def main(argv):
if len(argv) != 2:
print("Usage of this program:\npython main.py <path to images folder>")
return
folder = argv[1]
training_data = None
if not os.path.isfile(folder + ".pickle"):
training_data = load_images_from_folder(folder)
pickle.dump(training_data, open(folder + ".pickle", "wb"))
else:
training_data = pickle.load(open(folder + ".pickle", "rb"))
print(f"loaded {len(training_data)} images as numpy array.")
gan = None
if os.path.isfile("gan.model"):
gan = pickle.load(open("gan.model", "rb"))
else:
gan = Gan()
gan.train(training_data, 1000)
from datagen import load_dataset
# Global
import numpy as np
import random as rd
import matplotlib.pyplot as plt
from tensorflow import keras as ks
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LeakyReLU, Reshape, Conv2DTranspose, Conv2D, Flatten, Dropout
## Parameters and dataset
Ldim = 100
P = 10
Shape = (28, 28, 1)
X, Y = load_dataset()
## Gan
Gan = Gan(ldim=Ldim, p=P, shape=Shape)
Gan.load('C:/Users/meri2/Documents/Projects/MNSIT_GAN/Attempt_0')
Gan.make_gan()
losses, accuracies, times = Gan.train(
X,
Y,
epochs=0,
batch_size=256,
)
Gan.samples(7)