Keras-情节训练，验证和测试集准确性 [英] Keras - Plot training, validation and test set accuracy

问题描述

我想绘制这个简单的神经网络的输出:

I want to plot the output of this simple neural network:

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
history = model.fit(x_test, y_test, nb_epoch=10, validation_split=0.2, shuffle=True)

model.test_on_batch(x_test, y_test)
model.metrics_names

我绘制了训练和验证的准确性和损失:

I have plotted accuracy and loss of training and validation:

print(history.history.keys())
#  "Accuracy"
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
# "Loss"
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()

现在，我想从model.test_on_batch(x_test, y_test)添加并绘制测试集的精度，但是从model.metrics_names我获得了用于在训练数据plt.plot(history.history['acc'])上绘制精度的相同值'acc'.如何绘制测试仪的精度?

Now I want to add and plot test set's accuracy from model.test_on_batch(x_test, y_test), but from model.metrics_names I obtain the same value 'acc' utilized for plotting accuracy on training data plt.plot(history.history['acc']). How could I plot test set's accuracy?

推荐答案

这是相同的，因为您是在测试集中进行训练，而不是在训练集中进行训练.不要这样做，只需在训练集上进行训练即可:

It is the same because you are training on the test set, not on the train set. Don't do that, just train on the training set:

history = model.fit(x_test, y_test, nb_epoch=10, validation_split=0.2, shuffle=True)

更改为:

history = model.fit(x_train, y_train, nb_epoch=10, validation_split=0.2, shuffle=True)

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