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large-batch-training's Issues

Code for sharpness

Hi Nitish,

Could you include the code for computing sharpness as well.

Thanks,
Neelesh

pytorch gpu

Is there any problem that makes the implementation of GPU version difficult? I tried to get a linear combination of SB weights and LB weights in GPU mode, and got weird issues. Did you have similar problems before?

UnboundLocalError: local variable 'out' referenced before assignment

I run python plot_parametric_plot.py -n C1, and get following error:

Traceback (most recent call last):
  File "plot_parametric_plot.py", line 64, in <module>
    model = network_zoo.shallownet(nb_classes)
  File "/home//github/users/wenwei202/large-batch-training/network_zoo.py", line 37, in shallownet
    model.add(BatchNormalization(mode=2,axis=1))
  File "/home//anaconda2/lib/python2.7/site-packages/Keras-1.0.0-py2.7.egg/keras/models.py", line 139, in add
    output_tensor = layer(self.outputs[0])
  File "/home//anaconda2/lib/python2.7/site-packages/Keras-1.0.0-py2.7.egg/keras/engine/topology.py", line 485, in __call__
    self.add_inbound_node(inbound_layers, node_indices, tensor_indices)
  File "/home//anaconda2/lib/python2.7/site-packages/Keras-1.0.0-py2.7.egg/keras/engine/topology.py", line 543, in add_inbound_node
    Node.create_node(self, inbound_layers, node_indices, tensor_indices)
  File "/home//anaconda2/lib/python2.7/site-packages/Keras-1.0.0-py2.7.egg/keras/engine/topology.py", line 148, in create_node
    output_tensors = to_list(outbound_layer.call(input_tensors[0], mask=input_masks[0]))
  File "/home//anaconda2/lib/python2.7/site-packages/Keras-1.0.0-py2.7.egg/keras/layers/normalization.py", line 118, in call
    return out
UnboundLocalError: local variable 'out' referenced before assignment

Keras version: 1
Tensorflow: '1.4.0'

Problems when running `plot_parametric_plot.py`

It seems like keras has changed the parameter of the function BatchNormalization. The error msg:

Traceback (most recent call last):
  File "plot_parametric_plot.py", line 64, in <module>
    model = network_zoo.shallownet(nb_classes)
  File "/home/nqluo/experiement/large-batch-training-master/network_zoo.py", line 37, in shallownet
    model.add(BatchNormalization(mode=2,axis=1))
  File "/home/nqluo/anaconda3/envs/tf14-gpu/lib/python3.7/site-packages/keras/legacy/interfaces.py", line 34, in wrapper
    args, kwargs, converted = preprocessor(args, kwargs)
  File "/home/nqluo/anaconda3/envs/tf14-gpu/lib/python3.7/site-packages/keras/legacy/interfaces.py", line 451, in batchnorm_args_preprocessor
    raise TypeError('The `mode` argument of `BatchNormalization` '
TypeError: The `mode` argument of `BatchNormalization` no longer exists. `mode=1` and `mode=2` are no longer supported.

How much is small and how much is large in real problem?

Hi @keskarnitish, I would like to ask a question that how much is a small batch and how much is a large batch in the real problems. For example, in the object detection, segmentation and pose estimation, we even set mini-batch as 2. Sometimes we also set the minibatch as 32. So how to choose batch number in these problems. Thanks.

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