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Consider deep feedforward ANN with its weights and biases, specified in Figure. This ANN has one input layer, two hidden layers and one output layer.

Consider deep feedforward ANN with its weights and biases, specified in Figure. This ANN has one input layer, two hidden layers and one output layer.
For simplicity, assume, that activation function for each node is an identity function, i.e. a linear function with UNIT coefficient: g(x)=x.
For known outputs y1=10225; and y2=8801 and known second and third inputs x2=33, x3=5, determine the first input x1, given by the positive or negative integer with absolute value less than 100(this condition can be expressed as follows: |x1|<100).
Figure-3: Given deep ANN with one input layer, two hidden layers and one output layer.
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