In [1]:

    

# load the data
import numpy as np

X = np.random.randint(0, 10, size=10000)
Y = np.zeros((10000, 28, 28, 1))


    

In [2]:

    

from noxer.gm.gan import ACGANCategoryToImageGenerator

model = ACGANCategoryToImageGenerator(verbose=1)
model.fit(X, Y)


    

    




---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-2-7a48c3c0ae46> in <module>()
      2 
      3 model = ACGANCategoryToImageGenerator(verbose=1)
----> 4 model.fit(X, Y)

~/noxer/noxer/gm/acgan.py in fit(self, X, Y, **kwargs)
    279         )
    280 
--> 281         self.net.train(X, Y)
    282 
    283     def predict_noise(self, X, Z, **kwargs):

~/noxer/noxer/gm/acgan.py in train(self, X, Y)
    193                 self.D_optimizer.zero_grad()
    194 
--> 195                 D_real, C_real = self.D(x_)
    196                 D_real_loss = self.BCE_loss(D_real, self.y_real_)
    197                 C_real_loss = self.CE_loss(C_real, torch.max(y_vec_, 1)[1])

/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    222         for hook in self._forward_pre_hooks.values():
    223             hook(self, input)
--> 224         result = self.forward(*input, **kwargs)
    225         for hook in self._forward_hooks.values():
    226             hook_result = hook(self, input, result)

~/noxer/noxer/gm/acgan.py in forward(self, input)
     89 
     90     def forward(self, input):
---> 91         x = self.conv(input)
     92         x = x.view(-1, 128 * (self.input_height // 4) * (self.input_width // 4))
     93         x = self.fc1(x)

/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    222         for hook in self._forward_pre_hooks.values():
    223             hook(self, input)
--> 224         result = self.forward(*input, **kwargs)
    225         for hook in self._forward_hooks.values():
    226             hook_result = hook(self, input, result)

/usr/local/lib/python3.5/dist-packages/torch/nn/modules/container.py in forward(self, input)
     65     def forward(self, input):
     66         for module in self._modules.values():
---> 67             input = module(input)
     68         return input
     69 

/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    222         for hook in self._forward_pre_hooks.values():
    223             hook(self, input)
--> 224         result = self.forward(*input, **kwargs)
    225         for hook in self._forward_hooks.values():
    226             hook_result = hook(self, input, result)

/usr/local/lib/python3.5/dist-packages/torch/nn/modules/conv.py in forward(self, input)
    252     def forward(self, input):
    253         return F.conv2d(input, self.weight, self.bias, self.stride,
--> 254                         self.padding, self.dilation, self.groups)
    255 
    256 

/usr/local/lib/python3.5/dist-packages/torch/nn/functional.py in conv2d(input, weight, bias, stride, padding, dilation, groups)
     50     f = ConvNd(_pair(stride), _pair(padding), _pair(dilation), False,
     51                _pair(0), groups, torch.backends.cudnn.benchmark, torch.backends.cudnn.enabled)
---> 52     return f(input, weight, bias)
     53 
     54 

RuntimeError: Need input.size[1] == 1 but got 28 instead.