def main(): # os.nice(20) # os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # Inicializa e configura parĂ¢metros p = Parameters() d = Dataset() # Carrega as imagens do treino e do test com suas respectivas labels train = d.load_all_images(p.TRAIN_FOLDER, p.TEST_FOLDER, p.IMAGE_HEIGHT, p.IMAGE_WIDTH) train = train / 255.0 print("size of train: {}".format(len(train))) # Embaralhas as imagens train = d.shuffle(train, seed=42) print(train.shape) p.NUM_EPOCHS_FULL = 10 # Inicializa a rede n = Net(p) # Inicia treino n.treino(train)
# Train discriminator on both real and fake images _, __ = sess.run([d_trainer_real, d_trainer_fake], {self.x_placeholder: real_image_batch}) # Train generator _ = sess.run(g_trainer) if i % 10 == 0: # Update TensorBoard with summary statistics summary = sess.run(merged, {self.x_placeholder: real_image_batch}) writer.add_summary(summary, i) saver = tf.train.Saver() path_model = 'pretrained-model/' + datetime.datetime.now().strftime( "%Y%m%d-%H%M%S") + '_gan.ckpt' saver.save(sess, path_model) print("The model has saved in: " + path_model) if __name__ == "__main__": d = Dataset() _ = d.load_all_images('../data_part1/train', '../data_part1/test', height=28, width=28) #mnist = input_data.read_data_sets("MNIST_data/") print("Imagens carregadas!") net = Gan() print("Rede inicializada!") net.train(d)