ValueError: total size of new array must be unchanged #2270
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I got this report from someone else. I'm not able to reproduce it. To help On Wed, Nov 19, 2014 at 1:55 PM, Alex Rothberg notifications@github.com
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from pylearn2.config import yaml_parse
model_yl = """
!obj:pylearn2.models.mlp.MLP {input_space: !obj:pylearn2.space.Conv2DSpace {shape: [
300, 400], num_channels: 1}, layers: [!obj:pylearn2.models.mlp.ConvRectifiedLinear {
layer_name: 'h0', output_channels: 7, irange: .05, kernel_shape: [3, 3], pool_shape: [
2, 2], pool_stride: [2, 2], max_kernel_norm: 1.9365}, !obj:pylearn2.models.mlp.ConvRectifiedLinear {
layer_name: 'h1', output_channels: 16, irange: .05, kernel_shape: [3, 3], pool_shape: [
2, 2], pool_stride: [2, 2], max_kernel_norm: 1.9365}, !obj:pylearn2.models.mlp.ConvRectifiedLinear {
layer_name: 'h2', output_channels: 32, irange: .05, kernel_shape: [3, 3], pool_shape: [
2, 2], pool_stride: [2, 2], max_kernel_norm: 1.9365, init_bias: 0.1}, !obj:pylearn2.models.maxout.Maxout {
layer_name: 'h6', num_units: 57, num_pieces: 2, irange: 0.0575059125935, max_col_norm: 2.0,
init_bias: 1.}, !obj:pylearn2.models.mlp.Softmax {max_col_norm: 3.9365, layer_name: 'y',
n_classes: 2, irange: 0.005000}]}
"""
yaml_parse.load(model_yl) |
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This do not reproduce the problem here. Can you tell me which version of On Wed, Nov 19, 2014 at 2:32 PM, Alex Rothberg notifications@github.com
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I am seeing this issue on pylearn2 lisa-lab/pylearn2@a23672b (master) and Theano 4c513ba (master). |
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I was missing that the Theano flags need to be floatX=float32 and On Wed, Nov 19, 2014 at 2:56 PM, Alex Rothberg notifications@github.com
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In response to your replies in regards to changing the type.py in theano.gof, I got the following. I'm guessing that this does not help you any further: Input shape: (128, 128) File "", line 1, in File "C:\Python27\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 586, in runfile File "D:/Experiments/Papers/PatholCAD/scripts/trainConvolutionNetwork.py", line 14, in File "D:/Code/thirdpartylibs/pylearn2\pylearn2\config\yaml_parse.py", line 338, in load File "D:/Code/thirdpartylibs/pylearn2\pylearn2\config\yaml_parse.py", line 281, in _instantiate File "D:/Code/thirdpartylibs/pylearn2\pylearn2\config\yaml_parse.py", line 230, in _instantiate_proxy_tuple File "D:/Code/thirdpartylibs/pylearn2\pylearn2\config\yaml_parse.py", line 230, in File "D:/Code/thirdpartylibs/pylearn2\pylearn2\config\yaml_parse.py", line 281, in _instantiate File "D:/Code/thirdpartylibs/pylearn2\pylearn2\config\yaml_parse.py", line 231, in _instantiate_proxy_tuple File "D:/Code/thirdpartylibs/pylearn2\pylearn2\utils\call_check.py", line 99, in checked_call File "D:/Code/thirdpartylibs/pylearn2\pylearn2\models\mlp.py", line 464, in init File "D:/Code/thirdpartylibs/pylearn2\pylearn2\models\mlp.py", line 529, in _update_layer_input_spaces File "D:/Code/thirdpartylibs/pylearn2\pylearn2\sandbox\rnn\models\mlp_hook.py", line 334, in outer File "D:/Code/thirdpartylibs/pylearn2\pylearn2\sandbox\rnn\models\mlp_hook.py", line 334, in outer File "D:/Code/thirdpartylibs/pylearn2\pylearn2\models\mlp.py", line 3066, in set_input_space File "D:/Code/thirdpartylibs/pylearn2\pylearn2\models\mlp.py", line 3006, in initialize_output_space File "C:\Python27\lib\site-packages\theano-0.6.0-py2.7.egg\theano\gof\graph.py", line 431, in eval File "C:\Python27\lib\site-packages\theano-0.6.0-py2.7.egg\theano\compile\function_module.py", line 595, in call File "C:\Python27\lib\site-packages\theano-0.6.0-py2.7.egg\theano\compile\debugmode.py", line 2096, in deco File "C:\Python27\lib\site-packages\theano-0.6.0-py2.7.egg\theano\compile\debugmode.py", line 1806, in f File "C:\Python27\lib\site-packages\theano-0.6.0-py2.7.egg\theano\compile\debugmode.py", line 880, in _lessbroken_deepcopy File "C:\Python27\lib\copy.py", line 189, in deepcopy Error: un(deep)copyable object of type <type 'PyCObject'> My code is similar to that of cancan101, so you should get the same issue with that. If there is anything else I can do to help, let me know. Kind regards, Geert |
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I have a partial fix in a branch: https://github.com/nouiz/Theano/tree/mixed It fix the crash, but now the tests fails. So I need to fix them. With this branch, your example do not crash anymore. |
