From 06228fb5afde4180c697ce244b7465b7533d3cbc Mon Sep 17 00:00:00 2001 From: Vasil Zlatanov Date: Mon, 4 Mar 2019 16:59:31 +0000 Subject: Use tqdm in cgan --- cgan.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/cgan.py b/cgan.py index 68256f3..5ab0c10 100755 --- a/cgan.py +++ b/cgan.py @@ -10,6 +10,7 @@ from keras.models import Sequential, Model from keras.optimizers import Adam import matplotlib.pyplot as plt from IPython.display import clear_output +from tqdm import tqdm import numpy as np @@ -122,7 +123,7 @@ class CGAN(): xaxis = np.arange(epochs) loss = np.zeros((2,epochs)) - for epoch in range(epochs): + for epoch in tqdm(range(epochs)): # --------------------- # Train Discriminator @@ -154,9 +155,6 @@ class CGAN(): # Plot the progress #print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss)) - if epoch % 500 == 0: - clear_output() - print(epoch) loss[0][epoch] = d_loss[0] loss[1][epoch] = g_loss -- cgit v1.2.3