AI News, michaelfarrell76/End-To-End-Generative-Dialogue


This code implements multi-layer recurrent neural network encoder-decoder models (RNN, GRU, and LSTM) for training/sampling from conversation dialogue.

If you want to train on an Nvidia GPU using CUDA, you'll need to install the CUDA Toolkit as well as the cutorch and cunn packages: If you'd like to chat with your trained model, you'll need the penlight package: Input data is stored in the data directory.

Given a checkpoint file, we can generate responses to input dialogue examples: It's also possible to chat directly with a checkpoint: These models have a tendency to respond tersely and vaguely.

Once you've successfully added your ssh to the list of authorized keys, and allowed for remote login, you can run this through localhost with the following command: Ideally, you can have the clients farmed out to different remote computers instead of running locally.

First, zip the data folder that contains the MovieTriples dataset in the main End-To-End-Generative-Dialogue folder: Next navigate to the 'lua-lua' folder and follow the setup instructions under the 'Remote -gcloud' header.

Follow these instructions exactly as they are written through the section 'Adding ssh keys again', except in the section labeled 'Setup the disk', instead of running the command in the 'Setup the disk' step, you should run which contains the necessary changes to the setup for this task.

How to Check-Point Deep Learning Models in Keras

Deep learning models can take hours, days or even weeks to train.

The checkpoint may be used directly, or used as the starting point for a new run, picking up where it left off.

The ModelCheckpoint callback class allows you to define where to checkpoint the model weights, how the file should named and under what circumstances to make a checkpoint of the model.

The example below creates a small neural network for the Pima Indians onset of diabetes binary classification problem.

Checkpointing is setup to save the network weights only when there is an improvement in classification accuracy on the validation dataset (monitor=’val_acc’

Running the example produces the following output (truncated for brevity): You will see a number of files in your working directory containing the network weights in HDF5 format.

simpler check-point strategy is to save the model weights to the same file, if and only if the validation accuracy improves.

This can be done easily using the same code from above and changing the output filename to be fixed (not include score or epoch information).

It avoids you needing to include code to manually keep track and serialize the best model when training.

In the example below, the model structure is known and the best weights are loaded from the previous experiment, stored in the working directory in the file.

You learned two checkpointing strategies that you can use on your next deep learning project: You also learned how to load a checkpointed model and make predictions.

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