Error with Sequential()

Hi, i am just starting with Tensorflow for my AI and i ran into an error i don’t know how to solve

[2021-09-06 21:55:50.461476: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2

To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

2021-09-06 21:55:51.050032: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2781 MB memory: -> device: 0, name: NVIDIA GeForce GTX 970, pci bus id: 0000:01:00.0, compute capability: 5.2

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasoptimizer_v2optimizer_v2.py:355: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.

warnings.warn(

2021-09-06 21:55:51.508673: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)

Epoch 1/200

Traceback (most recent call last):

File “C:UsersGamereclipse-workspaceAItraining_jarvis.py”, line 69, in <module>

model.fit(np.array(training_1), np.array(training_2), epochs=200, batch_size=5, verbose=2)

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginetraining.py“, line 1184, in fit

tmp_logs = self.train_function(iterator)

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythoneagerdef_function.py”, line 885, in __call__

result = self._call(*args, **kwds)

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythoneagerdef_function.py”, line 933, in _call

self._initialize(args, kwds, add_initializers_to=initializers)

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythoneagerdef_function.py”, line 759, in _initialize

self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythoneagerfunction.py“, line 3066, in _get_concrete_function_internal_garbage_collected

graph_function, _ = self._maybe_define_function(args, kwargs)

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythoneagerfunction.py“, line 3463, in _maybe_define_function

graph_function = self._create_graph_function(args, kwargs)

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythoneagerfunction.py“, line 3298, in _create_graph_function

func_graph_module.func_graph_from_py_func(

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythonframeworkfunc_graph.py”, line 1007, in func_graph_from_py_func

func_outputs = python_func(*func_args, **func_kwargs)

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythoneagerdef_function.py”, line 668, in wrapped_fn

out = weak_wrapped_fn().__wrapped__(*args, **kwds)

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythonframeworkfunc_graph.py”, line 994, in wrapper

raise e.ag_error_metadata.to_exception(e)

TypeError: in user code:

​

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginetraining.py:853 train_function *

return step_function(self, iterator)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginetraining.py:842 step_function **

outputs = model.distribute_strategy.run(run_step, args=(data,))

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythondistributedistribute_lib.py:1286 run

return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythondistributedistribute_lib.py:2849 call_for_each_replica

return self._call_for_each_replica(fn, args, kwargs)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythondistributedistribute_lib.py:3632 _call_for_each_replica

return fn(*args, **kwargs)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginetraining.py:835 run_step **

outputs = model.train_step(data)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginetraining.py:787 train_step

y_pred = self(x, training=True)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginebase_layer.py:1037 __call__

outputs = call_fn(inputs, *args, **kwargs)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginesequential.py:369 call

return super(Sequential, self).call(inputs, training=training, mask=mask)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginefunctional.py:414 call

return self._run_internal_graph(

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginefunctional.py:550 _run_internal_graph

outputs = node.layer(*args, **kwargs)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginebase_layer.py:1037 __call__

outputs = call_fn(inputs, *args, **kwargs)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskeraslayerscore.py:212 call

output = control_flow_util.smart_cond(training, dropped_inputs,

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasutilscontrol_flow_util.py:105 smart_cond

return tf.__internal__.smart_cond.smart_cond(

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythonframeworksmart_cond.py:56 smart_cond

return true_fn()

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskeraslayerscore.py:208 dropped_inputs

noise_shape=self._get_noise_shape(inputs),

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskeraslayerscore.py:197 _get_noise_shape

for i, value in enumerate(self.noise_shape):

​

TypeError: ‘int’ object is not iterable]

​

I guess it’s about the model.fit line but i am not sure, for reference here is a bit of my code:

[training_1 = list(training_ai[:,0])

training_2 = list(training_ai[:,1])

​

model = Sequential()

model.add(Dense(128, input_shape=(len(training_1[0]),),activation=’relu’))

model.add(Dropout(0,5))

model.add(Dense(64, activation = ‘relu’))

model.add(Dropout(0,5))

model.add(Dense(len(training_2[0]),activation=’softmax’))

​

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

model.compile(loss=’categorical_crossenropy’, optimizer=sgd, metrics=[‘accuracy’])

​

model.fit(np.array(training_1), np.array(training_2), epochs=200, batch_size=5, verbose=2)]

​

I would be happy if you could help me with this Error