TypeError: bad operand type for unary -: 'str'

python version = 3.6 Numpy version = 1.14.3 scipy version = 1.10

Help me...

Can I know which version I used at the time of implementation?

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Problem description

*I have finished learning the TrainModel.ipynb，and the results looks good, but when i tried to run the testModel.ipynb, I met this error as the following as @llyhbuaa,but it does not work when i deleted the modes and runned the TrainModel.ipynb again . I have connected to AirSim Simulator

Problem details

OSError Traceback (most recent call last) in () ----> 1 model = load_model(MODEL_PATH) 2 3 client = CarClient() 4 client.confirmConnection() 5 client.enableApiControl(True)

C:\ProgramData\Anaconda3\lib\site-packages\keras\models.py in load_model(filepath, custom_objects, compile) 232 return custom_objects[obj] 233 return obj --> 234 with h5py.File(filepath, mode='r') as f: 235 # instantiate model 236 model_config = f.attrs.get('model_config')

C:\ProgramData\Anaconda3\lib\site-packages\h5py_hl\files.py in init(self, name, mode, driver, libver, userblock_size, swmr, **kwds) 267 with phil: 268 fapl = make_fapl(driver, libver, **kwds) --> 269 fid = make_fid(name, mode, userblock_size, fapl, swmr=swmr) 270 271 if swmr_support:

C:\ProgramData\Anaconda3\lib\site-packages\h5py_hl\files.py in make_fid(name, mode, userblock_size, fapl, fcpl, swmr) 97 if swmr and swmr_support: 98 flags |= h5f.ACC_SWMR_READ ---> 99 fid = h5f.open(name, flags, fapl=fapl) 100 elif mode == 'r+': 101 fid = h5f.open(name, h5f.ACC_RDWR, fapl=fapl)

h5py_objects.pyx in h5py._objects.with_phil.wrapper()

h5py_objects.pyx in h5py._objects.with_phil.wrapper()

h5py\h5f.pyx in h5py.h5f.open()

OSError: Unable to open file (unable to open file: name = 'D:\EndToEndDeepLearning\Model', errno = 13, error message = 'Permission denied', flags = 0, o_flags = 0)

Exepriment/Environment details

• Tutorial used: *(AirSimE2EDeepLearning)
• Environment used: (hawaii )
• Versions of artifacts used (if applicable): ( Python 3.6, Keras 2.1.2,cntk 2.5.1)

I trained the model successfully and tried to run TestModel.ipynb. but connection with Airsim is not works.

1. I changed MODEL_PATH with trained model. MODEL_PATH = 'model/models/model_model.26-0.0002362.h5' -> Runs well.

2. Run AD_Cookbook_Start_AirSim.ps1 using PowerShell. -> Runs well.

3. No further process during connecting to AirSim. Waiting for connection: -> Not works.

I've already tested examples of CNTK-AirSim (Drone, Car) and it works well in my laptop. Test Environments OS : Windows 10 64bit. GPU : GTX1060 6GB Anaconda3

I tried to run this Autonomous driving cookbook on other maps like the neighborhood. However, the steering angle values in " airsim_rec.txt" only have three values: -1, 0, and 1. I have attached one file below. airsim_rec.txt

Don't know how to deal with it. Has anyone ever met this issue? Thank you！

Because the shadow of trees in "Neighborhood" often causes confused, I tried to use the segmentation images recorded as training data set. However, the car cannot move properly during testing. I am wondering whether I can train it by segmentation images or not?

Thanks for writing this tutorial! But there is a question about the xbox controller.

1.Using the scene "AD_Cookbook_AirSim\Hawaii" with xbox one controller , the input steering and throttle value are too large. How to set it properly?

2.When problem 1 happened, pressing backspace would not make all values back to 0 . The car will keep rotating.

I've tried to run 'TrainModel.ipynb' using Jupyter Notebook. but It is difficult to install OpenCV libraries because of version matchings.

Can you tell me versions of additional packages such as openCV, CUDA, CuDNN?

I'm trying to run the code with Python 3.5.2 Anaconda 4.1.1 (64bit) CNTK 2.4 Keras 2.1.4 Tensorflow-gpu 1.5.0 CUDA9.1 CuDNN9.0 OpenCV??

Error message because of openCV as below.

