Comments (22)
@AryanBhardwaj
Could you provide the full Traceback of the error? I just want to see the files that produce this error.
from theano_alexnet.
@hma02
Thank you for your reply. I was able to resolve the error by making modification in alex_net.py, in line 26 y = T.ivector('y')
from theano_alexnet.
This problem was also mentioned in #32
from theano_alexnet.
@hma02
yes, I did refer it. Thank you. However, I am still getting this issue. Can you please suggest some solution.
Error:
epoch 56: validation loss nan
epoch 56: validation error nan %
Complete Output is here:
WARNING (theano.sandbox.cuda): The cuda backend is deprecated and will be removed in the next release (v0.10). Please switch to the gpuarray backend. You can get more information about how to switch at this URL:
https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29
Using gpu device 0: GeForce GTX 1080 (CNMeM is enabled with initial size: 80.0% of memory, cuDNN 5110)
WARNING (theano.sandbox.cuda): The cuda backend is deprecated and will be removed in the next release (v0.10). Please switch to the gpuarray backend. You can get more information about how to switch at this URL:
https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29
... building the model
conv (cudnn) layer with shape_in: (3, 227, 227, 256)
conv (cudnn) layer with shape_in: (96, 27, 27, 256)
conv (cudnn) layer with shape_in: (256, 13, 13, 256)
conv (cudnn) layer with shape_in: (384, 13, 13, 256)
conv (cudnn) layer with shape_in: (384, 13, 13, 256)
fc layer with num_in: 9216 num_out: 4096
dropout layer with P_drop: 0.5
fc layer with num_in: 4096 num_out: 4096
dropout layer with P_drop: 0.5
softmax layer with num_in: 4096 num_out: 1000
... training
epoch 1: validation loss nan
epoch 1: validation error nan %
weight saved: W_0_1
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epoch 2: validation loss nan
epoch 2: validation error nan %
weight saved: W_0_2
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epoch 3: validation loss nan
epoch 3: validation error nan %
weight saved: W_0_3
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epoch 4: validation loss nan
epoch 4: validation error nan %
weight saved: W_0_4
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epoch 5: validation loss nan
epoch 5: validation error nan %
weight saved: W_0_5
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epoch 6: validation loss nan
epoch 6: validation error nan %
weight saved: W_0_6
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epoch 7: validation loss nan
epoch 7: validation error nan %
weight saved: W_0_7
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epoch 8: validation loss nan
epoch 8: validation error nan %
weight saved: W_0_8
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epoch 9: validation loss nan
epoch 9: validation error nan %
weight saved: W_0_9
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weight saved: W0_1_9
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epoch 10: validation loss nan
epoch 10: validation error nan %
('Learning rate changed to:', array(0.0009999999310821295, dtype=float32))
weight saved: W_0_10
weight saved: b_0_10
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epoch 11: validation loss nan
epoch 11: validation error nan %
weight saved: W_0_11
weight saved: b_0_11
weight saved: W0_1_11
weight saved: W1_1_11
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epoch 12: validation loss nan
epoch 12: validation error nan %
weight saved: W_0_12
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weight saved: W0_1_12
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epoch 13: validation loss nan
epoch 13: validation error nan %
weight saved: W_0_13
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weight saved: W0_1_13
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epoch 14: validation loss nan
epoch 14: validation error nan %
weight saved: W_0_14
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weight saved: W0_1_14
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epoch 15: validation loss nan
epoch 15: validation error nan %
weight saved: W_0_15
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weight saved: W0_1_15
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epoch 16: validation loss nan
epoch 16: validation error nan %
weight saved: W_0_16
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weight saved: W0_1_16
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epoch 17: validation loss nan
epoch 17: validation error nan %
weight saved: W_0_17
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epoch 18: validation loss nan
epoch 18: validation error nan %
weight saved: W_0_18
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weight saved: W0_1_18
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epoch 19: validation loss nan
epoch 19: validation error nan %
weight saved: W_0_19
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weight saved: W0_1_19
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epoch 20: validation loss nan
epoch 20: validation error nan %
('Learning rate changed to:', array(9.99999901978299e-05, dtype=float32))
weight saved: W_0_20
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epoch 21: validation loss nan
epoch 21: validation error nan %
weight saved: W_0_21
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epoch 22: validation loss nan
epoch 22: validation error nan %
weight saved: W_0_22
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weight saved: W0_1_22
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epoch 23: validation loss nan
epoch 23: validation error nan %
weight saved: W_0_23
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epoch 24: validation loss nan
epoch 24: validation error nan %
weight saved: W_0_24
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weight saved: W0_1_24
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epoch 25: validation loss nan
epoch 25: validation error nan %
weight saved: W_0_25
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weight saved: W0_1_25
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epoch 26: validation loss nan
epoch 26: validation error nan %
