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)
