train_sz = X.shape[0] train_batch_sz = X.shape[0] train_batch_n = train_sz / train_batch_sz train_cost = np.zeros((2000,)) train_err = np.zeros((2000,)) test_cost = np.zeros((2000,)) test_err = np.zeros((2000,)) for epoch in range(2000): for batch_idx in range(train_batch_n): batch_X, batch_H, batch_C, batch_m, batch_y = prepare_batch(X=X, H=H, C=C, m=M, y=y, batch_idx=batch_idx, batch_sz=train_batch_sz) #v1 = f_watch(batch_X, batch_H, batch_C, batch_m, batch_y) #cost = f_grad_shared(batch_X, batch_H, batch_C, batch_m, batch_y) f_update() train_cost[epoch] = f_grad_shared(X, H, C, M, y) train_err[epoch] = f_pred_err(X, H, C, M, y) test_cost[epoch] = f_grad_shared(X1, H1, C1, M1, y1) test_err[epoch] = f_pred_err(X1, H1, C1, M1, y1) output_pred = f_pred_gross(X1, H1, C1, M1) output_pred[M1==0] = -1
train_batch_sz = 37 train_batch_n = train_sz / train_batch_sz train_cost = np.zeros((2000, )) train_err = np.zeros((2000, )) test_cost = np.zeros((2000, )) test_err = np.zeros((2000, )) for epoch in range(2000): for batch_idx in range(train_batch_n): batch_X, batch_H, batch_C, batch_m, batch_y = prepare_batch( X=X, H=H, C=C, m=M, y=y, batch_idx=batch_idx, batch_sz=train_batch_sz) #v1 = f_watch(batch_X, batch_H, batch_C, batch_m, batch_y) cost = f_grad_shared(batch_X, batch_H, batch_C, batch_m, batch_y) f_update() train_cost[epoch] = f_grad_shared(X, H, C, M, y) train_err[epoch] = f_pred_err(X, H, C, M, y) test_cost[epoch] = f_grad_shared(X1, H1, C1, M1, y1) test_err[epoch] = f_pred_err(X1, H1, C1, M1, y1) output_pred = f_pred_gross(X1, H1, C1, M1)