batch_data = np.empty([batch_size, N_IN_DATA], np.float32)
batch_desi = np.empty([batch_size, N_OUT_DATA], np.float32)
for i in range(batch_size):
x = randint(1, len(in_data)) - 1
batch_imgs[i], batch_data[i], batch_desi[i] = in_data[x].load_data()
return batch_imgs, batch_data, batch_desi
#---------------------------------------------#
# Running the Network
#---------------------------------------------#
# Load the network and the data
data = ld.load_data()
nn = Network()
if LOAD_NETWORK:
nn.load_network(LOAD_LOCATION)
# Main loop
for i in tqdm(range(4000, 10000000)):
# Generate the batch and train
img_batch, data_batch, desired_batch = get_batch(data, BATCH_SIZE)
loss = nn.train(img_batch, data_batch, desired_batch, USE_WEIGHTED_LOSS)
# Print the loss
if i % 20 == 0:
print i, loss, CHECKPOINT_END
# Save the network
if SAVE_NETWORK and (i + 1) % 1000 == 0:
nn.save_network(os.path.join(SAVE_LOCATION, str(i + 1) + CHECKPOINT_END))
