化的输出。
import tensorflow as tf
import cv2
import numpy as np
import time
import os
def main():
with tf.Session() as sess:
#[INFO] code for loading checkpoint
#---------------------------------------------------------------------
saver = tf.train.import_meta_graph("./bag-model-34000.meta")
saver.restore(sess, tf.train.latest_checkpoint("./"))
graph = tf.get_default_graph()
input_place = graph.get_tensor_by_name('input/image_input:0')
op = graph.get_tensor_by_name('output/image_output:0')
#----------------------------------------------------------------------
#[INFO] generating input data which is equal to input tensor shape
#----------------------------------------------------------------------
input_data = np.random.randint(255, size=(1,320,240, 3)).astype(float)
#----------------------------------------------------------------------
#[INFO] code to get all tensors_name
#----------------------------------------------------------------------
operations = sess.graph.get_operations()
ind = 0;
tens_name = [] # store all tensor name in list
for operation in operations:
#print(ind,"> ", operation.name, "=> \n", operation.values())
if (operation.values()):
name_of_tensor = str(operation.values()).split()[1][1:-1]
tens_name.append(name_of_tensor)
ind = ind + 1
#------------------------------------------------------------------------
#[INFO] printing Input to batch normalization, beta, moving mean and moving variance
# so I can calculate manually batch normalization output
#------------------------------------------------------------------------
tensor_number = 0
for tname in tens_name: # looping through each tensor name
if tensor_number <= 812: # I am interested in first 812 tensors
tensor = graph.get_tensor_by_name(tname)
tensor_values = sess.run(tensor, feed_dict={input_place: input_data})
print("tensor: ", tensor_number, ": ", tname, ": \n\t\t", tensor_values.shape)
# [INFO] 28'th tensor its name is "input/conv1/conv1_1/separable_conv2d:0"
# the output of this tensor is input to the batch normalization
if tensor_number == 28:
# here I am printing this tensor output
print(tensor_values) # [[[[-0.03182551 0.00226904 0.00440771 ...
print(tensor_values.shape) # (1, 320, 240, 32)
# [INFO] 31'th tensor its name is "conv1/conv1_1/BatchNorm/beta:0"
# the output of this tensor is all beta
if tensor_number == 31:
# here I am printing this beta's
print(tensor_values) # [ 0.04061257 -0.16322449 -0.10942575 ...
print(tensor_values.shape) # (32,)
# [INFO] 35'th tensor its name is "conv1/conv1_1/BatchNorm/moving_mean:0"
# the output of this tensor is all moving mean
if tensor_number == 35:
# here I am printing this moving means
print(tensor_values) # [-0.0013569 0.00618145 0.00248459 ...
print(tensor_values.shape) # (32,)
# [INFO] 39'th tensor its name is "conv1/conv1_1/BatchNorm/moving_variance:0"
# the output of this tensor is all moving_variance
if tensor_number == 39:
# here I am printing this moving variance
print(tensor_values) # [4.48082483e-06 1.21615967e-05 5.37582537e-06 ...
print(tensor_values.shape) # (32,)
# [INFO] 44'th tensor its name is "input/conv1/conv1_1/BatchNorm/FusedBatchNorm:0"
# here perform batch normalization and here I am printing the output of this tensor
if tensor_number == 44:
# here I am printing the output of this tensor
print(tensor_values) # [[[[-8.45019519e-02 1.23237416e-01 -4.60943699e-01 ...
print(tensor_values.shape) # (1, 320, 240, 32)
tensor_number = tensor_number + 1
#---------------------------------------------------------------------------------------------
if __name__ == "__main__":
main()
因此，从控制台运行上述代码后，我得到了批量归一化的输入，这是“input/conv1/conv1_1/separable_conv2d:0”这个张量的输出。
I am taking the first value from that output as x,
so, input x = -0.03182551
and beta, moving mean and moving variance is also printed on console.
and I am take the first value from each array.
beta = 0.04061257
moving mean = -0.0013569
moving variance = 4.48082483e-06
epsilon = 0.001 ... It is default value
and gamma is ignored. because I set training time as scale = false so gamma is ignored.
When I am calculate the output of batch normalization at inference time for given input x
x_hat = (x - moving_mean) / square_root_of(moving variance + epsilon)
= (-0.03182551 − (-0.0013569)) / √(0.00000448082483 + 0.001)
= −0.961350647
so x_hat is −0.961350647
y = gamma * x_hat + beta
gamma is ignored so equation becomes y = x_hat + beta
= −0.961350647 + 0.04061257
y = −0.920738077
So If I calculated manually y at inference time it gives as y = −0.920738077
but in program it showing y = -8.45019519e-02
It is output of "input/conv1/conv1_1/BatchNorm/FusedBatchNorm:0" tensor.
It is very very different from what I am calculated. Is my equation is wrong? So which modifications
I have to make to above x_hat and y equation so I can get this value.
