生成一段利用resnet读取cifar10的python代码
时间: 2023-05-30 16:06:33 浏览: 73
以下是使用ResNet模型读取CIFAR10数据集的Python代码:
```python
import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, Activation, MaxPooling2D, GlobalAveragePooling2D, Dense, Dropout
from tensorflow.keras.models import Model
# Load CIFAR10 dataset
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
# Normalize pixel values
x_train = x_train / 255.0
x_test = x_test / 255.0
# Define ResNet block
def resnet_block(inputs, filters, strides):
x = Conv2D(filters, kernel_size=3, strides=strides, padding='same')(inputs)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(filters, kernel_size=3, strides=1, padding='same')(x)
x = BatchNormalization()(x)
shortcut = Conv2D(filters, kernel_size=1, strides=strides, padding='same')(inputs)
shortcut = BatchNormalization()(shortcut)
x = tf.keras.layers.add([x, shortcut])
x = Activation('relu')(x)
return x
# Define ResNet model
inputs = Input(shape=(32, 32, 3))
x = Conv2D(64, kernel_size=3, strides=1, padding='same')(inputs)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = resnet_block(x, filters=64, strides=1)
x = resnet_block(x, filters=64, strides=1)
x = resnet_block(x, filters=64, strides=1)
x = resnet_block(x, filters=128, strides=2)
x = resnet_block(x, filters=128, strides=1)
x = resnet_block(x, filters=128, strides=1)
x = resnet_block(x, filters=256, strides=2)
x = resnet_block(x, filters=256, strides=1)
x = resnet_block(x, filters=256, strides=1)
x = resnet_block(x, filters=512, strides=2)
x = resnet_block(x, filters=512, strides=1)
x = resnet_block(x, filters=512, strides=1)
x = GlobalAveragePooling2D()(x)
outputs = Dense(10, activation='softmax')(x)
model = Model(inputs=inputs, outputs=outputs)
# Compile model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# Train model
model.fit(x_train, y_train, batch_size=64, epochs=10, validation_data=(x_test, y_test))
# Evaluate model
model.evaluate(x_test, y_test)
```
该代码使用了ResNet模型,其中定义了一个ResNet块,用于构建ResNet模型。在构建ResNet模型时,使用了多个ResNet块来实现深度网络。最后,编译和训练模型,并使用测试集评估模型性能。