基于卷积神经网络的水果图像识别设计一个ResNet残差网络，给出完整且具体的实验代码，绘制出准确率的折线图

好的,我来为您设计一个基于ResNet残差网络的水果图像识别系统,并提供完整的实验代码和准确率折线图。以下是具体实现步骤: ```python import tensorflow as tf from tensorflow.keras import layers, models, optimizers import matplotlib.pyplot as plt import numpy as np from tensorflow.keras.preprocessing.image import ImageDataGenerator # 设置随机种子 np.random.seed(42) tf.random.set_seed(42) # 数据预处理和增强 train_datagen = ImageDataGenerator( rescale=1./255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest') test_datagen = ImageDataGenerator(rescale=1./255) train_generator = train_datagen.flow_from_directory( 'data/train', target_size=(224, 224), batch_size=32, class_mode='categorical') validation_generator = test_datagen.flow_from_directory( 'data/validation', target_size=(224, 224), batch_size=32, class_mode='categorical') # 构建ResNet模型 def build_resnet_model(input_shape, num_classes): inputs = layers.Input(shape=input_shape) x = layers.Conv2D(64, (7, 7), strides=(2, 2), padding='same')(inputs) x = layers.BatchNormalization()(x) x = layers.Activation('relu')(x) x = layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x) # ResNet块 def res_block(x, filters, stride=1): residual = x x = layers.Conv2D(filters, (3, 3), strides=stride, padding='same')(x) x = layers.BatchNormalization()(x) x = layers.Activation('relu')(x) x = layers.Conv2D(filters, (3, 3), padding='same')(x) x = layers.BatchNormalization()(x) if stride != 1: residual = layers.Conv2D(filters, (1, 1), strides=stride)(residual) residual = layers.BatchNormalization()(residual) x = layers.add([x, residual]) x = layers.Activation('relu')(x) return x x = res_block(x, 64) x = res_block(x, 64) x = res_block(x, 128, stride=2) x = res_block(x, 128) x = res_block(x, 256, stride=2) x = res_block(x, 256) x = layers.GlobalAveragePooling2D()(x) outputs = layers.Dense(num_classes, activation='softmax')(x) model = models.Model(inputs, outputs) return model model = build_resnet_model((224, 224, 3), 4) model.compile(optimizer=optimizers.Adam(), loss='categorical_crossentropy', metrics=['accuracy']) # 训练模型 history = model.fit( train_generator, steps_per_epoch=train_generator.samples // train_generator.batch_size, epochs=50, validation_data=validation_generator, validation_steps=validation_generator.samples // validation_generator.batch_size) # 绘制准确率折线图 plt.plot(history.history['accuracy'], label='训练准确率') plt.plot(history.history['val_accuracy'], label='验证准确率') plt.xlabel('Epoch') plt.ylabel('准确率') plt.legend() plt.show() ``` 这段代码实现了一个基于ResNet的图像分类模型,并绘制了训练过程中的准确率变化折线图。