I solved the problem by making
input size: (95000,360,1) and
output size: (95000,22)
and changed the input shape to (360,1) in the code where model is defined:
model = Sequential()
model.add(LSTM(22, input_shape=(360,1)))
model.add(Dense(22, activation='softmax'))
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=['accuracy'])
print(model.summary())
model.fit(ml2_train_input, ml2_train_output_enc, epochs=2, batch_size=500)