C:\Anaconda3\lib\site-packages\cv2_init_.py in () 2 import os 3 ----> 4 from .cv2 import * 5 from .data import * 6

ImportError: DLL load failed:

Problem description

I am trying to run the TestModel and I have found a TypeError when connecting to AirSim. It seems to me that it could come from the Tornado version, but actually I have tried with more than one version and the problem continues.

Problem details

Details of the problem are as follow:

When I run the script .\AD_Cookbook_Start_Airsim.ps1 landscape, everything runs perfectly in UE4 and AirSim but once I run the Jupyter Notebook (Step 2: "Test the model") as follows, it is when I find the error after the second cell (posted below):

model = load_model(MODEL_PATH) client = CarClient() client.confirmConnection() client.enableApiControl(True) car_controls = CarControls() print('Connection established!')

Then I found the folowing error message:

TypeError Traceback (most recent call last) in 1 model = load_model(MODEL_PATH) 2 ----> 3 client = CarClient() 4 client.confirmConnection() 5 client.enableApiControl(True)

~\PycharmProjects\Cookbook\AutonomousDrivingCookbook-master\AutonomousDrivingCookbook-master\AirSimE2EDeepLearning\AirSimClient.py in init(self, ip) 504 if (ip == ""): 505 ip = "127.0.0.1" --> 506 super(CarClient, self).init(ip, 42451) 507 508 def setCarControls(self, controls):

~\PycharmProjects\Cookbook\AutonomousDrivingCookbook-master\AutonomousDrivingCookbook-master\AirSimE2EDeepLearning\AirSimClient.py in init(self, ip, port) 148 class AirSimClientBase: 149 def init(self, ip, port): --> 150 self.client = msgpackrpc.Client(msgpackrpc.Address(ip, port), timeout = 3600) 151 152 def ping(self):

~\Anaconda3\envs\Cookbook\lib\site-packages\msgpackrpc\client.py in init(self, address, timeout, loop, builder, reconnect_limit, pack_encoding, unpack_encoding) 13 14 if timeout: ---> 15 loop.attach_periodic_callback(self.step_timeout, 1000) # each 1s 16 17 @classmethod

~\Anaconda3\envs\Cookbook\lib\site-packages\msgpackrpc\loop.py in attach_periodic_callback(self, callback, callback_time) 37 38 self._periodic_callback = ioloop.PeriodicCallback(callback, callback_time, self._ioloop) ---> 39 self._periodic_callback.start() 40 41 def dettach_periodic_callback(self):

~\Anaconda3\envs\Cookbook\lib\site-packages\tornado\ioloop.py in start(self) 885 self._running = True 886 self._next_timeout = self.io_loop.time() --> 887 self._schedule_next() 888 889 def stop(self) -> None:

~\Anaconda3\envs\Cookbook\lib\site-packages\tornado\ioloop.py in _schedule_next(self) 913 def _schedule_next(self) -> None: 914 if self._running: --> 915 self._update_next(self.io_loop.time()) 916 self._timeout = self.io_loop.add_timeout(self._next_timeout, self._run) 917

~\Anaconda3\envs\Cookbook\lib\site-packages\tornado\ioloop.py in _update_next(self, current_time) 920 if self.jitter: 921 # apply jitter fraction --> 922 callback_time_sec *= 1 + (self.jitter * (random.random() - 0.5)) 923 if self._next_timeout <= current_time: 924 # The period should be measured from the start of one call

TypeError: unsupported operand type(s) for *: 'ZMQIOLoop' and 'float'

Experiment/Environment details

• Tutorial used: AirSimE2EDeepLearning
• Environment used: landscape (all my environment are running perfectly)
• Versions of artifacts used: -- Windows 10 -- Anaconda -- Python 3.5 -- Tornado 6.0.2

Thanks a lot in advance.

python35 cudnn5.1 keras2.1.2 numpy (1.14.2) Traceback (most recent call last): File "C:\Users\oleg\Anaconda3\lib\site-packages\traitlets\traitlets.py", line 526, in get value = obj._trait_values[self.name] KeyError: 'layout'

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "C:\Users\oleg\Anaconda3\lib\site-packages\traitlets\traitlets.py", line 526, in get value = obj._trait_values[self.name] KeyError: 'kernel'

