画两个网络准确率

#%%
#画准确率
import matplotlib.pyplot as plt
DC_Lenet_loss = [1.2643316984176636,
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DC_Lenet_accuracy = [0.5788888931274414,
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DC_Lenet_val_loss = [0.34025800228118896,
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DC_Lenet_val_accuracy = [0.9016666412353516,
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plt.subplot(1, 2, 1)
plt.plot(DC_Lenet_accuracy, label='Training Accuracy')
plt.plot(DC_Lenet_val_accuracy, label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(DC_Lenet_loss, label='Training Loss')
plt.plot(DC_Lenet_val_loss, label='Validation Loss')
plt.title('Training and Validation Loss')
plt.legend()
plt.show()

#%%
#LeNet5数据
Lenet_loss = [2.480301856994629,
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Lenet_accuracy = [0.24755555391311646,
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Lenet_val_loss = [2.1577064990997314,
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Lenet_val_accuracy = [0.39516666531562805,
  0.4925000071525574,
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plt.subplot(1, 2, 1)
plt.plot(Lenet_accuracy, label='Training Accuracy')
plt.plot(Lenet_val_accuracy, label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(Lenet_loss, label='Training Loss')
plt.plot(Lenet_val_loss, label='Validation Loss')
plt.title('Training and Validation Loss')
plt.legend()
plt.show()
#%%
plt.subplot(2, 1, 1)
plt.plot(Lenet_accuracy, label='Lenet_Training Accuracy')
plt.plot(DC_Lenet_accuracy, label='DC_Training Accuracy')
plt.title('Training Accuracy')
plt.legend()
plt.subplot(2, 1, 2)
plt.plot(Lenet_val_accuracy, label='Lenet_Validation Accuracy')
plt.plot(DC_Lenet_val_accuracy, label='DC_Validation Accuracy')
plt.title('Validation Accuracy')
plt.legend()


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