說起深度學習,目前流行的主要有TensorFlow和Pytorch。其中TensorFlow目前主要應用於工業界,Pytorch在學術界用的比較多。TensorFlow目前正在向2.0轉型,由於2.0與1.0差異較大,所以TensorFlow的生態社區目前並不是很友好。而Pytorch的生態社區較爲完善。
在官網上找到Windows下的安裝說明
所以,打開cmd,輸入conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
即可自動安裝。
編譯環境選擇在Vscode中進行,沒有Tab
索引功能,差評~
參考教程爲:《PyTorch深度學習實踐》完結合集
第一個例子,線性模型
import numpy as np
import matplotlib.pyplot as plt
x_data = [1.0, 2.0, 3.0]
y_data = [2.0, 4.0, 6.0]
def forward(x):
return x*w
def loss(x,y):
y_pred = forward(x)
return (y_pred-y)*(y_pred-y)
w_list = []
mse_list = []
for w in np.arange(0.0, 4.1, 0.1):
print("w=",w)
l_sum = 0
for x_val, y_val in zip(x_data,y_data):
y_pred_val = forward(x_val)
loss_val = loss(x_val,y_val)
l_sum += loss_val
print('\t',x_val,y_val,y_pred_val,loss_val)
print('MSE = ', l_sum/3)
w_list.append(w)
mse_list.append(l_sum/3)
plt.plot(w_list,mse_list)
plt.ylabel('Loss')
plt.xlabel('w')
plt.show()
輸出結果爲
代碼含義爲通過不斷遍歷嘗試w值,找到最小的損失值,其中loss函數定義爲 .
下面的代碼實現基於梯度下降的線性迴歸問題。注意顯示曲線的時候,需要將for循環中生成的變量保存在list中。
import numpy as np
import matplotlib.pyplot as plt
x_data = [1.0, 2.0, 3.0]
y_data = [2.0, 4.0, 6.0]
w = 1.0
def forward(x):
return x*w
def cost(xs,ys):
cost = 0
for x,y in zip(xs,ys):
y_pred = forward(x)
cost += (y_pred - y)**2
return cost / len(xs)
def gradient(xs,ys):
grad = 0
for x,y in zip(xs,ys):
grad += 2*x*(x*w - y)
return grad/ len(xs)
print('Predic (before training)',4, forward(4))
epoch_list = []
cost_list = []
for epoch in range(100):
cost_val = cost(x_data,y_data)
grad_val = gradient(x_data,y_data)
w -= 0.01*grad_val
print('Epoch:',epoch,'w=',w,'loss=',cost_val)
epoch_list.append(epoch)
cost_list.append(cost_val)
print('Predic (after training)',4,forward(4))
plt.plot(epoch_list,cost_list)
plt.ylabel('cost')
plt.xlabel('epoch')
plt.show()
示例三:使用反向梯度,此時已經開始import torch,不再使用numpy庫了,所以,梯度函數就不用定義了,一個backward解決問題。
import torch
import matplotlib.pyplot as plt
x_data = [1,2,3]
y_data = [2,4,6]
w = torch.Tensor([1.0])
w.requires_grad = True
def forward(x):
return x*w
def loss(x,y):
y_pred = forward(x)
return (y_pred - y)**2
epoch_list = []
loss_list = []
print("predict (before training)",4,forward(4).item())
for epoch in range(100):
for x,y in zip(x_data,y_data):
l = loss(x,y)
l.backward()
# print('\t grad:',x,y,w.grad.item())
w.data = w.data - 0.01*w.grad.data
w.grad.data.zero_()
epoch_list.append(epoch)
loss_list.append(l.item())
# print("progess:", epoch,l.item())
print("predict (after training)", 4, forward(4).item())
plt.plot(epoch_list,loss_list)
plt.xlabel("epoch")
plt.ylabel("loss")
plt.show()
下面的例子採用torch中的庫直接完成線性化模型的迴歸問題。
import torch
import matplotlib.pyplot as plt
x_data = torch.Tensor([[1.0],[2.0],[3.0]])
y_data = torch.Tensor([[2.0],[4.0],[6.0]])
class LinearModel(torch.nn.Module): #繼承於nn.Module
def __init__(self): #構造函數
super(LinearModel,self).__init__() #調用父類的構造
self.linear = torch.nn.Linear(1,1) #pytorch中的一個類,nn.linear,
#繼承於 Module
# 成員函數 weight,bias
def forward(self,x): #必須叫這個名字 ,父類中有forward這個函數
#這個地方相當於override
y_pred = self.linear(x)
return y_pred
model = LinearModel()
criterion = torch.nn.MSELoss(size_average=False)
optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)
# model.parameter()自動加載權重-all 權重 lr 自動學習率
for epoch in range(1000):
y_pred = model(x_data)
loss = criterion(y_pred,y_data)
print(epoch,loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('w=',model.linear.weight.item())
print('b=',model.linear.bias.item())
x_test = torch.Tensor([[4.0]])
y_test = model(x_test)
print('y_pred=',y_test.data)
下面的代碼表示二分類問題
import torch
import matplotlib.pyplot as plt
import torch.nn.functional as F
import numpy as np
x_data = torch.Tensor([[1.0],[2.0],[3.0]])
y_data = torch.Tensor([[0],[0],[1]])
class LogisticRegressionModel(torch.nn.Module): #繼承於nn.Module
def __init__(self): #構造函數
super(LogisticRegressionModel,self).__init__() #調用父類的構造
self.linear = torch.nn.Linear(1,1) #pytorch中的一個類,nn.linear,
#繼承於 Module
# 成員函數 weight,bias
def forward(self,x): #必須叫這個名字 ,父類中有forward這個函數
#這個地方相當於override
y_pred = torch.sigmoid(self.linear(x))
return y_pred
