utils.set_seed(seed) 什么意思

这段代码的作用是设置随机数种子，以确保程序每次运行的结果都是一致的。在深度学习中，许多操作都会使用随机数，如权重的初始化、数据的打乱等。如果每次运行时随机数不一致，那么模型的训练结果也会有所不同，导致难以进行复现和比较。因此，通过设置随机数种子，可以使得每次运行时随机数相同，从而保证模型训练结果的一致性。在代码中，`seed` 是一个整数类型的参数，表示要设置的随机数种子。

相关问题

self.env_wrapper.set_random_seed(seed*(n_agent+1)) File "D:\LearningData\graduate student\edge computing\experiment\D4PG-master\utils\env_wrapper.py", line 44, in set_random_seed self.env.seed(seed) TypeError: seed() takes 1 positional argument but 2 were given

同样的错误提示，说明在调用`seed()`函数时传递了多余的参数。根据错误信息，`self.env.seed()`函数只需要传递一个参数，但是您传递了`seed*(n_agent+1)`这个表达式，其中包含了两个参数。

您可以将`seed*(n_agent+1)`这个表达式简化为一个单独的参数，例如`int(seed*(n_agent+1))`。然后将其作为`self.env.seed()`函数的参数传递，就可以解决这个错误了。如果这个方法还是出现错误，您可以检查一下`env_wrapper.py`文件中`self.env`的定义，看看它需要传递几个参数，以及`seed()`函数的定义，看看它需要传递几个参数。找到问题所在，然后修改相关代码即可。

import numpy as np import tensorflow as tf from tensorflow import keras import matplotlib.pyplot as plt ## Let us define a plt function for simplicity def plt_loss(x,training_metric,testing_metric,ax,colors = ['b']): ax.plot(x,training_metric,'b',label = 'Train') ax.plot(x,testing_metric,'k',label = 'Test') ax.set_xlabel('Epochs') ax.set_ylabel('Accuarcy')# ax.set_ylabel('Categorical Crossentropy Loss') plt.legend() plt.grid() plt.show() tf.keras.utils.set_random_seed(1) ## We import the Minist Dataset using Keras.datasets (train_data, train_labels), (test_data, test_labels) = keras.datasets.mnist.load_data() ## We first vectorize the image (28*28) into a vector （784） train_data = train_data.reshape(train_data.shape[0],train_data.shape[1]*train_data.shape[2]) # 60000*784 test_data = test_data.reshape(test_data.shape[0],test_data.shape[1]*test_data.shape[2]) # 10000*784 ## We next change label number to a 10 dimensional vector, e.g., 1->[0,1,0,0,0,0,0,0,0,0] train_labels = keras.utils.to_categorical(train_labels,10) test_labels = keras.utils.to_categorical(test_labels,10) ## start to build a MLP model N_batch_size = 5000 N_epochs = 100 lr = 0.01 # ## we build a three layer model, 784 -> 64 -> 10 MLP_3 = keras.models.Sequential([ keras.layers.Dense(64, input_shape=(784,),activation='relu'), keras.layers.Dense(10,activation='softmax') ]) MLP_3.compile( optimizer=keras.optimizers.Adam(lr), loss= 'categorical_crossentropy', metrics = ['accuracy'] ) History = MLP_3.fit(train_data,train_labels, batch_size = N_batch_size, epochs = N_epochs,validation_data=(test_data,test_labels), shuffle=False) train_acc = History.history['accuracy'] test_acc = History.history['val_accuracy']模仿此段代码，写一个双隐层感知器（输入层784，第一隐层128，第二隐层64，输出层10）

import numpy as np
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt

## Let us define a plt function for simplicity
def plt_loss(x,training_metric,testing_metric,ax,colors = ['b']):
ax.plot(x,training_metric,'b',label = 'Train')
ax.plot(x,testing_metric,'k',label = 'Test')
ax.set_xlabel('Epochs')
ax.set_ylabel('Accuracy')
plt.legend()
plt.grid()
plt.show()

tf.keras.utils.set_random_seed(1)

## We import the Minist Dataset using Keras.datasets
(train_data, train_labels), (test_data, test_labels) = keras.datasets.mnist.load_data()

## We first vectorize the image (28*28) into a vector （784）
train_data = train_data.reshape(train_data.shape[0],train_data.shape[1]*train_data.shape[2]) # 60000*784
test_data = test_data.reshape(test_data.shape[0],test_data.shape[1]*test_data.shape[2]) # 10000*784

## We next change label number to a 10 dimensional vector, e.g., 1->[0,1,0,0,0,0,0,0,0,0]
train_labels = keras.utils.to_categorical(train_labels,10)
test_labels = keras.utils.to_categorical(test_labels,10)

## start to build a MLP model
N_batch_size = 5000
N_epochs = 100
lr = 0.01

## we build a three layer model, 784 -> 64 -> 10
MLP_3 = keras.models.Sequential([
keras.layers.Dense(128, input_shape=(784,),activation='relu'),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dense(10,activation='softmax')
])

MLP_3.compile(
optimizer=keras.optimizers.Adam(lr),
loss= 'categorical_crossentropy',
metrics = ['accuracy']
)

History = MLP_3.fit(train_data,train_labels, batch_size = N_batch_size, epochs = N_epochs,validation_data=(test_data,test_labels), shuffle=False)

train_acc = History.history['accuracy']
test_acc = History.history['val_accuracy']