# 查看当前挂载的数据集目录, 该目录下的变更重启环境后会自动还原 # View dataset directory. # This directory will be recovered automatically after resetting environment. !ls /home/aistudio/data
# 查看工作区文件, 该目录下的变更将会持久保存. 请及时清理不必要的文件, 避免加载过慢. # View personal work directory. # All changes under this directory will be kept even after reset. # Please clean unnecessary files in time to speed up environment loading. !ls /home/aistudio/work
# 如果需要进行持久化安装, 需要使用持久化路径, 如下方代码示例: # If a persistence installation is required, # you need to use the persistence path as the following: !mkdir /home/aistudio/external-libraries !pip install beautifulsoup4 -t /home/aistudio/external-libraries
# 同时添加如下代码, 这样每次环境(kernel)启动的时候只要运行下方代码即可:
# Also add the following code,
# so that every time the environment (kernel) starts,
# just run the following code:
import sys
sys.path.append('/home/aistudio/external-libraries')
请点击此处查看本环境基本用法.
Please click here for more detailed instructions.
import paddle import paddle.nn.functional as F from paddle.vision.transforms import ToTensor import numpy as np import matplotlib.pyplot as plt print(paddle.__version__)
2.1.2
transform=ToTensor() cifar10_train=paddle.vision.datasets.Cifar10(mode='train',transform=transform) cifar10_test=paddle.vision.datasets.Cifar10(mode='test',transform=transform)
Cache file /home/aistudio/.cache/paddle/dataset/cifar/cifar-10-python.tar.gz not found, downloading https://dataset.bj.bcebos.com/cifar/cifar-10-python.tar.gz Begin to download Download finished
class MyNet(paddle.nn.Layer):
def __init__(self, num_classes=1):
super(MyNet, self).__init__()
self.conv1 = paddle.nn.Conv2D(in_channels=3, out_channels=32, kernel_size=(2, 2))
self.pool1 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
self.conv2 = paddle.nn.Conv2D(in_channels=32, out_channels=64, kernel_size=(2,2))
self.pool2 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
self.conv3 = paddle.nn.Conv2D(in_channels=64, out_channels=128, kernel_size=(2,2))
self.pool3 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
self.conv4 = paddle.nn.Conv2D(in_channels=128, out_channels=128, kernel_size=(2,2))
self.flatten = paddle.nn.Flatten()
self.linear1 = paddle.nn.Linear(in_features=512, out_features=64)
self.linear2 = paddle.nn.Linear(in_features=64, out_features=num_classes)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.pool1(x)
x = self.conv2(x)
x = F.relu(x)
x = self.pool2(x)
x = self.conv3(x)
x = F.relu(x)
x = self.pool3(x)
x = self.conv4(x)
x = F.relu(x)
x = self.flatten(x)
x = self.linear1(x)
x = F.relu(x)
x = self.linear2(x)
return x
epoch_num=10 batch_size=32 learning_rate=0.001
val_acc_history = []
val_loss_history = []
def train(model):
print('start training ... ')
# turn into training mode
model.train()
opt = paddle.optimizer.Adam(learning_rate=learning_rate,
parameters=model.parameters())
train_loader = paddle.io.DataLoader(cifar10_train,
shuffle=True,
batch_size=batch_size)
valid_loader = paddle.io.DataLoader(cifar10_test, batch_size=batch_size)
for epoch in range(epoch_num):
for batch_id, data in enumerate(train_loader()):
x_data = data[0]
y_data = paddle.to_tensor(data[1])
y_data = paddle.unsqueeze(y_data, 1)
logits = model(x_data)
loss = F.cross_entropy(logits, y_data)
if batch_id % 1000 == 0:
print("epoch: {}, batch_id: {}, loss is: {}".format(epoch, batch_id, loss.numpy()))
loss.backward()
opt.step()
opt.clear_grad()
# evaluate model after one epoch
model.eval()
accuracies = []
losses = []
for batch_id, data in enumerate(valid_loader()):
x_data = data[0]
y_data = paddle.to_tensor(data[1])
y_data = paddle.unsqueeze(y_data, 1)
logits = model(x_data)
loss = F.cross_entropy(logits, y_data)
acc = paddle.metric.accuracy(logits, y_data)
accuracies.append(acc.numpy())
losses.append(loss.numpy())
avg_acc, avg_loss = np.mean(accuracies), np.mean(losses)
print("[validation] accuracy/loss: {}/{}".format(avg_acc, avg_loss))
val_acc_history.append(avg_acc)
val_loss_history.append(avg_loss)
model.train()
model = MyNet(num_classes=10)
train(model)
start training ... epoch: 0, batch_id: 0, loss is: [2.6684964]
plt.plot(val_acc_history, label = 'validation accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 0.8])
_history, label = 'validation accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 0.8])
plt.legend(loc='lower right')
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working if isinstance(obj, collections.Iterator): /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2366: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working return list(data) if isinstance(data, collections.MappingView) else data
[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-LxxLVrCe-1634309325209)(output_10_2.png)]
