Getting ready

Training a neural network is done in the following steps:

1. Import the relevant packages and datasets
2. Preprocess the targets (convert them into one-hot encoded vectors) so that we can perform optimization on top of them:
   • We shall be minimizing categorical cross entropy loss
3. Create train and test datasets:
   • We have the train dataset so that we create a model based on it
   • The test dataset is not seen by the model:
     • Hence, the accuracy on the test dataset is an indicator of how well the model is likely to work on data when the model is productionalized, as data in the production scenario (which might occur a few days/weeks after building the model) cannot be seen by the model
4. Initialize a model
5. Define the model architecture:
   • Specify the number of units in a hidden layer
   • Specify the activation function that is to be performed in a hidden layer
   • Specify the number of hidden layers
   • Specify the loss function that we want to minimize
   • Provide the optimizer that will minimize the loss function
6. Fit the model:
   • Mention the batch size to update weights
   • Mention the total number of epochs
7. Test the model:
   • Mention the validation data, otherwise, mention the validation split, which will consider the last x% of total data as test data
   • Calculate the accuracy and loss values on top of the test dataset
8. Check for anything interesting in the way in which loss value and accuracy values changed over an increasing number of epochs

Using this strategy, let's go ahead and build a neural network model in Keras, in the following section.