import paddle
import paddle.nn.functional as F
from paddle.vision.transforms import ToTensor
import numpy as np
import matplotlib.pyplot as plt
transform = ToTensor()
cifar10_train = paddle.vision.datasets.Cifar10(mode='train',
transform=transform)
cifar10_test = paddle.vision.datasets.Cifar10(mode='test',
transform=transform)
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=(3, 3))
self.pool1 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
self.conv2 = paddle.nn.Conv2D(in_channels=32, out_channels=64, kernel_size=(3,3))
self.pool2 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
self.conv3 = paddle.nn.Conv2D(in_channels=64, out_channels=64, kernel_size=(3,3))
self.flatten = paddle.nn.Flatten()
self.linear1 = paddle.nn.Linear(in_features=1024, 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.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)
plt.plot(val_acc_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/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
from collections import MutableMapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
from collections import Iterable, Mapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
from collections import Sized
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
start training ...
epoch: 0, batch_id: 0, loss is: [2.2984047]
epoch: 0, batch_id: 1000, loss is: [1.1130366]
[validation] accuracy/loss: 0.5728833675384521/1.1950840950012207
epoch: 1, batch_id: 0, loss is: [1.4412661]
epoch: 1, batch_id: 1000, loss is: [0.7821938]
[validation] accuracy/loss: 0.6347843408584595/1.0210740566253662
epoch: 2, batch_id: 0, loss is: [0.9672767]
epoch: 2, batch_id: 1000, loss is: [1.3251742]
[validation] accuracy/loss: 0.6820088028907776/0.924034059047699
epoch: 3, batch_id: 0, loss is: [0.948807]
epoch: 3, batch_id: 1000, loss is: [1.0935752]
[validation] accuracy/loss: 0.6902955174446106/0.899625837802887
epoch: 4, batch_id: 0, loss is: [0.68473774]
epoch: 4, batch_id: 1000, loss is: [0.9899424]
[validation] accuracy/loss: 0.699181318283081/0.8516970276832581
epoch: 5, batch_id: 0, loss is: [0.59179]
epoch: 5, batch_id: 1000, loss is: [0.60291755]
[validation] accuracy/loss: 0.6930910348892212/0.9029199481010437
epoch: 6, batch_id: 0, loss is: [0.86010605]
epoch: 6, batch_id: 1000, loss is: [0.8406735]
[validation] accuracy/loss: 0.7024760246276855/0.8776425719261169
epoch: 7, batch_id: 0, loss is: [1.0346094]
epoch: 7, batch_id: 1000, loss is: [0.2502644]
[validation] accuracy/loss: 0.7146565318107605/0.834501326084137
epoch: 8, batch_id: 0, loss is: [0.5335045]
epoch: 8, batch_id: 1000, loss is: [0.38619795]
[validation] accuracy/loss: 0.7243410348892212/0.8565839529037476
epoch: 9, batch_id: 0, loss is: [0.55277234]
epoch: 9, batch_id: 1000, loss is: [0.43163693]
[validation] accuracy/loss: 0.7071685194969177/0.9381765723228455
