import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Input
import numpy as np

model = Sequential()
model.add(Dense(32, input_dim=32, activation='relu'))
model.add(Dense(5))

model.compile(loss=keras.losses.mean_squared_error,
              optimizer=keras.optimizers.Adam())

data = np.random.normal(size=(512, 32))
ans = np.random.normal(size=(512, 5))

while True:
    model.fit(data, ans, epochs=100, verbose=0)
    print (model.predict(data[10:11, :]), '\n', ans[10:11, :])