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What you are looking for is called "reinforcement learning". A reinforcement learning algorithm will try to maximize a reward function. This reward represents how "good" or "bad" an action is in the actual context. For example, in the snake game, your reward will be positive for eating an apple and negative when the snake hits a ...


3

By convention, the $\mathrm{ReLU}$ activation is treated as if it is differentiable at zero (e.g. in [1]). Therefore it makes sense for TensorFlow to adopt this convention for tf.nn.relu. As you've found, of course, it's not true in general that we treat the gradient of the absolute value function as zero in the same situation; it makes sense for it to be an ...


1

Creating custom gradient for tf.abs may solve the problem: @tf.custom_gradient def abs_with_grad(x): y = tf.abs(x); def grad(div): # Derivation intermediate value g = 1; # Use 1 to make the chain rule just skip abs return div*g; return y,grad;


1

The link you have mentioned is using Dense layers. One thing to start with would be to use 1D CNNs (they will capture some local information). Also, since sequence matters in your case, refrain from one-hot encodings (just 1, 2, 3, 4). And for the 2D matrix, use a 2D CNN. Then, flatten your encoding for both 1D CNN and 2D CNN, then, finally combine them. ...


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