用卷积搭建五分类的EfficientNet代码,加上准确率和损失率可视化
时间: 2024-05-11 07:17:43 浏览: 10
以下是使用Keras框架搭建五分类的EfficientNet代码,包括准确率和损失率可视化:
```python
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard
from efficientnet.tfkeras import EfficientNetB0
# 加载数据集
train_data = # 加载训练数据
val_data = # 加载验证数据
# 定义EfficientNet模型
def create_model():
base_model = EfficientNetB0(input_shape=(224,224,3), include_top=False, weights='imagenet')
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
x = Dropout(0.5)(x)
predictions = Dense(5, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
return model
model = create_model()
# 编译模型
model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.0001), metrics=['accuracy'])
# 定义回调函数
early_stop = EarlyStopping(monitor='val_loss', patience=5, verbose=1, mode='min')
checkpoint = ModelCheckpoint('model.h5', monitor='val_accuracy', mode='max', save_best_only=True)
tensorboard = TensorBoard(log_dir='./logs', write_graph=True, write_images=True)
# 训练模型
history = model.fit(train_data, epochs=100, validation_data=val_data, callbacks=[early_stop, checkpoint, tensorboard])
# 可视化准确率和损失率
import matplotlib.pyplot as plt
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'b', label='Training accuracy')
plt.plot(epochs, val_acc, 'r', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'b', label='Training loss')
plt.plot(epochs, val_loss, 'r', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
```
上面的代码中,我们首先加载训练数据和验证数据,然后定义EfficientNet模型。在模型训练之前,我们需要先编译模型,指定损失函数、优化器和评估指标。接着我们定义了回调函数,包括早停、模型保存和TensorBoard可视化。最后,我们使用`fit()`函数训练模型,并使用Matplotlib库可视化训练过程中的准确率和损失率。