aaron-xichen / cnn-lstm-ctc Goto Github PK

how to add a uni-LSTM layer after the biLSTM layer?

i want to generate my dataset . how to get boudingboxs?
thanks

When I train the model from scratch, loss : nan phenomenon is detected in the first epoch.

in file 'utee.py' line 122
i don't have the folder 'split_tiny_images', where is the folder?

Traceback (most recent call last):
File "train/train.py", line 9, in
from layers.net import Net
ImportError: No module named layers.net

thanks for your time to solve my problem.
Can you share '99.pkl' trained model file to us or give me a link to download it. so that I can make some data set to test it and I neednt train it again.
thank you very much

Hello, Aaron

Recently I wanna do some tests about OCR, then I found your code,
I think maybe it's a good start.can you give us some data&&img samples?
BTW: Would you like to implement this with (Keras)[https://github.com/fchollet/keras] ?

Best Regards!

hello,请问为什么我使用您的程序ctc loss从一开始就为nan呢？希望您指导一下，非常感谢~
下面是显示的内容：
Using gpu device 0: GeForce GTX 980 Ti (CNMeM is disabled, cuDNN not available)
C:\Anaconda\lib\site-packages\theano\tensor\signal\downsample.py:6: UserWarning: downsample module has been moved to the theano.tensor.signal.pool module.
"downsample module has been moved to the theano.tensor.signal.pool module.")
loaded 29143 samples from D:\xugang\OCR\cnn-lstm-ctc-master\dataset\english_sentence\train_img_list.txt
loaded 2914 samples from D:\xugang\OCR\cnn-lstm-ctc-master\dataset\english_sentence\val_img_list.txt
building symbolic tensors(0.0799999237061)
setting parameters(0.0799999237061)
('n_classes: ', 95)
('multi-step: ', set([79625, 68250, 45500]))
building the model(0.0799999237061)
computing updates and function(0.240000009537)
using normal sgd and learning_rate:0.00999999977648
('bw_lstm_b', <class 'theano.sandbox.cuda.var.CudaNdarraySharedVariable'>)
('fw_lstm_W', <class 'theano.sandbox.cuda.var.CudaNdarraySharedVariable'>)
('fw_lstm_U', <class 'theano.sandbox.cuda.var.CudaNdarraySharedVariable'>)
('fw_lstm_b', <class 'theano.sandbox.cuda.var.CudaNdarraySharedVariable'>)
('bw_lstm_W', <class 'theano.sandbox.cuda.var.CudaNdarraySharedVariable'>)
('bw_lstm_U', <class 'theano.sandbox.cuda.var.CudaNdarraySharedVariable'>)
('hidden_b', <class 'theano.sandbox.cuda.var.CudaNdarraySharedVariable'>)
('hidden_W', <class 'theano.sandbox.cuda.var.CudaNdarraySharedVariable'>)
building training function(1.78999996185)
building validating function(29.6099998951)
begin to train(32.8609998226)
.epoch 1/200 begin(32.861)
[prefetch]height: 28, x_max_step:141.0, y_max_width:50
D:\xugang\OCR\cnn-lstm-ctc-master - 1.0\layers\utee.py:137: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
x = np.zeros((batch_size, 1, height, x_max_len)). astype(config.floatX)
D:\xugang\OCR\cnn-lstm-ctc-master - 1.0\layers\utee.py:138: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
x_mask = np.zeros((batch_size, x_max_len)).astype(config.floatX)
..loss: nan, iter:1/455(1, 1.076s)
..detect nan
..loss: nan, iter:1/455(1.076)
Traceback (most recent call last):
File "D:\xugang\OCR\cnn-lstm-ctc-master - 1.0\train.py", line 150, in
sys.exit()
SystemExit

Can u give me a web link or sth that I can get data sets?
thanks very much.

