new_contents: import numpy as np
  import tensorflow as tf
  tf.python.control_flow_ops = tf
  from keras.models import Sequential
  from keras.layers.core import Dense, Activation, Flatten, Dropout
  from keras.layers.convolutional import Convolution2D
  from keras.layers.pooling import MaxPooling2D
  from sklearn import model_selection
  from pandas.io import parsers
  import os
  from data import generate_samples, preprocess
  
  
  local_project_path = '/'
  local_data_path = os.path.join(local_project_path, 'data')
  
  
  if __name__ == '__main__':
      # Read the data
      df = parsers.read_csv(os.path.join(local_data_path, 'driving_log.csv'))
      # Filter out frames with steering angles close to 0
      df = df[np.absolute(df.steering) > 0.001]
      # Split data into training and validation sets
      df_train, df_valid = model_selection.train_test_split(df, test_size=0.2)
  
      model = Sequential()
      model.add(Convolution2D(24, 5, 5, subsample=(2, 2), input_shape=(66, 200, 3), activation='relu'))
      model.add(Convolution2D(36, 5, 5, subsample=(2, 2), activation='relu'))
      model.add(Convolution2D(48, 3, 3, activation='relu'))
      model.add(Convolution2D(64, 3, 3, activation='relu'))
      model.add(Convolution2D(64, 3, 3, activation='relu'))
      model.add(Flatten())
      model.add(Dense(1164, activation='relu'))
      model.add(Dropout(0.5))
      model.add(Dense(100, activation='relu'))
      model.add(Dropout(0.25))
      model.add(Dense(50, activation='relu'))
      model.add(Dropout(0.1))
      model.add(Dense(10, activation='relu'))
      model.add(Dense(1))
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      history = model.fit_generator(
          generate_samples(df_train, local_data_path),
          samples_per_epoch=df_train.count()[0],
          nb_epoch=20,
          validation_data=generate_samples(df_valid, local_data_path),
          nb_val_samples=df_valid.count()[0]
      )
  
      model.save_weights(os.path.join(local_project_path, 'behavioral_cloning.h5'))
      with open(os.path.join(local_project_path, 'behavioral_cloning.json'), 'w') as file:
          file.write(model.to_json())
      print('Model saved.')
subject: Add script with the Keras model.