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I would like to ask for help. I have a dataset which has a column called pixels (I loaded with Pandas):

enter image description here

I would like to train this image classification dataset. So, I used split function inside apply pandas function

dfemotrain['pic']=dfemotrain['pixels'].apply(lambda x: np.array(x.split()).reshape(48, 48))

Later on, I used train_test_split from sklearn

X = dfemotrain['pic'].values
y = dfemotrain['emotion'].values
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.25, stratify=y, random_state=42)
print(X_train.shape, X_val.shape, y_train.shape, y_val.shape)

I got (21750,) (7250,) (21750,) (7250,) and I am not sure how to convert to a numpy array n_samplesx48x48 to input to a Deep Learning model

enter image description here

Please any suggestion to solve this issue. Thanks in advance

Improve this question
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  • I believe you want to use reshape stackoverflow.com/questions/12575421/… Commented Mar 23, 2023 at 21:23
  • 1
    X = np.array(dfemotrain['pic'].astype('int').to_list()) Commented Mar 23, 2023 at 21:33
  • 1
    np.array(x.split(),dtype=int) should turn those cell values into numeric arrays. What did you load this from? From a csv? The frame cells contain strings, not lists or arrays. Did you create the source file yourself, or get it from someone else? Commented Mar 23, 2023 at 21:45
  • I believe this question is not related to scikit-learn or tensorflow, therefore the tags are removed. Please try not to use irrelevant tags. Commented Mar 24, 2023 at 0:32
  • don't you have a data sample ? Commented Mar 24, 2023 at 2:38
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