InvalidArgumentError: Default MaxPoolingOp only supports NHWC on device type CPU

Question

np.random.seed(1212)
import keras
from keras.models import Model
from keras.layers import *
from keras import optimizers
from keras.layers import Input, Dense
#from keras.models import Sequential
#from keras.layers import Dense
#from keras.layers import Dropout
#from keras.layers import Flatten
#from keras.layers.convolutional import Conv2D
#from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
K.set_image_data_format('channels_last')
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, Flatten, Dense,Dropout,MaxPooling2D

I am trying equation solver using deep learning. but while making the model I'm getting an error. Im giving input for model np.array(l)

df_train=pd.read_csv('train_final3.csv',index_col=False)
labels=df_train[['784']]
df_train.drop(df_train.columns[[784]],axis=1,inplace=True)

the dimension of the above dataframe is 5X784

labels=np.array(labels)
from keras.utils.np_utils import to_categorical
cat=to_categorical(labels,num_classes=13)

L array

l=[]
for i in range(47504):
    l.append(np.array(df_train[i:i+1]).reshape(1,28,28))

model defination

np.random.seed(7)
model = Sequential()
model.add(Conv2D(30, (5, 5), input_shape=(1 , 28, 28), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(15, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(50, activation='relu'))
model.add(Dense(13, activation='softmax'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
from keras.models import model_from_json

when i am trying to fit model im getting error

model.fit(np.array(l), cat, epochs=10, batch_size=200,shuffle=True,verbose=1)

error

Epoch 1/10
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-14-68276a2674ff> in <module>
----> 1 model.fit(np.array(l), cat, epochs=10, batch_size=200,shuffle=True,verbose=1)

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self, *args, **kwargs)
    106   def _method_wrapper(self, *args, **kwargs):
    107     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
--> 108       return method(self, *args, **kwargs)
    109 
    110     # Running inside `run_distribute_coordinator` already.

~\AppData\Roaming\Python\Python37\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)
   1096                 batch_size=batch_size):
   1097               callbacks.on_train_batch_begin(step)
-> 1098               tmp_logs = train_function(iterator)
   1099               if data_handler.should_sync:
   1100                 context.async_wait()

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
    778       else:
    779         compiler = "nonXla"
--> 780         result = self._call(*args, **kwds)
    781 
    782       new_tracing_count = self._get_tracing_count()

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
    838         # Lifting succeeded, so variables are initialized and we can run the
    839         # stateless function.
--> 840         return self._stateless_fn(*args, **kwds)
    841     else:
    842       canon_args, canon_kwds = \

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\function.py in __call__(self, *args, **kwargs)
   2827     with self._lock:
   2828       graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 2829     return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
   2830 
   2831   @property

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\function.py in _filtered_call(self, args, kwargs, cancellation_manager)
   1846                            resource_variable_ops.BaseResourceVariable))],
   1847         captured_inputs=self.captured_inputs,
-> 1848         cancellation_manager=cancellation_manager)
   1849 
   1850   def _call_flat(self, args, captured_inputs, cancellation_manager=None):

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
   1922       # No tape is watching; skip to running the function.
   1923       return self._build_call_outputs(self._inference_function.call(
-> 1924           ctx, args, cancellation_manager=cancellation_manager))
   1925     forward_backward = self._select_forward_and_backward_functions(
   1926         args,

~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\function.py in call(self, ctx, args, cancellation_manager)
    548               inputs=args,
    549               attrs=attrs,
--> 550               ctx=ctx)
    551         else:
    552           outputs = execute.execute_with_cancellation(

~\AppData\Roaming\Python\Python37\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:  Default MaxPoolingOp only supports NHWC on device type CPU
     [[node sequential/max_pooling2d/MaxPool (defined at <ipython-input-14-68276a2674ff>:1) ]] [Op:__inference_train_function_963]

Function call stack:
train_function

please any one guide me error Default MaxPoolingOp only supports NHWC on device type CPU Thank you

Answer 1

NHWC stands for

(n_samples, height, width, channels)

When you reshape your array into (1, 28, 28), you're using this format instead:

(n_samples, channels, height, width)

Which Keras can't work with, at least on CPU. Unless you have a very good reason to do so, I suggest you just use the NHWC format. So use this:

reshape(28, 28, 1)

And change the input shape of your neural net:

input_shape=(28, 28, 1)