Deep Learning Quick Reference

Data preparation glue

We've done a lot of transformation in this example. Before moving on to training, I think it might be a good idea to see how this all fits together. We will use one more function, as shown here, to tie all these steps together:

def prep_data(df_train, df_test, lags):    df_train = diff_data(df_train)    scaler, df_train = scale_data(df_train)    df_test = diff_data(df_test)    scaler, df_test = scale_data(df_test, scaler)    df_train = lag_dataframe(df_train, lags=lags)    df_test = lag_dataframe(df_test, lags=lags)    X_train = df_train.drop("y", axis=1)    y_train = df_train.y    X_test = df_test.drop("y", axis=1)    y_test = df_test.y    X_train = np.reshape(X_train.values, (X_train.shape[0], X_train.shape[1], 1)) X_test = np.reshape(X_test.values ...