cnn_prediction.py 文件源码

def buildModel(cameraFormat=(3, 480, 640)):
  """
  Build and return a CNN; details in the comments.
  The intent is a scaled down version of the model from "End to End Learning
  for Self-Driving Cars": https://arxiv.org/abs/1604.07316.

  Args:
    cameraFormat: (3-tuple) Ints to specify the input dimensions (color
        channels, rows, columns).
  Returns:
    A compiled Keras model.
  """
  print "Building model..."
  ch, row, col = cameraFormat

  model = Sequential()

  # Use a lambda layer to normalize the input data
  model.add(Lambda(
      lambda x: x/127.5 - 1.,
      input_shape=(ch, row, col),
      output_shape=(ch, row, col))
  )

  # Several convolutional layers, each followed by ELU activation
  # 8x8 convolution (kernel) with 4x4 stride over 16 output filters
  model.add(Convolution2D(16, 8, 8, subsample=(4, 4), border_mode="same"))
  model.add(ELU())
  # 5x5 convolution (kernel) with 2x2 stride over 32 output filters
  model.add(Convolution2D(32, 5, 5, subsample=(2, 2), border_mode="same"))
  model.add(ELU())
  # 5x5 convolution (kernel) with 2x2 stride over 64 output filters
  model.add(Convolution2D(64, 5, 5, subsample=(2, 2), border_mode="same"))
  # Flatten the input to the next layer
  model.add(Flatten())
  # Apply dropout to reduce overfitting
  model.add(Dropout(.2))
  model.add(ELU())
  # Fully connected layer
  model.add(Dense(512))
  # More dropout
  model.add(Dropout(.5))
  model.add(ELU())
  # Fully connected layer with one output dimension (representing the speed).
  model.add(Dense(1))

  # Adam optimizer is a standard, efficient SGD optimization method
  # Loss function is mean squared error, standard for regression problems
  model.compile(optimizer="adam", loss="mse")

  return model