Question: A few days back, I got the same error at 12th epoch. This time, it happens at the 1st. I have no idea why that is happening as I did not make any changes to the model. I only normalized the input to give X_train.max() as 1 after scaling like it should be. Does it have something to do with patch size? Should I reduce it? Why do I get this error and how can I fix it? my_model.summary() Model: "U-Net" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_6 (InputLayer) [(None, 64, 64, 64, 0 [] 3)] conv3d_95 (Conv3D) (None, 64, 64, 64, 5248 ['input_6[0][0]'] 64) batch_normalization_90 (BatchN (None, 64, 64, 64, 256 ['conv3d_95[0][0]'] ormalization) 64) activation_90 (Activation) (None, 64, 64, 64, 0 ['batch_normalization_90[0][0]'] 64) conv3d_96 (Conv3D) (None, 64, 64, 64, 110656 ['activation_90[0][0]'] 64) batch_normalization_91 (BatchN (None, 64, 64, 64, 256 ['conv3d_96[0][0]'] ormalization) 64) activation_91 (Activation) (None, 64, 64, 64, 0 ['batch_normalization_91[0][0]'] 64) max_pooling3d_20 (MaxPooling3D (None, 32, 32, 32, 0 ['activation_91[0][0]'] ) 64) conv3d_97 (Conv3D) (None, 32, 32, 32, 221312 ['max_pooling3d_20[0][0]'] 128) batch_normalization_92 (BatchN (None, 32, 32, 32, 512 ['conv3d_97[0][0]'] ormalization) 128) activation_92 (Activation) (None, 32, 32, 32, 0 ['batch_normalization_92[0][0]'] 128) conv3d_98 (Conv3D) (None, 32, 32, 32, 442496 ['activation_92[0][0]'] 128) batch_normalization_93 (BatchN (None, 32, 32, 32, 512 ['conv3d_98[0][0]'] ormalization) 128) activation_93 (Activation) (None, 32, 32, 32, 0 ['batch_normalization_93[0][0]'] 128) max_pooling3d_21 (MaxPooling3D (None, 16, 16, 16, 0 ['activation_93[0][0]'] ) 128) conv3d_99 (Conv3D) (None, 16, 16, 16, 884992 ['max_pooling3d_21[0][0]'] 256) batch_normalization_94 (BatchN (None, 16, 16, 16, 1024 ['conv3d_99[0][0]'] ormalization) 256) activation_94 (Activation) (None, 16, 16, 16, 0 ['batch_normalization_94[0][0]'] 256) conv3d_100 (Conv3D) (None, 16, 16, 16, 1769728 ['activation_94[0][0]'] 256) batch_normalization_95 (BatchN (None, 16, 16, 16, 1024 ['conv3d_100[0][0]'] ormalization) 256) activation_95 (Activation) (None, 16, 16, 16, 0 ['batch_normalization_95[0][0]'] 256) max_pooling3d_22 (MaxPooling3D (None, 8, 8, 8, 256 0 ['activation_95[0][0]'] ) ) conv3d_101 (Conv3D) (None, 8, 8, 8, 512 3539456 ['max_pooling3d_22[0][0]'] ) batch_normalization_96 (BatchN (None, 8, 8, 8, 512 2048 ['conv3d_101[0][0]'] ormalization) ) activation_96 (Activation) (None, 8, 8, 8, 512 0 ['batch_normalization_96[0][0]'] ) conv3d_102 (Conv3D) (None, 8, 8, 8, 512 7078400 ['activation_96[0][0]'] ) batch_normalization_97 (BatchN (None, 8, 8, 8, 512 2048 ['conv3d_102[0][0]'] ormalization) ) activation_97 (Activation) (None, 8, 8, 8, 512 0 ['batch_normalization_97[0][0]'] ) max_pooling3d_23 (MaxPooling3D (None, 4, 4, 4, 512 0 ['activation_97[0][0]'] ) ) conv3d_103 (Conv3D) (None, 4, 4, 4, 102 14156800 ['max_pooling3d_23[0][0]'] 4) batch_normalization_98 (BatchN (None, 4, 4, 4, 102 4096 ['conv3d_103[0][0]'] ormalization) 4) activation_98 (Activation) (None, 4, 4, 4, 102 0 ['batch_normalization_98[0][0]'] 4) conv3d_104 (Conv3D) (None, 4, 4, 4, 102 28312576 ['activation_98[0][0]'] 4) batch_normalization_99 (BatchN (None, 4, 4, 4, 102 4096 ['conv3d_104[0][0]'] ormalization) 4) activation_99 (Activation) (None, 4, 4, 4, 102 0 ['batch_normalization_99[0][0]'] 4) conv3d_transpose_20 (Conv3DTra (None, 8, 8, 8, 512 4194816 ['activation_99[0][0]'] nspose) ) concatenate_20 (Concatenate) (None, 8, 8, 8, 102 0 ['conv3d_transpose_20[0][0]', 4) 'activation_97[0][0]'] conv3d_105 (Conv3D) (None, 8, 8, 8, 512 14156288 ['concatenate_20[0][0]'] ) batch_normalization_100 (Batch (None, 8, 8, 8, 512 2048 ['conv3d_105[0][0]'] Normalization) ) activation_100 (Activation) (None, 8, 8, 8, 512 0 ['batch_normalization_100[0][0]'] ) conv3d_106 (Conv3D) (None, 8, 8, 8, 512 7078400 ['activation_100[0][0]'] ) batch_normalization_101 (Batch (None, 8, 8, 8, 512 2048 ['conv3d_106[0][0]'] Normalization) ) activation_101 (Activation) (None, 8, 8, 8, 512 0 ['batch_normalization_101[0][0]'] ) conv3d_transpose_21 (Conv3DTra (None, 16, 16, 16, 1048832 ['activation_101[0][0]'] nspose) 256) concatenate_21 (Concatenate) (None, 16, 16, 16, 0 ['conv3d_transpose_21[0][0]', 512) 'activation_95[0][0]'] conv3d_107 (Conv3D) (None, 16, 16, 16, 3539200 ['concatenate_21[0][0]'] 256) batch_normalization_102 (Batch (None, 16, 16, 16, 1024 ['conv3d_107[0][0]'] Normalization) 256) activation_102 (Activation) (None, 16, 16, 16, 0 ['batch_normalization_102[0][0]'] 256) conv3d_108 (Conv3D) (None, 16, 16, 16, 1769728 ['activation_102[0][0]'] 256) batch_normalization_103 (Batch (None, 16, 16, 16, 1024 ['conv3d_108[0][0]'] Normalization) 256) activation_103 (Activation) (None, 16, 16, 16, 0 ['batch_normalization_103[0][0]'] 256) conv3d_transpose_22 (Conv3DTra (None, 32, 32, 32, 262272 ['activation_103[0][0]'] nspose) 128) concatenate_22 (Concatenate) (None, 32, 32, 32, 0 ['conv3d_transpose_22[0][0]', 256) 'activation_93[0][0]'] conv3d_109 (Conv3D) (None, 32, 32, 32, 884864 ['concatenate_22[0][0]'] 128) batch_normalization_104 (Batch (None, 32, 32, 32, 512 ['conv3d_109[0][0]'] Normalization) 128) activation_104 (Activation) (None, 32, 32, 32, 0 ['batch_normalization_104[0][0]'] 128) conv3d_110 (Conv3D) (None, 32, 32, 32, 442496 ['activation_104[0][0]'] 128) batch_normalization_105 (Batch (None, 32, 32, 32, 512 ['conv3d_110[0][0]'] Normalization) 128) activation_105 (Activation) (None, 32, 32, 32, 0 ['batch_normalization_105[0][0]'] 128) conv3d_transpose_23 (Conv3DTra (None, 64, 64, 64, 65600 ['activation_105[0][0]'] nspose) 64) concatenate_23 (Concatenate) (None, 64, 64, 64, 0 ['conv3d_transpose_23[0][0]', 128) 'activation_91[0][0]'] conv3d_111 (Conv3D) (None, 64, 64, 64, 221248 ['concatenate_23[0][0]'] 64) batch_normalization_106 (Batch (None, 64, 64, 64, 256 ['conv3d_111[0][0]'] Normalization) 64) activation_106 (Activation) (None, 64, 64, 64, 0 ['batch_normalization_106[0][0]'] 64) conv3d_112 (Conv3D) (None, 64, 64, 64, 110656 ['activation_106[0][0]'] 64) batch_normalization_107 (Batch (None, 64, 64, 64, 256 ['conv3d_112[0][0]'] Normalization) 64) activation_107 (Activation) (None, 64, 64, 64, 0 ['batch_normalization_107[0][0]'] 64) conv3d_113 (Conv3D) (None, 64, 64, 64, 260 ['activation_107[0][0]'] 4) ================================================================================================== Total params: 90,319,876 Trainable params: 90,308,100 Non-trainable params: 11,776 __________________________________________________________________________________________________ None  Error Message Log: Epoch 1/100 --------------------------------------------------------------------------- ResourceExhaustedError Traceback (most recent call last) <ipython-input-52-ec522ff5ad08> in <module>() 5 epochs=100, 6 verbose=1, ----> 7 validation_data=(X_test, y_test)) 1 frames /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name) 53 ctx.ensure_initialized() 54 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, ---> 55 inputs, attrs, num_outputs) 56 except core._NotOkStatusException as e: 57 if name is not None: ResourceExhaustedError: Graph execution error: Detected at node 'U-Net/concatenate_23/concat' defined at (most recent call last): File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main "__main__", mod_spec) File "/usr/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) File "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py", line 16, in <module> app.launch_new_instance() File "/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py", line 846, in launch_instance app.start() File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py", line 499, in start self.io_loop.start() File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 132, in start self.asyncio_loop.run_forever() File "/usr/lib/python3.7/asyncio/base_events.py", line 541, in run_forever self._run_once() File "/usr/lib/python3.7/asyncio/base_events.py", line 1786, in _run_once handle._run() File "/usr/lib/python3.7/asyncio/events.py", line 88, in _run self._context.run(self._callback, *self._args) File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 122, in _handle_events handler_func(fileobj, events) File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper return fn(*args, **kwargs) File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 452, in _handle_events self._handle_recv() File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 481, in _handle_recv self._run_callback(callback, msg) File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 431, in _run_callback callback(*args, **kwargs) File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper return fn(*args, **kwargs) File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher return self.dispatch_shell(stream, msg) File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell handler(stream, idents, msg) File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request user_expressions, allow_stdin) File "/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py", line 208, in do_execute res = shell.run_cell(code, store_history=store_history, silent=silent) File "/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py", line 537, in run_cell return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs) File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell interactivity=interactivity, compiler=compiler, result=result) File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2822, in run_ast_nodes if self.run_code(code, result): File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-52-ec522ff5ad08>", line 7, in <module> validation_data=(X_test, y_test)) File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler return fn(*args, **kwargs) File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1384, in fit tmp_logs = self.train_function(iterator) File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1021, in train_function return step_function(self, iterator) File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1010, in step_function outputs = model.distribute_strategy.run(run_step, args=(data,)) File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1000, in run_step outputs = model.train_step(data) File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 859, in train_step y_pred = self(x, training=True) File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler return fn(*args, **kwargs) packages/keras/layers/merge.py", line 531, in _merge_function return backend.concatenate(inputs, axis=self.axis) File "/usr/local/lib/python3.7/dist-packages/keras/backend.py", line 3313, in concatenate return tf.concat([to_dense(x) for x in tensors], axis) Node: 'U-Net/concatenate_23/concat' OOM when allocating tensor with shape[8,128,64,64,64] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [[{{node U-Net/concatenate_23/concat}}]] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode. [Op:__inference_train_function_24517]  GPU details: nvidia-smi command: +-----------------------------------------------------------------------------+ | NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 | | N/A 72C P0 73W / 149W | 11077MiB / 11441MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=============================================================================| +-----------------------------------------------------------------------------+  I'm new to Tensorflow and all of this ML stuff honestly. Would really appreciate any help. Thanks.

Answer: I had the same error as you ,it's a resource exhausted problem, resolved by just reducing batch_size value(I had a Model which try to learn from dataset of big images I reduce it's value from 32 to 16) .and it's worked fine