weight saved: W_0_26
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weight saved: W0_1_26
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epoch 27: validation loss nan
epoch 27: validation error nan %
weight saved: W_0_27
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epoch 28: validation loss nan
epoch 28: validation error nan %
weight saved: W_0_28
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weight saved: W0_1_28
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epoch 29: validation loss nan
epoch 29: validation error nan %
weight saved: W_0_29
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weight saved: W0_1_29
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epoch 30: validation loss nan
epoch 30: validation error nan %
weight saved: W_0_30
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weight saved: W0_1_30
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epoch 31: validation loss nan
epoch 31: validation error nan %
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weight saved: W0_1_31
weight saved: W1_1_31
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epoch 32: validation loss nan
epoch 32: validation error nan %
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epoch 33: validation loss nan
epoch 33: validation error nan %
weight saved: W_0_33
weight saved: b_0_33
weight saved: W0_1_33
weight saved: W1_1_33
weight saved: b0_1_33
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epoch 34: validation loss nan
epoch 34: validation error nan %
weight saved: W_0_34
weight saved: b_0_34
weight saved: W0_1_34
weight saved: W1_1_34
weight saved: b0_1_34
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epoch 35: validation loss nan
epoch 35: validation error nan %
weight saved: W_0_35
weight saved: b_0_35
weight saved: W0_1_35
weight saved: W1_1_35
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epoch 36: validation loss nan
epoch 36: validation error nan %
weight saved: W_0_36
weight saved: b_0_36
weight saved: W0_1_36
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epoch 37: validation loss nan
epoch 37: validation error nan %
weight saved: W_0_37
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weight saved: W0_1_37
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epoch 38: validation loss nan
epoch 38: validation error nan %
weight saved: W_0_38
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epoch 39: validation loss nan
epoch 39: validation error nan %
weight saved: W_0_39
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weight saved: W0_1_39
weight saved: W1_1_39
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epoch 40: validation loss nan
epoch 40: validation error nan %
weight saved: W_0_40
weight saved: b_0_40
weight saved: W0_1_40
weight saved: W1_1_40
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epoch 41: validation loss nan
epoch 41: validation error nan %
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epoch 42: validation loss nan
epoch 42: validation error nan %
weight saved: W_0_42
weight saved: b_0_42
weight saved: W0_1_42
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epoch 43: validation loss nan
epoch 43: validation error nan %
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epoch 44: validation loss nan
epoch 44: validation error nan %
weight saved: W_0_44
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weight saved: W0_1_44
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epoch 45: validation loss nan
epoch 45: validation error nan %
weight saved: W_0_45
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weight saved: W0_1_45
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epoch 46: validation loss nan
epoch 46: validation error nan %
weight saved: W_0_46
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epoch 47: validation loss nan
epoch 47: validation error nan %
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epoch 48: validation loss nan
epoch 48: validation error nan %
weight saved: W_0_48
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weight saved: W0_1_48
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epoch 49: validation loss nan
epoch 49: validation error nan %
weight saved: W_0_49
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epoch 50: validation loss nan
epoch 50: validation error nan %
weight saved: W_0_50
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weight saved: W0_1_50
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epoch 51: validation loss nan
epoch 51: validation error nan %
weight saved: W_0_51
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epoch 52: validation loss nan
epoch 52: validation error nan %
weight saved: W_0_52
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weight saved: W0_1_52
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epoch 53: validation loss nan
epoch 53: validation error nan %
weight saved: W_0_53
weight saved: b_0_53
weight saved: W0_1_53
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epoch 54: validation loss nan
epoch 54: validation error nan %
weight saved: W_0_54
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weight saved: W0_1_54
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epoch 55: validation loss nan
epoch 55: validation error nan %
weight saved: W_0_55
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epoch 56: validation loss nan
epoch 56: validation error nan %
weight saved: W_0_56
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epoch 57: validation loss nan
epoch 57: validation error nan %
weight saved: W_0_57
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epoch 58: validation loss nan
epoch 58: validation error nan %
weight saved: W_0_58
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epoch 59: validation loss nan
epoch 59: validation error nan %
weight saved: W_0_59
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epoch 60: validation loss nan
epoch 60: validation error nan %
weight saved: W_0_60
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weight saved: W0_1_60
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Optimization complete.
PyCUDA ERROR: The context stack was not empty upon module cleanup.
