Merck / DeepNeuralNet-QSAR

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About DeepNeuralNet-QSAR #1

Open Bigrock-dd opened 2 years ago

Bigrock-dd commented 2 years ago

Saving the trained Neural Net to models/multi_sparse_1/DeepNeuralNetParameters.npz Traceback (most recent call last): File "DeepNeuralNetTrain.py", line 555, in main() File "DeepNeuralNetTrain.py", line 534, in main num.savez(saveModelPath, *NNParametersDict); File "/root/miniconda3/envs/my-env/lib/python2.7/site-packages/numpy/lib/npyio.py", line 595, in savez _savez(file, args, kwds, False) File "/root/miniconda3/envs/my-env/lib/python2.7/site-packages/numpy/lib/npyio.py", line 714, in _savez format.write_array(fid, np.asanyarray(val), File "/root/miniconda3/envs/my-env/lib/python2.7/site-packages/numpy/core/numeric.py", line 544, in asanyarray return array(a, dtype, copy=False, order=order, subok=True) File "/root/DeepNeuralNet-QSAR-master/gnumpy.py", line 1059, in iter for i in tuple(xrange(len(self))): yield self[i] File "/root/DeepNeuralNet-QSAR-master/gnumpy.py", line 1026, in getitem ret = self[selectors[:axisI] + (_short_slice(_read_single_index(axisSelector, axisLen)),)] .reshape(_deleteT2(self.shape, axisI)) File "/root/DeepNeuralNet-QSAR-master/gnumpy.py", line 1045, in getitem if axisI==0: return garray(_cm_row_slice_read(self._base_shaped(1), sFrom, sTo), retShape, self) # slice on axis 0 is free, using _cm_row_slice_read File "/root/DeepNeuralNet-QSAR-master/gnumpy.py", line 587, in _base_shaped def _base_shaped(self, nDimsAsRows): return _cm_reshape(self._base, (_prodT(self.shape[:nDimsAsRows]), _prodT(self.shape[nDimsAsRows:]))) File "/root/DeepNeuralNet-QSAR-master/gnumpy.py", line 165, in _cm_reshape else: return cm.reshape(tuple(reversed(newShape))) File "/root/DeepNeuralNet-QSAR-master/npmat.py", line 99, in reshape self.numpy_array.resize(shape) ValueError: resize only works on single-segment arrays

I have this problem during the training of the model, how can I solve it? I would appreciate if you can reply!

xuyuting commented 2 years ago

I hope you could include more details: Were you training the model using the example dataset or your own dataset? Which version of numpy?