Keras: Dimension Error with Sparse Categorical Crossentropy I'm trying to make an NN that, given the time on the clock, would try to predict which class (out of 32 in this example) is making a request to the system. As a first attempt, I've tried to use categorical_crossentropy, but this will obviously not work because the targets are very sparse, so the system will be heavily rewarded by just always predicting the non-requests.

Now I'm trying to use sparse_categorical_crossentropy, but I keep getting a dimension mismatch error (the train and test sets are the same in this case because I just wanted to evaluate performace in the training set at first):

Error when checking target: expected dense_90 to have shape (1,) but got array with shape (32,)


The DataFrame is here (a simple clock and another column for the requests) and the code is:

from keras.models import Sequential
from keras.layers import Dense
from keras.utils import to_categorical
import tensorflow as tf

requests = df['requests'].values
requests_cat = to_categorical(requests, 32)

length = len(df['clock'])
train = np.reshape(df['clock'].values, (length, 1))
train = train.astype(np.int)
target = requests_cat

model = Sequential()
model.add(Dense(25, activation = 'relu', input_shape = (train.shape[1],)))
model.add(Dense(25, activation = 'relu'))
model.add(Dense(32, activation = 'softmax'))

model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy')
model.fit(x = train, y = target, epochs = 100, validation_data = (train, target))


On a sidenote:


This architecture doesn't seem to be the best in this case. As a second prototype I was thinking about doing something with an LSTM, since past requests can affect later ones. Is there a standard architecture for scheduling?
What would be the proper way of splitting sparse sets into training and testing ones?
I thing you've misunderstood what the difference between categorical_crossentropy and sparse_categorical_crossentropy is. The sparse part doesn't refer to the sparsity of the data but the format of the labels.


If your labels are one-hot encoded: use categorical_crossentropy
If your labels are encoded as integers: use sparse_categorical_crossentropy