old_contents: import numpy as np
  import os
  os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
  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 keras.callbacks import EarlyStopping
  from keras.optimizers import Adam
  from keras import backend
  from sklearn import model_selection
  from pandas.io import parsers
  from data import generate_samples, preprocess
  from weights_logger_callback import WeightsLogger
  
  
  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.load_weights(os.path.join(local_project_path, 'behavioral_cloning.h5'))
  
      model.compile(optimizer=Adam(lr=0.0001), loss='mean_squared_error')
  
      history = model.fit_generator(
          generate_samples(df_train, local_data_path),
          samples_per_epoch=df_train.count()[0],
          nb_epoch=10,
          validation_data=generate_samples(df_valid, local_data_path, augment=False),
          nb_val_samples=df_valid.count()[0],
          callbacks=[ProgbarLogger()],
          callbacks=[ProgbarLogger(), WeightsLogger(root_path=local_project_path)],
          verbose=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.')
      backend.clear_session()
new_contents: import numpy as np
  import os
  os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
  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 keras.optimizers import Adam
  from keras import backend
  from sklearn.model_selection import train_test_split
  from pandas.io import parsers
  from data import generate_samples, preprocess
  from weights_logger_callback import WeightsLogger
  
  
  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 = 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.load_weights(os.path.join(local_project_path, 'behavioral_cloning.h5'))
  
      model.compile(optimizer=Adam(lr=0.0001), loss='mean_squared_error')
  
      history = model.fit_generator(
          generate_samples(df_train, local_data_path),
          samples_per_epoch=df_train.count()[0],
          nb_epoch=10,
          validation_data=generate_samples(df_valid, local_data_path, augment=False),
          nb_val_samples=df_valid.count()[0],
          callbacks=[ProgbarLogger()],
          callbacks=[ProgbarLogger(), WeightsLogger(root_path=local_project_path)],
          verbose=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.')
      backend.clear_session()
subject: Refactor imports.