# we will then load and unpickle the file.ĭata = pickle_load(f, encoding='latin-1')ġ3. ValueError: You are tring to use the old GPU back-end. # Download the mnist dataset if it is not yet available. => So, you can re-generate your pkle file and try again. => The second main reason is that: you are using pkle file generted not by your current environments, it's also caused by enum, this is a bug ! => maybe first you need to do is just install: $ sudo pip install enum34 But since Python 3.x has already adapted a new enum type, it might be wiser to uninstall the old enum and rewrite your code to use enum34. You could remove enum34 and get rid of this error. enum34 overrides the old enum files and causes this error. Many packages have started to use it and so it will be installed implicitly while installing another package. enum34 is the backport for Python 2.x of the standard enum in Python 3.4. => This error is caused when the enum34 module has been installed alongside the old enum module. Theano framework shown an error like this:įile '/usr/local/lib/python2.7/dist-packages/enum.py', line 199, in _init_ raise EnumBadKeyError(key) The version of theano is not matched with the code. ValueError: GpuJoin: Wrong inputs for input 2 related to inputs 0.!Īpply node that caused the error: GpuJoin(TensorConstant(, )]') Storage_map=getattr(self.fn, 'storage_map', None))įile '/usr/local/lib/python2.7/dist-packages/theano/gof/link.py', line 314, in raise_with_opįile '/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.py', line 859, in _call_ G_obj, D_obj, G_cost = model.D_trainFunction(batch_input,batch_output,batch_patch) #add batch_patch ?įile '/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.py', line 871, in _call_ Salgan_batch_iterator(model, train_data, None)įile 'wangxiao-02-train.py', line 108, in salgan_batch_iterator ValueError: GpuJoin: Wrong inputs for input 2 related to inputs 0.!įile 'wangxiao-02-train.py', line 171, in train how to use specific GPU with Theano framework ?Ĥ. It will display on the terminal: loss: ~~~ģ. Such as: hist = Deep_Q_Network.fit(X_train, y_train, batch_size=batch_size, nb_epoch=1, verbose=1) Some deep learning framework such as torch or theano, use this flag to control whether shown some training information on the terminal. verbose = 1 or 0, does it have any difference ? Summary on deep learning framework - Theano & LasagneĢ.

Evoscan 2.9 map tracer no values shown