General changes, got basic visualization of an activation function working for a

convolutinoal layer.

tests/test_activation_function.py

@@ -0,0 +1,38 @@
+from manim import *
+from PIL import Image
+import numpy as np
+
+from manim_ml.neural_network.layers.convolutional_2d import Convolutional2DLayer
+from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
+from manim_ml.neural_network.layers.image import ImageLayer
+from manim_ml.neural_network.neural_network import NeuralNetwork
+
+# Make the specific scene
+config.pixel_height = 1200
+config.pixel_width = 1900
+config.frame_height = 6.0
+config.frame_width = 6.0
+
+class CombinedScene(ThreeDScene):
+    def construct(self):
+        image = Image.open("../assets/mnist/digit.jpeg")
+        numpy_image = np.asarray(image)
+        # Make nn
+        nn = NeuralNetwork([
+                ImageLayer(numpy_image, height=1.5),
+                Convolutional2DLayer(1, 7, filter_spacing=0.32),
+                Convolutional2DLayer(3, 5, 3, filter_spacing=0.32, activation_function="ReLU"),
+                FeedForwardLayer(3),
+            ],
+            layer_spacing=0.25,
+        )
+        # Center the nn
+        nn.move_to(ORIGIN)
+        self.add(nn)
+        # Play animation
+        forward_pass = nn.make_forward_pass_animation(
+            corner_pulses=False, 
+            all_filters_at_once=False
+        )
+        self.wait(1)
+        self.play(forward_pass)