Keras - 绘制训练、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
我已经绘制了训练和验证的准确度和损失:
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)
添加和绘制测试集的准确性,但是从 model.metrics_names
我获得相同的值 'acc' 用于绘制训练数据的准确性plt.plot(history.history['acc'])
.我如何绘制测试集的准确性?
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
这是一样的,因为你是在测试集上训练,而不是在训练集上.不要那样做,只需在训练集上训练:
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)
变成:
history = model.fit(x_train, y_train, nb_epoch=10, validation_split=0.2, shuffle=True)
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