Updated README + added tests to CI

.github/workflows/tests.yml

@@ -82,3 +82,6 @@ jobs:
 
       - name: Test library accessibility
         run: python -c "from t_loss import TLoss"
+
+      - name: Run tests
+        run: pytest -v tests/

README.md

@@ -18,15 +18,30 @@ pip install git+https://github.com/andreped/t-loss-tf.git
 ## Usage
 As the t-loss contains a trainable parameter, in keras the loss needed to be implemented as a custom layer.
 Hence, instead of setting the loss as normally through `model.compile(loss=[...])`, just add it to the model
-at appropriate place.
+at appropriate place. An example can be seen below:
 
 ```python
 import tensorflow as tf
 from t_loss import TLoss
 
-model = tf.keras.Sequential()
-model.add(TLoss(image_size=512))
-[...]
+input_shape = (16, 16, 1)
+# create dummy inputs and GTs
+x = tf.ones((32,) + input_shape, dtype="float32")
+y = tf.ones((32,) + input_shape, dtype="float32")
+
+input_x = tf.keras.Input(shape=input_shape)
+input_y = tf.keras.Input(shape=input_shape)
+
+z = tf.keras.layers.Conv2D(filters=4, kernel_size=(1, 1), activation="relu")(input_x)
+z = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(z)
+z = tf.keras.layers.UpSampling2D(size=(2, 2))(z)
+z = tf.keras.layers.Conv2D(filters=1, kernel_size=(1, 1), activation="sigmoid")(z)
+z = TLoss(tensor_shape=input_shape, image_size=input_shape[0])(z, input_y)
+model = tf.keras.Model(inputs=[input_x, input_y], outputs=[z])
+print(model.summary())
+
+model.compile(optimizer="adam")
+model.fit(x=[x, y], y=y, batch_size=2, epochs=1, verbose="auto")
 ```
 
 ## License