Update readme and setup code to be pushed to pip repository.

Readme.md

@@ -60,6 +60,12 @@ class VariationalAutoencoderScene(Scene):
         self.play(neural_network.make_forward_pass_animation(run_time=15))
 ```
 
+### Convolutional Neural Network
+
+This is a visualization of a Convolutional Neural Network.
+
+<img src="examples/media/CNNScene.gif">
+
 ### Generative Adversarial Network
 
 This is a visualization of a Generative Adversarial Network made using ManimML. 

examples/cnn/cnn.py

@@ -3,40 +3,26 @@
 from manim import *
 from PIL import Image
 
-<<<<<<< HEAD
 from manim_ml.neural_network.layers.convolutional3d import Convolutional3DLayer
-=======
-from manim_ml.neural_network.layers import Convolutional3DLayer
->>>>>>> 0bc3ad561ba224f3d33e9f843665c1d50d64a68b
 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
 
-<<<<<<< HEAD
 # Make the specific scene
 config.pixel_height = 1200
 config.pixel_width = 1900
 config.frame_height = 7.0
 config.frame_width = 7.0
-=======
 ROOT_DIR = Path(__file__).parents[2]
->>>>>>> 0bc3ad561ba224f3d33e9f843665c1d50d64a68b
 
 def make_code_snippet():
     code_str = """
         # Make nn
         nn = NeuralNetwork([
-<<<<<<< HEAD
             ImageLayer(numpy_image, height=1.5),
             Convolutional3DLayer(1, 7, 7, 3, 3),
             Convolutional3DLayer(3, 5, 5, 3, 3),
             Convolutional3DLayer(5, 3, 3, 1, 1),
-=======
-            ImageLayer(numpy_image),
-            Convolutional3DLayer(3, 3, 3),
-            Convolutional3DLayer(5, 2, 2),
-            Convolutional3DLayer(10, 2, 1),
->>>>>>> 0bc3ad561ba224f3d33e9f843665c1d50d64a68b
             FeedForwardLayer(3),
             FeedForwardLayer(3),
         ])
@@ -64,7 +50,6 @@ def construct(self):
         numpy_image = np.asarray(image)
         # Make nn
         nn = NeuralNetwork([
-<<<<<<< HEAD
                 ImageLayer(numpy_image, height=1.5),
                 Convolutional3DLayer(1, 7, 7, 3, 3, filter_spacing=0.32),
                 Convolutional3DLayer(3, 5, 5, 3, 3, filter_spacing=0.32),
@@ -75,16 +60,6 @@ def construct(self):
             layer_spacing=0.25,
         )
         # Center the nn
-=======
-            ImageLayer(numpy_image, height=3.5),
-            Convolutional3DLayer(3, 3, 3, filter_spacing=0.2),
-            Convolutional3DLayer(5, 2, 2, filter_spacing=0.2),
-            Convolutional3DLayer(10, 2, 1, filter_spacing=0.2),
-            FeedForwardLayer(3, rectangle_stroke_width=4, node_stroke_width=4).scale(2),
-            FeedForwardLayer(1, rectangle_stroke_width=4, node_stroke_width=4).scale(2)
-        ], layer_spacing=0.2)
-        nn.scale(0.9)
->>>>>>> 0bc3ad561ba224f3d33e9f843665c1d50d64a68b
         nn.move_to(ORIGIN)
         self.add(nn)
         # Make code snippet

examples/code_snippet/image_nn_code_snippet.py

@@ -0,0 +1,85 @@
+from manim import *
+from manim_ml.neural_network.layers import FeedForwardLayer, ImageLayer
+from manim_ml.neural_network.neural_network import NeuralNetwork
+from PIL import Image
+import numpy as np
+
+config.pixel_height = 720
+config.pixel_width = 1280
+config.frame_height = 6.0
+config.frame_width = 6.0
+
+class ImageNeuralNetworkScene(Scene):
+
+    def make_code_snippet(self):
+        code_str = """
+            # Make image object
+            image = Image.open('images/image.jpeg')
+            numpy_image = np.asarray(image)
+            # Make Neural Network
+            layers = [
+                ImageLayer(numpy_image, height=1.4),
+                FeedForwardLayer(3), 
+                FeedForwardLayer(5),
+                FeedForwardLayer(3)
+            ]
+            nn = NeuralNetwork(layers)
+            self.add(nn)
+            # Play animation
+            self.play(
+                nn.make_forward_pass_animation()
+            )
+        """
+
+        code = Code(
+            code = code_str, 
+            tab_width=4,
+            background_stroke_width=1,
+            background_stroke_color=WHITE,
+            insert_line_no=False,
+            style='monokai',
+            #background="window",
+            language="py",
+        )
+        code.scale(0.2)
+
+        return code
+
+    def construct(self):
+        image = Image.open('../../tests/images/image.jpeg')
+        numpy_image = np.asarray(image)
