Keras - 绘制训练、验证和测试集准确性

import keras
from matplotlib import pyplot as plt
history = model1.fit(train_x, train_y,validation_split = 0.1, epochs=50, batch_size=4)
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.show()

https://i.stack.imgur.com/FmcMs.png

plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.show()

https://i.stack.imgur.com/3HL0C.png

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：

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)

尝试

pd.DataFrame(history.history).plot(figsize=(8,5))
plt.show()

这将构建一个图表，其中包含历史的所有数据集的可用历史指标。例子：

https://i.stack.imgur.com/LfFC3.png

如下所示在测试数据上验证模型，然后绘制准确率和损失

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
history = model.fit(X_train, y_train, nb_epoch=10, validation_data=(X_test, y_test), shuffle=True)

你也可以这样做......

regressor.compile(optimizer = 'adam', loss = 'mean_squared_error',metrics=['accuracy'])
earlyStopCallBack = EarlyStopping(monitor='loss', patience=3)
history=regressor.fit(X_train, y_train, validation_data=(X_test, y_test), epochs = EPOCHS, batch_size = BATCHSIZE, callbacks=[earlyStopCallBack])

对于情节 - 我喜欢情节......所以

import plotly.graph_objects as go
from plotly.subplots import make_subplots

# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add traces
fig.add_trace(
    go.Scatter( y=history.history['val_loss'], name="val_loss"),
    secondary_y=False,
)

fig.add_trace(
    go.Scatter( y=history.history['loss'], name="loss"),
    secondary_y=False,
)

fig.add_trace(
    go.Scatter( y=history.history['val_accuracy'], name="val accuracy"),
    secondary_y=True,
)

fig.add_trace(
    go.Scatter( y=history.history['accuracy'], name="val accuracy"),
    secondary_y=True,
)

# Add figure title
fig.update_layout(
    title_text="Loss/Accuracy of LSTM Model"
)

# Set x-axis title
fig.update_xaxes(title_text="Epoch")

# Set y-axes titles
fig.update_yaxes(title_text="<b>primary</b> Loss", secondary_y=False)
fig.update_yaxes(title_text="<b>secondary</b> Accuracy", secondary_y=True)

fig.show()

https://i.stack.imgur.com/dDpga.png

两种处理方法都没有错。请注意，Plotly 图有两个尺度，1 表示损失，另一个表示准确性。