print("Wrong input")
override = 1
if override == True:
print("Over Riding all input requirements with default values")
# Datasets
if override == 0:
dataset_option = (input(
"Which Dataset you want to run the ConvNet Model on?\nX: Image-Classification\nS : SIGNS Dataset\nN: Hand-Written Digit Classification"
+
"\nNb : Hand-Written Digits BigDatset [MNIST Datset]\nH: Happy Face Detection\t"
))
if dataset_option == "X":
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset(
)
train_set_x = train_set_x_orig
print(train_set_x.shape)
print(test_set_x_orig.shape)
test_set_x = test_set_x_orig
print(test_set_x.shape)
num_px = train_set_x_orig.shape[1]
X = train_set_x / 255
Y = train_set_y
X_test = test_set_x / 255
Y_test = test_set_y
print(Y)
print(Y_test)
print("Y_test.shape : " + str(Y_test.shape))
print("Wrong input")
override = 1
if override == True:
print("Over Riding all input requirements with default values")
# Datasets
if override == 0:
dataset_option = (input(
"Which Dataset you want to run the ConvNet Model on?\nX: Image-Classification\nS : SIGNS Dataset\nN: Hand-Written Digit Classification"
+
"\nNb : Hand-Written Digits BigDatset [MNIST Datset]\nH: Happy Face Detection\t"
))
if dataset_option == "X":
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset(
)
train_set_x = train_set_x_orig
print(train_set_x.shape)
print(test_set_x_orig.shape)
test_set_x = test_set_x_orig
print(test_set_x.shape)
num_px = train_set_x_orig.shape[1]
X = train_set_x / 255
Y = train_set_y
X_test = test_set_x / 255
Y_test = test_set_y
print(Y_test)
print("Y_test.shape : " + str(Y_test.shape))
print("X_test.shape : " + str(X_test.shape))