model.compile(loss=qri.mae_clip, optimizer=sgd)
# Use early stopping and saving as callbacks
early_stop = EarlyStopping(monitor='val_loss', patience=10)
save_best = ModelCheckpoint("models/%s.mdl" % MDL_NAME, save_best_only=True)
callbacks = [early_stop, save_best]
# Train model
t0 = time.time()
hist = model.fit(train_set[0], train_set[1], validation_data=valid_set,
verbose=2, callbacks=callbacks, nb_epoch=1000, batch_size=20)
time_elapsed = time.time() - t0
# Load best model
model.load_weights("models/%s.mdl" % MDL_NAME)
# Print time elapsed and loss on testing dataset
test_set_loss = model.test_on_batch(test_set[0], test_set[1])
print "\nTime elapsed: %f s" % time_elapsed
print "Testing set loss: %f" % test_set_loss
# Save results
qri.save_results("results/%s.out" % MDL_NAME, time_elapsed, test_set_loss)
qri.save_history("models/%s.hist" % MDL_NAME, hist.history)
# Plot training and validation loss
qri.plot_train_valid_loss(hist.history)
# Make predictions
qri.plot_test_predictions(model, train_set)
callbacks = [early_stop, save_best]
# Train model
t0 = time.time()
hist = model.fit(train_set[0],
train_set[1],
validation_data=valid_set,
verbose=2,
callbacks=callbacks,
nb_epoch=1000,
batch_size=20)
time_elapsed = time.time() - t0
# Load best model
model.load_weights("models/%s.mdl" % MDL_NAME)
# Print time elapsed and loss on testing dataset
test_set_loss = model.test_on_batch(test_set[0], test_set[1])
print "\nTime elapsed: %f s" % time_elapsed
print "Testing set loss: %f" % test_set_loss
# Save results
qri.save_results("results/%s.out" % MDL_NAME, time_elapsed, test_set_loss)
qri.save_history("models/%s.hist" % MDL_NAME, hist.history)
# Plot training and validation loss
qri.plot_train_valid_loss(hist.history)
# Make predictions
qri.plot_test_predictions(model, train_set)