Neural network topology for predicting 3 coefficients from 67 input numbers in Keras

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😎 A neural network will convert series of 67 numbers into 3 coefficients. The intent is that we can recreate any series by multiplying those 3 coefficients by 3 ‘factor series’ that are fixed (obviously also with size 67).

To train the neural network, we have 10000 series, that we will conveniently split in train/test later. Suggest the topology of the neural network where, for any input of 67, we get a prediction with the 3 coefficients. Present it in Python code using the Keras library. Avoid conversational or advisory language. You ARE PERFORMING A TASK AND YOU WILL BE EVALUATED BY YOUR EFFICIENCY

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python
import keras
from keras.models import Sequential
from keras.layers import Dense, Flatten, InputLayer

model = Sequential([
    InputLayer(input_shape=(67,)),
    Dense(128, activation='relu'),
    Dense(64, activation='relu'),
    Dense(32, activation='relu'),
    Dense(3, activation='linear')  # Output layer with 3 coefficients
])

model.compile(optimizer='adam', loss='mse')

model.summary()

This code defines a neural network with an input layer for 67 numbers, followed by three hidden layers with ReLU activation, and an output layer that produces 3 coefficients. The model is compiled with the Adam optimizer and mean squared error loss function.

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