1.create a basic nerual network model with pytorch

数据集 Iris UCI Machine Learning Repository

fully connected  

目标:创建从输入层的代码开始，向前移动到隐藏层，最后到输出层

# %%
import torch
import torch.nn as nn
import torch.nn.functional as F# %%
# create a model class that inherits nn.Module 这里是Module 不是model
class Model(nn.Module):#input layer (4 features of the flower) -->#  Hidden layer1 (number of neurons) -->#  H2(n) --> output (3 classed of iris flowers)def __init__(self, in_features = 4, h1 = 8, h2 = 9, out_features = 3):super().__init__() # instantiate out nn.Module 实例化self.fc1 = nn.Linear(in_features= in_features, out_features= h1)self.fc2 = nn.Linear(in_features= h1, out_features= h2)self.out = nn.Linear(in_features= h2, out_features= out_features)# moves everything forward def forward(self, x):# rectified linear unit 修正线性单元 大于0则保留，小于0另其等于0x = F.relu(self.fc1(x))x = F.relu(self.fc2(x))x = self.out(x)return x# %%
# before we turn it on we need to create a manual seed, because networks involve randomization every time.
# say hey start here and then go randomization, then we'll get basically close to the same outputs# pick a manual seed for randomization
torch.manual_seed(seed= 41)
# create an instance of model
model = Model()

2.load data and train nerual network model 

torch.optim

torch.optim — PyTorch 2.2 documentation

1. optimizer.zero_grad()

• 作用: 清零梯度。在训练神经网络时，每次参数更新前，需要将梯度清零。因为如果不清零，梯度会累加到已有的梯度上，这是PyTorch的设计决策，目的是为了处理像RNN这样的网络结构，它们在一个循环中多次计算梯度。

• 原理: PyTorch在进行反向传播（backward）时，会累计梯度，而不是替换掉当前的梯度值。因此，如果不手动清零，梯度值会不断累积，导致训练过程出错。

2. loss.backward()

• 作用: 计算梯度。这一步会根据损失函数对模型参数进行梯度的计算。在神经网络中，损失函数衡量的是模型输出与真实标签之间的差异，通过反向传播算法，可以计算出损失函数关于模型各个参数的梯度。

• 原理: 反向传播是一种有效计算梯度的算法，它首先计算输出层的梯度，然后逆向逐层传播至输入层。这个过程依赖于链式法则，是深度学习训练中的核心。

3. optimizer.step()

• 作用: 更新参数。基于计算出的梯度，更新模型的参数。这一步实际上是在执行优化算法（如SGD、Adam等），根据梯度方向和设定的学习率调整参数值，以减小损失函数的值。

• 原理: 优化器根据梯度下降（或其它优化算法）更新模型参数。梯度指示了损失函数增长最快的方向，因此通过向相反方向调整参数，模型的预测误差会逐渐减小。

• # %%
  import pandas as pd
  import matplotlib.pyplot as plt
  %matplotlib inline# %%
  # url = 'https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/0e7a9b0a5d22642a06d3d5b9bcbad9890c8ee534/iris.csv'
  my_df = pd.read_csv('dataset/iris.csv')# %%
  # change last column from strings to integers
  my_df['species'] = my_df['species'].replace('setosa', 0.0)
  my_df['species'] = my_df['species'].replace('versicolor', 1.0)
  my_df['species'] = my_df['species'].replace('virginica', 2.0)
  my_df# my_df.head()
  # my_df.tail()# %%
  # train test split ，set X,Y    
  X = my_df.drop('species', axis = 1) # 删除指定列
  y = my_df['species']# %%
  #Convert these to numpy arrays
  X = X.values
  y = y.values
  # X# %%
  # train test split
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.2, random_state= 41)# %%
  # convert X features to float tensors
  X_train = torch.FloatTensor(X_train)
  X_test = torch.FloatTensor(X_test)
  #convert y labels to long tensors
  y_train = torch.LongTensor(y_train)
  y_test = torch.LongTensor(y_test)# %%
  # set the criterion of model to measure the error,how far off the predicitons are from the data
  criterion = nn.CrossEntropyLoss()
  # choose Adam optimizer, lr = learing rate (if error does not go down after a bunch of 
  # iterations(epochs), lower our learning rate),学习率越低，学习所需时间越长
  optimizer = torch.optim.Adam(model.parameters(), lr= 0.01)
  # 传进去的参数包括fc1, fc2, out
  # model.parameters# %%
  # train our model
  # epochs? (one run through all the training data in out network )
  epochs = 100
  losses = []
  for i in range(epochs):# go forward and get a predictiony_pred = model.forward(X_train) # get a predicted results#measure the loss/error, gonna be high at firstloss = criterion(y_pred, y_train) # predicted values vs y_train# keep track of our losses#detach()不再跟踪计算图中的梯度信息，numpy(): 这个方法将PyTorch张量转换成NumPy数组。因为NumPy数组在Python科学计算中非常普遍，很多库和函数需要用到NumPy数组作为输入。losses.append(loss.detach().numpy()) #print every 10 epochesif i % 10 == 0:print(f'Epoch: {i} and loss: {loss}')# do some back propagation: take the error rate of forward propagation and feed it back# thru the network to fine tune the weights# optimizer.zero_grad() 清零梯度，为新的梯度计算做准备。# loss.backward() 计算梯度，即对损失函数进行微分，获取参数的梯度。# optimizer.step() 更新参数，根据梯度和学习率调整参数值以最小化损失函数。optimizer.zero_grad()loss.backward()optimizer.step()# %%
  # graph it out
  plt.plot(range(epochs), losses)
  plt.ylabel("loss/error")
  plt.xlabel("Epoch")
  

  

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