In the Week3 video ‘Object Localisation’, Andrew mentioned a loss function which contained ‘if’ statements. (~10:30 mark). I’m wondering if there are existing implementations for loss functions like this (i.e., combining both object detection and localisation)?

I tried to implement this myself and encountered shape issues. Below is a minimal working example:

```
from numpy import *
from tensorflow.keras.layers import Input, Dense
import tensorflow as tf
#dummy variables
X_dummy=array([1.0,0.0,1.0])
#here the first element =1 if object is detected, =0 if not
Y_dummy=array([ [1.0,0.0],[0.0,0.0],[1.0,0.0] ])
def loss_loc(y_true, y_pred):
#if the object is detected, use MSE for all pred
loss_w_obj=tf.keras.losses.MeanSquaredError()(y_true, y_pred)
#if the object is not detected, just find the squared difference for the first element
loss_wo_obj=(y_true[0]- y_pred[0])**2
#case 1 when object is not detected, y_true[0]=0
loss1= tf.keras.backend.switch(
tf.keras.backend.equal(y_true[0],0.0), loss_wo_obj, 1e9)
#case 2 when object is detected, y_true[0]=1
loss2=tf.keras.backend.switch(
tf.keras.backend.equal(y_true[0],1.0), loss_w_obj , 1e9)
return tf.math.minimum(loss1, loss2)
#compile and fit the model
input_dummy = Input(shape=[1,], name='input')
X_dummy_2=tf.keras.layers.Dense(units=2)(input_dummy)
model = tf.keras.models.Model(input_dummy, X_dummy_2)
model.compile(optimizer='adam', loss=loss_loc)
model.fit(X_dummy, Y_dummy, epochs=1, verbose=1)
```

model summary:

```
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input (InputLayer) [(None, 1)] 0
dense_6 (Dense) (None, 2) 4
=================================================================
Total params: 4
Trainable params: 4
Non-trainable params: 0
_________________________________________________________________
```

The loss function works if I just pass arrays into it (e.g., loss_loc(Y_dummy[0],Y_dummy[0])) but once I start training the model, I get the following error:

```
ValueError: in user code:
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/keras/engine/training.py", line 1160, in train_function *
return step_function(self, iterator)
File "<ipython-input-2-da8c334ac548>", line 18, in loss_loc *
loss2=tf.keras.backend.switch(
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/keras/backend.py", line 5223, in switch
raise ValueError(
ValueError: Rank of `condition` should be less than or equal to rank of `then_expression` and `else_expression`. ndim(condition)=1, ndim(then_expression)=0
```

Seems like a shape issue, but I couldn’t figure out what tensorflow is doing during batch processing. Any help is appreciated. Thanks!