绘图训练、验证和测试集精度

我想画出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

我绘制了培训和验证的 准确性和 失去:

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)中添加和绘制测试集的精度，但是从 model.metrics_names中我获得了与在训练数据 plt.plot(history.history['acc'])上绘制精度所使用的 “ acc”值相同的值。我如何绘制测试集的准确度？

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小开
最佳答案

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)

Change into:

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

Validate the model on the test data as shown below and then plot the accuracy and loss

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)
小开 
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()

Model Accuracy

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()

Model Loss

小开

Try

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

This builds a graph with the available metrics of the history for all datasets of the history. Example:

enter image description here

小开

You could do it this way also ....

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])

For the plotting - I like plotly ... so

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()

enter image description here

Nothing wrong with either of the proceeding methods. Please note the Plotly graph has two scales , 1 for loss the other for accuracy.


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