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plant_disease_efficientnetb4.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4f34b8da5c996362a6d20582a090f1f9a67926e591922156a780046c66493fed
+size 98030480

train_v2.py

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+import tensorflow as tf
+from tensorflow.keras import layers, models, applications, optimizers, callbacks
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+import matplotlib.pyplot as plt
+
+# 参数设置
+IMAGE_SIZE = (380, 380)  # EfficientNetB4的推荐输入尺寸
+BATCH_SIZE = 8
+EPOCHS = 15 # 20/10/5
+NUM_CLASSES = 38  # PlantVillage数据集有38个类别（包含健康叶片）
+DATA_DIR = "./PlantVillage-Dataset-master/raw/color"  # 替换为你的数据集路径
+
+# 数据增强和预处理
+def create_data_generator():
+    # 使用EfficientNet的专用预处理方法
+    return ImageDataGenerator(
+        preprocessing_function=applications.efficientnet.preprocess_input,
+        rotation_range=40,
+        width_shift_range=0.2,
+        height_shift_range=0.2,
+        shear_range=0.2,
+        zoom_range=0.2,
+        horizontal_flip=True,
+        vertical_flip=True,
+        validation_split=0.05  # 保留5%数据作为验证集
+    )
+
+# 创建数据生成器
+train_datagen = create_data_generator()
+
+# 训练数据流
+train_generator = train_datagen.flow_from_directory(
+    DATA_DIR,
+    target_size=IMAGE_SIZE,
+    batch_size=BATCH_SIZE,
+    class_mode="categorical",
+    subset="training",
+    shuffle=True
+)
+
+# 验证数据流
+val_generator = train_datagen.flow_from_directory(
+    DATA_DIR,
+    target_size=IMAGE_SIZE,
+    batch_size=BATCH_SIZE,
+    class_mode="categorical",
+    subset="validation",
+    shuffle=False
+)
+
+# 构建模型
+def build_model():
+    # 加载预训练基模型
+    base_model = applications.EfficientNetB4(
+        weights="imagenet",
+        include_top=False,
+        input_shape=(*IMAGE_SIZE, 3)
+    )
+    
+    # 冻结预训练层（初始训练阶段）
+    base_model.trainable = False
+    
+    # 自定义顶层
+    inputs = layers.Input(shape=(*IMAGE_SIZE, 3))
+    x = base_model(inputs)
+    x = layers.GlobalAveragePooling2D()(x)
+    x = layers.Dense(256, activation="relu")(x)
+    x = layers.Dropout(0.5)(x)
+    outputs = layers.Dense(NUM_CLASSES, activation="softmax")(x)
+    
+    model = models.Model(inputs, outputs)
+    return model
+
+model = build_model()
+
+# 编译模型
+model.compile(
+    optimizer=optimizers.Adam(learning_rate=1e-3),
+    loss="categorical_crossentropy",
+    metrics=["accuracy"]
+)
+
+# 回调函数
+callbacks_list = [
+    callbacks.EarlyStopping(
+        monitor="val_loss",
+        patience=5,
+        restore_best_weights=True
+    ),
+    callbacks.ModelCheckpoint(
+        "best_model_initial",  # 去后缀或使用.keras
+        save_best_only=True,
+        monitor="val_accuracy",
+        save_format="tf"  # 显式指定保存格式
+    ),
+    callbacks.ReduceLROnPlateau(
+        monitor="val_loss",
+        factor=0.2,
+        patience=3
+    )
+]
+
+# 初始训练（仅训练自定义顶层）
+history = model.fit(
+    train_generator,
+    epochs=EPOCHS,
+    validation_data=val_generator,
+    callbacks=callbacks_list
+)
+
+# 解冻部分层进行微调
+def fine_tune_model(model):
+    # 解冻顶层卷积块
+    model.get_layer("efficientnetb4").trainable = True
+    for layer in model.layers[1].layers[:-10]:  # 保留最后10层可训练
+        layer.trainable = False
+    
+    # 重新编译模型（使用更小的学习率）
+    model.compile(
+        optimizer=optimizers.Adam(learning_rate=1e-5),
+        loss="categorical_crossentropy",
+        metrics=["accuracy"]
+    )
+    return model
+
+model = fine_tune_model(model)
+
+# 微调训练
+fine_tune_history = model.fit(
+    train_generator,
+    initial_epoch=history.epoch[-1],
+    epochs=history.epoch[-1] + 10,  # 再训练10个epoch
+    validation_data=val_generator,
+    callbacks=[
+        callbacks.ModelCheckpoint(
+            "best_model_finetuned.h5",
+            save_best_only=True,
+            monitor="val_accuracy"
+        )
+    ]
+)
+
+# 保存最终模型
+model.save("plant_disease_efficientnetb4.h5")
+
+# 可视化训练过程
+def plot_history(history, title):
+    plt.figure(figsize=(12, 4))
+    
+    # 准确率曲线
+    plt.subplot(1, 2, 1)
+    plt.plot(history.history['accuracy'])
+    plt.plot(history.history['val_accuracy'])
+    plt.title(f'{title} Accuracy')
+    plt.ylabel('Accuracy')
+    plt.xlabel('Epoch')
+    plt.legend(['Train', 'Validation'], loc='upper left')  # 与第一个文件一致
+    
+    # 损失曲线
+    plt.subplot(1, 2, 2)
+    plt.plot(history.history['loss'])
+    plt.plot(history.history['val_loss'])
+    plt.title(f'{title} Loss')
+    plt.ylabel('Loss')
+    plt.xlabel('Epoch')
+    plt.legend(['Train', 'Validation'], loc='upper left')  # 统一图例位置
+    
+    plt.tight_layout()
+    plt.show()
+# 修改调用方式（替换最后两行plot_training调用）
+plot_history(history, "Initial Training")
+plot_history(fine_tune_history, "Fine-tuning")
+
+# 评估模型
+def evaluate_model(model_path):
+    model = models.load_model(model_path)
+    loss, acc = model.evaluate(val_generator)
+    print(f"Validation accuracy: {acc*100:.2f}%")
+    print(f"Validation loss: {loss:.4f}")
+
+print("Initial model evaluation:")
+evaluate_model("best_model_initial.h5")
+
+print("\nFine-tuned model evaluation:")
+evaluate_model("best_model_finetuned.h5")