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model_nvidia.py
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model_nvidia.py
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# Recreation of the original PilotNet architecture described in the paper(https://arxiv.org/abs/1704.07911)
import tensorflow.python.keras as tfk
image_height = 105
image_width = 240
def build_nvidia(img_height, img_width):
model = tfk.Sequential()
model.add(tfk.layers.Conv2D(filters=24, kernel_size=(5, 5), strides=(2, 2),
input_shape=(img_height, img_width, 3), kernel_initializer='he_normal'))
model.add(tfk.layers.BatchNormalization())
model.add(tfk.layers.Activation('relu'))
model.add(tfk.layers.Conv2D(filters=36, kernel_size=(5, 5), strides=(2, 2), kernel_initializer='he_normal'))
model.add(tfk.layers.BatchNormalization())
model.add(tfk.layers.Activation('relu'))
model.add(tfk.layers.Conv2D(filters=48, kernel_size=(5, 5), strides=(2, 2), kernel_initializer='he_normal'))
model.add(tfk.layers.BatchNormalization())
model.add(tfk.layers.Activation('relu'))
model.add(tfk.layers.Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), kernel_initializer='he_normal'))
model.add(tfk.layers.BatchNormalization())
model.add(tfk.layers.Activation('relu'))
model.add(tfk.layers.Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), kernel_initializer='he_normal'))
model.add(tfk.layers.BatchNormalization())
model.add(tfk.layers.Activation('relu'))
model.add(tfk.layers.Flatten())
model.add(tfk.layers.Dense(1000, kernel_initializer='he_normal'))
model.add(tfk.layers.BatchNormalization())
model.add(tfk.layers.Activation('relu'))
model.add(tfk.layers.Dense(100, kernel_initializer='he_normal'))
model.add(tfk.layers.BatchNormalization())
model.add(tfk.layers.Activation('relu'))
model.add(tfk.layers.Dense(50, kernel_initializer='he_normal'))
model.add(tfk.layers.BatchNormalization())
model.add(tfk.layers.Activation('relu'))
model.add(tfk.layers.Dense(10, kernel_initializer='he_normal'))
model.add(tfk.layers.BatchNormalization())
model.add(tfk.layers.Activation('relu'))
model.add(tfk.layers.Dense(1))
return model
if __name__ == '__main__':
model = build_nvidia(image_height, image_width)
print(model.summary())