Changes

'''Model Training/Prediction (classification_MMM_LLL.py)''' : this is where the deep neural network is. The "MMM" represents the model. For example, currently I have "1DConvolution", "2DConvolution" and "LSTM". "LLL" represents the name of the label. Notice that for the same text inputs we can predict for different things using the same model literally. For example, "classification_LSTM_indu.py" is a LSTM model to predict the industray based on the descriptions. And "classification_LSTM_ipo.py" is a LSTM model to predict the IPO status based on the same descriptions. Again you need to name your files properly! Different tasks will have different hyper-parameter configurations though the model and the inputs can be totally the same. This Python file, no matter what the model is, will always load in a pickle file you generated in the previous step and train the neural network. At the end, the well trained neural network will predict on your test examples (the examples you don't see during the training) and print the accuracy.

with open('longdescription_ipo.pkl', 'rb') as file:

model.add(Dense(2, activation='softmax'))