ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject

[Billnewgate327]

When I try to run the program I get following error:

---

ValueError Traceback (most recent call last)
in
2 import tensorflow as tf
3
----> 4 from deepswarm.backends import Dataset, TFKerasBackend
5 from deepswarm.deepswarm import DeepSwarm
6

~\AppData\Local\Programs\Python\Python38\Scripts\DeepSwarm-master\deepswarm\backends.py in
7
8 from abc import ABC, abstractmethod
----> 9 from sklearn.model_selection import train_test_split
10 from tensorflow.keras import backend as K
11

c:\users\user\appdata\local\programs\python\python38\lib\site-packages\sklearn_init_.py in
62 else:
63 from . import __check_build
---> 64 from .base import clone
65 from .utils._show_versions import show_versions
66

c:\users\user\appdata\local\programs\python\python38\lib\site-packages\sklearn\base.py in
12 from scipy import sparse
13 from .externals import six
---> 14 from .utils.fixes import signature
15 from .utils import _IS_32BIT
16 from . import version

c:\users\user\appdata\local\programs\python\python38\lib\site-packages\sklearn\utils_init_.py in
10 from scipy.sparse import issparse
11
---> 12 from .murmurhash import murmurhash3_32
13 from .class_weight import compute_class_weight, compute_sample_weight
14 from . import _joblib

init.pxd in init sklearn.utils.murmurhash()

ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject

I use:
python 3.8
numpy 1.20.1

I would appreciate your help.

[Pattio]

Hey, I would suggest to either downgrade Python or try to play around with scikit-learn version in requirements.txt, see scikit-learn-contrib/hdbscan#457 for reference. I hope, that helps 🤞

[Billnewgate327]

After I had solved the problem, ran the example -----> cifar10.py, and get following error:

Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
170500096/170498071 [==============================] - 78s 0us/step
-------------------------------DeepSwarm settings-------------------------------
{
"DeepSwarm": {
"aco": {
"ant_count": 16,
"greediness": 0.5,
"pheromone": {
"decay": 0.1,
"evaporation": 0.1,
"start": 0.1,
"verbose": false
}
},
"backend": {
"batch_size": 64,
"epochs": 15,
"loss": "sparse_categorical_crossentropy",
"patience": 5,
"verbose": false
},
"flat_nodes": [
"FlattenNode",
"DenseNode",
"DropoutFlatNode",
"BatchNormalizationFlatNode"
],
"max_depth": 15,
"metrics": "accuracy",
"reuse_patience": 1,
"save_folder": null,
"spatial_nodes": [
"InputNode",
"Conv2DNode",
"DropoutSpatialNode",
"BatchNormalizationNode",
"Pool2DNode"
]
},
"Nodes": {
"BatchNormalizationFlatNode": {
"attributes": {},
"transitions": {
"DenseNode": 1.1,
"DropoutFlatNode": 1.1,
"OutputNode": 0.9
},
"type": "BatchNormalization"
},
"BatchNormalizationNode": {
"attributes": {},
"transitions": {
"Conv2DNode": 1.1,
"DropoutSpatialNode": 1.0,
"FlattenNode": 1.0,
"Pool2DNode": 1.1
},
"type": "BatchNormalization"
},
"Conv2DNode": {
"attributes": {
"activation": [
"ReLU"
],
"filter_count": [
32,
64,
128
],
"kernel_size": [
1,
3,
5
]
},
"transitions": {
"BatchNormalizationNode": 1.2,
"Conv2DNode": 0.8,
"DropoutSpatialNode": 1.1,
"FlattenNode": 1.0,
"Pool2DNode": 1.2
},
"type": "Conv2D"
},
"DenseNode": {
"attributes": {
"activation": [
"ReLU",
"Sigmoid"
],
"output_size": [
64,
128
]
},
"transitions": {
"BatchNormalizationFlatNode": 1.2,
"DenseNode": 0.8,
"DropoutFlatNode": 1.2,
"OutputNode": 1.0
},
"type": "Dense"
},
"DropoutFlatNode": {
"attributes": {
"rate": [
0.1,
0.3
]
},
"transitions": {
"BatchNormalizationFlatNode": 1.0,
"DenseNode": 1.0,
"OutputNode": 0.9
},
"type": "Dropout"
},
"DropoutSpatialNode": {
"attributes": {
"rate": [
0.1,
0.3
]
},
"transitions": {
"BatchNormalizationNode": 1.1,
"Conv2DNode": 1.1,
"FlattenNode": 1.0,
"Pool2DNode": 1.0
},
"type": "Dropout"
},
"FlattenNode": {
"attributes": {},
"transitions": {
"BatchNormalizationFlatNode": 0.9,
"DenseNode": 1.0,
"OutputNode": 0.8
},
"type": "Flatten"
},
"InputNode": {
"attributes": {
"shape": [
[
28,
28,
1
]
]
},
"transitions": {
"Conv2DNode": 1.0
},
"type": "Input"
},
"OutputNode": {
"attributes": {
"activation": [
"Softmax"
],
"output_size": [
10
]
},
"transitions": {},
"type": "Output"
},
"Pool2DNode": {
"attributes": {
"pool_size": [
2
],
"pool_type": [
"max",
"average"
],
"stride": [
2,
3
]
},
"transitions": {
"BatchNormalizationNode": 1.1,
"Conv2DNode": 1.1,
"FlattenNode": 1.0
},
"type": "Pool2D"
}
},
"script": "ipykernel_launcher.py",
"settings_file": "C:\Users\USER\AppData\Local\Programs\Python\Python38\Scripts\DeepSwarm-master\settings\default.yaml"
}
------------------------------STARTING ACO SEARCH-------------------------------

ValueError Traceback (most recent call last)
in
24 deepswarm = DeepSwarm(backend=backend)
25 # Find the topology for a given dataset
---> 26 topology = deepswarm.find_topology()
27 # Evaluate discovered topology
28 deepswarm.evaluate_topology(topology)

~\AppData\Local\Programs\Python\Python38\Scripts\DeepSwarm-master\deepswarm\deepswarm.py in find_topology(self)
43 self.aco = ACO(backend=self.backend, storage=self.storage)
44
---> 45 best_ant = self.aco.search()
46 best_model = self.storage.load_specified_model(self.backend, best_ant.path_hash)
47 return best_model

~\AppData\Local\Programs\Python\Python38\Scripts\DeepSwarm-master\deepswarm\aco.py in search(self)
30 Log.header("STARTING ACO SEARCH", type="GREEN")
31 self.best_ant = Ant(self.graph.generate_path(self.random_select))
---> 32 self.best_ant.evaluate(self.backend, self.storage)
33 Log.info(self.best_ant)
34 else:

~\AppData\Local\Programs\Python\Python38\Scripts\DeepSwarm-master\deepswarm\aco.py in evaluate(self, backend, storage)
248
249 # Train model
--> 250 new_model = backend.train_model(new_model)
251 # Evaluate model
252 self.loss, self.accuracy = backend.evaluate_model(new_model)

~\AppData\Local\Programs\Python\Python38\Scripts\DeepSwarm-master\deepswarm\backends.py in train_model(self, model)
270
271 # Train model
--> 272 model.fit(**fit_parameters)
273
274 # Load model from checkpoint

~\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1098 _r=1):
1099 callbacks.on_train_batch_begin(step)
-> 1100 tmp_logs = self.train_function(iterator)
1101 if data_handler.should_sync:
1102 context.async_wait()

~\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\eager\def_function.py in call(self, *args, **kwds)
826 tracing_count = self.experimental_get_tracing_count()
827 with trace.Trace(self._name) as tm:
--> 828 result = self._call(*args, **kwds)
829 compiler = "xla" if self._experimental_compile else "nonXla"
830 new_tracing_count = self.experimental_get_tracing_count()

~\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
869 # This is the first call of call, so we have to initialize.
870 initializers = []
--> 871 self._initialize(args, kwds, add_initializers_to=initializers)
872 finally:
873 # At this point we know that the initialization is complete (or less

~\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to)
723 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
724 self._concrete_stateful_fn = (
--> 725 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
726 *args, **kwds))
727

~\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2967 args, kwargs = None, None
2968 with self._lock:
-> 2969 graph_function, _ = self._maybe_define_function(args, kwargs)
2970 return graph_function
2971

~\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs)
3359
3360 self._function_cache.missed.add(call_context_key)
-> 3361 graph_function = self._create_graph_function(args, kwargs)
3362 self._function_cache.primary[cache_key] = graph_function
3363