A context was still active when the context stack was being
cleaned up. At this point in our execution, CUDA may already
have been deinitialized, so there is no way we can finish
cleanly. The program will be aborted now.
Use Context.pop() to avoid this problem.
This application has requested the Runtime to terminate it in an unusual way.
Please contact the application's support team for more information.
Process finished with exit code 0
Also:
If para_load: True
then I get this error
LogicError: cuIpcGetMemHandle failed: OS call failed or operation not supported on this OS
(C:\Users\arjun\Anaconda2) D:\xxxxxyyyy>python train.py
Process Process-2:
Traceback (most recent call last):
File "C:\Users\arjun\Anaconda2\lib\multiprocessing\process.py", line 258, in _bootstrap
self.run()
File "C:\Users\arjun\Anaconda2\lib\multiprocessing\process.py", line 114, in run
self._target(self._args, **self._kwargs)
File "D:\Rough\random\xxx\xxxxxyyyy\proc_load.py", line 98, in fun_load
sock.bind('tcp://:{0}'.format(sock_data))
File "zmq/backend/cython/socket.pyx", line 495, in zmq.backend.cython.socket.Socket.bind (zmq\backend\cython\socket.c:5653)
File "zmq/backend/cython/checkrc.pxd", line 25, in zmq.backend.cython.checkrc._check_rc (zmq\backend\cython\socket.c:10014)
raise ZMQError(errno)
ZMQError: Address in use
PyCUDA ERROR: The context stack was not empty upon module cleanup.
A context was still active when the context stack was being
cleaned up. At this point in our execution, CUDA may already
have been deinitialized, so there is no way we can finish
cleanly. The program will be aborted now.
Use Context.pop() to avoid this problem.
This application has requested the Runtime to terminate it in an unusual way.
Please contact the application's support team for more information.
WARNING (theano.sandbox.cuda): The cuda backend is deprecated and will be removed in the next release (v0.10). Please switch to the gpuarray backend. You can get more information about how to switch at this URL:
https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29
Using gpu device 0: GeForce GTX 1080 (CNMeM is enabled with initial size: 80.0% of memory, cuDNN 5110)
... building the model
conv (cudnn) layer with shape_in: (3, 227, 227, 256)
conv (cudnn) layer with shape_in: (96, 27, 27, 256)
conv (cudnn) layer with shape_in: (256, 13, 13, 256)
conv (cudnn) layer with shape_in: (384, 13, 13, 256)
conv (cudnn) layer with shape_in: (384, 13, 13, 256)
fc layer with num_in: 9216 num_out: 4096
dropout layer with P_drop: 0.5
fc layer with num_in: 4096 num_out: 4096
dropout layer with P_drop: 0.5
softmax layer with num_in: 4096 num_out: 1000
... training
Process Process-1:
Traceback (most recent call last):
File "C:\Users\arjun\Anaconda2\lib\multiprocessing\process.py", line 258, in _bootstrap
self.run()
File "C:\Users\arjun\Anaconda2\lib\multiprocessing\process.py", line 114, in run
self._target(*self._args, **self._kwargs)
File "D:\Rough\random\xxx\xxxxxyyyy\train.py", line 69, in train_net
h = drv.mem_get_ipc_handle(gpuarray_batch.ptr)
LogicError: cuIpcGetMemHandle failed: OS call failed or operation not supported on this OS
PyCUDA ERROR: The context stack was not empty upon module cleanup.
A context was still active when the context stack was being
cleaned up. At this point in our execution, CUDA may already
have been deinitialized, so there is no way we can finish
cleanly. The program will be aborted now.
Use Context.pop() to avoid this problem.
This application has requested the Runtime to terminate it in an unusual way.
Please contact the application's support team for more information.
It will be really helpful, if i can get some suggestions or approximate solution. Thank you in advance.
from theano_alexnet.
The "ZMQError: Address in use" error happens when the previous run failed and the socket port opened in the previous run was not closed properly causing port conflict in the next run. You can search the process opening the port by:
netstat -ltnp
and kill the corresponding process.
For the NAN issue, if it happened from the first epoch, this could be caused by input batch not being fed or preprocessed correctly. Or it can be caused by using too large learning rate. See issue #27.
from theano_alexnet.
@hma02
Thanks for sharing. I am trying the suggested solutions. Do we have any solution on windows os 10 for
LogicError: cuIpcGetMemHandle failed: OS call failed or operation not supported on this OS
from theano_alexnet.
@hma02
Thank you for your suggestions for "For the NAN issue", the problem was it was not able to read the training data. Now it is training. Now, I want to know how to get the good accuracy results.
