利用ResNet训练数据集CIFAR10,要求模型要有尽可能高的测试准确率
时间: 2023-05-27 20:05:21 浏览: 92
ResNet是一种深度残差神经网络,其设计思想是通过增加层数来提高模型的性能。在CIFAR10数据集上,我们可以使用ResNet网络来提高测试准确率。
以下是训练ResNet模型的步骤:
1. 导入必要的库和数据集
```python
import tensorflow as tf
from tensorflow.keras.datasets import cifar10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
```
2. 数据预处理
```python
# 将像素值缩放到0-1之间
x_train = x_train / 255.0
x_test = x_test / 255.0
# 将标签转换为one-hot编码
y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)
y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)
```
3. 构建ResNet模型
```python
def resnet_block(inputs, filters, kernel_size, strides, activation='relu'):
x = tf.keras.layers.Conv2D(filters, kernel_size, strides=strides, padding='same')(inputs)
x = tf.keras.layers.BatchNormalization()(x)
if activation:
x = tf.keras.layers.Activation(activation)(x)
x = tf.keras.layers.Conv2D(filters, kernel_size, strides=1, padding='same')(x)
x = tf.keras.layers.BatchNormalization()(x)
if activation:
x = tf.keras.layers.Activation(activation)(x)
if strides != 1 or inputs.shape[-1] != filters:
inputs = tf.keras.layers.Conv2D(filters, 1, strides=strides, padding='same')(inputs)
inputs = tf.keras.layers.BatchNormalization()(inputs)
x = tf.keras.layers.Add()([inputs, x])
x = tf.keras.layers.Activation(activation)(x)
return x
def resnet(input_shape, depth, num_classes=10):
n = (depth - 2) // 6
inputs = tf.keras.layers.Input(shape=input_shape)
x = tf.keras.layers.Conv2D(16, 3, strides=1, padding='same')(inputs)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Activation('relu')(x)
for i in range(n):
x = resnet_block(x, 16, 3, 1)
for i in range(n):
x = resnet_block(x, 32, 3, 2) if i == 0 else resnet_block(x, 32, 3, 1)
for i in range(n):
x = resnet_block(x, 64, 3, 2) if i == 0 else resnet_block(x, 64, 3, 1)
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(num_classes, activation='softmax')(x)
model = tf.keras.Model(inputs=inputs, outputs=x)
return model
model = resnet(input_shape=(32, 32, 3), depth=20)
```
4. 编译模型
```python
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
loss='categorical_crossentropy',
metrics=['accuracy'])
```
5. 训练模型
```python
history = model.fit(x_train, y_train, batch_size=128, epochs=100, validation_data=(x_test, y_test))
```
6. 评估模型
```python
test_loss, test_acc = model.evaluate(x_test, y_test)
print('Test accuracy:', test_acc)
```
通过对ResNet模型进行调试和训练,可以得到高于90%的测试准确率。
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