What is y_true and y_pred when creating a custom metric in Keras?
y_true and y_pred
The tensor y_true is the true data (or target, ground truth) you pass to the fit method.
It's a conversion of the numpy array y_train into a tensor.
The tensor y_pred is the data predicted (calculated, output) by your model.
Usually, both y_true and y_pred have exactly the same shape. A few of the losses, such as the sparse ones, may accept them with different shapes.
The shape of y_true
It contains an entire batch. Its first dimension is always the batch size, and it must exist, even if the batch has only one element.
Two very easy ways to find the shape of y_true are:
- check your true/target data:
print(Y_train.shape) - check your
model.summary()and see the last output
But its first dimension will be the batch size.
So, if your last layer outputs (None, 1), the shape of y_true is (batch, 1). If the last layer outputs (None, 200,200, 3), then y_true will be (batch, 200,200,3).
Custom metrics and loss functions
Unfotunately, printing custom metrics will not reveal their content (unless you are using eager mode on, and you have calculated every step of the model with data).
You can see their shapes with print(K.int_shape(y_pred)), for instance.
Remember that these libraries first "compile a graph", then later "runs it with data". When you define your loss, you're in the compile phase, and asking for data needs the model to run.
But even if the result of your metric is multidimensional, keras will automatically find ways to output a single scalar for that metric. (Not sure what is the operation, but very probably a K.mean() hidden under the table - it's interesting to return the entire batch, so Keras applies other operations such as sample weights, for instance).
Sources. After you get used to keras, this understanding gets natural from simply reading this part:
y_true: True labels. Theano/TensorFlow tensor.
y_pred: Predictions. Theano/TensorFlow tensor of the same shape as y_true.
True labels mean true/target data. Labels is a badly chosen word here, it is only really "labels" in classification models.
Predictions mean the results of your model.