CODEWITHSUNDEEP
pythonkerasmetric
Kroshtan
Kroshtan

Reputation: 677

Custom Keras Metric throwing error

While trying to implement Intersection over Union (IoU), I've run into a python/keras error that I can't seem to place. In a seperate file I define the following metric:

def computeIoU(y_pred_batch, y_true_batch):
    print y_true_batch.shape[0]
    return np.mean(np.asarray([imageIoU(y_pred_batch[i], y_true_batch[i]) for i in range(y_true_batch.shape[0])]))

def imageIoU(y_pred, y_true):
    y_pred = np.argmax(y_pred, axis=2)
    y_true = np.argmax(y_true, axis=2)
    inter = 0
    union = 0
    for x in range(imCols):
        for y in range(imRows):
            for i in range(num_classes):
                inter += (y_pred[y][x] == y_true[y][x] == i)
                union += (y_pred[y][x] == i or y_true[y][x] == i)
    print inter
    print union
    return float(inter)/union

In the main file I have imported the function and use the metric as follows:

fcn32_model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy', computeIoU])

The error that is thrown is 

TypeError: __int__ should return int object

After implementing the above algorithm with the suggested Keras/tf syntax from answers here and on another question the code was changed to:

def iou(y_pred_batch, y_true_batch):
    intersection = tf.zeros(())
    union = tf.zeros(())
    y_pred_batch = K.argmax(y_pred_batch, axis=-1)
    y_true_batch = K.argmax(y_true_batch, axis=-1)
    for i in range(num_classes):
        iTensor = tf.to_int64(tf.fill(y_pred_batch.shape, i))
        intersection = tf.add(intersection, tf.to_float(tf.count_nonzero(tf.logical_and(K.equal(y_true_batch, y_pred_batch), K.equal(y_true_batch, iTensor)))))
        union = tf.add(union, tf.to_float(tf.count_nonzero(tf.logical_or(K.equal(y_true_batch, iTensor), K.equal(y_pred_batch, iTensor)))))
    return intersection/union

Upvotes: 1

Views: 274

Answers (1)

thefifthjack005
thefifthjack005

Reputation: 638

The issue seems to be that you are trying to compute in plain integer,instead its a keras variable.

intersection = K.sum(K.abs(y_true * y_pred), axis=-1)
union_sum = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1)
IOU = (intersection) / (union_sum- intersection)

Upvotes: 2

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