Custom Loss Function in TF2.0 - python-3.x

Question

For a image segmentation problem, I need to write a custom loss function. I am getting below said error.
Code Base: https://www.tensorflow.org/tutorials/images/segmentation
Last layer:
Conv2DTrans (128,128,2) [Note that in my case it is only 2 values]
def call(self, y_true, y_pred):
ytrue = ytrue.numpy()
.....
Error:
AttributeError: 'Tensor' object has no attribute 'numpy'
I tried py_function and numpy_function but both return the same error
and also
with tf.compat.v1.Session() as sess:
for i,j in enumerate(sess.run(y_true),sess.run(y_pred)):
Current Model Layers:
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_82 (InputLayer) [(None, 128, 128, 3) 0
__________________________________________________________________________________________________
model_80 (Model) [(None, 64, 64, 96), 1841984 input_82[0][0]
__________________________________________________________________________________________________
sequential_160 (Sequential) (None, 8, 8, 512) 1476608 model_80[1][4]
__________________________________________________________________________________________________
concatenate_160 (Concatenate) (None, 8, 8, 1088) 0 sequential_160[0][0]
model_80[1][3]
__________________________________________________________________________________________________
sequential_161 (Sequential) (None, 16, 16, 256) 2507776 concatenate_160[0][0]
__________________________________________________________________________________________________
concatenate_161 (Concatenate) (None, 16, 16, 448) 0 sequential_161[0][0]
model_80[1][2]
__________________________________________________________________________________________________
sequential_162 (Sequential) (None, 32, 32, 128) 516608 concatenate_161[0][0]
__________________________________________________________________________________________________
concatenate_162 (Concatenate) (None, 32, 32, 272) 0 sequential_162[0][0]
model_80[1][1]
__________________________________________________________________________________________________
sequential_163 (Sequential) (None, 64, 64, 64) 156928 concatenate_162[0][0]
__________________________________________________________________________________________________
concatenate_163 (Concatenate) (None, 64, 64, 160) 0 sequential_163[0][0]
model_80[1][0]
__________________________________________________________________________________________________
conv2d_transpose_204 (Conv2DTra (None, 128, 128, 2) 2882 concatenate_163[0][0]
==================================================================================================
I need a numpy array to focus only more on 1's and not on zeroes. Now the metric and accuracy are overwhelmed by the presence of lot of zeros.
def tumor_loss(y_true,y_pred):
y_true = y_true.reshape((SHAPE,SHAPE))
y_pred = y_pred.reshape((SHAPE,SHAPE))
y_true_ind = np.where(y_true ==1)[1]
y_pred_ind = np.where(y_pred==1)[1]
if np.array_equal(y_true_ind,y_pred_ind):
return 0
if y_true_ind.shape[0] > y_pred_ind.shape[0]:
return y_true_ind.shape[0] - np.setdiff1d(y_true_ind,y_pred_ind).shape[0]
else:
return y_true_ind.shape[0] - np.setdiff1d(y_pred_ind,y_true_ind).shape[0]

Answer 1

If you are running on tf version >= 2.0, try using
model.compile(loss=custom_loss, optimizer='adam', run_eagerly=True)
if you are using Keras api.