# Softmax activation predictions not summing to 1

I am a beginner with rnns, consider this sample code

from tensorflow import keras
import numpy as np

if __name__ == '__main__':
model = keras.Sequential((
keras.layers.SimpleRNN(5, activation="softmax", input_shape=(1, 3)),
))
X = [
[1, 2, 3],
[4, 5, 6]
]
y = [
[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0]
]
X = np.array(X)
X = np.reshape(X, (2, 1, 3))
y = np.array(y)
# print(X)
# print(y)
print(model.summary())
loss="categorical_crossentropy")
model.fit(X, y, epochs=100)
p = model.predict(X)
print(p)
p = list(np.squeeze(p))
print(p)
print(np.sum(p,axis=1))


I am using a simple rnn with batch size=2, 3 input features and 1 timestep,as the activation is softmax the last line prints [1,1] as the sum of predictions of a softmax is 1. But when when I change the layer from a SimpleRNN to

keras.layers.LSTM(5, activation="softmax", input_shape=
(1,3),recurrent_activation="softmax")


The sum of predictions is no longer 1, why is that?