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()