基于pytorch的cnn卷积神经网络——代码
李宏毅老师的深度学习课程，讲到CNN,Mark一下。
代码实现：
# -*- encoding: utf-8 -*-
import sys
import csv
import time
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import TensorDataset, DataLoader
def readfile(path):
print("Reading File...")
x_train = []
x_label = []
val_data = []
val_label = []
raw_train = np.genfromtxt(path, delimiter=\',\', dtype=str, skip_header=1)
for i in range(len(raw_train)):
tmp = np.array(raw_train[i, 1].split(\' \')).reshape(1, 48, 48)
if (i % 10 == 0):
val_data.append(tmp)
val_label.append(raw_train[i][0])
else:
x_train.append(tmp)
x_train.append(np.flip(tmp, axis=2)) # simple example of data augmentation
x_label.append(raw_train[i][0])
x_label.append(raw_train[i][0])
x_train = np.array(x_train, dtype=float) / 255.0
val_data = np.array(val_data, dtype=float) / 255.0
x_label = np.array(x_label, dtype=int)
val_label = np.array(val_label, dtype=int)
x_train = torch.FloatTensor(x_train)
val_data = torch.FloatTensor(val_data)
x_label = torch.LongTensor(x_label)
val_label = torch.LongTensor(val_label)
return x_train, x_label, val_data, val_label
x_train, x_label, val_data, val_label = readfile(\'Data/train.csv\') # \'train.csv\'
train_set = TensorDataset(x_train, x_label)
val_set = TensorDataset(val_data, val_label)
batch_size = 256
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=0)
val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False, num_workers=0)
def gaussian_weights_init(m):
classname = m.__class__.__name__
if classname.find(\'Conv\') != -1 and classname.find(\'Conv\') == 0:
m.weight.data.normal_(0.0, 0.02)
class Classifier(nn.Module):
def __init__(self):
super(Classifier, self).__init__()
self.cnn = nn.Sequential(
nn.Conv2d(1, 64, 4, 2, 1), # [64, 24, 24]
nn.BatchNorm2d(64),
nn.LeakyReLU(0.2),
nn.Conv2d(64, 64, 3, 1, 1),
nn.BatchNorm2d(64),
nn.LeakyReLU(0.2),
nn.MaxPool2d(2, 2, 0), # [64, 12, 12]
nn.Conv2d(64, 128, 3, 1, 1),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2),
nn.Conv2d(128, 128, 3, 1, 1),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2),
nn.MaxPool2d(2, 2, 0), # [128, 6, 6]
nn.Conv2d(128, 256, 3, 1, 1),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.2),
nn.Conv2d(256, 256, 3, 1, 1),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.2),
nn.MaxPool2d(2, 2, 0) # [256, 3, 3]
)
self.fc = nn.Sequential(
nn.Linear(256*3*3, 1024),
nn.LeakyReLU(0.2),
nn.Dropout(p=0.5),
nn.Linear(1024, 512),
nn.LeakyReLU(0.2),
nn.Dropout(p=0.5),
nn.Linear(512, 7)
)
self.cnn.apply(gaussian_weights_init)
self.fc.apply(gaussian_weights_init)
def forward(self, x):
out = self.cnn(x)
out = out.view(out.size()[0], -1)
return self.fc(out)
model = Classifier().cuda()
# print(model)
loss = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
best_acc = 0.0
for i, data in enumerate(train_loader):
if i == 0:
print(data[0])
num_epoch = 100
for epoch in range(num_epoch):
epoch_start_time = time.time()
train_acc = 0.0
train_loss = 0.0
val_acc = 0.0
val_loss = 0.0
model.train()
for i, data in enumerate(train_loader):
optimizer.zero_grad()
train_pred = model(data[0].cuda()) #预测
batch_loss = loss(train_pred, data[1].cuda()) #误差计算
batch_loss.backward() #误差的反向传播
optimizer.step() #参数更新
train_acc += np.sum(np.argmax(train_pred.cpu().data.numpy(), axis=1) == data[1].numpy())
train_loss += batch_loss.item()
progress = (\'#\' * int(float(i) / len(train_loader) * 40)).ljust(40)
print(\'[%03d/%03d] %2.2f sec(s) | %s |\' % (epoch + 1, num_epoch, \
(time.time() - epoch_start_time), progress), end=\'\r\', flush=True)
model.eval()
for i, data in enumerate(val_loader):
val_pred = model(data[0].cuda())
batch_loss = loss(val_pred, data[1].cuda())
val_acc += np.sum(np.argmax(val_pred.cpu().data.numpy(), axis=1) == data[1].numpy())
val_loss += batch_loss.item()
progress = (\'#\' * int(float(i) / len(val_loader) * 40)).ljust(40)
print(\'[%03d/%03d] %2.2f sec(s) | %s |\' % (epoch + 1, num_epoch, \
(time.time() - epoch_start_time), progress), end=\'\r\', flush=True)
val_acc = val_acc / val_set.__len__()
print(\'[%03d/%03d] %2.2f sec(s) Train Acc: %3.6f Loss: %3.6f | Val Acc: %3.6f loss: %3.6f\' % \
(epoch + 1, num_epoch, time.time() - epoch_start_time, \
train_acc / train_set.__len__(), train_loss, val_acc, val_loss))
if (val_acc > best_acc):
with open(\'save/acc.txt\', \'w\') as f:
f.write(str(epoch) + \'\t\' + str(val_acc) + \'\n\')
torch.save(model.state_dict(), \'save/model.pth\')
best_acc = val_acc
print(\'Model Saved!\')
