correct_prediction = tf.equal(tf.argmax(pred_y, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print(sess.run(accuracy, feed_dict={X: test_x, Y: test_y}))
print(sess.run(pred_y, feed_dict={X: test_x}))
from sklearn.model_selection import train_test_split # all'inizio
# in sostituzione della parte random splitta outsample 20%
train_x, test_x, train_y, test_y = train_test_split(f,l,test_size=0.1, shuffle =False)