I’m trying to train a network with an unbalanced data. I have A (198 samples), B (436 samples), C (710 samples), D (272 samples) and I have read about the “weighted_cross_entropy_with_logits” but all the examples I found are for binary classification so I’m not very confident in how to set those weights.
Let’s suppose I have a sequence of integers:
I’ve ran into serious incompatibility problems for the same code ran with one vs. the other; e.g.:
Trying to install the gpu version of Tensorflow on my machine, but I’m getting this error when trying ‘import tensorflow’
The code that I have (that I can’t change) uses the Resnet with my_input_tensor as the input_tensor.
Here is my code fore training the complete model and saving it:
I’m having trouble recovering a tensor by name, I don’t even know if it’s possible.
Given a trained LSTM model I want to perform inference for single timesteps, i.e. seq_length = 1 in the example below. After each timestep the internal LSTM (memory and hidden) states need to be remembered for the next ‘batch’. For the very beginning of the inference the internal LSTM states init_c, init_h are computed given the input. These are then stored in a LSTMStateTuple object which is passed to the LSTM. During training this state is updated every timestep. However for inference I want the state to be saved in between batches, i.e. the initial states only need to be computed at the very beginning and after that the LSTM states should be saved after each ‘batch’ (n=1).
I currently have the following code for a series of chained together RNNs in tensorflow. I am not using MultiRNN since I was to do something later on with the output of each layer.
I have a set of fairly complicated models that I am training and I am looking for a way to save and load the model optimizer states. The “trainer models” consist of different combinations of several other “weight models”, of which some have shared weights, some have frozen weights depending on the trainer, etc. It is a bit too complicated of an example to share, but in short, I am not able to use model.save('model_file.h5') and keras.models.load_model('model_file.h5') when stopping and starting my training.