def get_nvidia_model2():
'''
this model is inspired by the NVIDIA paper
https://images.nvidia.com/content/tegra/automotive/images/2016/solutions/pdf/end-to-end-dl-using-px.pdf
Activation is ELU
Nvidia uses YUV plane inputs
Final dense layers are adjusted for the lower resolutions in use
channel last order is used because it results in fewer final weights and performs better
on limited cpu resources, but does not match the recommended order for Tensorflow.
Check get_nvidia_model_sw for a model using Tensorflow recommended ordering of channels
'''
row, col, ch = conf.row, conf.col, conf.ch
input_shape=(row, col, ch)
model = Sequential()
model.ch_order = 'channel_last'
model.add(Lambda(lambda x: x/127.5 - 1.,
input_shape=(row, col, ch),
output_shape=(row, col, ch)))
#model.add(Cropping2D(cropping=((20,20), (0,0))))
model.add(Convolution2D(64, 5, 5, subsample=(2, 2), border_mode="same"))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(36, 5, 5, subsample=(2, 2), border_mode="same"))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(48, 3, 3, subsample=(2, 2), border_mode="same"))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3, subsample=(2, 2), border_mode="same"))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3, subsample=(1, 1), border_mode="same"))
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dropout(.2))
model.add(Activation('relu'))
model.add(Dense(1000))
model.add(Dropout(.5))
model.add(Activation('relu'))
model.add(Dense(100))
model.add(Activation('relu'))
model.add(Dense(10))
model.add(Activation('tanh'))
#two floats for steering and throttle commands
model.add(Dense(1))
#choose a loss function and optimizer
model.compile(loss='mse', optimizer='adam')
show_model_summary(model)
return model
def get_nvidia_model():
'''
this model is inspired by the NVIDIA paper
https://images.nvidia.com/content/tegra/automotive/images/2016/solutions/pdf/end-to-end-dl-using-px.pdf
Activation is ELU
Nvidia uses YUV plane inputs
Final dense layers are adjusted for the lower resolutions in use
channel last order is used because it results in fewer final weights and performs better
on limited cpu resources, but does not match the recommended order for Tensorflow.
Check get_nvidia_model_sw for a model using Tensorflow recommended ordering of channels
'''
row, col, ch = conf.row, conf.col, conf.ch
model = Sequential()
model.ch_order = 'channel_first'
model.add(
Lambda(lambda x: x / 127.5 - 1.,
input_shape=(ch, col, row),
output_shape=(ch, col, row)))
model.add(Convolution2D(24, 5, 5, subsample=(2, 2), border_mode="same"))
model.add(ELU())
model.add(Convolution2D(36, 5, 5, subsample=(2, 2), border_mode="same"))
model.add(ELU())
model.add(Convolution2D(48, 3, 3, subsample=(2, 2), border_mode="same"))
model.add(ELU())
model.add(Convolution2D(64, 3, 3, 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(256))
model.add(ELU())
model.add(Dense(128))
model.add(ELU())
model.add(Dense(2))
model.compile(optimizer="adam", loss="mse")
return model
def get_simple_model():
'''
trying for a more simple model
'''
row, col, ch = conf.row, conf.col, conf.ch
input_shape=(row, col, ch)
model = Sequential()
model.ch_order = 'channel_last'
model.add(Lambda(lambda x: x/127.5 - 1.,
input_shape=(row, col, ch),
output_shape=(row, col, ch)))
model.add(MaxPooling2D((2, 2)))
model.add(Convolution2D(9, 5, 5, subsample=(1, 1), border_mode="same"))
model.add(Activation('relu'))
model.add(Dropout(.5))
model.add(MaxPooling2D((2, 2)))
model.add(Convolution2D(9, 3, 3, subsample=(2, 2), border_mode="same"))
model.add(Activation('relu'))
model.add(Dropout(.5))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dropout(.2))
model.add(Activation('relu'))
model.add(Dense(1000))
model.add(Dropout(.5))
model.add(Dense(10))
model.add(Activation('tanh'))
#two floats for steering and throttle commands
model.add(Dense(1))
#choose a loss function and optimizer
model.compile(loss='mse', optimizer='adam')
show_model_summary(model)
return model
def get_nvidia_model_sw():
'''
this model is based on the NVIDIA paper
https://images.nvidia.com/content/tegra/automotive/images/2016/solutions/pdf/end-to-end-dl-using-px.pdf
This follows a similar approach to model above, but sets the channel order
to the recommended for Tensorflow. This results in nearly 5x more trainiable weights
and did not result in better overal performance in my tests.
'''
row, col, ch = conf.row, conf.col, conf.ch
model = Sequential()
model.ch_order = 'channel_last'
model.add(
Lambda(lambda x: x / 127.5 - 1.,
input_shape=(row, col, ch),
output_shape=(row, col, ch)))
model.add(Convolution2D(24, 5, 5, subsample=(2, 2), border_mode="same"))
model.add(Activation('relu'))
model.add(Convolution2D(36, 5, 5, subsample=(2, 2), border_mode="same"))
model.add(Activation('relu'))
model.add(Convolution2D(48, 3, 3, subsample=(2, 2), border_mode="same"))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3, subsample=(2, 2), border_mode="same"))
model.add(Flatten())
model.add(Dropout(.2))
model.add(Activation('relu'))
model.add(Dense(512))
model.add(Dropout(.5))
model.add(Activation('relu'))
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dense(128))
model.add(Activation('tanh'))
model.add(Dense(2))
model.compile(optimizer="adam", loss="mse")
return model
def model():
row, col, ch = 160, 128, 3
# The model
model = Sequential()
model.ch_order = 'channel_first'
model.add(
Lambda(lambda x: x / 127.5 - 1.0,
input_shape=(col, row, ch),
output_shape=(col, row, ch)))
model.add(
Convolution2D(24, kernel_size=(5, 5), strides=(2, 2), padding="same"))
model.add(ELU())
model.add(
Convolution2D(36, kernel_size=(5, 5), strides=(2, 2), padding="same"))
model.add(ELU())
model.add(
Convolution2D(48, kernel_size=(3, 3), strides=(2, 2), padding="same"))
model.add(ELU())
model.add(
Convolution2D(64, kernel_size=(3, 3), strides=(2, 2), padding="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(256))
model.add(ELU())
model.add(Dense(128))
model.add(ELU())
model.add(Dense(2))
# Build and return it
model.compile(optimizer="adam", loss="mse")
return model