scorks / gan_drl_ids Goto Github PK

Hi,

Thanks for sharing this cpde. I tried your code and i'm facing this error:

AxisError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_21096\874485016.py in
7 #print(train_n['Class'].value_counts(dropna=False))
8 #print(train_n.head(10))
----> 9 models = train_with_data(train_new)

~\AppData\Local\Temp\ipykernel_21096\1681573222.py in train_with_data(train)
17
18 #Training the GAN model chosen: Vanilla GAN, CGAN, DCGAN, etc.
---> 19 synthesizer = model(gan_args)
20 synthesizer.train(train, train_args)
21 models = {'GAN': ['GAN', False, synthesizer.generator]}

~\AppData\Local\Temp\ipykernel_21096\1858274551.py in init(self, gan_args)
23
24 self.discriminator = Discriminator(self.batch_size).
---> 25 build_model(input_shape=self.data_dim, dim=layers_dim, num_classes= self.num_classes)
26
27 optimizer = Adam(lr, 0.5)

~\AppData\Local\Temp\ipykernel_21096\1858274551.py in build_model(self, input_shape, dim, num_classes)
183 label = Input(shape= (1,), dtype = 'int32')
184
--> 185 label_embedding = Embedding(input_dim = num_classes, output_dim = np.prod(input_shape, self.batch_size), input_length = 1)(label)
186 label_embedding = Flatten()(label_embedding)
187 label_embedding = Reshape((input_shape, self.batch_size))(label_embedding)

<array_function internals> in prod(*args, **kwargs)

~.conda\envs\myenv\lib\site-packages\numpy\core\fromnumeric.py in prod(a, axis, dtype, out, keepdims, initial, where)
2909 """
2910 return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,
-> 2911 keepdims=keepdims, initial=initial, where=where)
2912
2913

~.conda\envs\myenv\lib\site-packages\numpy\core\fromnumeric.py in _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs)
88 return reduction(axis=axis, out=out, **passkwargs)
89
---> 90 return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
91
92

AxisError: axis 64 is out of bounds for array of dimension 0

May you advise please ?

I kindly request your assistance in obtaining the "NSL-KDD Features - Sheet1.csv" file. It would greatly facilitate my understanding and application of the code in the repository. If possible, could you please provide me with the mentioned file or guide me on where I can access it?