Layer mismatch causes session to to terminate abruptly about singa HOT 6 CLOSED

from singa import autograd
from singa import module
from singa import opt
from singa import tensor

class MLP():
    def __init__(self):
        self.linear1 = autograd.Linear(3,4)
        self.linear2 = autograd.Linear(4,3)
    def forward(self,x):
        y = self.linear1(x)
        return self.linear2(y)
    def loss(self, out, ty):
        return autograd.softmax_cross_entropy(out, ty)
    def optim(self, loss):
        self.optimizer.backward_and_update(loss)
    def set_optimizer(self, optimizer):
        self.optimizer = optimizer


if __name__ == '__main__':
    x=tensor.Tensor((3,3)).gaussian(1,1)
    y=tensor.Tensor((3,3)).gaussian(1,1)

    autograd.training = True
    m = MLP()
    sgd = opt.SGD()
    m.set_optimizer(sgd)
    out = m.forward(x)
    loss = m.loss(out, y)
    m.optim(loss)
    print(loss)

from singa import autograd
from singa import module
from singa import opt
from singa import tensor
from singa import device

class MLP():
    def __init__(self):
        self.linear1 = autograd.Linear(3, 4)
        self.linear2 = autograd.Linear(4, 3)
    def forward(self,x):
        y = self.linear1(x)
        return self.linear2(y)
    def loss(self, out, ty):
        return autograd.softmax_cross_entropy(out, ty)
    def optim(self, loss):
        self.optimizer.backward_and_update(loss)
    def set_optimizer(self, optimizer):
        self.optimizer = optimizer

def train(model, x, t, dev=device.get_default_device(), epochs=100):
    for i in range(epochs):
        y = model.forward(x)
        loss = autograd.mse_loss(y, t)
        print("loss: ", loss)
        sgd = opt.SGD()
        for p, gp in autograd.backward(loss):
            sgd.update(p, gp)
        sgd.step()


if __name__ == '__main__':
    x=tensor.Tensor((3,3)).gaussian(1,1)
    y=tensor.Tensor((3,3)).gaussian(1,1)
    
    autograd.training = True
    m = MLP()
    sgd = opt.SGD()
    m.set_optimizer(sgd)
    out = m.forward(x)
    loss = m.loss(out, y)
    m.optim(loss)
    print(loss)
    train(m,x,y)

from singa import autograd
from singa import module
from singa import opt
from singa import tensor
from singa import device

class MLP():
    def __init__(self):
        self.linear1 = autograd.Linear(3, 4)
        self.linear2 = autograd.Linear(4, 3)
    def forward(self,x):
        y = self.linear1(x)
        return self.linear2(y)
    def loss(self, out, ty):
        return autograd.softmax_cross_entropy(out, ty)
    def optim(self, loss):
        self.optimizer.backward_and_update(loss)
    def set_optimizer(self, optimizer):
        self.optimizer = optimizer

def train(model, x, t, dev=device.get_default_device(), epochs=100):
    for i in range(epochs):
        y = model.forward(x)
        loss = autograd.mse_loss(y, t)
        print("loss: ", loss)
        sgd = opt.SGD()
        for p, gp in autograd.backward(loss):
            sgd.update(p, gp)
        sgd.step()


if __name__ == '__main__':
    x=tensor.Tensor((3,3)).gaussian(1,1)
    y=tensor.Tensor((3,4)).gaussian(1,1)
    
    autograd.training = True
    m = MLP()
    sgd = opt.SGD()
    m.set_optimizer(sgd)
    out = m.forward(x)
    loss = m.loss(out, y)
    m.optim(loss)
    print(loss)
    train(m,x,y)

autograd.softmax_cross_entropy(tx, ty)
autograd.mse_loss(tx, ty)
autograd.equal(tx,ty)