网络八股中打印acc曲线与loss曲线

灰太狼 2021-09-25 14:22 345阅读 0赞

history=model.fit(训练集数据, 训练集标签, batch\_size=, epochs=,  
validation\_split=用作测试数据的比例,validation\_data=测试集, validation\_freq=测试频率)  
history：  
loss：训练集loss  
val\_loss：测试集loss  
sparse\_categorical\_accuracy：训练集准确率  
val\_sparse\_categorical\_accuracy：测试集准确率

acc = history.history['sparse_categorical_accuracy']
    val_acc = history.history['val_sparse_categorical_accuracy']
    loss = history.history['loss']
    val_loss = history.history['val_loss']
    
    print(acc)
    print(val_acc)
    print(loss)
    print(val_loss)
    # [0.9254000186920166, 0.9668499827384949, 0.9761666655540466, 0.9825999736785889, 0.9862499833106995]
    # [0.9592999815940857, 0.9706000089645386, 0.9775000214576721, 0.9761999845504761, 0.9765999913215637]
    # [0.26014444231987, 0.113783098757267, 0.07880879193544388, 0.0564233660697937, 0.0446171797811985]
    # [0.1373901069164276, 0.09307443350553513, 0.0812087282538414, 0.08124100416898727, 0.07900043576955795]
    fig = plt.figure(figsize=(10,10))
    ax=fig.add_subplot(1,2,1)
    ax.plot(acc,label="TA")
    ax.plot(val_acc,label="VTA")
    ax.set_title("Training And Validation Accuracy")
    plt.legend()
    
    ax=fig.add_subplot(1,2,2)
    ax.plot(loss,label="TL")
    ax.plot(val_loss,label="VTL")
    ax.set_title("Training And Validation Loss")
    plt.legend()
    plt.show()

测试结果：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_Q1NETiBA5piO5aSp5aSp5piOfg_size_18_color_FFFFFF_t_70_g_se_x_16][]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_Q1NETiBA5piO5aSp5aSp5piOfg_size_18_color_FFFFFF_t_70_g_se_x_16]: /images/20210923/f98a1c752f394931ac021082503c7e79.png