Multi-Output Regression with neural network in Keras
I found some mistakes:


input data must be numpy objects, not pandas
this Network has 6 output nodes, not 2
the number of layers is completely exagerated IMHO
the Flatten() layer at the beginning is not correct
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.