Can anyone tell me what's wrong here in cnn own model in mxnet? - mxnet

Question

def acc(output, label):
correct_preds = output.argmax(axis=1) == label.astype('float32')
return correct_preds.mean().asscalar()
for epoch in range(10):
train_loss, train_acc, valid_acc = 0., 0., 0.
tic = time()
for data, label in train_data:
data = data.copyto(mx.cpu(0))
label = label.copyto(mx.cpu(0))
with autograd.record():
output = net(data)
loss = softmax_cross_entropy(output, label)
loss.backward()
trainer.step(batch_size)
train_loss += loss.mean().asscalar()
train_acc += acc(output, label)
When running this part I get the error and my dataset is in pascol voc format
ValueError
Traceback (most recent call last)
<ipython-input-7-9926ba7deb21> in <module>()
12 label = label.copyto(mx.cpu(0))
13 with autograd.record():
---> 14 output = net(data)
15 loss = softmax_cross_entropy(output, label)
16
/home/manasi/.local/lib/python2.7/site-packages/mxnet/gluon/block.pyc in __call__(self, *args)
539 hook(self, args)
540
--> 541 out = self.forward(*args)
542
543 for hook in self._forward_hooks.values():
/home/manasi/.local/lib/python2.7/site-packages/mxnet/gluon/nn/basic_layers.pyc in forward(self, x)
51 def forward(self, x):
52 for block in self._children.values():
---> 53 x = block(x)
54 return x
55
/home/manasi/.local/lib/python2.7/site-packages/mxnet/gluon/block.pyc in __call__(self, *args)
539 hook(self, args)
540
--> 541 out = self.forward(*args)
542
543 for hook in self._forward_hooks.values():
/home/manasi/.local/lib/python2.7/site-packages/mxnet/gluon/block.pyc in forward(self, x, *args)
911 params = {i: j.data(ctx) for i, j in self._reg_params.items()}
912 except DeferredInitializationError:
--> 913 self._deferred_infer_shape(x, *args)
914 for _, i in self.params.items():
915 i._finish_deferred_init()
/home/manasi/.local/lib/python2.7/site-packages/mxnet/gluon/block.pyc in _deferred_infer_shape(self, *args)
792 error_msg = "Deferred initialization failed
because shape"\
793 " cannot be inferred. {}".format(e)
--> 794 raise ValueError(error_msg)
795
796 def _call_cached_op(self, *args):
ValueError: Deferred initialization failed because shape cannot be inferred. Error in operator conv2_fwd: [10:56:15] src/operator/nn/convolution.cc:196: Check failed: dilated_ksize_x <= AddPad(dshape[3], param_.pad[1]) (5 vs. 3) kernel size exceed input

Answer 1

kernel size exceed input error is usually seen when your input image is too small for the network. You either need to resize your input image, or change the network architecture to remove layers that reduce the spatial dimensions of the feature maps (e.g. pooling layers, or convolution with stride).