class Prediction:
def __init__(self):
self.bridge = cv_bridge.CvBridge()
self.IMG_HEIGHT = 200
self.IMG_WIDTH = 200
#self.sub=rospy.Subscriber("/cam_front/raw",Image,self.image_cb)
self.image = cv2.imread("5.jpg")
self._session = tf.Session()
self.model = Sequential([
Conv2D(16,
3,
padding='same',
activation='relu',
input_shape=(self.IMG_HEIGHT, self.IMG_WIDTH, 3)),
MaxPooling2D(),
Conv2D(32, 3, padding='same', activation='relu'),
MaxPooling2D(),
Conv2D(64, 3, padding='same', activation='relu'),
MaxPooling2D(),
Flatten(),
Dense(512, activation='relu'),
Dense(1)
])
self.model.compile(
optimizer='adam',
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=['accuracy'])
# Restore the weights
self.model.load_weights('./checkpoints/lidar_checkpoint')
self.model._make_predict_function()
self.model.summary()
val = self.model.predict([self.image.reshape(-1, 200, 200, 3)])
if (val[0][0] > 0):
print("person")
else:
print("car")
# Defining our DQN
dqn = DQNAgent(
model=model,
nb_actions=len(env_player.action_space),
policy=policy,
memory=memory,
nb_steps_warmup=1000,
gamma=0.5,
target_model_update=1,
delta_clip=0.01,
enable_double_dqn=True,
)
dqn.compile(Adam(lr=0.00025), metrics=["mae"])
model._make_predict_function()
# Training
env_player.play_against(
env_algorithm=dqn_training,
opponent=opponent,
env_algorithm_kwargs={
"dqn": dqn,
"nb_steps": NB_TRAINING_STEPS
},
)
model.save("model_%d" % NB_TRAINING_STEPS)
model._make_predict_function()
# Evaluation
