net = nn.ConvNet(10, C, H=128) net = solver_fun( net, X_train[0], y_train[0], val_set=(X_val[0], y_val[0]), mb_size=mb_size, alpha=alpha, n_iter=n_iter, print_after=print_after ) y_pred = net.predict(X_test[0]) accs[k] = np.mean(y_pred == y_test[0]) ''' #multi worker net = [] for i in range(worker_num): if net_type == 'ff': net.append(nn.FeedForwardNet(D, C, H=128, lam=reg, p_dropout=p_dropout, loss=loss, nonlin=nonlin)) net1 = nn.FeedForwardNet(D, C, H=128,lam=reg,p_dropout=p_dropout,loss = loss,nonlin = nonlin) net2 = nn.FeedForwardNet(D, C, H=128,lam=reg,p_dropout=p_dropout,loss = loss,nonlin = nonlin) elif net_type == 'cnn': net.append(nn.ConvNet(10, C, H=128)) net1 = nn.ConvNet(10,C,H=128) net2 = nn.ConvNet(10,C,H=128) net = solver_fun( net, X_train, y_train, worker_num=worker_num,val_set=val1_set, mb_size=mb_size, alpha=alpha, n_iter=n_iter, print_after=print_after ) y_pred = [] accs=[] for i in range(worker_num): y_pred.append(net[i].predict(X_test))
solver_fun = solvers[solver] accs = np.zeros(n_experiment) print() print('Experimenting on {}'.format(solver)) print() for k in range(n_experiment): print('Experiment-{}'.format(k + 1)) # Reset model if net_type == 'ff': net = nn.FeedForwardNet(D, C, H=128, lam=reg, p_dropout=p_dropout, loss=loss, nonlin=nonlin) elif net_type == 'cnn': net = nn.ConvNet(10, C, H=128) net = solver_fun(net, X_train, y_train, val_set=(X_val, y_val), mb_size=mb_size, alpha=alpha, n_iter=n_iter, print_after=print_after)