Keras - 绘制培训,验证和测试集准确性

Question

我想绘制这个简单神经网络的输出:

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

我绘制了准确性和失去的培训和验证:

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()

现在我想添加并绘制测试集的准确度model.test_on_batch(x_test, y_test),但是从model.metrics_names我获得用于绘制训练数据准确性的相同值'acc'plt.plot(history.history['acc']).我怎么能绘制测试集的准确性？

Answer 1

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()

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()

Answer 2

这是相同的，因为您正在测试集上而不是训练集上进行训练。不要那样做，只需在训练集上进行训练即可：

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)

Answer 3

尝试

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

这将构建一个图表，其中包含所有历史数据集的可用历史指标。例子：

Answer 4

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()

这两种处理方法都没有问题。请注意，Plotly 图表有两个刻度，1 表示损失，另一个表示准确性。

Answer 5

在测试数据上验证模型，如下所示，然后绘制精度和损失

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)