'only supports NHWC tensor format' in Conv3D on M1 Mac

[mj-mayank]

I am trying to run tensorflow on M1 Mac(installed via miniforge) and while fitting the model I am getting error for Invalid argument.
Can some one please help with that is wrong?
Below is the code which I am trying to run:

Code: To fit data using below.

model_1.fit(train_generator, steps_per_epoch=steps_per_epoch, epochs=20, verbose=1, callbacks=callbacks_list, validation_data=val_generator, validation_steps=validation_steps, class_weight=None, workers=1, initial_epoch=0)

The input format I am using is as below:

model_1 = Sequential() model_1.add(Conv3D(8,kernel_size=(3,3,3),padding='same',input_shape=(30, 120, 120, 3))) model_1.add(BatchNormalization()) model_1.add(Activation('relu'))

The error is as below:

`File ~/miniforge3/envs/tensorflow/lib/python3.9/site-packages/tensorflow/python/keras/engine/training_generator_v1.py:202, in model_iteration(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)
199 break
201 # Setup work for each epoch.
--> 202 model.reset_metrics()
203 epoch_logs = {}
204 if mode == ModeKeys.TRAIN:

File ~/miniforge3/envs/tensorflow/lib/python3.9/site-packages/tensorflow/python/keras/engine/training_v1.py:1003, in Model.reset_metrics(self)
1001 metrics = self._get_training_eval_metrics()
1002 for m in metrics:
-> 1003 m.reset_state()
1005 # Reset metrics on all the distributed (cloned) models.
1006 if self._distribution_strategy:

File ~/miniforge3/envs/tensorflow/lib/python3.9/site-packages/tensorflow/python/keras/metrics.py:260, in Metric.reset_state(self)
258 return self.reset_states()
259 else:
--> 260 backend.batch_set_value([(v, 0) for v in self.variables])

File ~/miniforge3/envs/tensorflow/lib/python3.9/site-packages/tensorflow/python/util/dispatch.py:206, in add_dispatch_support..wrapper(*args, **kwargs)
204 """Call target, and fall back on dispatchers if there is a TypeError."""
205 try:
--> 206 return target(*args, **kwargs)
207 except (TypeError, ValueError):
208 # Note: convert_to_eager_tensor currently raises a ValueError, not a
209 # TypeError, when given unexpected types. So we need to catch both.
210 result = dispatch(wrapper, args, kwargs)

File ~/miniforge3/envs/tensorflow/lib/python3.9/site-packages/tensorflow/python/keras/backend.py:3829, in batch_set_value(tuples)
3827 assign_ops.append(assign_op)
3828 feed_dict[assign_placeholder] = value
-> 3829 get_session().run(assign_ops, feed_dict=feed_dict)

File ~/miniforge3/envs/tensorflow/lib/python3.9/site-packages/tensorflow/python/keras/backend.py:745, in get_session(op_input_list)
743 if not _MANUAL_VAR_INIT:
744 with session.graph.as_default():
--> 745 _initialize_variables(session)
746 return session

File ~/miniforge3/envs/tensorflow/lib/python3.9/site-packages/tensorflow/python/keras/backend.py:1193, in _initialize_variables(session)
1189 candidate_vars.append(v)
1190 if candidate_vars:
1191 # This step is expensive, so we only run it on variables not already
1192 # marked as initialized.
-> 1193 is_initialized = session.run(
1194 [variables_module.is_variable_initialized(v) for v in candidate_vars])
1195 # TODO(kathywu): Some metric variables loaded from SavedModel are never
1196 # actually used, and do not have an initializer.
1197 should_be_initialized = [
1198 (not is_initialized[n]) and v.initializer is not None
1199 for n, v in enumerate(candidate_vars)]

File ~/miniforge3/envs/tensorflow/lib/python3.9/site-packages/tensorflow/python/client/session.py:967, in BaseSession.run(self, fetches, feed_dict, options, run_metadata)
964 run_metadata_ptr = tf_session.TF_NewBuffer() if run_metadata else None
966 try:
--> 967 result = self._run(None, fetches, feed_dict, options_ptr,
968 run_metadata_ptr)
969 if run_metadata:
970 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

File ~/miniforge3/envs/tensorflow/lib/python3.9/site-packages/tensorflow/python/client/session.py:1190, in BaseSession._run(self, handle, fetches, feed_dict, options, run_metadata)
1187 # We only want to really perform the run if fetches or targets are provided,
1188 # or if the call is a partial run that specifies feeds.
1189 if final_fetches or final_targets or (handle and feed_dict_tensor):
-> 1190 results = self._do_run(handle, final_targets, final_fetches,
1191 feed_dict_tensor, options, run_metadata)
1192 else:
1193 results = []

File ~/miniforge3/envs/tensorflow/lib/python3.9/site-packages/tensorflow/python/client/session.py:1368, in BaseSession._do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1365 return self._call_tf_sessionprun(handle, feed_dict, fetch_list)
1367 if handle is None:
-> 1368 return self._do_call(_run_fn, feeds, fetches, targets, options,
1369 run_metadata)
1370 else:
1371 return self._do_call(_prun_fn, handle, feeds, fetches)

File ~/miniforge3/envs/tensorflow/lib/python3.9/site-packages/tensorflow/python/client/session.py:1394, in BaseSession._do_call(self, fn, *args)
1389 if 'only supports NHWC tensor format' in message:
1390 message += ('\nA possible workaround: Try disabling Grappler optimizer'
1391 '\nby modifying the config for creating the session eg.'
1392 '\nsession_config.graph_options.rewrite_options.'
1393 'disable_meta_optimizer = True')
-> 1394 raise type(e)(node_def, op, message)

InvalidArgumentError: Node 'training/Adam/gradients/gradients/dropout_1/cond_grad/StatelessIf': Connecting to invalid output 1 of source node dropout_1/cond which has 1 outputs. Try using tf.compat.v1.experimental.output_all_intermediates(True).`

[radarhere]

Your question doesn't seem to have anything to do with Pillow.

I would suggest https://stackoverflow.com/ or https://github.com/tensorflow/tensorflow instead as better places to find people with the expertise to solve your problem.

[mj-mayank]

my bad. I will post it there. Thank you.