def default_lstm(self):
'''
'''
img_seq_shape = (self.seq_length, ) + self.image_shape
img_in = Input(shape=img_seq_shape, name='img_in')
x = img_in
x = TD(Cropping2D(cropping=((60, 0), (0, 0))))(x)
x = TD(Convolution2D(24, (5, 5), strides=(2, 2), activation='relu'))(x)
x = TD(Convolution2D(32, (5, 5), strides=(2, 2), activation='relu'))(x)
x = TD(Convolution2D(64, (3, 3), strides=(2, 2), activation='relu'))(x)
x = TD(Convolution2D(64, (3, 3), strides=(1, 1), activation='relu'))(x)
x = TD(Convolution2D(64, (3, 3), strides=(1, 1), activation='relu'))(x)
x = TD(Flatten(name='flattened'))(x)
x = TD(Dense(100, activation='relu'))(x)
x = TD(Dropout(.1))(x)
x = LSTM(128, return_sequences=True, name="LSTM_seq")(x)
x = Dropout(.1)(x)
x = LSTM(128, return_sequences=False, name="LSTM_out")(x)
x = Dropout(.1)(x)
x = Dense(50, activation='relu')(x)
x = Dropout(.1)(x)
angle_out = Dense(1, activation='linear', name='angle_out')(x)
# continous output of throttle
throttle_out = Dense(1, activation='linear', name='throttle_out')(x)
model = Model(inputs=[img_in], outputs=[angle_out, throttle_out])
model.compile(optimizer='adam',
loss={
'angle_out': 'mean_squared_error',
'throttle_out': 'mean_squared_error'
},
loss_weights={
'angle_out': 0.5,
'throttle_out': .5
})
return model
def rnn_lstm(seq_length=3, num_outputs=2, image_shape=(120, 160, 3)):
img_seq_shape = (seq_length, ) + image_shape
img_in = Input(batch_shape=img_seq_shape, name='img_in')
drop_out = 0.3
x = Sequential()
x.add(TD(Cropping2D(cropping=((40, 0), (0, 0))),
input_shape=img_seq_shape)) #trim 60 pixels off top
x.add(TD(BatchNormalization()))
x.add(TD(Convolution2D(24, (5, 5), strides=(2, 2), activation='relu')))
x.add(TD(Dropout(drop_out)))
x.add(TD(Convolution2D(32, (5, 5), strides=(2, 2), activation='relu')))
x.add(TD(Dropout(drop_out)))
x.add(TD(Convolution2D(32, (3, 3), strides=(2, 2), activation='relu')))
x.add(TD(Dropout(drop_out)))
x.add(TD(Convolution2D(32, (3, 3), strides=(1, 1), activation='relu')))
x.add(TD(Dropout(drop_out)))
x.add(TD(MaxPooling2D(pool_size=(2, 2))))
x.add(TD(Flatten(name='flattened')))
x.add(TD(Dense(100, activation='relu')))
x.add(TD(Dropout(drop_out)))
x.add(LSTM(128, return_sequences=True, name="LSTM_seq"))
x.add(Dropout(.1))
x.add(LSTM(128, return_sequences=False, name="LSTM_out"))
x.add(Dropout(.1))
x.add(Dense(128, activation='relu'))
x.add(Dropout(.1))
x.add(Dense(64, activation='relu'))
x.add(Dense(10, activation='relu'))
x.add(Dense(num_outputs, activation='linear', name='model_outputs'))
return x
def rnn_lstm(seq_length=3,
num_outputs=2,
input_shape=(120, 160, 3),
roi_crop=(0, 0)):
#we now expect that cropping done elsewhere. we will adjust our expeected image size here:
input_shape = adjust_input_shape(input_shape, roi_crop)
img_seq_shape = (seq_length, ) + input_shape
img_in = Input(batch_shape=img_seq_shape, name='img_in')
drop_out = 0.3
x = Sequential()
x.add(
TD(Convolution2D(24, (5, 5), strides=(2, 2), activation='relu'),
input_shape=img_seq_shape))
x.add(TD(Dropout(drop_out)))
x.add(TD(Convolution2D(32, (5, 5), strides=(2, 2), activation='relu')))
x.add(TD(Dropout(drop_out)))
x.add(TD(Convolution2D(32, (3, 3), strides=(2, 2), activation='relu')))
x.add(TD(Dropout(drop_out)))
x.add(TD(Convolution2D(32, (3, 3), strides=(1, 1), activation='relu')))
x.add(TD(Dropout(drop_out)))
x.add(TD(MaxPooling2D(pool_size=(2, 2))))
x.add(TD(Flatten(name='flattened')))
x.add(TD(Dense(100, activation='relu')))
x.add(TD(Dropout(drop_out)))
x.add(LSTM(128, return_sequences=True, name="LSTM_seq"))
x.add(Dropout(.1))
x.add(LSTM(128, return_sequences=False, name="LSTM_fin"))
x.add(Dropout(.1))
x.add(Dense(128, activation='relu'))
x.add(Dropout(.1))
x.add(Dense(64, activation='relu'))
x.add(Dense(10, activation='relu'))
x.add(Dense(num_outputs, activation='linear', name='model_outputs'))
return x
def rnn_lstm(seq_length=2, num_outputs=2, image_shape=(120, 2 * 160)):
from tensorflow.python.keras.layers.merge import concatenate
from tensorflow.python.keras.layers import LSTM
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers.wrappers import TimeDistributed as TD
drop_out = 0.3
img_seq_shape = (seq_length, ) + image_shape
img_in = Input(batch_shape=img_seq_shape, name='img_in')
x = Sequential()
x.add(
TD(Reshape(target_shape=image_shape + (1, )),
input_shape=img_seq_shape))
x.add(TD(Cropping2D(cropping=((40, 0), (0, 0))),
input_shape=img_seq_shape))
x.add(TD(BatchNormalization()))
x.add(TD(Conv2D(24, (5, 5), strides=(2, 2), activation='relu')))
x.add(TD(Dropout(drop_out)))
x.add(TD(Conv2D(32, (5, 5), strides=(2, 2), activation='relu')))
x.add(TD(Dropout(drop_out)))
x.add(TD(Conv2D(32, (3, 3), strides=(2, 2), activation='relu')))
x.add(TD(Dropout(drop_out)))
x.add(TD(Conv2D(32, (3, 3), strides=(1, 1), activation='relu')))
x.add(TD(Dropout(drop_out)))
x.add(TD(MaxPool2D(pool_size=(2, 2))))
x.add(TD(Flatten(name='flattened')))
x.add(TD(Dense(100, activation='relu')))
x.add(TD(Dropout(drop_out)))
x.add(LSTM(128, return_sequences=True, name="LSTM_seq"))
x.add(Dropout(.1))
x.add(LSTM(128, return_sequences=False, name="LSTM_out"))
x.add(Dropout(.1))
x.add(Dense(128, activation='relu'))
x.add(Dropout(.1))
x.add(Dense(64, activation='relu'))
x.add(Dense(10, activation='relu'))
x.add(Dense(num_outputs, activation='linear', name='model_outputs'))
return x
