Python Resnet 网络搭建与训练：CIFAR-10 数据集实战

以下是用 Python 搭建 Resnet 网络并进行训练的示例代码：

首先，加载必要的库：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import cifar10

接着，定义一个 residual block：

def residual_block(x, filters, stride):
    shortcut = x
    x = layers.Conv2D(filters, 3, strides=stride, padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = layers.ReLU()(x)
    x = layers.Conv2D(filters, 3, strides=1, padding='same')(x)
    x = layers.BatchNormalization()(x)
    if stride != 1 or shortcut.shape[-1] != filters:
        shortcut = layers.Conv2D(filters, 1, strides=stride, padding='same')(shortcut)
        shortcut = layers.BatchNormalization()(shortcut)
    x = layers.Add()([x, shortcut])
    x = layers.ReLU()(x)
    return x

然后，定义一个 Resnet 网络：

def resnet(input_shape, num_classes):
    inputs = keras.Input(shape=input_shape)
    x = layers.Conv2D(64, 7, strides=2, padding='same')(inputs)
    x = layers.BatchNormalization()(x)
    x = layers.ReLU()(x)
    x = layers.MaxPooling2D(pool_size=3, strides=2, padding='same')(x)
    for filters in [64, 128, 256, 512]:
        stride = 1 if filters == 64 else 2
        x = residual_block(x, filters, stride)
        for _ in range(1, 3):
            x = residual_block(x, filters, 1)
    x = layers.GlobalAveragePooling2D()(x)
    outputs = layers.Dense(num_classes, activation='softmax')(x)
    model = keras.Model(inputs, outputs)
    return model

接下来，加载数据集并进行预处理：

(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)

然后，把数据划分为训练集、测试集和验证集：

x_train, x_val = x_train[:40000], x_train[40000:]
y_train, y_val = y_train[:40000], y_train[40000:]

最后，编译模型并进行训练：

model = resnet((32, 32, 3), 10)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=64, epochs=50, validation_data=(x_val, y_val))

这样就完成了用 Python 搭建 Resnet 网络并进行训练的过程。