def Test():
print("Making model")
model = Model()
model.build_model_from_pickle("Graphs_and_Results/dual/" + version +
"/SAVED_WEIGHTS.pkl")
datafeeder = Prep(TEST_AMOUNT, VALID_AMOUNT, SELECTION_LIST)
datafeeder.load_train_to_RAM()
data, label = datafeeder.GetTest_dom()
predictions, l2loss = model.call(data)
assert len(label) == len(
predictions), "something is wrong with the loaded model or labels"
conf = np.zeros(shape=[len(label[0]), len(predictions[0])])
for i in range(len(predictions)):
k = np.argmax(predictions[i])
l = np.argmax(label[i])
conf[k][l] += 1
test = open("Graphs_and_Results/dual/" + version + "/confusion.csv", "w")
logger = csv.writer(test, lineterminator="\n")
test_ = open("Graphs_and_Results/dual/" + version + "/results.csv", "w")
logger_ = csv.writer(test_, lineterminator="\n")
logger_.writerow([accuracy(predictions, label)])
for iterate in conf:
logger.writerow(iterate)
right, wrong = record_error(data, label, predictions)
try:
os.mkdir("Graphs_and_Results/dual/" + version + "/wrong/")
os.mkdir("Graphs_and_Results/dual/" + version + "/right/")
except:
shutil.rmtree("Graphs_and_Results/dual/" + version + "/wrong/")
shutil.rmtree("Graphs_and_Results/dual/" + version + "/right/")
os.mkdir("Graphs_and_Results/dual/" + version + "/wrong/")
os.mkdir("Graphs_and_Results/dual/" + version + "/right/")
for i in range(len(wrong)):
print("Saving wrong image {}".format(i))
carrier = np.reshape(wrong[i], [100, 100])
util.save_image(
255 * carrier,
"Graphs_and_Results/dual/" + version + "/wrong/" + str(i) + ".jpg",
"L")
for i in range(len(right)):
print("Saving right image {}".format(i))
carrier = np.reshape(right[i], [100, 100])
util.save_image(
255 * carrier,
"Graphs_and_Results/dual/" + version + "/right/" + str(i) + ".jpg",
"L")
print("This is the test set accuracy: {}".format(
accuracy(predictions, label)))
def Test():
print("Making model")
model = Model()
model.build_model_from_pickle(base_directory + "SAVED_WEIGHTS.pkl")
datafeeder = Prep(TEST_AMOUNT, VALID_AMOUNT, [version])
datafeeder.load_train_to_RAM()
data, label = datafeeder.GetTest_dom()
data = data[0] # this is because we now have multiple images in the pickle
predictions, l2loss = model.call(data)
print("This is the test set accuracy: {}".format(
accuracy(predictions, label)))
def Test():
print("Making model")
model = Model()
model.build_model_from_pickle("Graphs_and_Results/basic/" + version + "/" +
"SAVED_WEIGHTS.pkl")
datafeeder = Prep(TEST_AMOUNT, VALID_AMOUNT, [version])
datafeeder.load_train_to_RAM()
data, label = datafeeder.GetTest_dom()
data = data[0] # this is because we now have multiple images in the pickle
predictions, l2loss = model.call(data)
assert len(label) == len(
predictions), "something is wrong with the loaded model or labels"
right, wrong, wrong_list = record_error(data, label, predictions)
print("This is the test set accuracy: {}".format(
accuracy(predictions, label)))
try:
os.mkdir("Graphs_and_Results/basic/" + version + "/wrong/")
os.mkdir("Graphs_and_Results/basic/" + version + "/right/")
os.mkdir("Graphs_and_Results/wrongs/")
except:
shutil.rmtree("Graphs_and_Results/basic/" + version + "/wrong/")
shutil.rmtree("Graphs_and_Results/basic/" + version + "/right/")
os.mkdir("Graphs_and_Results/basic/" + version + "/wrong/")
os.mkdir("Graphs_and_Results/basic/" + version + "/right/")
wrong_logger = csv.writer(open(
"Graphs_and_Results/wrongs/" + version + "SimpleCNN.csv", "w"),
lineterminator="\n")
for element in wrong_list:
wrong_logger.writerow([element])
for i in range(len(wrong)):
print("Saving wrong image {}".format(i))
carrier = np.reshape(wrong[i], [100, 100])
util.save_image(
255 * carrier, "Graphs_and_Results/basic/" + version + "/wrong/" +
str(i) + ".jpg", "L")
for i in range(len(right)):
print("Saving right image {}".format(i))
carrier = np.reshape(right[i], [100, 100])
util.save_image(
255 * carrier, "Graphs_and_Results/basic/" + version + "/right/" +
str(i) + ".jpg", "L")