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Hi Fred, After applying your fix it work up till right after epoch 0, as soon as it starts with epoch 1 I get: .... File "", line 1, in File "C:\Python27\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 586, in runfile File "D:/Experiments/Papers/PatholCAD/scripts/trainConvolutionNetwork.py", line 15, in File "D:/Code/thirdpartylibs/pylearn2\pylearn2\train.py", line 207, in main_loop File "D:/Code/thirdpartylibs/pylearn2\pylearn2\training_algorithms\sgd.py", line 453, in train File "C:\Python27\lib\site-packages\theano-0.6.0-py2.7.egg\theano\compile\function_module.py", line 605, in call File "C:\Python27\lib\site-packages\theano-0.6.0-py2.7.egg\theano\compile\function_module.py", line 595, in call RuntimeError: GpuDnnPoolGrad: error doing operation: An incorrect value was passed in. HINT: Re-running with most Theano optimization disabled could give you a back-trace of when this node was created. This can be done with by setting the Theano flag 'optimizer=fast_compile'. If that does not work, Theano optimizations can be disabled with 'optimizer=None'. |
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Thanks for the info. The example I had didn't cover this case. This mean A workaround is to not use the cudnn pooling for now. This can be done with On Wed, Nov 19, 2014 at 6:17 PM, GeertLitjens notifications@github.com
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Just to tell that yesterday we disabled this fonctionality in Theano. So if On Thu, Nov 20, 2014 at 9:18 AM, Frédéric Bastien <
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Okay. Hopefully you are to turn the example I made into a unit test. On Fri Nov 21 2014 at 2:00:46 PM Frédéric Bastien notifications@github.com
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Can't use pylearn2 stuff in Theano. But I'll make sure to cover the On Fri, Nov 21, 2014 at 2:06 PM, Alex Rothberg notifications@github.com
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Understood on pylearn2. Wouldn't want circular dependencies. On Fri Nov 21 2014 at 2:08:31 PM Frédéric Bastien notifications@github.com
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We reenabled this funcitonality in gh-2281. The consequence of the bug could be crash or that we do ignore the border when we where asked to do not ignore it. |
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I think with master I am now seeing: |
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And here is the python code that replicates the issue: from pylearn2.config import yaml_parse
import numpy as np
from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix
from pylearn2.config import yaml_parse
y_labels = 4
labels = [0] * 100
y = np.array(labels)[:,np.newaxis]
y[0] = 1
y[0] = 2
y[0] = 3
train_set = DenseDesignMatrix(topo_view=np.zeros((100,300,400,1)), y=y, y_labels=y_labels)
valid = DenseDesignMatrix(topo_view=np.zeros((100,300,400,1)), y=y, y_labels=y_labels)
model_yl = """
!obj:pylearn2.models.mlp.MLP {input_space: !obj:pylearn2.space.Conv2DSpace {shape: [
300, 400], num_channels: 1}, layers: [!obj:pylearn2.models.mlp.ConvRectifiedLinear {
layer_name: 'h0', output_channels: 7, irange: .05, kernel_shape: [3, 3], pool_shape: [
2, 2], pool_stride: [2, 2], max_kernel_norm: 1.9365}, !obj:pylearn2.models.mlp.ConvRectifiedLinear {
layer_name: 'h1', output_channels: 16, irange: .05, kernel_shape: [3, 3], pool_shape: [
2, 2], pool_stride: [2, 2], max_kernel_norm: 1.9365}, !obj:pylearn2.models.mlp.ConvRectifiedLinear {
layer_name: 'h2', output_channels: 32, irange: .05, kernel_shape: [3, 3], pool_shape: [
2, 2], pool_stride: [2, 2], max_kernel_norm: 1.9365, init_bias: 0.1}, !obj:pylearn2.models.maxout.Maxout {
layer_name: 'h6', num_units: 57, num_pieces: 2, irange: 0.0575059125935, max_col_norm: 2.0,
init_bias: 1.}, !obj:pylearn2.models.mlp.Softmax {max_col_norm: 3.9365, layer_name: 'y',
n_classes: 2, irange: 0.005000}]}
"""
model = yaml_parse.load(model_yl)
train_yml = """
!obj:pylearn2.train.Train {dataset: !import '__main__.train_set',
model: null, algorithm: !obj:pylearn2.training_algorithms.sgd.SGD {batch_size: 10,
learning_rate: 0.059662, learning_rule: !obj:pylearn2.training_algorithms.learning_rule.Momentum {
init_momentum: 0.15}, cost: !obj:pylearn2.costs.cost.SumOfCosts {costs: [!obj:pylearn2.models.mlp.WeightDecay {
coeffs: [0.0, 0.0, 0.0, 0.0, 0.012569075841283767]}, !obj:pylearn2.costs.mlp.dropout.Dropout {
input_include_probs: {'h0': 1, 'h1': 1, 'h2': 1}, input_scales: {'h0': 1,
'h1': 1, 'h2': 1}}]}, monitor_iteration_mode: "even_shuffled_sequential",
train_iteration_mode: "even_shuffled_sequential", monitoring_dataset: {'valid': !import '__main__.valid'},
termination_criterion: !obj:pylearn2.termination_criteria.And {criteria: [!obj:pylearn2.termination_criteria.MonitorBased {
channel_name: "valid_y_misclass", prop_decrease: 0.01, N: 30}, !obj:pylearn2.termination_criteria.EpochCounter {
max_epochs: 50, new_epochs: false}]}}, extensions: [
!obj:pylearn2.training_algorithms.learning_rule.MomentumAdjustor {
start: 1, saturate: 40, final_momentum: 0.60}, !obj:pylearn2.training_algorithms.sgd.LinearDecayOverEpoch {
start: 20, saturate: 100, decay_factor: 0.1}
],
}
"""
train = yaml_parse.load(train_yml)
train.model = model
train.main_loop() |
Sometime between 52cb8ec and d8ffecc I started getting:
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