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Previously I used Sequential model for this problem, but later I read from the TensorFlow website that it is not suitable to use it. I'd like to have a NN model to predict 100+ values from the given 25 values. The code below is my previous implementation. What kind of changes should I make to achieve better results?

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import tensorflow

X_train, X_test, Y_train, Y_test = train_test_split( 
    inputdata, outputdata, test_size=0.2)


model = tensorflow.keras.models.Sequential()
model.add(tensorflow.keras.Input(shape=(25)))
model.add(tensorflow.keras.layers.Dense(64, activation='relu'))
model.add(tensorflow.keras.layers.Dense(103))

model.compile(loss="mean_absolute_error", optimizer="adam",
          metrics=["mean_squared_error"])
model.fit(X_train, Y_train, epochs=20)
predictions = model.predict(predict)
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  • $\begingroup$ A sequential model is perfectly fine in total lack of any other information. Maybe you can tell us what these 25 values and 100+ values mean and what is their relationship, and a link to the place where you read that a sequential model is not suitable for this. $\endgroup$
    – noe
    Jan 4 at 10:08
  • $\begingroup$ tensorflow.org/guide/keras/sequential_model Please, see the section where it is mentioned that it is not appropriate (Any of your layers has multiple inputs or multiple outputs). Maybe I misunderstood it. Those are band values of a hyperspectral image. Those 100 values were passed through multiple processes and became 25 (values changed completely) and I need to recover the previous 100 from 25. It is a sort of compression and decompression. I can't say they have a strong relation between at this point. $\endgroup$
    – k-sky
    Jan 4 at 10:22

1 Answer 1

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In the Tensorflow documentation that you linked, "multiple inputs/outputs", refers to multiple input/output tensors. Here you have one input tensor (of size 25) and one output tensor (of size 100+).

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  • $\begingroup$ Thank you for the information. But what is the algorithm here? Is it backpropagation? What is it exactly? It works but I can't understand what it is. $\endgroup$
    – k-sky
    Jan 4 at 10:52
  • $\begingroup$ Yes, it's backpropagation. All the main neural network frameworks nowadays use backpropagation. $\endgroup$
    – noe
    Jan 4 at 11:08
  • $\begingroup$ Thank you very much! $\endgroup$
    – k-sky
    Jan 4 at 12:05

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