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "E:/AutonomousDriving/AirSimE2EDeepLearning/test2.py", line 122, in validation_data=eval_generator, validation_steps=num_eval_examples//batch_size, verbose=2) File "C:\Users\oleg\Anaconda3\lib\site-packages\keras\legacy\interfaces.py", line 87, in wrapper return func(*args, **kwargs) File "C:\Users\oleg\Anaconda3\lib\site-packages\keras\engine\training.py", line 2072, in fit_generator callbacks.on_train_begin() File "C:\Users\oleg\Anaconda3\lib\site-packages\keras\callbacks.py", line 126, in on_train_begin callback.on_train_begin(logs) File "C:\Users\oleg\Anaconda3\lib\site-packages\keras_tqdm\tqdm_callback.py", line 129, in on_train_begin total=epochs) File "C:\Users\oleg\Anaconda3\lib\site-packages\keras_tqdm\tqdm_callback.py", line 67, in build_tqdm_outer return self.tqdm(desc=desc, total=total, leave=self.leave_outer) File "C:\Users\oleg\Anaconda3\lib\site-packages\keras_tqdm\tqdm_notebook_callback.py", line 33, in tqdm return tqdm_notebook(desc=desc, total=total, leave=leave) File "C:\Users\oleg\Anaconda3\lib\site-packages\tqdm_init_.py", line 22, in tqdm_notebook return _tqdm_notebook(*args, **kwargs) File "C:\Users\oleg\Anaconda3\lib\site-packages\tqdm_tqdm_notebook.py", line 176, in init self.sp = self.status_printer(self.fp, self.total, self.desc) File "C:\Users\oleg\Anaconda3\lib\site-packages\tqdm_tqdm_notebook.py", line 96, in status_printer pbar = IntProgress(min=0, max=total) File "C:\Users\oleg\Anaconda3\lib\site-packages\ipywidgets\widgets\widget_int.py", line 57, in init super(cls, self).init(**kwargs) File "C:\Users\oleg\Anaconda3\lib\site-packages\ipywidgets\widgets\widget_int.py", line 95, in init super(_BoundedInt, self).init(**kwargs) File "C:\Users\oleg\Anaconda3\lib\site-packages\ipywidgets\widgets\widget_int.py", line 76, in init super(_Int, self).init(**kwargs) File "C:\Users\oleg\Anaconda3\lib\site-packages\ipywidgets\widgets\domwidget.py", line 90, in init super(DOMWidget, self).init(*pargs, **kwargs) File "C:\Users\oleg\Anaconda3\lib\site-packages\ipywidgets\widgets\widget.py", line 184, in init self.open() File "C:\Users\oleg\Anaconda3\lib\site-packages\ipywidgets\widgets\widget.py", line 197, in open state, buffer_keys, buffers = self._split_state_buffers(self.get_state()) File "C:\Users\oleg\Anaconda3\lib\site-packages\ipywidgets\widgets\widget.py", line 291, in get_state value = to_json(getattr(self, k), self) File "C:\Users\oleg\Anaconda3\lib\site-packages\traitlets\traitlets.py", line 554, in get return self.get(obj, cls) File "C:\Users\oleg\Anaconda3\lib\site-packages\traitlets\traitlets.py", line 533, in get value = self._validate(obj, dynamic_default()) File "C:\Users\oleg\Anaconda3\lib\site-packages\ipywidgets\widgets\domwidget.py", line 23, in _layout_default return Layout() File "C:\Users\oleg\Anaconda3\lib\site-packages\ipywidgets\widgets\widget.py", line 184, in init self.open() File "C:\Users\oleg\Anaconda3\lib\site-packages\ipywidgets\widgets\widget.py", line 203, in open self.comm = Comm(**args) File "C:\Users\oleg\Anaconda3\lib\site-packages\ipykernel\comm\comm.py", line 56, in init self.open(data=data, metadata=metadata, buffers=buffers) File "C:\Users\oleg\Anaconda3\lib\site-packages\ipykernel\comm\comm.py", line 83, in open comm_manager = getattr(self.kernel, 'comm_manager', None) File "C:\Users\oleg\Anaconda3\lib\site-packages\traitlets\traitlets.py", line 554, in get return self.get(obj, cls) File "C:\Users\oleg\Anaconda3\lib\site-packages\traitlets\traitlets.py", line 533, in get value = self._validate(obj, dynamic_default()) File "C:\Users\oleg\Anaconda3\lib\site-packages\traitlets\traitlets.py", line 589, in _validate value = self.validate(obj, value) File "C:\Users\oleg\Anaconda3\lib\site-packages\traitlets\traitlets.py", line 1681, in validate self.error(obj, value) File "C:\Users\oleg\Anaconda3\lib\site-packages\traitlets\traitlets.py", line 1528, in error raise TraitError(e) traitlets.traitlets.TraitError: The 'kernel' trait of a Comm instance must be a Kernel, but a value of class 'NoneType' (i.e. None) was specified.