model = LogisticRegressionModel()
criterion = torch.nn.BCELoss(size_average=False)
optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)
# model.parameter()自動加載權重-all 權重 lr 自動學習率
for epoch in range(1000):
y_pred = model(x_data)
loss = criterion(y_pred,y_data)
print(epoch,loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('w=',model.linear.weight.item())
print('b=',model.linear.bias.item())
x_test = torch.Tensor([[4.0]])
y_test = model(x_test)
print('y_pred=',y_test.data)
x = np.linspace(0,10,200)
x_t = torch.Tensor(x).view((200,1))
y_t = model(x_t)
y = y_t.data.numpy()
plt.plot(x,y)
plt.plot([0,10],[0.5,0.5],c='r')
plt.xlabel('Hours')
plt.ylabel('Probaility of Pass')
plt.grid()
plt.show()
上面的代碼表示的一層神經網絡,下面的代碼增加難度,改成多層神經網絡串聯,所以在模型定義的時候進行了相應的修改。
import torch
import matplotlib.pyplot as plt
import torch.nn.functional as F
import numpy as np
# x_data = torch.Tensor([[1.0],[2.0],[3.0]])
# y_data = torch.Tensor([[0],[0],[1]])
xy = np.loadtxt('diabetes.csv.gz',delimiter=',',dtype=np.float32)
x_data = torch.from_numpy(xy[:,:-1])
y_data = torch.from_numpy(xy[:,[-1]])
class Model(torch.nn.Module): #繼承於nn.Module
def __init__(self): #構造函數
super(Model,self).__init__() #調用父類的構造
self.linear1 = torch.nn.Linear(8,6) #pytorch中的一個類,nn.linear,
#繼承於 Module
# 成員函數 weight,bias
self.linear2 = torch.nn.Linear(6,4)
self.linear3 = torch.nn.Linear(4,1)
self.sigmoid = torch.nn.Sigmoid()
def forward(self,x): #必須叫這個名字 ,父類中有forward這個函數
#這個地方相當於override
x = self.sigmoid(self.linear1(x))
x = self.sigmoid(self.linear2(x))
x = self.sigmoid(self.linear3(x))
# y_pred = torch.sigmoid(self.linear(x))
return x
model = Model()
epoch_list = []
loss_list = []
criterion = torch.nn.BCELoss(size_average=True)
optimizer = torch.optim.SGD(model.parameters(), lr = 0.1)
# model.parameter()自動加載權重-all 權重 lr 自動學習率
for epoch in range(1000):
y_pred = model(x_data)
loss = criterion(y_pred,y_data)
print(epoch,loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
epoch_list.append(epoch)
loss_list.append(loss.item())
plt.plot(epoch_list,loss_list)
# plt.plot([0,10],[0.5,0.5],c='r')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.grid()
plt.show()
顯示結果如下圖所示:
上述例子,在導入數據的時候,採用的方式爲全部數據一次性導入,這對於大的數據集會把內存消耗完。所以可以採用dataloader解決上述問題。示例程序如下:
import torch
import matplotlib.pyplot as plt
import torch.nn.functional as F
import numpy as np
from torch.utils.data import Dataset,DataLoader
class DiabetesDataset(Dataset):
def __init__(self, filepath):
xy = np.loadtxt(filepath, delimiter=',',dtype=np.float32)
self.len = xy.shape[0]
self.x_data = torch.from_numpy(xy[:,:-1])
self.y_data = torch.from_numpy(xy[:,[-1]])
def __getitem__(self, index):
return self.x_data[index], self.y_data[index]
def __len__(self):
return self.len
dataset = DiabetesDataset('diabetes.csv.gz')
train_loader = DataLoader(dataset = dataset,
batch_size = 32,
shuffle = True,
num_workers =2)
class Model(torch.nn.Module): #繼承於nn.Module
def __init__(self): #構造函數
super(Model,self).__init__() #調用父類的構造
self.linear1 = torch.nn.Linear(8,6) #pytorch中的一個類,nn.linear,
#繼承於 Module
# 成員函數 weight,bias
self.linear2 = torch.nn.Linear(6,4)
self.linear3 = torch.nn.Linear(4,1)
self.sigmoid = torch.nn.Sigmoid()
def forward(self,x): #必須叫這個名字 ,父類中有forward這個函數
#這個地方相當於override
x = self.sigmoid(self.linear1(x))
x = self.sigmoid(self.linear2(x))
x = self.sigmoid(self.linear3(x))
# y_pred = torch.sigmoid(self.linear(x))
return x
model = Model()
epoch_list = []
loss_list = []
criterion = torch.nn.BCELoss(reduction='sum')
optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)
# model.parameter()自動加載權重-all 權重 lr 自動學習率
if __name__=='__main__':
for epoch in range(100):
for i,data in enumerate(train_loader,0):
inputs,labels = data
y_pred = model(inputs)
loss = criterion(y_pred,labels)
print(epoch,i,loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
epoch_list.append(epoch)
loss_list.append(loss.item())
plt.plot(epoch_list,loss_list)
# plt.plot([0,10],[0.5,0.5],c='r')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.grid()
plt.show()
上述程序中,存在如下問題
解決方法爲: