import tensorflow as tf
import keras.backend as K

nonlabels = list(np.where(predictions<threshold)[1])
loss_fg_nT = 0
probs_nT = tf.gather(probs_fg,nonlabels,axis=3)
for i in range(len(nonlabels)):
    probs_temp = tf.reshape(probs_nT[:,:,:,i],[-1])
    prob_max = tf.math.reduce_max(probs_temp)
    const = tf.constant(0.000001)
    prob_max = tf.math.add(prob_max, const)
    print(prob_max)
    print(K.eval(prob_max))
    prob_max = tf.math.subtract(tf.constant(1.0), prob_max)
    print(prob_max)
    print(K.eval(prob_max))
    val = K.log(prob_max)