This issue maybe silly, I'm new to this area, Sorry to have your time.

When I run into the TrainModel.ipynb

def draw_image_with_label(img, label, prediction=None):
    theta = label * 0.69 #Steering range for the car is +- 40 degrees -> 0.69 radians
    line_length = 50
    line_thickness = 3
    label_line_color = (255, 0, 0)
    prediction_line_color = (0, 0, 255)
    pil_image = image.array_to_img(img, K.image_data_format(), scale=True)
    print('Actual Steering Angle = {0}'.format(label))
    draw_image = pil_image.copy()
    image_draw = ImageDraw.Draw(draw_image)
    first_point = (int(img.shape[1]/2),img.shape[0])
    second_point = (int((img.shape[1]/2) + (line_length * math.sin(theta))), int(img.shape[0] - (line_length * math.cos(theta))))
    image_draw.line([first_point, second_point], fill=label_line_color, width=line_thickness)
    
    if (prediction is not None):
        print('Predicted Steering Angle = {0}'.format(prediction))
        print('L1 Error: {0}'.format(abs(prediction-label)))
        theta = prediction * 0.69
        second_point = (int((img.shape[1]/2) + (line_length * math.sin(theta))), int(img.shape[0] - (line_length * math.cos(theta))))
        image_draw.line([first_point, second_point], fill=prediction_line_color, width=line_thickness)
    
    del image_draw
    plt.imshow(draw_image)
    plt.show()

[sample_batch_train_data, sample_batch_test_data] = next(train_generator)
for i in range(0, 3, 1):
    draw_image_with_label(sample_batch_train_data[0][i], sample_batch_test_data[i])

The error message as below:

TypeError                                 Traceback (most recent call last)
<ipython-input-13-6955bc10ff72> in <module>
     24     plt.show()
     25 
---> 26 [sample_batch_train_data, sample_batch_test_data] = next(train_generator)
     27 for i in range(0, 3, 1):
     28     draw_image_with_label(sample_batch_train_data[0][i], sample_batch_test_data[i])

~\AppData\Local\conda\conda\envs\my_airsim\lib\site-packages\keras_preprocessing\image.py in __next__(self, *args, **kwargs)
   1524 
   1525     def __next__(self, *args, **kwargs):
-> 1526         return self.next(*args, **kwargs)
   1527 
   1528     def _get_batches_of_transformed_samples(self, index_array):

D:\AirSim\AutonomousDrivingCookbook\AirSimE2EDeepLearning\Generator.py in next(self)
    240         # so it can be done in parallel
    241 
--> 242         return self.__get_indexes(index_array)
    243 
    244     def __get_indexes(self, index_array):

D:\AirSim\AutonomousDrivingCookbook\AirSimE2EDeepLearning\Generator.py in __get_indexes(self, index_array)
    262                 x_images = x_images[self.roi[0]:self.roi[1], self.roi[2]:self.roi[3], :]
    263 
--> 264             transformed = self.image_data_generator.random_transform_with_states(x_images.astype(K.floatx()))
    265             x_images = transformed[0]
    266             is_horiz_flipped.append(transformed[1])

D:\AirSim\AutonomousDrivingCookbook\AirSimE2EDeepLearning\Generator.py in random_transform_with_states(self, x, seed)
    138             x = image.random_channel_shift(x,
    139                                            self.channel_shift_range,
--> 140                                      img_channel_axis)
    141         if self.horizontal_flip:
    142             if np.random.random() < 0.5:

~\AppData\Local\conda\conda\envs\my_airsim\lib\site-packages\keras_preprocessing\image.py in random_channel_shift(x, intensity_range, channel_axis)
    199         Numpy image tensor.
    200     """
--> 201     intensity = np.random.uniform(-intensity_range, intensity_range)
    202     return apply_channel_shift(x, intensity, channel_axis=channel_axis)
    203 

TypeError: bad operand type for unary -: 'str'

It seems the data type of "intensity_range" is 'str', which should be a number. but I cannot find where the channel_shift_range been assigned as 'str' Please help me out.

First of all, thank you for making these tutorials! They've been incredibly helpful for getting started with autonomous driving. My question is, do you have any tips for collecting useful data in new environments, and how to improve/optimize the model's performance?

