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[MXNET-696] Fix undefined name errors #12137

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Aug 15, 2018
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[MXNET-696] Fix undefined name errors #12137

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4f61e50
Fix undefined name error in neural style example
vandanavk Aug 10, 2018
bf81dcb
Fix import exception error
vandanavk Aug 11, 2018
6505674
Fix undefined name in AUCMetric
vandanavk Aug 10, 2018
774bf01
Fix undefined name in a3c example
vandanavk Aug 10, 2018
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4 changes: 2 additions & 2 deletions example/deep-embedded-clustering/model.py
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Expand Up @@ -22,7 +22,7 @@
import numpy as np
try:
import cPickle as pickle
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why use cPickle? why not just pickle. In fact cPickle is not used in python3. most functions of cPickle is there in pickle

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This is correct usage... We first try to use cPickle which is compiled C code in Python 2 and is nice and fast but if we fail to import that (because it is not pip installed on Python 2 or because we are running on Python 3) then we use the normal standard library pickle.

except ModuleNotFoundError:
except ImportError:
import pickle


Expand Down Expand Up @@ -75,4 +75,4 @@ def load(self, fname):
self.args[key][:] = v

def setup(self, *args, **kwargs):
raise NotImplementedError("must override this")
raise NotImplementedError("must override this")
1 change: 1 addition & 0 deletions example/neural-style/end_to_end/model_vgg19.py
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Expand Up @@ -90,6 +90,7 @@ def get_executor_with_style(style, content, input_size, ctx):
arg_dict=arg_dict)

def get_executor_content(content, input_size, ctx):
out = mx.sym.Group([content])
arg_shapes, output_shapes, aux_shapes = content.infer_shape(data=(1, 3, input_size[0], input_size[1]))
arg_names = out.list_arguments()
arg_dict = dict(zip(arg_names, [mx.nd.zeros(shape, ctx=ctx) for shape in arg_shapes]))
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2 changes: 1 addition & 1 deletion example/reinforcement-learning/a3c/a3c.py
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Expand Up @@ -203,7 +203,7 @@ def test():
mx.gpu(int(i)) for i in args.gpus.split(',')]

# module
dataiter = robo_data.RobosimsDataIter('scenes', args.batch_size, args.input_length, web_viz=True)
dataiter = rl_data.GymDataIter('scenes', args.batch_size, args.input_length, web_viz=True)
print(dataiter.provide_data)
net = sym.get_symbol_thor(dataiter.act_dim)
module = mx.mod.Module(net, data_names=[d[0] for d in dataiter.provide_data], label_names=('policy_label', 'value_label'), context=devs)
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4 changes: 3 additions & 1 deletion example/sparse/factorization_machine/metric.py
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Expand Up @@ -107,7 +107,9 @@ def update(self, labels, preds):
label_sum = label_weight.sum()
if label_sum == 0 or label_sum == label_weight.size:
raise Exception("AUC with one class is undefined")


label_one_num = np.count_nonzero(label_weight)
label_zero_num = len(label_weight) - label_one_num
total_area = label_zero_num * label_one_num
height = 0
width = 0
Expand Down