neural nets optimizer shape mismatch during backward pass

[srs3]

@ddbourgin Have an issue where updates to gradients cannot be performed since shapes conflict during backprop... specifically in the optimizer file.

Error reads:

C[param_name]["mean"] = d1 * mean + (1 - d1) * param_grad
ValueError: operands could not be broadcast together with shapes (100,10) (3072,100) 

Model architecture is as follows:

Input -> n_samples, 3072 FC1 -> 3072, 100 FC2 -> 100, 10

The model code is as follows:

def _build_model(self):
    self.model = OrderedDict()
    self.model['fc1'] = FullyConnected(n_out=self.layers[0],
                                       act_fn=ReLU(),
                                       init=self.initializer,
                                       optimizer=self.optimizer)

    self.model['fc2'] = FullyConnected(n_out=self.layers[1],
                                       act_fn=Affine(slope=1, intercept=0),
                                       init=self.initializer,
                                       optimizer=self.optimizer)

    self.model['out'] = Softmax(dim=-1,
                                optimizer=self.optimizer)

@property
def parameters(self):
    return {k: v.parameters for k, v in self.model.items()}

@property
def hyperparameters(self):
    return {k: v.hyperparameters for k, v in self.model.items()}

@property
def derived_variables(self):
    return {k: v.derived_variables for k, v in self.model.items()}

@property
def gradients(self):
    return {k: v.gradients for k, v in self.model.items()}

def forward(self, x):
    out = x
    for k, v in self.model.items():
        out = v.forward(out)
    return out

def backward(self, y, y_pred):
    """Compute dLdy and then backprop through the layers in self.model"""
    dY_pred = self.loss.grad(y, y_pred)
    for k, v in reversed(list(self.model.items())):
        dY_pred = v.backward(dY_pred)
        self._dv['d' + k] = dY_pred
    return dY_pred

def update(self, cur_loss):
    """Perform gradient updates"""
    for k, v in reversed(list(self.model.items())):
        v.update(cur_loss)
    self.flush_gradients()

Hoping we can fix this and also create an example for people to follow. Thanks