I am currently working on a change detection project for my university course and I was stuck at writing a custom loss function.I know i have to use function closure to be able to use data from layers of the model but i don't know enough tensorflow/keras knowledge to write effecient code. The loss function equation
This is the modified cross entropy loss equation that i'm trying to turn into code.The loss needs the matrix W which I have to calculate using the inputs to the model, that is X1 and X2. So at the moment I have this.
def cmg_loss(X1,X2):
def loss(y_true,y_pred):
print(X1)
if X1.shape[0] == None:
X1 = tf.reshape(X1,(224,224,3))
X2 = tf.reshape(X2,(224,224,3))
cmm = [get_cmm(X1,X2)]
else:
cmm = [get_cmm(X1[i],X2[i]) for i in range(X1.shape[0])]
N = tf.convert_to_tensor(y_true.shape[0],dtype=tf.float32)
N_val = y_true.shape[0]
loss = tf.convert_to_tensor(0.0)
if(N_val == None):
loss = get_cmgloss(y_true[0],y_pred[0],cmm[0])
loss = tf.math.multiply(-1.0,loss)
return tf.math.divide(loss,N)
else:
for i in range(N_val):
print(i)
print("CMM len ", len(cmm))
x = get_cmgloss(y_true[i],y_pred[i],cmm[i])
loss = tf.math.add(loss,get_cmgloss(y_true[i],y_pred[i],cmm[i]))
loss = tf.math.multiply(-1.0,loss)
return tf.math.divide(loss,N)
return loss
def get_cmgloss(y_true,y_pred,W):
y_true = tf.cast(y_true,dtype=tf.float32)
y_pred = tf.cast(y_pred, dtype=tf.float32)
betaminus = findbetaminus(y_pred)
betaplus = 1 - betaminus
betaminus = betaminus.astype('float32')
betaplus = betaplus.astype('float32')
loss = tf.convert_to_tensor(0.0)
N = tf.convert_to_tensor(y_true.shape[0] * y_true.shape[0],dtype=tf.float32)
betaminus_matrix = tf.fill((224,224), betaminus)
betaplus_matrix = tf.fill((224,224), betaplus)
one_matrix = tf.fill((224,224), 1.0)
first_term = tf.math.multiply(betaminus_matrix,tf.math.multiply(y_true,tf.math.log(y_pred)))
second_term = tf.math.multiply(betaplus_matrix,tf.math.multiply(tf.math.subtract(one_matrix,y_true), tf.math.log(tf.math.subtract(one_matrix,y_pred))))
sum_first_second = tf.math.add(first_term, second_term)
prod = tf.math.multiply(W,sum_first_second)
loss = tf.math.reduce_sum(prod)
#loss = K.sum(K.sum(betaminus_matrix * y_true * tf.math.log(y_pred),betaplus_matrix * (1 - y_true) * tf.math.log(1 - y_pred)))
loss = tf.math.multiply(-1.0, loss)
return tf.math.divide(loss,N)
def findbetaminus(gt):
count_1 = tf.math.count_nonzero(gt == 1)
size = gt.shape[0] * gt.shape[1]
return count_1 / size;
def get_cmm(x1,x2):
b1_diff_sq = tf.math.squared_difference(x1[:,:,0],x2[:,:,0])
b2_diff_sq = tf.math.squared_difference(x1[:,:,1],x2[:,:,1])
b3_diff_sq = tf.math.squared_difference(x1[:,:,2],x2[:,:,2])
sum_3bands = tf.math.add(tf.math.add(b1_diff_sq,b2_diff_sq),b3_diff_sq)
cmm = tf.math.sqrt(sum_3bands)
#print(cmm)
max_val = tf.reduce_max(cmm)
#print("MAX_VAL ", max_val)
max_val_matrix = tf.fill((224,224), max_val)
cmm_bar = tf.divide(cmm,max_val_matrix)
#print(cmm_bar)
mean_cmm_bar = tf.reduce_mean(cmm_bar)
#print("MEAN_VAL ", mean_cmm_bar)
mean_cmm_bar_matrix = tf.fill((224,224), mean_cmm_bar)
#print(mean_cmm_bar_matrix)
condition = tf.math.greater(mean_cmm_bar_matrix, cmm_bar)
return tf.where(condition, mean_cmm_bar_matrix, cmm_bar)
#print(weights)
It would be great help if you could guide me on how to develop a loss function that makes use of data from other layers and also call multiple functions in its computation.
