I'm working on a project where I have 3 inputs (v, f, n) and 1 output (delta(t)).
I'm trying to test the effect of the inputs on the output and to figure out which input is the most effective in different situations, therefore I would like to predict new output values that depend on new inputs values.
I have been testing this system and I got the following data table:
This table contains 1000 rows.
I'm new to this whole Neural Network thing, so I don't know what should be the Activation function, the loss function, etc.
I've been trying use some Keras models, but I'm getting wrong predictions when trying model.predict() some inputs values.
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
model = Sequential()
model.add(Dense(16, activation='relu', input_shape=(3,)))
model.add(Dense(16, activation='relu'))
model.add(Dense(1))
model.compile(optimizer=Adam(), loss='mse')
data = np.array(pd.read_excel(r'Data.xlsx'))
x = data[:, :3]
y = data[:, 3]
target = model.fit(x, y, validation_split=0.2, epochs=15000,
batch_size=256)
# check some predictions:
print(model.predict([[0.9, 840370875, 240]]))