Multiclass classification label error using TensoFlow - tensorflow

Question

I am doing a multiclass classification using TensorFlow. Target has 4 values[0, 1, 13, 14]. That is why I took 4 in the last Dense Layer. I am using sparse_categorical_crossentropy in my loss function. My code is as follows:-
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Conv1D, MaxPool1D, Dropout, Conv2D
import matplotlib.pyplot as plt
x_train, x_test, y_train, y_test = train_test_split(X, data['seg_type'], test_size=0.33, random_state=1)
#The known number of output classes.
labels = [0, 1, 13, 14]
num_classes = 4
# # label encoding
# encoder = LabelEncoder()
# y_train = encoder.fit_transform(y_train)
# y_test = encoder.fit_transform(y_test)
# # one hot encoding
# y_train = tensorflow.keras.utils.to_categorical(y_train, num_classes)
# y_test = tensorflow.keras.utils.to_categorical(y_test, num_classes)
y_train = np.array(y_train)
y_test = np.array(y_test)
# build CNN model
model = Sequential()
model.add(Conv2D(32, (1, 1) , input_shape = (1, 3, 1), activation='relu'))
model.add(Conv2D(64, (1, 1) , input_shape = (1, 3, 1), activation='relu'))
model.add(Conv2D(128, (1, 1) ,activation='relu'))
model.add(Flatten()) # flatten
model.add(Dense(32, activation='relu')) # fc
model.add(Dense(64, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(4, activation='softmax'))
# model compile
model.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.summary()
batch_size = 32
epochs = 20
history = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=2)
But after fitting the model it shows the error: -
InvalidArgumentError: Received a label value of 14 which is outside the valid range of [0, 4). Label values: 14 14 13 13 13 14 13 13 14 1 13 13 14 14 14 1 14 13 1 14 13 14 14 13 1 14 13 14 1 14 14 14
[[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits (defined at <ipython-input-81-33f0652484e5>:6) ]] [Op:__inference_train_function_574154]
Function call stack:
train_function
Full code - My notebook
Data - Access the data here
Thanks in advance!!!!!!!!!!

Answer 1

As you have used four labels in your code here,
#The known number of output classes.
labels = [0, 1, 13, 14]
num_classes = 4
The valid range of your labels is 0,1,2 & 3. So the values while passing for prediction should always come from this range itself, the model does not recognizes 13 and 14 values.
You need to use any encoder to convert your input labels to valid range and do reverse while predicting for them.
You can just change your labels to range with in [0,3] for workaround.

Answer 2

change the loss function from sparse_categorical_crossentropy to categorical crossentroy and convert data into the categorical data
from keras.utils import to_categorical
y_data = to_categorical(y_data, classes = 4)
Now it will work, the present situation is the classes should be like [0, 1, 2, 3] but you are using [0, 1, 13, 14]. the solution for this is to categorical function