+        # Make nn
+        layers = [
+            ImageLayer(numpy_image, height=1.4),
+            FeedForwardLayer(3), 
+            FeedForwardLayer(5),
+            FeedForwardLayer(3),
+            FeedForwardLayer(6)
+        ]
+        nn = NeuralNetwork(layers)
+        nn.scale(0.9)
+        # Center the nn
+        nn.move_to(ORIGIN)
+        nn.rotate(-PI/2)
+        nn.layers[0].image_mobject.rotate(PI/2)
+        nn.layers[0].image_mobject.shift([0, -0.4, 0])
+        nn.shift([1.5, 0.3, 0])
+        self.add(nn)
+        # Make code snippet
+        code_snippet = self.make_code_snippet()
+        code_snippet.scale(1.9)
+        code_snippet.shift([-1.25, 0, 0])
+        self.add(code_snippet)
+        # Play animation
+        self.play(
+            nn.make_forward_pass_animation(run_time=10)
+        )
+
+if __name__ == "__main__":
+    """Render all scenes"""
+    # Feed Forward Neural Network
+    ffnn_scene = FeedForwardNeuralNetworkScene()
+    ffnn_scene.render()
+    # Neural Network 
+    nn_scene = NeuralNetworkScene()
+    nn_scene.render()

examples/code_snippet/vae_code_landscape.py

@@ -0,0 +1,85 @@
+from manim import *
+from manim_ml.neural_network.layers import FeedForwardLayer, ImageLayer, EmbeddingLayer
+from manim_ml.neural_network.neural_network import NeuralNetwork
+from PIL import Image
+import numpy as np
+
+config.pixel_height = 720
+config.pixel_width = 720
+config.frame_height = 6.0
+config.frame_width = 6.0
+
+class VAECodeSnippetScene(Scene):
+
+    def make_code_snippet(self):
+        code_str = """
+            # Make Neural Network
+            nn = NeuralNetwork([
+                ImageLayer(numpy_image, height=1.2),
+                FeedForwardLayer(5),
+                FeedForwardLayer(3),
+                EmbeddingLayer(),
+                FeedForwardLayer(3),
+                FeedForwardLayer(5),
+                ImageLayer(numpy_image, height=1.2),
+            ], layer_spacing=0.1)
+            # Play animation
+            self.play(nn.make_forward_pass_animation())
+        """
+
+        code = Code(
+            code = code_str, 
+            tab_width=4,
+            background_stroke_width=1,
+            # background_stroke_color=WHITE,
+            insert_line_no=False,
+            background="window",
+            # font="Monospace",
+            style='one-dark',
+            language="py",
+        )
+
+        return code
+
+    def construct(self):
+        image = Image.open('../../tests/images/image.jpeg')
+        numpy_image = np.asarray(image)
+        embedding_layer = EmbeddingLayer(dist_theme="ellipse", point_radius=0.04).scale(1.0)
+        # Make nn
+        nn = NeuralNetwork([
+            ImageLayer(numpy_image, height=1.2),
+            FeedForwardLayer(5),
+            FeedForwardLayer(3),
+            embedding_layer,
+            FeedForwardLayer(3),
+            FeedForwardLayer(5),
+            ImageLayer(numpy_image, height=1.2),
+        ], layer_spacing=0.1)
+
+        nn.scale(1.1)
+        # Center the nn
+        nn.move_to(ORIGIN)
+        # nn.rotate(-PI/2)
+        # nn.all_layers[0].image_mobject.rotate(PI/2)
+        # nn.all_layers[0].image_mobject.shift([0, -0.4, 0])
+        # nn.all_layers[-1].image_mobject.rotate(PI/2)
+        # nn.all_layers[-1].image_mobject.shift([0, -0.4, 0])
+        nn.shift([0, -1.4, 0])
+        self.add(nn)
+        # Make code snippet
+        code_snippet = self.make_code_snippet()
+        code_snippet.scale(0.52)
+        code_snippet.shift([0, 1.25, 0])
+        # code_snippet.shift([-1.25, 0, 0])
+        self.add(code_snippet)
+        # Play animation
+        self.play(
+            nn.make_forward_pass_animation(),
+            run_time=10
+        )
+
+if __name__ == "__main__":
+    """Render all scenes"""
+    # Neural Network 
+    nn_scene = VAECodeSnippetScene()
+    nn_scene.render()

examples/code_snippet/vae_nn_code_snippet.py

@@ -0,0 +1,92 @@
+from manim import *
+from manim_ml.neural_network.layers import FeedForwardLayer, ImageLayer, EmbeddingLayer
+from manim_ml.neural_network.neural_network import NeuralNetwork
+from PIL import Image
+import numpy as np
+
+config.pixel_height = 720
+config.pixel_width = 1280
+config.frame_height = 6.0
