broadcastable shapes error occurs in model.fit

MounaSah 0
Viewed 27

I am trying to test and adapt a UNET code on my 2D MR images which are of (512* 512* 24) dimensions to train my model. Here is the code I used:

inputs = Input((512, 512, 24))
conv1 = Conv2D(32, 3, activation='relu', padding='same')(inputs)
conv1 = Conv2D(32, 3, activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

conv2 = Conv2D(64, 3, activation='relu', padding='same')(pool1)
conv2 = Conv2D(64, 3, activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

conv3 = Conv2D(128, 3, activation='relu', padding='same')(pool2)
conv3 = Conv2D(128, 3, activation='relu', padding='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

conv4 = Conv2D(256, 3, activation='relu', padding='same')(pool3)
conv4 = Conv2D(256, 3, activation='relu', padding='same')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)

conv5 = Conv2D(512, 3, activation='relu', padding='same')(pool4)
conv5 = Conv2D(512, 3, activation='relu', padding='same')(conv5)
    
up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv5), conv4], axis=-1)
conv6 = Conv2D(256, 3, activation='relu', padding='same')(up6)
conv6 = Conv2D(256, 3, activation='relu', padding='same')(conv6)

up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6), conv3], axis=-1)
conv7 = Conv2D(128, 3, activation='relu', padding='same')(up7)
conv7 = Conv2D(128, 3, activation='relu', padding='same')(conv7)

up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv7), conv2], axis=-1)
conv8 = Conv2D(64, 3, activation='relu', padding='same')(up8)
conv8 = Conv2D(64, 3, activation='relu', padding='same')(conv8)

up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv8), conv1], axis=-1)
conv9 = Conv2D(32, 3, activation='relu', padding='same')(up9)
conv9 = Conv2D(32, 3, activation='relu', padding='same')(conv9)

conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)

model = Model(inputs=[inputs], outputs=[conv10])

model.compile(optimizer=Adam(lr=1e-5),loss=dice_coef_loss, metrics=[dice_coef, 'accuracy'])

When trying to .fit the model by doing:

model_checkpoint = ModelCheckpoint('weights2021.h5', monitor='val_loss', save_best_only=True)
history = model.fit(imgs_train, masks_train, batch_size=128, epochs=5, verbose=1, shuffle=True, validation_split=0.2, callbacks=[model_checkpoint])

an error occurred. Here is the full log I got:

Epoch 1/5
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-42-6b4fb6c38414> in <module>
      1 model_checkpoint = ModelCheckpoint('weights2021.h5', monitor='val_loss', save_best_only=True)
----> 2 history = model.fit(imgs_train, masks_train, batch_size=128, epochs=5, verbose=1, shuffle=True, validation_split=0.2, callbacks=[model_checkpoint])

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:
InvalidArgumentError: Graph execution error:
Detected at node 'dice_coef_loss/mul' 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 612, 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/ioloop.py", line 758, in _run_callback
      ret = callback()
    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/tornado/gen.py", line 1233, in inner
      self.run()
    File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 1147, in run
      yielded = self.gen.send(value)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 365, in process_one
      yield gen.maybe_future(dispatch(*args))
    File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 326, in wrapper
      yielded = next(result)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 268, in dispatch_shell
      yield gen.maybe_future(handler(stream, idents, msg))
    File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 326, in wrapper
      yielded = next(result)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 545, in execute_request
      user_expressions, allow_stdin,
    File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 326, in wrapper
      yielded = next(result)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py", line 306, 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 536, in run_cell
      return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2855, in run_cell
      raw_cell, store_history, silent, shell_futures)
    File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2881, in _run_cell
      return runner(coro)
    File "/usr/local/lib/python3.7/dist-packages/IPython/core/async_helpers.py", line 68, in _pseudo_sync_runner
      coro.send(None)
    File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 3058, in run_cell_async
      interactivity=interactivity, compiler=compiler, result=result)
    File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 3249, in run_ast_nodes
      if (await self.run_code(code, result,  async_=asy)):
    File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 3326, in run_code
      exec(code_obj, self.user_global_ns, self.user_ns)
    File "<ipython-input-42-6b4fb6c38414>", line 2, in <module>
      history = model.fit(imgs_train, masks_train, batch_size=128, epochs=5, verbose=1, shuffle=True, validation_split=0.2, callbacks=[model_checkpoint])
    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 860, in train_step
      loss = self.compute_loss(x, y, y_pred, sample_weight)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 919, in compute_loss
      y, y_pred, sample_weight, regularization_losses=self.losses)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py", line 201, in __call__
      loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 141, in __call__
      losses = call_fn(y_true, y_pred)
    File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 245, in call
      return ag_fn(y_true, y_pred, **self._fn_kwargs)
    File "<ipython-input-26-bfe16c112741>", line 11, in dice_coef_loss
      return -dice_coef(y_true, y_pred)
    File "<ipython-input-26-bfe16c112741>", line 6, in dice_coef
      intersection = K.sum(y_true_f * y_pred_f)
Node: 'dice_coef_loss/mul'
required broadcastable shapes
     [[{{node dice_coef_loss/mul}}]] [Op:__inference_train_function_28414]

Here is the dice_coef_loss function I used

smooth = 1
def dice_coef(y_true, y_pred):
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum(y_true_f * y_pred_f)
    return K.mean(2. * intersection) / (K.sum(y_true_f + y_pred_f) + smooth)

def dice_coef_loss(y_true, y_pred):
    return -dice_coef(y_true, y_pred)

Please could any one help me understanding and solving this issue (I'm beginner to python and machine learning). Thank you in advance.

unet-neural-network deep-learning keras tensorflow python
1 Answers

Without knowing exactly the shape of your dataset, it is not completely clear. But looking at the stack trace, you can see that the issue is on this line

intersection = K.sum(y_true_f * y_pred_f)

which means that the shape of y_true_f and y_pred_f are not broadcastable, in this case, your ground truth and the output of your model don't have the same shape, but they should since the model should output the same type of answer as the ground truth.

Try feeding a single example of your dataset to your model, and running the loss function with the output and your ground truth, print the shapes and you should see what is causing the missmatch.

Bernat Gene0
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