def visualise_features(P, centroids):
PC = np.dot(centroids, P.T)
plt.figure(figsize=(12, 8))
utils.visualise_filters(PC.T,
settings['patch_height'],
settings['patch_width'],
posneg=False)
plt.draw()
plt.clf()
plt.plot(objs_train_so_far, label='train')
plt.plot(objs_valid_so_far, label='validation')
plt.title("objective")
plt.legend()
plt.draw()
plt.figure(3)
plt.clf()
plt.plot(ces_train_so_far, label='train')
plt.plot(ces_valid_so_far, label='validation')
plt.title("cross-entropy")
plt.legend()
plt.draw()
plt.figure(4)
plt.clf()
visualise_filters(rbm.W.var.get_value(), dim=28)
plt.colorbar()
plt.title("filters")
plt.draw()
print "Epoch %d" % epoch
print "training set: MSE = %.6f, CE = %.6f, objective = %.6f" % (mse_train, ce_train, obj_train)
print "validation set: MSE = %.6f, CE = %.6f, objective = %.6f" % (mse_valid, ce_valid, obj_valid)
plt.draw()
plt.figure(2)
plt.clf()
plt.plot(objs_train_so_far, label='train')
plt.plot(objs_valid_so_far, label='validation')
plt.title("objective")
plt.legend()
plt.draw()
plt.figure(3)
plt.clf()
plt.plot(ces_train_so_far, label='train')
plt.plot(ces_valid_so_far, label='validation')
plt.title("cross-entropy")
plt.legend()
plt.draw()
plt.figure(4)
plt.clf()
visualise_filters(rbm.W.var.get_value(), dim=28)
plt.colorbar()
plt.title("filters")
plt.draw()
print "Epoch %d" % epoch
print "training set: MSE = %.6f, CE = %.6f, objective = %.6f" % (
mse_train, ce_train, obj_train)
print "validation set: MSE = %.6f, CE = %.6f, objective = %.6f" % (
mse_valid, ce_valid, obj_valid)
def visualise_features(P, centroids):
PC = np.dot(centroids, P.T)
plt.figure(figsize=(12,8))
utils.visualise_filters(PC.T, settings['patch_height'], settings['patch_width'], posneg=False)
plt.draw()