Problem details

I've worked through the Autonomous Driving using End-to-End Deep Learning: an AirSim tutorial, and am now trying apply the concepts discussed to train a model to drive in the AirSim Neighborhood environment. I've already collected training data (using a steering wheel), and, using the same network architecture and parameter values as the tutorial, I've been able to train a model where the car drives around for a few minutes before swerving off the road or crashing into a parked car, etc.

I have begun to try modifying different parameters and variables to optimize the model's performance, such as following the suggestions in the tutorials: modifying the region of interest, zero drop percentage, network architecture, etc. However, I've run into a few problems.

My main problem is that a trained model with lower validation loss doesn't necessarily correspond to a model with better driving performance. For example, one model I trained had a validation loss of .0002429 and crashed while making its first turn, but a different model with a val loss of .0005796 was able to drive pretty well for about five minutes. As a general trend, I've found a lower val loss does indicate a better performing model, but not enough that I can rely on it to optimize performance. This has costs me quite a bit of time, as each time I make changes and retrain, I have to manually test the model and watch the car drive, instead of just being able to rely on minimizing the val loss.

My best guess as to why this is happening is poor training data. I understand that if the training data is "bad", then no matter how low you are able to get your val loss, your model will perform poorly, as it has learned to match the "bad" training data. I did my best to follow the ideas outlined in the Data Exploration and Preparation jupyter notebook when I collected my training data. The majority of the data was collected by driving normally. Then I also collected data to take care of edge cases and deviations from the ideal (like the swerve data does in the tutorial), but I have no idea how "good" the data I collected is, since we're using an end-to-end model where most everything is abstracted, so I don't know if this is actually a problem with my data or with something else.

At this point, I've just been iteratively modifying and testing the model manually. I know there's got to be a better way to approach this, but I'm at a loss for what to try. It's difficult to optimize performance when it doesn't directly correspond to validation loss or another quantitative value (as far as I can tell). I would greatly appreciate any tips anyone can offer for collecting better training data or improving model performance. Thank you!

My training is not starting. I have used python 3.6 with tensorflow gpu 1.8.0 and keras 2.1.2. Also I have a Geforce GTX 3060 running on my computer. So it shouldnt be a problem. I also installed Norton antivirus on this new computer. On the older computer which has a bad GPU I had Panda Dome, but there training was running. But after over 1 hour, the training was only on 1%. Thats why I bought a new computer with a good GPU and CPU. Some of this work is going to be presented in my master thesis. I would appreciate any help soon.

Just to share my port on Kaggle AirSim End-to-End Learning

To make it work on Kaggle with Keras 2.6.0, Cooking.py and Generator.py are modified in my github fork

It seems I got better prediction than the tutorial notebook.

Problem description

I am trying to use images in batches of frames and use Conv3D in my model; so I need to enter the images in the same order in the folders. I could fix other necessary changes and it is the only remaining problem. I also consider changing the order of folders so that the model also learns the swerve Images. Of course, I know it would not be a good way of data preprocessing but please give me your ideas on this matter. Thanks

Problem details

I tried some changes but none of them works; in one of them I made generateDataMapAirSim in Cooking.py not to shuffle the mappings :

mappings = [(key, all_mappings[key]) for key in all_mappings] # random.shuffle(mappings) return mappings

Experiment/Environment details

• Tutorial used: AirSimE2EDeepLearning
• Environment used: landscape
• Versions of artifacts used (if applicable): Win 10, Python 3.8, Keras 2.4.3

JSONDecodeError: Expecting value: line 14 column 1 (char 354)

Is it a problem with the version? You get the same error when you try different versions. How do I solve this?

Problem description

Getting IndexError: list index out of range while running TrainModel.ipynb ([in 5]).

Problem details

IndexError Traceback (most recent call last) in 1 image_input_shape = sample_batch_train_data[0].shape[1:] ----> 2 state_input_shape = sample_batch_train_data[1].shape[1:] 3 activation = 'relu' 4 5 #Create the convolutional stacks

IndexError: list index out of range

Experiment/Environment details

• Tutorial used: AirSimE2EDeepLearning/TrainModel.ipynb
• Environment used: none
• Versions of artifacts used (if applicable): (Python 3.6, Keras 2.1.2 ,TF 1.8)

i want try to do modifications on the code so the code will predict throttle and steering angle too. where i should add the code? i am using landscape mountain environment with deep learning method.