python在mindspore框架下实现卷积网络模型分类afhq图像数据集
时间: 2024-03-19 14:44:14 浏览: 178
基于python手工搭建三层卷积神经网络实现图像分类任务源码+数据集(期末课程大作业).zip
好的,让我来为你提供一些关于使用MindSpore框架在Python中实现卷积神经网络模型分类afhq图像数据集的步骤。
首先,你需要安装MindSpore框架和下载afhq数据集。然后,你需要按照以下步骤进行:
1. 导入必要的库和模块:
```python
import os
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as CV
import mindspore.nn as nn
from mindspore import context
from mindspore.train.callback import LossMonitor
from mindspore.train.serialization import load_checkpoint, save_checkpoint
from mindspore.common.initializer import TruncatedNormal
from mindspore.common import dtype as mstype
```
2. 定义数据集路径和超参数:
```python
data_path = "/path/to/afhq/dataset"
batch_size = 32
num_classes = 3
num_epochs = 100
learning_rate = 0.01
```
3. 加载数据集并进行数据增强:
```python
train_transforms = [
CV.RandomCrop(224),
CV.RandomHorizontalFlip(0.5),
CV.ColorJitter(0.5, 0.5, 0.5, 0.5),
CV.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
]
train_dataset = ds.ImageFolderDataset(data_path + "/train",
num_parallel_workers=8,
shuffle=True)
train_dataset = train_dataset.map(input_columns="image",
num_parallel_workers=8,
operations=train_transforms)
train_dataset = train_dataset.batch(batch_size, drop_remainder=True)
```
4. 定义卷积神经网络模型:
```python
class Net(nn.Cell):
def __init__(self, num_classes):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, has_bias=True, weight_init=TruncatedNormal(stddev=0.02))
self.bn1 = nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, gamma_init=1, beta_init=0, moving_mean_init=0, moving_var_init=1)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
self.flatten = nn.Flatten()
self.fc1 = nn.Dense(64 * 56 * 56, 256, weight_init=TruncatedNormal(stddev=0.02), bias_init='zeros')
self.fc2 = nn.Dense(256, num_classes, weight_init=TruncatedNormal(stddev=0.02), bias_init='zeros')
def construct(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
return x
net = Net(num_classes)
```
5. 定义损失函数和优化器:
```python
loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
optimizer = nn.Momentum(params=net.trainable_params(), learning_rate=learning_rate, momentum=0.9)
```
6. 定义训练和验证函数:
```python
def train(net, train_loader, optimizer, loss_fn):
net.set_train()
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.clear_grad()
output = net(data)
loss = loss_fn(output, target)
loss.backward()
optimizer.step()
def validate(net, val_loader, loss_fn):
net.set_train(False)
loss = 0
correct = 0
total = 0
for data, target in val_loader:
output = net(data)
loss += loss_fn(output, target).asnumpy().mean()
pred = output.argmax(1)
correct += (pred == target.asnumpy()).sum().item()
total += target.shape[0]
return loss / len(val_loader), correct / total
```
7. 开始训练模型:
```python
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
net = Net(num_classes)
optimizer = nn.Momentum(params=net.trainable_params(), learning_rate=learning_rate, momentum=0.9)
loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
train_loader = train_dataset.create_tuple_iterator()
for epoch in range(num_epochs):
train(net, train_loader, optimizer, loss_fn)
val_loss, val_acc = validate(net, val_loader, loss_fn)
print(f"Epoch {epoch + 1}, Validation Loss: {val_loss:.4f}, Validation Accuracy: {val_acc:.4f}")
save_checkpoint(net, "afhq_classification.ckpt")
```
这样就完成了在MindSpore框架下实现卷积神经网络模型分类afhq图像数据集的步骤。
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