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pytorch和tensorflow的爱恨情仇之定义可训练的参数

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pytorch和tensorflow的爱恨情仇之基本数据类型

 

pytorch和tensorflow的爱恨情仇之张量

 

pytorch版本:1.6.0

 

tensorflow版本:1.15.0

 

之前我们就已经了解了pytorch和tensorflow中的变量,本节我们深入了解可训练的参数-变量

 

接下来我们将使用sklearn自带的iris数据集来慢慢品味。

 

1、

 

(1)第一种方式,不使用nn.Module或nn.Sequntial()来建立模型的情况下自定义参数;

 

加载数据集并转换为tensot:

 

import torch
import torch.nn.functional as F
import numpy as np
from sklearn.datasets import load_iris
iris = load_iris()
data=iris.data
target = iris.target

 

data = torch.from_numpy(data).float()  #(150,4)
target = torch.from_numpy(target).long()  #(150,3)
batch_size=data.shape[0]  #设置batchsize的大小就是所有数据
dataset = torch.utils.data.TensorDataset(data, target)      # 设置数据集
train_iter = torch.utils.data.DataLoader(dataset, batch_size, shuffle=True) # 设置获取数据方式

 

自己定义好要训练的参数:

 

classes = 3
input = 4
hidden = 10
w_0 = torch.tensor(np.random.normal(0, 0.01, (input, hidden)), dtype=torch.float)
b_0 = torch.zeros(hidden, dtype=torch.float)
w_1 = torch.tensor(np.random.normal(0, 0.01, (hidden, classes)), dtype=torch.float)
b_1 = torch.zeros(classes, dtype=torch.float)

 

我们可以在定义参数的时候指定requires_grad=True使其为可训练的参数,也可以使用如下方式:

 

params = [w_0, b_0, w_1, b_1]
for param in params:
    param.requires_grad_(requires_grad=True)

 

定义学习率、优化器、损失函数、网络

 

lr = 5
optimizer = None
criterion = torch.nn.CrossEntropyLoss()
epoch = 1000
def sgd(params, lr, batch_size):
    for param in params:
        param.data -= lr * param.grad / batch_size # 注意这里更改param时用的param.data  
def net(x):
    h = torch.matmul(x,w_0)+b_0
    h = F.relu(h)
    output = torch.matmul(h,w_1)+b_1
    #output = F.softmax(output,dim=1)
    return output

 

为了更加清楚参数训练的过程,这里我们不使用pytorch自带的,而是我们自己定义的随机梯度下降。

 

定义训练主函数:

 

def train(net,params,lr,train_iter):
    for i in range(1,epoch+1):
        for x,y in train_iter:
            output = net(x)
            loss = criterion(output,y) 
            # 梯度清零
            if optimizer is not None:
                optimizer.zero_grad()
            elif params is not None and params[0].grad is not None:
                for param in params:
                    param.grad.data.zero_()
            loss.backward()
            if optimizer is None:
                sgd(params, lr, batch_size)
            else:
                optimizer.step()  # “softmax回归的简洁实现”一节将用到
            acc = (output.argmax(dim=1) == y).sum().item() / data.shape[0]
            print("epoch:{:03d} loss:{:.4f} acc:{:.4f}".format(i,loss.item(),acc))
train(net=net,params=params,lr=lr,train_iter=train_iter)

 

从这里我们也可以看到optimizer.zero_grad()和optimizer.step()的作用了,以上便是我们自定义训练参数的完整过程了,看下结果:

 

epoch:994 loss:0.0928 acc:0.9800
epoch:995 loss:0.0927 acc:0.9800
epoch:996 loss:0.0926 acc:0.9800
epoch:997 loss:0.0926 acc:0.9800
epoch:998 loss:0.0925 acc:0.9800
epoch:999 loss:0.0925 acc:0.9800
epoch:1000 loss:0.0924 acc:0.9800

 

(2)使用nn.Sequential()来构建模型,进行参数初始化:

 

导入相应的包并加载数据集:

 

import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
import numpy as np
from sklearn.datasets import load_iris
iris = load_iris()
data=iris.data
target = iris.target

 

转换为pytorch数据格式:

 

data = torch.from_numpy(data).float()
target = torch.from_numpy(target).long()
batch_size=data.shape[0]
dataset = torch.utils.data.TensorDataset(data, target)      # 设置数据集
train_iter = torch.utils.data.DataLoader(dataset, batch_size, shuffle=True) # 设置获取数据方式

 

定义相关超参数:

 

classes = 3
input = 4
hidden = 10
lr = 4
optimizer = None

 

