Finalized lens work. Started initial unit tests.

examples/tensorflow/denoising_autoencoder.py

@@ -0,0 +1,41 @@
+from displayarray import display
+import numpy as np
+from tensorflow.keras import layers, models
+import tensorflow as tf
+from examples.videos import test_video_2
+
+for gpu in tf.config.experimental.list_physical_devices("GPU"):
+    tf.compat.v2.config.experimental.set_memory_growth(gpu, True)
+
+displayer = display(test_video_2)
+displayer.wait_for_init()
+autoencoder = models.Sequential()
+autoencoder.add(
+    layers.Conv2D(
+        20, (3, 3), activation="sigmoid", input_shape=displayer.frames[0].shape
+    )
+)
+autoencoder.add(
+    layers.Conv2D(
+        20, (3, 3), activation="sigmoid", input_shape=displayer.frames[0].shape
+    )
+)
+autoencoder.add(layers.Conv2DTranspose(3, (3, 3), activation="sigmoid"))
+autoencoder.add(layers.Conv2DTranspose(3, (3, 3), activation="sigmoid"))
+
+autoencoder.compile(loss="mse", optimizer="adam")
+
+while displayer:
+    displayer.update()
+    grab = tf.convert_to_tensor(
+        next(iter(displayer.FRAME_DICT.values()))[np.newaxis, ...].astype(np.float32) / 255.0
+    )
+    grab_noise = tf.convert_to_tensor(
+        ((next(iter(displayer.FRAME_DICT.values()))[np.newaxis, ...].astype(
+            np.float32) + np.random.uniform(0, 255, grab.shape)) / 2)
+        / 255.0
+    )
+    displayer.update((grab_noise.numpy()[0] * 255.0).astype(np.uint8), "uid for grab noise")
+    autoencoder.fit(grab_noise, grab, steps_per_epoch=1, epochs=1)
+    output_image = autoencoder.predict(grab, steps=1)
+    displayer.update((output_image[0] * 255.0).astype(np.uint8), "uid for autoencoder output")