输出上述损失和正确率的可视化图像
时间: 2023-06-11 13:10:10 浏览: 34
好的,可以使用Python的Matplotlib库来可视化损失和正确率。以下是一个示例代码:
```python
import matplotlib.pyplot as plt
# 假设loss和accuracy是两个包含训练结果的列表
# 绘制损失函数曲线
plt.plot(loss)
plt.title('Loss over epochs')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.show()
# 绘制正确率曲线
plt.plot(accuracy)
plt.title('Accuracy over epochs')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.show()
```
你可以根据自己的需求进一步修改代码和调整图像。
相关问题
用卷积搭建五分类的EfficientNet代码,加上准确率和损失率可视化
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。
用代码实现逻辑回归分类,输出其准确率并实现可视化
好的,以下是用Python实现逻辑回归分类的代码,并输出准确率和可视化结果:
首先导入相关的库:
```python
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
from sklearn.metrics import accuracy_score
```
然后生成随机的二分类数据集:
```python
X, y = make_classification(n_samples=100, n_features=2, n_redundant=0, n_informative=1, n_clusters_per_class=1, random_state=42)
```
接着拟合逻辑回归模型并输出准确率:
```python
clf = LogisticRegression(random_state=42)
clf.fit(X, y)
y_pred = clf.predict(X)
print("Accuracy:", accuracy_score(y, y_pred))
```
最后将决策边界可视化:
```python
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100), np.linspace(y_min, y_max, 100))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.figure(1, figsize=(4, 3))
plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)
plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', cmap=plt.cm.Paired)
plt.xlabel('X1')
plt.ylabel('X2')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
plt.show()
```
完整代码如下: