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)