定义网络:

 

net = nn.Sequential(
    nn.Linear(input,hidden),
    nn.ReLU(),
    nn.Linear(hidden,classes),
)

 

参数初始化:

 

for name,param in net.named_parameters(): #使用model.named_parameters()可以获得相应层的名字的参数以及具体值
    if "weight" in name:
        init.normal_(param, mean=0, std=0.01)
    if "bias" in name:
        init.zeros_(param)

 

自定义随机梯度下降优化器:

 

def sgd(params, lr, batch_size):
    for param in params:
        param.data -= lr * param.grad / batch_size # 注意这里更改param时用的param.data

 

训练主循环:

 

epoch = 1000
criterion = torch.nn.CrossEntropyLoss()
def train(net,lr,train_iter):
    for i in range(1,epoch+1):
        for x,y in train_iter:
            output = net(x)
            loss = criterion(output,y) 
            # 梯度清零
            if optimizer is not None:
                optimizer.zero_grad()
            elif net.parameters() is not None:
                for param in net.parameters():
                    if param.grad is not None:
                        param.grad.data.zero_()
            loss.backward()
            if optimizer is None:
                sgd(net.parameters(), lr, batch_size)
            else:
                optimizer.step()  # “softmax回归的简洁实现”一节将用到
            acc = (output.argmax(dim=1) == y).sum().item() / data.shape[0]
            print("epoch:{:03d} loss:{:.4f} acc:{:.4f}".format(i,loss.item(),acc))
    return 
train(net=net,lr=lr,train_iter=train_iter)

 

结果:

 

 

(3) 使用pytorch自带的优化器

 

我们只需要将opyimizer设置为以下即可:

 

optimizer = torch.optim.SGD(net.parameters(), lr=0.05)

 

需要注意的是学习率这里需要设置的比较小一点,和上面设置的有所不同,结果如下:

 

 

(4) 使用nn.Module来构建网络,自定义参数并进行初始化

 

我们只需要修改以下地方即可:

 

class Net(nn.Module):
    def __init__(self,input,hidden,classes):
        super(Net, self).__init__()
        self.input = input
        self.hidden = hidden
        self.classes = classes
        
        self.w0 = nn.Parameter(torch.Tensor(self.input,self.hidden))
        self.b0 = nn.Parameter(torch.Tensor(self.hidden))
        self.w1 = nn.Parameter(torch.Tensor(self.hidden,self.classes))
        self.b1 = nn.Parameter(torch.Tensor(self.classes))
        self.reset_parameters()
        
    def reset_parameters(self):
        nn.init.normal_(self.w0)
        nn.init.constant_(self.b0,0)
        nn.init.normal_(self.w1)
        nn.init.constant_(self.b1,0)
        
        
    def forward(self,x):
        out = torch.matmul(x,self.w0)+self.b0
        out = F.relu(out)
        out = torch.matmul(out,self.w1)+self.b1
        return out
net = Net(input,hidden,classes)
optimizer = torch.optim.SGD(net.parameters(), lr=0.05)

 

结果:

 

 

(5) 使用nn.Module()构建网路,并使用各层中的参数并进行初始化

 

class Net(nn.Module):
    def __init__(self,input,hidden,classes):
        super(Net, self).__init__()
        self.input = input
        self.hidden = hidden
        self.classes = classes
        self.fc1 = nn.Linear(self.input,self.hidden)
        self.fc2 = nn.Linear(self.hidden,self.classes)
        
        for m in self.modules():
            if isinstance(m, nn.Linear):
                nn.init.normal_(m.weight,0,0.01)
                nn.init.constant_(m.bias, 0)      
        
    def forward(self,x):
        out = self.fc1(x)
        out = F.relu(out)
        out = self.fc2(out)
        return out
net = Net(input,hidden,classes)
optimizer = torch.optim.SGD(net.parameters(), lr=0.05)

 

结果:

 

 

PyTorch 中参数的默认初始化在各个层的 reset_parameters() 方法

 

我们看下官方的Linear层的实现:

 

官方Linear层:
class Linear(Module):
    def __init__(self, in_features, out_features, bias=True):
        super(Linear, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.Tensor(out_features, in_features))
        if bias:
            self.bias = Parameter(torch.Tensor(out_features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()
    def reset_parameters(self):
        stdv = 1. / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)
    def forward(self, input):
        return F.linear(input, self.weight, self.bias)
    def extra_repr(self):
        return 'in_features={}, out_features={}, bias={}'.format(
            self.in_features, self.out_features, self.bias is not None
        )

 