Can you share, after how many iterations I should expect for accuracy. Also, if you can share optimized hyper parameters file config.yml.
current status is:
('training error rate:', array(0.984375))
('training @ iter = ', 2765)
('training cost:', array(6.374232292175293, dtype=float32))
('training error rate:', array(0.99609375))
('training @ iter = ', 2770)
('training cost:', array(6.3500189781188965, dtype=float32))
('training error rate:', array(0.984375))
('training @ iter = ', 2775)
('training cost:', array(6.216220855712891, dtype=float32))
('training error rate:', array(0.98828125))
('training @ iter = ', 2780)
('training cost:', array(6.231907844543457, dtype=float32))
('training error rate:', array(0.98828125))
('training @ iter = ', 2785)
('training cost:', array(6.30079460144043, dtype=float32))
('training error rate:', array(0.99609375))
from theano_alexnet.
@hma02
Hi, I have this experiment running with this current results: Can you suggest any improvements to, achieve better accuracy and less training error.
('training cost:', array(4.295770168304443, dtype=float32))
('training error rate:', array(0.8046875))
('training @ iter = ', 8165)
('training cost:', array(4.224380016326904, dtype=float32))
('training error rate:', array(0.8125))
('training @ iter = ', 8170)
('training cost:', array(4.512507438659668, dtype=float32))
('training error rate:', array(0.90234375))
('training @ iter = ', 8175)
('training cost:', array(4.5337233543396, dtype=float32))
('training error rate:', array(0.8515625))
('training @ iter = ', 8180)
('training cost:', array(4.498597145080566, dtype=float32))
('training error rate:', array(0.82421875))
('training @ iter = ', 8185)
('training cost:', array(4.465353012084961, dtype=float32))
('training error rate:', array(0.84375))
('training @ iter = ', 8190)
('training cost:', array(4.593122482299805, dtype=float32))
('training error rate:', array(0.82421875))
from theano_alexnet.
Your training cost looks okay so far. Are you training on ImageNet data? If you follow the preprocess steps in this project, you will see 5004 batch files of batch size 256 for single GPU training. That means one epoch will take 5004 iterations. The hyperparams in config.yaml are already the optimized values found so far. That means you need to train for 60 epochs or 60*5004 iterations in total.
from theano_alexnet.
@hma02
Thank you for the quick reply. yes. you are correct, but may be number of batch files is little different. However, why we have two training data folders, _hkl_b256_b_128 and train_hkl_b256_b_256. Is there specific reason to have size 128 folder.
from theano_alexnet.
@AryanBhardwaj
This preprocessing setup is for doing multi-GPU training. Specifically, single GPU trains with batch_size=256, two GPUs train with batch_size=128 on each GPU, and 4 GPUs will train with batch_size=64 on each GPU...etc.
This is to preserve the effective batch size (n_GPUs*batch_size) when scaling to multiple GPUs.
from theano_alexnet.
@hma02
Thank you for this insight. Just wondering how long it should take to complete the training. Also if you know some way to understand the weights better. As in if I can read the weights and bias and understand it better.
I mean visualize hidden layer weight and bias values, read the values with some tool or may be some text or reference to know in detail about hidden layer weights and bias.
from theano_alexnet.
@hma02
Is there any specific naming patterns that's used for naming the weights for different layers of the network?
For my understanding, any suggestions?
Also if you can share some insight on using "group" in the convolution layers..
thank you in advance.
from theano_alexnet.
We benchmarked training speed on GTX 1080 and Tesla K80.
For GTX 1080, it takes 0.91h per epoch.
For Tesla K80, it takes 1.96h per epoch.
Totally 60 epochs, so it takes around 54h for GTX 1080 and around 120h for Tesla K80.
We didn't experiment on visualizing weights. You can simply read those weight files using numpy.load().
To visualize the activation like here, you can construct another theano function to output the self.output of each layer and plot them using imshow from matplotlib.
The naming pattern of saved weights is defined in this function, basically just "layer_index" + "epoch". Some weights has a number following W or b like W0 or b0 and W1 or b1, because they are from the alexnet grouped convolution layer. Inside those layers, there are two parallel sub-convolutions. Each has a weight.
from theano_alexnet.
@hma02
I am able to train the alexnet now, thank you for all the suggestions.
Now, I am trying to train on imagenet using my network. But the training error or validation error does not improve at all.
Any suggestions!!!