~\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3194 arg_names = base_arg_names + missing_arg_names
3195 graph_function = ConcreteFunction(
-> 3196 func_graph_module.func_graph_from_py_func(
3197 self._name,
3198 self._python_function,

~\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\framework\func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
988 _, original_func = tf_decorator.unwrap(python_func)
989
--> 990 func_outputs = python_func(*func_args, **func_kwargs)
991
992 # invariant: func_outputs contains only Tensors, CompositeTensors,

~\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds)
632 xla_context.Exit()
633 else:
--> 634 out = weak_wrapped_fn().wrapped(*args, **kwds)
635 return out
636

~\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args, **kwargs)
975 except Exception as e: # pylint:disable=broad-except
976 if hasattr(e, "ag_error_metadata"):
--> 977 raise e.ag_error_metadata.to_exception(e)
978 else:
979 raise

ValueError: in user code:

C:\Users\USER\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\keras\engine\training.py:805 train_function  *
    return step_function(self, iterator)
C:\Users\USER\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\keras\engine\training.py:795 step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
C:\Users\USER\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1259 run
    return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Users\USER\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2730 call_for_each_replica
    return self._call_for_each_replica(fn, args, kwargs)
C:\Users\USER\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3417 _call_for_each_replica
    return fn(*args, **kwargs)
C:\Users\USER\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\keras\engine\training.py:788 run_step  **
    outputs = model.train_step(data)
C:\Users\USER\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\keras\engine\training.py:754 train_step
    y_pred = self(x, training=True)
C:\Users\USER\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:998 __call__
    input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
C:\Users\USER\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\keras\engine\input_spec.py:271 assert_input_compatibility
    raise ValueError('Input ' + str(input_index) +

ValueError: Input 0 is incompatible with layer model: expected shape=(None, 28, 28, 1), found shape=(None, 32, 32, 3)

[Pattio]

You should be using cifar10 settings file. Currently you are running cifar10 example with default.yaml. Input shape for cifar10 should be [!!python/tuple [32, 32, 3]]

[Billnewgate327]

I changed to version 3.6 of python, but it doesn't work again.

ValueError Traceback (most recent call last)
in
24 deepswarm = DeepSwarm(backend=backend)
25 # Find the topology for a given dataset
---> 26 topology = deepswarm.find_topology()
27 # Evaluate discovered topology
28 deepswarm.evaluate_topology(topology)

~\Desktop\DeepSwarm-master\deepswarm\deepswarm.py in find_topology(self)
43 self.aco = ACO(backend=self.backend, storage=self.storage)
44
---> 45 best_ant = self.aco.search()
46 best_model = self.storage.load_specified_model(self.backend, best_ant.path_hash)
47 return best_model

~\Desktop\DeepSwarm-master\deepswarm\aco.py in search(self)
37 while self.graph.current_depth <= cfg['max_depth']:
38 Log.header("Current search depth is %i" % self.graph.current_depth, type="GREEN")
---> 39 ants = self.generate_ants()
40
41 # Sort ants using user selected metric

~\Desktop\DeepSwarm-master\deepswarm\aco.py in generate_ants(self)
76 ant.path = self.graph.generate_path(self.aco_select)
77 # Evaluate how good is the new path
---> 78 ant.evaluate(self.backend, self.storage)
79 ants.append(ant)
80 Log.info(ant)

~\Desktop\DeepSwarm-master\deepswarm\aco.py in evaluate(self, backend, storage)
242 if existing_model is None:
243 # Generate model
--> 244 new_model = backend.generate_model(self.path)
245 else:
246 # Re-use model

~\Desktop\DeepSwarm-master\deepswarm\backends.py in generate_model(self, path)
142
143 # Return generated model
--> 144 model = tf.keras.Model(inputs=input_layer, outputs=layer)
145 self.compile_model(model)
146 return model

~\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\training\tracking\base.py in _method_wrapper(self, *args, **kwargs)
515 self._self_setattr_tracking = False # pylint: disable=protected-access
516 try:
--> 517 result = method(self, *args, **kwargs)
518 finally:
519 self._self_setattr_tracking = previous_value # pylint: disable=protected-access

~\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\keras\engine\functional.py in init(self, inputs, outputs, name, trainable, **kwargs)
118 generic_utils.validate_kwargs(kwargs, {})
119 super(Functional, self).init(name=name, trainable=trainable)
--> 120 self._init_graph_network(inputs, outputs)
121
122 @trackable.no_automatic_dependency_tracking

