reduce_learning_rate = ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=5,
verbose=1)
callbacks = [csv_logger, model_checkpoint, reduce_learning_rate]
history = model.fit_generator(
generator=generator.flow(mode='train'),
steps_per_epoch=int(num_training_samples / batch_size),
epochs=num_epochs,
verbose=1,
callbacks=callbacks,
validation_data=generator.flow(mode='validation'),
validation_steps=int(num_validation_samples / batch_size))
model.save(os.path.join('../trained_models/nuswide', "weights_final.hdf5"))
# list all data in history
print(history.history.keys())
# summarize history for 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', 'test'], loc='upper left')
# plt.show()
plt.savefig('../results/acc_val_acc.png')
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
reduce_learning_rate = ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=5,
verbose=1)
callbacks = [csv_logger, model_checkpoint, reduce_learning_rate]
history = model.fit_generator(
generator=generator.flow(mode='train'),
steps_per_epoch=num_training_samples // batch_size,
epochs=num_epochs,
verbose=1,
callbacks=callbacks,
validation_data=generator.flow(mode='validation'),
validation_steps=num_validation_samples // batch_size)
model.save("trained_models/image_text/weights_final.hdf5")
# list all data in history
print(history.history.keys())
# summarize history for 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', 'test'], loc='upper left')
# plt.show()
plt.savefig('./results/acc_val_acc.png')
plt.cla() # 清除axes
plt.clf() # 清除当前 figure 的所有axes
plt.close()