+config.frame_width = 6.0
+
+class VAECodeSnippetScene(Scene):
+
+    def make_code_snippet(self):
+        code_str = """
+            # Make image object
+            image = Image.open('images/image.jpeg')
+            numpy_image = np.asarray(image)
+            # Make Neural Network
+            nn = NeuralNetwork([
+                ImageLayer(numpy_image, height=1.2),
+                FeedForwardLayer(5),
+                FeedForwardLayer(3),
+                EmbeddingLayer(),
+                FeedForwardLayer(3),
+                FeedForwardLayer(5),
+                ImageLayer(numpy_image, height=1.2),
+            ], layer_spacing=0.1)
+            self.add(nn)
+            # Play animation
+            self.play(
+                nn.make_forward_pass_animation()
+            )
+        """
+
+        code = Code(
+            code = code_str, 
+            tab_width=4,
+            background_stroke_width=1,
+            # background_stroke_color=WHITE,
+            insert_line_no=False,
+            background="window",
+            # font="Monospace",
+            style='one-dark',
+            language="py",
+        )
+        code.scale(0.2)
+
+        return code
+
+    def construct(self):
+        image = Image.open('../../tests/images/image.jpeg')
+        numpy_image = np.asarray(image)
+        embedding_layer = EmbeddingLayer(dist_theme="ellipse", point_radius=0.04).scale(1.0)
+        # Make nn
+        nn = NeuralNetwork([
+            ImageLayer(numpy_image, height=1.0),
+            FeedForwardLayer(5),
+            FeedForwardLayer(3),
+            embedding_layer,
+            FeedForwardLayer(3),
+            FeedForwardLayer(5),
+            ImageLayer(numpy_image, height=1.0),
+        ], layer_spacing=0.1)
+
+        nn.scale(0.65)
+        # Center the nn
+        nn.move_to(ORIGIN)
+        nn.rotate(-PI/2)
+        nn.all_layers[0].image_mobject.rotate(PI/2)
+        # nn.all_layers[0].image_mobject.shift([0, -0.4, 0])
+        nn.all_layers[-1].image_mobject.rotate(PI/2)
+        # nn.all_layers[-1].image_mobject.shift([0, -0.4, 0])
+        nn.shift([1.5, 0.0, 0])
+        self.add(nn)
+        # Make code snippet
+        code_snippet = self.make_code_snippet()
+        code_snippet.scale(1.9)
+        code_snippet.shift([-1.25, 0, 0])
+        self.add(code_snippet)
+        # Play animation
+        self.play(
+            nn.make_forward_pass_animation(),
+            run_time=10
+        )
+
+
+if __name__ == "__main__":
+    """Render all scenes"""
+    # Neural Network 
+    nn_scene = VAECodeSnippetScene()
+    nn_scene.render()

examples/logo/logo.py

@@ -0,0 +1,41 @@
+"""
+    Logo for Manim Machine Learning
+"""
+from manim import *
+from manim_ml.neural_network.neural_network import FeedForwardNeuralNetwork
+
+config.pixel_height = 500
+config.pixel_width = 500
+config.frame_height = 4.0
+config.frame_width = 4.0
+
+class ManimMLLogo(Scene):
+
+    def construct(self):
+        self.text = Text("ManimML")
+        self.text.scale(1.0)
+        self.neural_network = FeedForwardNeuralNetwork([3, 5, 3, 6, 3], layer_spacing=0.3, node_color=BLUE)
+        self.neural_network.scale(1.0)
+        self.neural_network.move_to(self.text.get_bottom())
+        self.neural_network.shift(1.25 * DOWN)
+        self.logo_group = Group(self.text, self.neural_network) 
+        self.logo_group.scale(1.0)
+        self.logo_group.move_to(ORIGIN)
+        self.play(Write(self.text))
+        self.play(Create(self.neural_network))
+        # self.surrounding_rectangle = SurroundingRectangle(self.logo_group, buff=0.3, color=BLUE)
+        underline = Underline(self.text, color=BLUE)
+        animation_group = AnimationGroup(
+            self.neural_network.make_forward_pass_animation(run_time=5),
+            Create(underline),
+        #    Create(self.surrounding_rectangle)
+        )
+        # self.surrounding_rectangle = SurroundingRectangle(self.logo_group, buff=0.3, color=BLUE)
+        underline = Underline(self.text, color=BLUE)
+        animation_group = AnimationGroup(
+            self.neural_network.make_forward_pass_animation(run_time=5),
+            Create(underline),
+        #    Create(self.surrounding_rectangle)
+        )
+        self.play(animation_group)
+        self.wait(5)