('training @ iter = ', 61040)
('training cost:', array(6.920103549957275, dtype=float32))
('training error rate:', array(0.9921875))
('training @ iter = ', 61045)
('training cost:', array(6.905889511108398, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61050)
('training cost:', array(6.9157304763793945, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61055)
('training cost:', array(6.915121078491211, dtype=float32))
('training error rate:', array(0.9921875))
('training @ iter = ', 61060)
('training cost:', array(6.9073486328125, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61065)
('training cost:', array(6.910022735595703, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61070)
('training cost:', array(6.898440361022949, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61075)
('training cost:', array(6.900564193725586, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61080)
('training cost:', array(6.9025468826293945, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61085)
('training cost:', array(6.906184196472168, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61090)
('training cost:', array(6.913963317871094, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61095)
('training cost:', array(6.90643310546875, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61100)
('training cost:', array(6.9034423828125, dtype=float32))
('training error rate:', array(0.9921875))
('training @ iter = ', 61105)
('training cost:', array(6.9006123542785645, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61110)
('training cost:', array(6.908158302307129, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61115)
('training cost:', array(6.901939392089844, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61120)
('training cost:', array(6.902793884277344, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61125)
('training cost:', array(6.899314880371094, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61130)
('training cost:', array(6.9046478271484375, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61135)
('training cost:', array(6.907194137573242, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61140)
('training cost:', array(6.91206169128418, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61145)
('training cost:', array(6.901838302612305, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61150)
('training cost:', array(6.904903411865234, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61155)
('training cost:', array(6.90507698059082, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61160)
('training cost:', array(6.911441802978516, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61165)
('training cost:', array(6.907763957977295, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61170)
('training cost:', array(6.909838676452637, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61175)
('training cost:', array(6.905656814575195, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61180)
('training cost:', array(6.905083179473877, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61185)
('training cost:', array(6.907958984375, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61190)
('training cost:', array(6.904727935791016, dtype=float32))
('training error rate:', array(0.9921875))
('training @ iter = ', 61195)
('training cost:', array(6.9050397872924805, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61200)
('training cost:', array(6.90727424621582, dtype=float32))
('training error rate:', array(0.9921875))
('training @ iter = ', 61205)
('training cost:', array(6.905116558074951, dtype=float32))
('training error rate:', array(1.0))
('training @ iter = ', 61210)
('training cost:', array(6.899809837341309, dtype=float32))
('training error rate:', array(1.0))
from theano_alexnet.
@hma02
If possible please suggest something on the above-mentioned issue. Also, if there any relation between the depth of network and learning rate.
from theano_alexnet.
@AryanBhardwaj usually you can try small learning rates until you see some training progress on training data (if you don't see training loss decrease at all, usually there's a bug, maybe in the data pipeline). and then try larger learning rate to learn faster.
from theano_alexnet.
@AryanBhardwaj
Yes, data pipeline would be the first to check. Verify that your training data matches the training labels.
The cost not decreasing issue could be due to a bad network initialization as well. For example, try tweaking the mean and std of your gaussian initializer. You can follow some of the standard ways of initializing weights like here.
You can also monitor the gradient flow along training to see if the gradient is in a reasonable magnitude (e.g. 1e-1 to 1e-3). Try constructing a theano function that outputs self.grads
.
from theano_alexnet.
@gwding and @hma02
Thank you, I will try to find the solution on these directions.
from theano_alexnet.
I want to thank you for your suggestions they were helpful.
I am currently trying to test the results using actual images from google that if the learned weights can label that images correctly. Do we have any existing sample to refer? Or please suggest any ideas that may be helpful.
from theano_alexnet.
@hma02 If possible please suggest something on the above-mentioned issue.
from theano_alexnet.
from theano_alexnet.
Related Issues (20)
- can't reproduce results with pre-trained models HOT 5
- crop size and difference between cudnn and cuda-convnet HOT 4
- meta data files HOT 1
- Momentum loading HOT 4
- Testing network on CPU HOT 4
- Validate Performance HOT 1
- Step_idx bug when resuming training
- Training Cost NAN HOT 11
- Forward and Backward Propagation HOT 1
- Error with theano v0.9.0 HOT 3
- Merge split layer weights HOT 3
- Execute alexnet using ligbpuarray backend HOT 8
- pycuda mem_get_ipc_handle() error on Windows 10 HOT 6
- For new dataset HOT 1
- An update must have the same type as the original shared variable HOT 1
- Preprocessing with respect to aspect ratio
- latest update to conda and I started getting this issue: TypeError: slice indices must be integers or None or have an __index__ method HOT 2
- version of dependencies HOT 1
- ValueError: GpuElemwise. Input dimension mis-match
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from theano_alexnet.