Keras - Plot training, validation and test set accuracy

Question

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

model.compile(loss=\'binary_crossentropy\', optimizer=\'adam\', metrics=[\'accuracy\'])
history = m         


        

Answer 1

try

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

Answer 2

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 3

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)

Answer 4

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)