I am using this model for similar data except that our images contain Sanskrit words. I created the train, val , test files similar to the ones(i.e image_name followed by ordinals for characters) used in this model.
But in our case, the number of characters(i.e n_classes) is 118(instead of 95 in original one) and y_max_len=200(instead of 50 in original one).
When I train the model , I am getting the following error

loaded 25996 samples from ./dataset/train_img_list.txt
loaded 756 samples from ./dataset/val_img_list.txt
building symbolic tensors(0.84720993042)
('#Train samples: ', 25996)
('#Val samples: ', 756)
('#Train Iterations: ', 406)
('#Val Iterations: ', 11)
setting parameters(0.848186016083)
('n_classes: ', 118)
('multi-step: ', set([40600, 71050, 60900]))
building the model(0.848335027695)
Subtensor{int64}.0
Shape.0
computing updates and function(1.2518889904)
using normal sgd and learning_rate:0.00999999977648
('bw_lstm_b', <class 'theano.tensor.sharedvar.TensorSharedVariable'>)
('fw_lstm_W', <class 'theano.tensor.sharedvar.TensorSharedVariable'>)
('fw_lstm_U', <class 'theano.tensor.sharedvar.TensorSharedVariable'>)
('fw_lstm_b', <class 'theano.tensor.sharedvar.TensorSharedVariable'>)
('bw_lstm_W', <class 'theano.tensor.sharedvar.TensorSharedVariable'>)
('bw_lstm_U', <class 'theano.tensor.sharedvar.TensorSharedVariable'>)
('hidden_b', <class 'theano.tensor.sharedvar.TensorSharedVariable'>)
('hidden_W', <class 'theano.tensor.sharedvar.TensorSharedVariable'>)
building training function(2.72188806534)
building validating function(25.7086689472)
begin to train(27.9824080467)
.epoch 1/200 begin(27.982)
[prefetch]height: 150, x_max_step:900.0, y_max_width:200
Traceback (most recent call last):
File "train/train.py", line 148, in
loss = train()
File "/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.py", line 898, in call
storage_map=getattr(self.fn, 'storage_map', None))
File "/usr/local/lib/python2.7/dist-packages/theano/gof/link.py", line 325, in raise_with_op
reraise(exc_type, exc_value, exc_trace)
File "/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.py", line 884, in call
self.fn() if output_subset is None else
File "/usr/local/lib/python2.7/dist-packages/theano/gof/op.py", line 872, in rval
r = p(n, [x[0] for x in i], o)
File "/usr/local/lib/python2.7/dist-packages/theano/tensor/subtensor.py", line 2173, in perform
out[0] = inputs[0].getitem(inputs[1:])
IndexError: index 121 is out of bounds for axis 2 with size 119
Apply node that caused the error: AdvancedSubtensor(Reshape{3}.0, SliceConstant{None, None, None}, InplaceDimShuffle{0,x}.0, <TensorType(int32, matrix)>)
Toposort index: 463
Inputs types: [TensorType(float32, 3D), <theano.tensor.type_other.SliceType object at 0x7f6a4d6d9510>, TensorType(int64, col), TensorType(int32, matrix)]
Inputs shapes: [(900, 64, 119), 'No shapes', (64, 1), (64, 200)]
Inputs strides: [(30464, 476, 4), 'No strides', (8, 8), (800, 4)]
Inputs values: ['not shown', slice(None, None, None), 'not shown', 'not shown']
Outputs clients: [[Reshape{2}(AdvancedSubtensor.0, MakeVector{dtype='int64'}.0), Shape_i{2}(AdvancedSubtensor.0), Shape_i{1}(AdvancedSubtensor.0), Shape_i{0}(AdvancedSubtensor.0)]]

Backtrace when the node is created(use Theano flag traceback.limit=N to make it longer):
File "train/train.py", line 84, in
mid_layer_type = BLSTMLayer, forget=False)
File "/misc/me/rohits/aaron-cnn-lstm-ctc/layers/net.py", line 40, in init
blank = options['blank'], log_space = True)
File "/misc/me/rohits/aaron-cnn-lstm-ctc/layers/ctc_layer.py", line 25, in init
self.log_ctc(labels_len_const = labels_len_const)
File "/misc/me/rohits/aaron-cnn-lstm-ctc/layers/ctc_layer.py", line 94, in log_ctc
x1 = self.x[:, T.arange(n_samples)[:, None], self.y]