Why does the neural network keep giving out the same output for every input?

Question

Made a neural network using TensorFlow's Keras that is supposed to match an IP to one of the 7 type of vulnerabilities and give out what type of vulnerability that IP has.
model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(50, activation=tf.nn.relu),
  tf.keras.layers.Dense(7, activation=tf.nn.softmax)
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(xs, ys, epochs=500)

xs is the list of IPs and ys is its corresponding vulnerability from 0 to 6 (seven in total).
The output for this remains the same for every input, i.e.,:
[[0.22258884 0.20329571 0.36828393 0.11352853 0.04444532 0.02388807 0.02396955]]

desertnaut · Answer

Most probably, this is because your model is way too simple - a single 50-node hidden layer for a 7-class problem does not sound adequate.
Try adding more hidden layers (and not quite sure if you really need a Flatten layer just after the input), e.g.,:
model = tf.keras.models.Sequential([
 tf.keras.layers.Dense(100, activation=tf.nn.relu),
 tf.keras.layers.Dense(50, activation=tf.nn.relu)
 tf.keras.layers.Dense(50, activation=tf.nn.relu),
 tf.keras.layers.Dense(7, activation=tf.nn.softmax)
])

Experiment with the exact number and size of the layers.

SrJ · Answer

It depends on the represent of the ip address. If you are feeding the ip address with those dot in it. It might perform worse. Rather i would suggest to split the ip into 4 seperate input(seperated by dot) and feed the 4 input individually to the network. And then evaluate.

Answered by SrJ on January 8, 2021

Why does the neural network keep giving out the same output for every input?

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