# Accuracy
        with tf.name_scope("accuracy"):
            correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
            self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name="accuracy")

confusion_matrix[16][7]+=1

y_ = tf.placeholder(tf.float32, shape=[None, 2])
y = last_layer(d/2,2,h_fc1_drop)
confusion_matrix_tf = tf.confusion_matrix(tf.argmax(y, 1), tf.argmax(y_, 1))

cm = confusion_matrix_tf.eval(feed_dict={x: X_train, y_: y_train, keep_prob: 1.0})

def beautyCM(cm, ind=['True pos', 'True neg'], cols=['Pred pos', 'Pred neg']):
    return pd.DataFrame(cm, index=ind, columns=cols)

def precision(cm):
     # prec = TP / (TP + FP)
    try:
        return round(float(cm.loc['True pos', 'Pred pos']) /
                     (cm.loc['True pos', 'Pred pos'] + cm.loc['True neg', 'Pred pos']), 4)
    except ZeroDivisionError:
        return 1.0


def recall(cm):
    # prec = TP / (TP + FN)
    try:
        return round(float(cm.loc['True pos', 'Pred pos']) /
                 (cm.loc['True pos', 'Pred pos'] + cm.loc['True pos', 'Pred neg']), 4)
    except ZeroDivisionError:
        return 1.0