所以，我很困惑为什么我的计算结果与结果值非常不同？
我还使用 tf.compat.v1.global_variables() 检查了 beta、移动均值和移动方差。所有值都与控制台上打印的 beta、移动均值和移动方差值相匹配。
那么为什么我在等式x_hat和y中手动替换这些值后得到错误的结果？
我还在这里提供我的控制台输出，从tensor_number 28到44...
tensor: 28 : input/conv1/conv1_1/separable_conv2d:0 :
(1, 320, 240, 32)
[[[[-0.03182551 0.00226904 0.00440771 ... -0.01204819 0.02620635
tensor: 29 : input/conv1/conv1_1/BatchNorm/Const:0 :
(32,)
tensor: 30 : conv1/conv1_1/BatchNorm/beta/Initializer/zeros:0 :
(32,)
tensor: 31 : conv1/conv1_1/BatchNorm/beta:0 :
(32,)
[ 0.04061257 -0.16322449 -0.10942575 0.05056419 -0.13785222 0.4060304
tensor: 32 : conv1/conv1_1/BatchNorm/beta/Assign:0 :
(32,)
tensor: 33 : conv1/conv1_1/BatchNorm/beta/read:0 :
(32,)
tensor: 34 : conv1/conv1_1/BatchNorm/moving_mean/Initializer/zeros:0 :
(32,)
tensor: 35 : conv1/conv1_1/BatchNorm/moving_mean:0 :
(32,)
[-0.0013569 0.00618145 0.00248459 0.00340403 0.00600711 0.00291052
tensor: 36 : conv1/conv1_1/BatchNorm/moving_mean/Assign:0 :
(32,)
tensor: 37 : conv1/conv1_1/BatchNorm/moving_mean/read:0 :
(32,)
tensor: 38 : conv1/conv1_1/BatchNorm/moving_variance/Initializer/ones:0 :
(32,)
tensor: 39 : conv1/conv1_1/BatchNorm/moving_variance:0 :
(32,)
[4.48082483e-06 1.21615967e-05 5.37582537e-06 1.40261754e-05
tensor: 40 : conv1/conv1_1/BatchNorm/moving_variance/Assign:0 :
(32,)
tensor: 41 : conv1/conv1_1/BatchNorm/moving_variance/read:0 :
(32,)
tensor: 42 : input/conv1/conv1_1/BatchNorm/Const_1:0 :
(0,)
tensor: 43 : input/conv1/conv1_1/BatchNorm/Const_2:0 :
(0,)
tensor: 44 : input/conv1/conv1_1/BatchNorm/FusedBatchNorm:0 :
(1, 320, 240, 32)
[[[[-8.45019519e-02 1.23237416e-01 -4.60943699e-01 ... 3.77691090e-01
最佳答案
我解决了这个问题，对于批量归一化操作，它认为它正在训练中。
因此，它使用批量均值、批量方差和 beta 为 0，而不是提供移动均值、移动方差和 beta。
因此，我计算了批处理均值、批处理方差，并将这些值代入方程中，现在它给出了正确的输出。
那么如何强制他使用移动均值和移动方差以及提供的贝塔值呢？ 我尝试通过将训练设置为 false 来进行此更改。但它不起作用。
for tname in tens_name: # looping through each tensor name
if tensor_number <= 812: # I am interested in first 812 tensors
training = tf.placeholder(tf.bool, name = 'training')
is_training = tf.placeholder(tf.bool, name = 'is_training')
tensor = graph.get_tensor_by_name(tname)
tensor_values = sess.run(tensor, feed_dict={is_training: False, training: False, input_place: input_data})
在实际代码中 is_training 为 true
def load_cnn(self,keep_prob = 0.5, num_filt = 32, num_layers = 2,is_training=True):
self.reuse=False
with tf.name_scope('input'):
self.image_input=tf.placeholder(tf.float32,shape=[None,None,None,3],name='image_input')
net=self.image_input
with slim.arg_scope([slim.separable_conv2d],
depth_multiplier=1,
normalizer_fn=slim.batch_norm,
normalizer_params={'is_training':is_training},
activation_fn=tf.nn.relu,weights_initializer=tf.truncated_normal_initializer(0.0, 0.01),
weights_regularizer=slim.l2_regularizer(0.0005)):
# Down Scaling
# Block 1
net=slim.repeat(net, 2, slim.separable_conv2d, num_filt, [3, 3], scope = 'conv1')
print('en_conv1',net.shape,net.name) # 320x240x3 -> 316x236x32
self.cnn_layer1=net
#Down Sampling
net=slim.max_pool2d(net,[2,2],scope='pool1')
print('en_maxpool1',net.shape,net.name) # 316x236x32 -> 158x118x32
关于python - tensorflow 推理时的批量归一化，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/59270063/