How to create a neural network for regression?

Question

I am trying to use Keras to make a neural network. The data I am using is https://archive.ics.uci.edu/ml/datasets/Yacht+Hydrodynamics . My code is as follows:

import numpy as np
from keras.layers import Dense, Activation
from keras.models import Sequential
from sklearn.model_selection import train_test_split

data = np.genfromtxt(r"""file location""", delimiter=',')

model = Sequential()
model.add(Dense(32, activation = 'relu', input_dim = 6))
model.add(Dense(1,))
model.compile(optimizer='adam', loss='mean_squared_error', metrics = ['accuracy'])

Y = data[:,-1]
X = data[:, :-1]

From here I have tried using model.fit(X, Y), but the accuracy of the model appears to remain at 0. I am new to Keras so this is probably an easy solution, apologies in advance.

My question is what is the best way to add regression to the model so that the accuracy increases? Thanks in advance.

Answer 1

I am trying to use Keras to make a neural network. The data I am using is https://archive.ics.uci.edu/ml/datasets/Yacht+Hydrodynamics . My code is as follows:

import numpy as np
from keras.layers import Dense, Activation
from keras.models import Sequential
from sklearn.model_selection import train_test_split

data = np.genfromtxt(r"""file location""", delimiter=',')

model = Sequential()
model.add(Dense(32, activation = 'relu', input_dim = 6))
model.add(Dense(1,))
model.compile(optimizer='adam', loss='mean_squared_error', metrics = ['accuracy'])

Y = data[:,-1]
X = data[:, :-1]

From here I have tried using model.fit(X, Y), but the accuracy of the model appears to remain at 0. I am new to Keras so this is probably an easy solution, apologies in advance.

My question is what is the best way to add regression to the model so that the accuracy increases? Thanks in advance.

Answer 2

Excelente. Saludos. Tengo una duda, la predicción se realiza en base a X_test o sea con datos conocidos para verificar si aprendió bien. Como se realiza la predicción a futuro? sin X_test?