Keras: TypeError: fit() got an unexpected keyword argument 'nb_epoch'

[AlexXx-Wu]

I'm training the model using fit with parameter nb_epoch. However, it shows the error:
`---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
in
1 #fit model
----> 2 model.fit(X_train, Y_train, batch_size=32, nb_epoch=10, verbose=1)

/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, args, *kwargs)
64 def _method_wrapper(self, args, *kwargs):
65 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
---> 66 return method(self, args, *kwargs)
67
68 # Running inside run_distribute_coordinator already.

TypeError: fit() got an unexpected keyword argument 'nb_epoch'`
I wanna know how to fix it?

[fchollet]

It's just epochs now. nb_epoch was deprecated years ago.

[AlexXx-Wu]

It's just epochs now. nb_epoch was deprecated years ago.

Thanks for your help. But when I used epochs , I got another error:

`Epoch 1/10

InvalidArgumentError Traceback (most recent call last)
in
1 #fit model
----> 2 model.fit(X_train, Y_train, batch_size=32, epochs=10, verbose=1)

/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, args, *kwargs)
64 def _method_wrapper(self, args, *kwargs):
65 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
---> 66 return method(self, args, *kwargs)
67
68 # Running inside run_distribute_coordinator already.

/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/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)
846 batch_size=batch_size):
847 callbacks.on_train_batch_begin(step)
--> 848 tmp_logs = train_function(iterator)
849 # Catch OutOfRangeError for Datasets of unknown size.
850 # This blocks until the batch has finished executing.

/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py in __call__(self, args, *kwds)
578 xla_context.Exit()
579 else:
--> 580 result = self._call(args, *kwds)
581
582 if tracing_count == self._get_tracing_count():

/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py in _call(self, args, *kwds)
609 # In this case we have created variables on the first call, so we run the
610 # defunned version which is guaranteed to never create variables.
--> 611 return self._stateless_fn(args, *kwds) # pylint: disable=not-callable
612 elif self._stateful_fn is not None:
613 # Release the lock early so that multiple threads can perform the call

/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/eager/function.py in __call__(self, args, *kwargs)
2418 with self._lock:
2419 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 2420 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
2421
2422 @property

/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/eager/function.py in _filtered_call(self, args, kwargs)
1663 if isinstance(t, (ops.Tensor,
1664 resource_variable_ops.BaseResourceVariable))),
-> 1665 self.captured_inputs)
1666
1667 def _call_flat(self, args, captured_inputs, cancellation_manager=None):

/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1744 # No tape is watching; skip to running the function.
1745 return self._build_call_outputs(self._inference_function.call(
-> 1746 ctx, args, cancellation_manager=cancellation_manager))
1747 forward_backward = self._select_forward_and_backward_functions(
1748 args,

/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
596 inputs=args,
597 attrs=attrs,
--> 598 ctx=ctx)
599 else:
600 outputs = execute.execute_with_cancellation(

/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
58 ctx.ensure_initialized()
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
62 if name is not None:

InvalidArgumentError: Conv2DCustomBackpropFilterOp only supports NHWC.
[[node gradient_tape/sequential_5/conv2d_8/Conv2DBackpropFilter (defined at :2) ]] [Op:__inference_train_function_2109]

Function call stack:
train_function`
Is this question related to the version of my laptop or python?
I'm using macOS 10.13.6, Python 3.6.4, tensorflow 2.2.0.

[toppylawz]

please check your code very well, 'epochs' work for me.

[Srichitra-S]

please check your code very well, 'epochs' work for me.

InvalidArgumentError: Default MaxPoolingOp only supports NHWC on device type CPU

Getting this error when epochs is used .