old_columns = df.columns df.dropna(inplace=True, axis=1) print('dropped_columns: \n\t{}'.format( list(set(old_columns) - set(df.columns)))) # Convert a Pandas dataframe to the x,y inputs that TensorFlow needs print('===== to_xy =====') x, y = common.to_xy(df, 'marker') print('x.shape: {}, {}'.format(x.shape, type(x))) print('y.shape: {}, {}'.format(y.shape, type(y))) # Create time sequences of x, y with time_step print('===== create sequences =====') print('step_size = {}'.format(options.step_size)) x_seq = common.to_sequences(x, step_size=options.step_size) y_seq = y[options.step_size - 1:] print('x_seq.shape: {}, {}'.format(x_seq.shape, type(x_seq))) print('y_seq.shape: {}, {}'.format(y_seq.shape, type(y_seq))) # Create a test/train split. 20% test print('validation_split = {}%'.format(VALIDATION_SPLIT * 100)) x_train, x_test, y_train, y_test = train_test_split(x_seq, y_seq, test_size=VALIDATION_SPLIT, random_state=42) # SimpleRNN stuff print('===== setup SimpleRNN =====') callback_monitor = 'val_acc' earlyStopping = EarlyStopping(monitor=callback_monitor,
old_columns = df.columns df.dropna(inplace=True, axis=1) print('dropped_columns: \n\t{}'.format( list(set(old_columns) - set(df.columns)))) # Convert a Pandas dataframe to the x,y inputs that TensorFlow needs print('===== to_xy =====') x, y = common.to_xy(df, 'marker') print('x.shape: {}, {}'.format(x.shape, type(x))) print('y.shape: {}, {}'.format(y.shape, type(y))) # Create time sequences of x, y with time_step print('===== create sequences =====') print('step_size = {}'.format(options['step_size'])) x_seq, y_seq = common.to_sequences(x, y, step_size=options['step_size']) # Create a test/train split. 20% test print('validation_split = {}%'.format(VALIDATION_SPLIT * 100)) x_train, x_test, y_train, y_test = train_test_split(x_seq, y_seq, test_size=VALIDATION_SPLIT, random_state=42) # GRU stuff print('===== setup GRU =====') monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=5, verbose=1, mode='auto')