test_wrong = T.neq(T.argmax(test_prediction, axis=1), T.argmax(target_var, axis=1))
# Compile a second function computing the validation loss and accuracy:
# val_fn = theano.function([input_var, target_var, labeled_var], [loss2*lr[1], test_acc], allow_input_downcast=True)
val_fn = theano.function([input_var, target_var, labeled_var],
[test_loss,
losses_ratio[0] * test_loss1.mean(),
losses_ratio[1] * test_loss2.mean(),
losses_ratio[2] * l2_penalties.mean(),
classification, test_acc, test_wrong],
allow_input_downcast=True, on_unused_input='ignore')
# ----------------------------RUN-----------------------------------#
MODE = input('"TEST" OR "TRAIN"?\n')
if MODE == 'TEST':
# load saved best model
io.read_model_data([unsupervised_graph, supervised_graph], BEST_MODEL_PATH)
run_test(val_fn, teX, teY)
elif MODE == 'TRAIN':
# if last model exists, load last model:
best_validation_acc = 0
best_validation_err = float('inf')
if os.path.isfile(LAST_MODEL_PATH):
choice = input(
'PREVIOUS MODEL FOUND, CONTINUE TRAINING OR OVERRIDE OR END? (ANSWER: "CONTINUE", "OVERRIDE", "END")\n')
if choice == 'CONTINUE':
best_validation_err, best_validation_acc, old_lr = io.read_model_data(
[unsupervised_graph, supervised_graph], LAST_MODEL_PATH)
sgd_lr.set_value(old_lr)
elif choice == 'OVERRIDE':
best_validation_acc = 0
best_validation_err = float('inf')
dtype=theano.config.floatX)
# Compile a second function computing the validation loss and accuracy:
#val_fn = theano.function([input_var, target_var, labeled_var], [loss2*lr[1], test_acc], allow_input_downcast=True)
val_fn = theano.function([input_var, target_var, labeled_var],
[test_loss,
lr[0]*test_loss1.mean(),
lr[1]*test_loss2.mean(),
lr[2]*l2_penalties.mean(),
test_acc], allow_input_downcast=True,
on_unused_input='ignore')
# ----------------------------RUN-----------------------------------#
MODE = input('"TEST" OR "TRAIN"?\n')
if MODE == 'TEST':
# load saved best model
io.read_model_data([unsupervised_graph, supervised_graph], BEST_MODEL_PATH)
run_test(val_fn, teX, teY)
elif MODE == 'TRAIN':
# if last model exists, load last model:
best_validation_acc = 0
if os.path.isfile(LAST_MODEL_PATH + '.' + io.PARAM_EXTENSION):
choice = input(
'PREVIOUS MODEL FOUND, CONTINUE TRAINING OR OVERRIDE OR END? (ANSWER: "CONTINUE", "OVERRIDE", "END")\n')
if choice == 'CONTINUE':
best_validation_acc = io.read_model_data([unsupervised_graph, supervised_graph], BEST_MODEL_PATH)
elif choice == 'OVERRIDE':
best_validation_acc = 0
else:
sys.exit('Terminated by user choice.')
print 'Training...'
# number of epochs to train
