model = MyModel(n_classes).model # define optimizers model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) model.summary() print(Config) # training hist = model.fit(train_generator, validation_data=valid_generator, epochs=Config['num_epochs'], callbacks=[checkpoint]) loss = model.evaluate(test_generator) plt.plot(hist.history['accuracy']) plt.plot(hist.history['val_accuracy']) plt.title('Test accuracy: ' + str(loss[1])) plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.legend(['Train', 'valid'], loc='upper left') plt.savefig(Config['checkpoint_path'] + '/' + name + '.png') print(loss) if Config['shutown']: import os os.system("sudo shutdown -P now")
# read dataset dataset = Dataset("training-1.csv") # create column features for further fit them into a model dataset.create_feature_columns() # preprocess it dataset.preprocess() #split on train/test dataset.split_data() # initialize model model = MyModel() # build it model.build(dataset.feature_columns) # train model.train(dataset.train_ds, dataset.val_ds) # evaluate model.evaluate(dataset.test_ds)