(6) 最后我们来看下从网络中获取参数名字和参数值的一些例子

 

我们以这个网络为例:

 

class Net(nn.Module):
    def __init__(self,input,hidden,classes):
        super(Net, self).__init__()
        self.input = input
        self.hidden = hidden
        self.classes = classes
        self.fc1 = nn.Linear(self.input,self.hidden)
        self.fc2 = nn.Linear(self.hidden,self.classes)
        
        for m in self.modules():
            if isinstance(m, nn.Linear):
                nn.init.normal_(m.weight,0,0.01)
                nn.init.constant_(m.bias, 0)      
        
    def forward(self,x):
        out = self.fc1(x)
        out = F.relu(out)
        out = self.fc2(out)
        return out
net = Net(input,hidden,classes)

 

首先是model.state_dict():是一个参数字典,键是参数的名称,值是参数的值:

 

for name,value in net.state_dict().items():
    print(name,value)

 

 

接着是:model.parameters():返回的是一个generator,我们之前也经常使用,通过param.data,param.data.grad来获取参数的值以及梯度

 

for param in net.parameters():
    print(param.data,param.grad)

 

 

接着是model.named_parameters():返回的是一个具名参数,也就是包含了参数的名称

 

for name,param in net.named_parameters():
    print(name,param)

 

 

最后讲下的是self.modules():一般是在网络初始化中使用,返回的是网络中的具体层,我们可以通过其对不同层进行参数初始化,比如nn.Conv2d、nn.Linear等;

 

参考:

 

https://www.cnblogs.com/KaifengGuan/p/12332072.html

 

pytorch学习笔记(十一):pytorch实现多层感知机

 

https://blog.csdn.net/weixin_44058333/article/details/92691656

 

(2)

 

导入相应的包并加载数据:

 

import tensorflow as tf
import numpy as np
from sklearn.datasets import load_iris
from sklearn.preprocessing import OneHotEncoder
iris = load_iris()
data=iris.data
target = iris.target

 

将标签转换为onehot编码:

 

oneHotEncoder = OneHotEncoder(sparse=False)
onehot_target = oneHotEncoder.fit_transform(target.reshape(-1,1))
print(onehot_target)

 

定义超参数以及可训练的参数:

 

input=4
hidden=10
classes=3
w0=tf.Variable(tf.random.normal([input,hidden],stddev=0.01,seed=1))
b0=tf.Variable(tf.zeros([hidden]))
w1=tf.Variable(tf.random.normal([hidden,classes],stddev=0.01,seed=1))
b1=tf.Variable(tf.zeros([classes]))

 

定义计算图中的占位符:

 

x = tf.placeholder(tf.float32,shape=(None,input),name="x-input") #输入数据
y_ = tf.placeholder(tf.float32,shape=(None,classes),name="y-input") #真实标签

 

定义网络、损失函数和优化器:

 

def net(x):
    hid = tf.add(tf.matmul(x,w0),b0)
    hid = tf.nn.relu(hid)
    out = tf.add(tf.matmul(hid,w1),b1)
    out = tf.nn.softmax(out)
    return out
y = net(x)  
cross_entropy = -tf.reduce_mean(y_*tf.log(tf.clip_by_value(y,1e-10,1.0)) \
                    + (1-y_)*tf.log(tf.clip_by_value(1-y,1e-10,1.0)))
optimizer=tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.05).minimize(cross_entropy)

 

训练循环:

 

epoch = 1000
with tf.compat.v1.Session() as sess: #建立会话
    init_op = tf.global_variables_initializer() #初始化参数
    sess.run(init_op)
    for epoch in range(1,epoch+1):
        sess.run(optimizer,feed_dict={x:data,y_:onehot_target}) #传入数据给优化器
        y_pred = sess.run(y,feed_dict={x:data}) #计算输出
        total_cross_entropy = sess.run(cross_entropy,feed_dict={y:y_pred,y_:onehot_target}) #计算交叉熵
        pred = tf.argmax(y_pred,axis = 1) # 取出行中最大值的索引,也就是取出其中概率最大的索引
        
        correct = tf.cast(tf.equal(pred,target),dtype=tf.int32) # 判断与测试集的标签是否相等并且转换bool为int型
        correct = tf.reduce_sum(correct) # 沿着指定维度的和,不指定axis则默认为所有元素的和
        acc = correct.eval() / data.shape[0]
        print("epoch:{} loss:{:.4f} acc:{:.4f}".format(epoch, total_cross_entropy,acc))

 

结果:

 

 

但感觉训练1000个epoch比pytorch慢好多。。

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