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#!/usr/bin/env python
Steering angle prediction model
import argparse
import json
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Lambda, ELU
from keras.layers.convolutional import Convolution2D
from server import client_generator
def gen(hwm, host, port):
  for tup in client_generator(hwm=hwm, host=host, port=port):
    X, Y, _ = tup
    Y = Y[:, -1]
    if X.shape[1] == 1:  # no temporal context
      X = X[:, -1]
    yield X, Y
def get_model(time_len=1):
  ch, row, col = 3, 160, 320  # camera format
  model = Sequential()
  model.add(Lambda(lambda x: x/127.5 - 1.,
            input_shape=(ch, row, col),
            output_shape=(ch, row, col)))
  model.add(Convolution2D(16, 8, 8, subsample=(4, 4), border_mode="same"))
  model.add(ELU())
  model.add(Convolution2D(32, 5, 5, subsample=(2, 2), border_mode="same"))
  model.add(ELU())
  model.add(Convolution2D(64, 5, 5, subsample=(2, 2), border_mode="same"))
  model.add(Flatten())
  model.add(Dropout(.2))
  model.add(ELU())
  model.add(Dense(512))
  model.add(Dropout(.5))
  model.add(ELU())
  model.add(Dense(1))
  model.compile(optimizer="adam", loss="mse")
  return model
if __name__ == "__main__":
  parser = argparse.ArgumentParser(description='Steering angle model trainer')
  parser.add_argument('--host', type=str, default="localhost", help='Data server ip address.')
  parser.add_argument('--port', type=int, default=5557, help='Port of server.')
  parser.add_argument('--val_port', type=int, default=5556, help='Port of server for validation dataset.')
  parser.add_argument('--batch', type=int, default=64, help='Batch size.')
  parser.add_argument('--epoch', type=int, default=200, help='Number of epochs.')
  parser.add_argument('--epochsize', type=int, default=10000, help='How many frames per epoch.')
  parser.add_argument('--skipvalidate', dest='skipvalidate', action='store_true', help='Multiple path output.')
  parser.set_defaults(skipvalidate=False)
  parser.set_defaults(loadweights=False)
  args = parser.parse_args()
  model = get_model()
  model.fit_generator(
    gen(20, args.host, port=args.port),
    samples_per_epoch=10000,
    nb_epoch=args.epoch,
    validation_data=gen(20, args.host, port=args.val_port),
    nb_val_samples=1000
  print("Saving model weights and configuration file.")
  if not os.path.exists("./outputs/steering_model"):
      os.makedirs("./outputs/steering_model")
  model.save_weights("./outputs/steering_model/steering_angle.keras", True)
  with open('./outputs/steering_model/steering_angle.json', 'w') as outfile:
    json.dump(model.to_json(), outfile)

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