imranshaikmuma

I am working on the same thing from few weeks. Methods I currently found to use model to train on Multiple GPUs on a single instance: 1. Manually assign GPU...

@BenjamWhite i am trying to fix this problem from two days. please help me if you find it too..

what i feel is file is not having weights of 'time_distributed_1' layer. i googled and find nothing on this. it has only [array(['bias:0', 'kernel:0'], dtype='

weights[0] and weights[1] should be an array of weights. we are not getting that from our trained model file. if you can solve this, everything will be automatically solved

check this link it helped me a lot (Cell 94) https://github.com/rtlee9/recipe-summarization/blob/master/src/predict.ipynb here when we are using fit_generator, weights are produced till dropout3 only. and there is no layer for time...

i am able to run but i am not getting the desired output. i am using a different data like job description and generating a job title my data is...

are you using python3? if so change the statement **sample_lengths = map(len, samples)** in keras_rnn_predict function as below: **sample_lengths = list(map(len, samples))**

rsarxiv can you help me in getting the data(training and test)? please

I am facing the same issue

guys the answer to all your problems is cudann https://developer.nvidia.com/cudnn install cudnn from above link install instructions https://stackoverflow.com/questions/42013316/after-building-tensorflow-from-source-seeing-libcudart-so-and-libcudnn-errors MY TOKENS AFTER I USE THIS: 2017-07-12 15:42:33.111509: I tensorflow/core/common_runtime/gpu/gpu_device.cc:961] DMA: 0...