~\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\training\tracking\base.py in _method_wrapper(self, *args, **kwargs)
515 self._self_setattr_tracking = False # pylint: disable=protected-access
516 try:
--> 517 result = method(self, *args, **kwargs)
518 finally:
519 self._self_setattr_tracking = previous_value # pylint: disable=protected-access

~\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\keras\engine\functional.py in _init_graph_network(self, inputs, outputs)
201
202 # Keep track of the network's nodes and layers.
--> 203 nodes, nodes_by_depth, layers, _ = _map_graph_network(
204 self.inputs, self.outputs)
205 self._network_nodes = nodes

~\AppData\Local\Programs\Python\Python38\Scripts\tf2-gpu\lib\site-packages\tensorflow\python\keras\engine\functional.py in _map_graph_network(inputs, outputs)
998 for name in all_names:
999 if all_names.count(name) != 1:
-> 1000 raise ValueError('The name "' + name + '" is used ' +
1001 str(all_names.count(name)) + ' times in the model. '
1002 'All layer names should be unique.')

ValueError: The name "1617975030.9342244" is used 2 times in the model. All layer names should be unique.

[Pattio]

What configuration you are using and are you running test example?

[Billnewgate327]

Yes, I am running the test example - cifar10

My configuration:
python: 3.6.8
tensorflow:.13.1
scikit-learn:0.20.3
numpy:1.19.5
colorama:0.4.1

Thank you so much for your help!

[Pattio]

Hmm it's weird it seems that few layers were created at the exact same time. Did you change anything in .yaml config file? As a quick fix you can open backends.py and change

parameters = {'name': str(time.time())}

to

parameters = {'name': str(uuid.uuid4())}

[Billnewgate327]

No, I didn't change anything. And I try your suggestion that change:

parameters = {'name': str(time.time())}

to

parameters = {'name': str(uuid.uuid4())}

but it didn't work.

ValueError: The name "1618915107.6525285" is used 2 times in the model. All layer names should be unique.

------------------------------STARTING ACO SEARCH-------------------------------
------------------------------STARTING ACO SEARCH-------------------------------

Ant: 0x23ec6fce198
Loss: 14.506286
Accuracy: 0.100000
Path: InputNode(shape:(32, 32, 3)) -> Conv2DNode(filter_count:256, kernel_size:5, activation:ReLU) -> FlattenNode() -> OutputNode(output_size:10, activation:Softmax)
Hash: 379ca2a5fe67b9b672cceea03a5c7547dcc60ab92c65474c264f8c318fd122b7

=======
Ant: 0x23ec6fce198
Loss: 14.506286
Accuracy: 0.100000
Path: InputNode(shape:(32, 32, 3)) -> Conv2DNode(filter_count:256, kernel_size:5, activation:ReLU) -> FlattenNode() -> OutputNode(output_size:10, activation:Softmax)
Hash: 379ca2a5fe67b9b672cceea03a5c7547dcc60ab92c65474c264f8c318fd122b7

---------------------------Current search depth is 1----------------------------
---------------------------Current search depth is 1----------------------------
--------------------------------GENERATING ANT 1--------------------------------
--------------------------------GENERATING ANT 1--------------------------------

Ant: 0x23ec759a550
Loss: 14.506286
Accuracy: 0.100000
Path: InputNode(shape:(32, 32, 3)) -> Conv2DNode(filter_count:256, kernel_size:1, activation:ReLU) -> FlattenNode() -> OutputNode(output_size:10, activation:Softmax)
Hash: 05db29859484c06781891dfaff8ada2e0c0ed5e5b17d58d4b2c3b9bd17a4799f

=======
Ant: 0x23ec759a550
Loss: 14.506286
Accuracy: 0.100000
Path: InputNode(shape:(32, 32, 3)) -> Conv2DNode(filter_count:256, kernel_size:1, activation:ReLU) -> FlattenNode() -> OutputNode(output_size:10, activation:Softmax)
Hash: 05db29859484c06781891dfaff8ada2e0c0ed5e5b17d58d4b2c3b9bd17a4799f

--------------------------------GENERATING ANT 2--------------------------------
--------------------------------GENERATING ANT 2--------------------------------

