batch_size = 32 num_classes = 2 epochs = 3 model = Sequential() model.add(Dense(512, activation='relu', input_shape=(25653,))) model.add(Dropout(0.2)) model.add(Dense(512, activation='relu')) model.add(Dropout(0.2)) model.add(Dense(num_classes, activation='softmax')) model.summary() model.compile(loss='categorical_crossentropy', optimizer=RMSprop(), metrics=['accuracy']) #模型训练 #verbose:z history = model.fit(X_train,y_train1, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(X_test, y_test1)) #模型打分 score = model.evaluate(X_test, y_test, verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1]) 编写一个代码 对上述模型调参并绘制正确率变化 损失图 正确率图和损失图横排放置
时间: 2024-03-11 11:48:21 浏览: 26
好的,我理解了你的问题。以下是对上述模型的调参代码和正确率、损失图的绘制代码:
```
batch_size = 64
num_classes = 2
epochs = 10
model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(25653,)))
model.add(Dropout(0.5))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy', optimizer=RMSprop(lr=0.001), metrics=['accuracy'])
history = model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(X_test, y_test))
score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
# 绘制正确率和损失图
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 = range(epochs)
plt.figure(figsize=(20, 10))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
```
调参的改动包括将 batch_size 改为 64,将 Dropout 比例改为 0.5,将学习率 lr 设为 0.001。绘制的正确率和损失图会横排放置,分别展示训练集和验证集在多个 epoch 下的正确率和损失变化情况。你可以根据需要修改代码中的参数来调整模型的表现。