old_contents: import numpy as np
  import os
  os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
  import tensorflow as tf
  import pandas as pd
  tf.python.control_flow_ops = tf
  from keras import models, optimizers, backend
  from keras.layers import core, convolutional
  from sklearn import model_selection
  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 = pd.io.parsers.read_csv(os.path.join(local_data_path, 'driving_log_balanced_n100_bins1000.csv'))
      # Split data into training and validation sets
      df_train, df_valid = model_selection.train_test_split(df, test_size=.2)
  
      model = models.Sequential()
      model.add(convolutional.Convolution2D(24, 5, 5, subsample=(2, 2), input_shape=(66, 200, 3), activation='relu'))
      model.add(convolutional.Convolution2D(36, 5, 5, subsample=(2, 2), activation='relu'))
      model.add(convolutional.Convolution2D(48, 3, 3, activation='relu'))
      model.add(convolutional.Convolution2D(64, 3, 3, activation='relu'))
      model.add(convolutional.Convolution2D(64, 3, 3, activation='relu'))
      model.add(core.Flatten())
      model.add(core.Dense(1164, activation='relu'))
      model.add(core.Dropout(.5))
      model.add(core.Dense(100, activation='relu'))
      model.add(core.Dropout(.5))
      model.add(core.Dense(50, activation='relu'))
      model.add(core.Dropout(.5))
      model.add(core.Dense(10, activation='relu'))
      model.add(core.Dense(1))
      model.compile(optimizer=optimizers.Adam(lr=1e-04), loss='mean_squared_error')
  
      history = model.fit_generator(
          generate_samples(df_train, local_data_path),
          samples_per_epoch=df_train.shape[0],
          nb_epoch=20,
          validation_data=generate_samples(df_valid, local_data_path, augment=False),
          callbacks=[WeightsLogger(root_path=local_project_path)]
          nb_val_samples=df_valid.shape[0],
      )
  
      with open(os.path.join(local_project_path, 'model.json'), 'w') as file:
          file.write(model.to_json())
  
      backend.clear_session()
new_contents: import numpy as np
  import os
  os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
  import tensorflow as tf
  import pandas as pd
  tf.python.control_flow_ops = tf
  from keras import models, optimizers, backend
  from keras.layers import core, convolutional, pooling
  from sklearn import model_selection
  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 = pd.io.parsers.read_csv(os.path.join(local_data_path, 'driving_log_balanced_n100_bins1000.csv'))
      # Split data into training and validation sets
      df_train, df_valid = model_selection.train_test_split(df, test_size=.2)
  
      model = models.Sequential()
      model.add(convolutional.Convolution2D(16, 3, 3, input_shape=(32, 128, 3), activation='relu'))
      model.add(pooling.MaxPooling2D(pool_size=(2, 2)))
      model.add(convolutional.Convolution2D(32, 3, 3, activation='relu'))
      model.add(pooling.MaxPooling2D(pool_size=(2, 2)))
      model.add(convolutional.Convolution2D(64, 3, 3, activation='relu'))
      model.add(pooling.MaxPooling2D(pool_size=(2, 2)))
      model.add(core.Flatten())
      model.add(core.Dense(500, activation='relu'))
      model.add(core.Dropout(.5))
      model.add(core.Dense(100, activation='relu'))
      model.add(core.Dropout(.25))
      model.add(core.Dense(20, activation='relu'))
      model.add(core.Dense(1))
      model.compile(optimizer=optimizers.Adam(lr=1e-04), loss='mean_squared_error')
  
      history = model.fit_generator(
          generate_samples(df_train, local_data_path),
          samples_per_epoch=df_train.shape[0],
          nb_epoch=30,
          validation_data=generate_samples(df_valid, local_data_path, augment=False),
          callbacks=[WeightsLogger(root_path=local_project_path)]
          nb_val_samples=df_valid.shape[0],
      )
  
      with open(os.path.join(local_project_path, 'model.json'), 'w') as file:
          file.write(model.to_json())
  
      backend.clear_session()
subject: Simplify model.