Ant: 0x23eb8f0abe0
Loss: 14.506286
Accuracy: 0.100000
Path: InputNode(shape:(32, 32, 3)) -> Conv2DNode(filter_count:128, kernel_size:1, activation:ReLU) -> FlattenNode() -> OutputNode(output_size:10, activation:Softmax)
Hash: 798a3f7ee91b9cb0b7d5478c63a06007643c702a44f24c69f7d400b032c2b0ba

=======
Ant: 0x23eb8f0abe0
Loss: 14.506286
Accuracy: 0.100000
Path: InputNode(shape:(32, 32, 3)) -> Conv2DNode(filter_count:128, kernel_size:1, activation:ReLU) -> FlattenNode() -> OutputNode(output_size:10, activation:Softmax)
Hash: 798a3f7ee91b9cb0b7d5478c63a06007643c702a44f24c69f7d400b032c2b0ba

--------------------------------GENERATING ANT 3--------------------------------
--------------------------------GENERATING ANT 3--------------------------------

Ant: 0x23ec8cf0470
Loss: 14.506286
Accuracy: 0.100000
Path: InputNode(shape:(32, 32, 3)) -> Conv2DNode(filter_count:64, kernel_size:1, activation:ReLU) -> FlattenNode() -> OutputNode(output_size:10, activation:Softmax)
Hash: ebe0ed98f850445b751b8368068efeb5c3b27f3b5c8b88ae5553ac81daf31a8e

=======
Ant: 0x23ec8cf0470
Loss: 14.506286
Accuracy: 0.100000
Path: InputNode(shape:(32, 32, 3)) -> Conv2DNode(filter_count:64, kernel_size:1, activation:ReLU) -> FlattenNode() -> OutputNode(output_size:10, activation:Softmax)
Hash: ebe0ed98f850445b751b8368068efeb5c3b27f3b5c8b88ae5553ac81daf31a8e

--------------------------------GENERATING ANT 4--------------------------------
--------------------------------GENERATING ANT 4--------------------------------

Ant: 0x23eb902d9e8
Loss: 14.506286
Accuracy: 0.100000
Path: InputNode(shape:(32, 32, 3)) -> Conv2DNode(filter_count:64, kernel_size:3, activation:ReLU) -> FlattenNode() -> OutputNode(output_size:10, activation:Softmax)
Hash: 78ac2dbb4781a729435be2ef23f54c34d34344dd8db21c78c64afd10ffcd929f

=======
Ant: 0x23eb902d9e8
Loss: 14.506286
Accuracy: 0.100000
Path: InputNode(shape:(32, 32, 3)) -> Conv2DNode(filter_count:64, kernel_size:3, activation:ReLU) -> FlattenNode() -> OutputNode(output_size:10, activation:Softmax)
Hash: 78ac2dbb4781a729435be2ef23f54c34d34344dd8db21c78c64afd10ffcd929f

--------------------------------GENERATING ANT 5--------------------------------
--------------------------------GENERATING ANT 5--------------------------------

Ant: 0x23ec88cfbe0
Loss: 14.506286
Accuracy: 0.100000
Path: InputNode(shape:(32, 32, 3)) -> Conv2DNode(filter_count:128, kernel_size:5, activation:ReLU) -> FlattenNode() -> OutputNode(output_size:10, activation:Softmax)
Hash: 28f75dc576370825f7420103b23c4de54624b85fb7260837199bde2c5ef120ec

=======
Ant: 0x23ec88cfbe0
Loss: 14.506286
Accuracy: 0.100000
Path: InputNode(shape:(32, 32, 3)) -> Conv2DNode(filter_count:128, kernel_size:5, activation:ReLU) -> FlattenNode() -> OutputNode(output_size:10, activation:Softmax)
Hash: 28f75dc576370825f7420103b23c4de54624b85fb7260837199bde2c5ef120ec

--------------------------------GENERATING ANT 6--------------------------------
--------------------------------GENERATING ANT 6--------------------------------

[Billnewgate327]

I'm not sure if this is the reason. I run the program in jupyter notebook. Cause it is easy to debug for me.

[Pattio]

How did you install DeepSwarm?

[Billnewgate327]

I install DeepSwarm in Command prompt.

[Pattio]

Sorry I was busy with other life stuff, does the problem still occur?