Multi-Output Regression with neural network in Keras

Question

I have got an .xlsx Excel file with an input an 2 output columns. And there are some coordinates and outputs in that file such as: x= 10 y1=15 y2=20 x= 20 y1=14 y2=22 ... I am trying to do that regression using tensorflow. But somehow i can't manage to do it. I am leaving my code here, I would appraciate it if someone could help! I also have test datas ready as well.

training_data = pd.read_excel(...\training_data.xlsx',sheet_name="i1-o2")

training_data_X = training_data['i1']

training_data_Y = training_data[['o1','o2']]

testing_data = data = pd.read_excel(....\testing_data.xlsx',sheet_name="i1-o2")

testing_data_X = testing_data['i1']

testing_data_Y = testing_data[['o1','o2']]


model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation=tf.nn.relu),
    tf.keras.layers.Dense(128, activation=tf.nn.relu),
    tf.keras.layers.Dense(128, activation=tf.nn.relu),
    tf.keras.layers.Dense(128, activation=tf.nn.relu),
    tf.keras.layers.Dense(128, activation=tf.nn.relu),
    tf.keras.layers.Dense(128, activation=tf.nn.relu),
    tf.keras.layers.Dense(6, activation='linear')
])

model.compile(optimizer='adam',
              loss='mean_squared_error',
              metrics=['accuracy'])
model.fit(training_data_X,training_data_Y,epochs=10,batch_size=100)

val_loss,val_acc = model.evaluate(testing_data_X,testing_data_Y)
print(val_loss,val_acc)

Answer 1

I found some mistakes:

1. input data must be numpy objects, not pandas
2. this Network has 6 output nodes, not 2
3. the number of layers is completely exagerated IMHO
4. the Flatten() layer at the beginning is not correct
5. the way you called ReLU's is not correct

This should be enough:

from tf.keras.models import Sequential
from tf.keras.layers import Dense
from tf.keras.activations import relu

model = Sequential([
    tf.keras.layers.Dense(128, activation = relu),

    tf.keras.layers.Dense(128, activation = relu),

    tf.keras.layers.Dense(2, activation = None)
])

Check if the loss works at this point. Alternatively, you need to write your own custom loss function using Keras backend functions.

Answer 2

Why would you use a Flatten layer in this? seems like it is already a numerical data.

and as @desertnaut mentioned, since this is a regression setup, you should be using mse or mae