Finshed adding construct_layer methods for refactor.

manim_ml/neural_network/layers/convolutional_2d_to_convolutional_2d.py

@@ -286,6 +286,9 @@ class Convolutional2DToConvolutional2D(ConnectiveLayer, ThreeDLayer):
         self.show_grid_lines = show_grid_lines
         self.highlight_color = highlight_color
 
+    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+        return super().construct_layer(input_layer, output_layer, **kwargs)
+
     def get_rotated_shift_vectors(self):
         """
         Rotates the shift vectors

manim_ml/neural_network/layers/convolutional_2d_to_feed_forward.py

@@ -26,6 +26,9 @@ class Convolutional2DToFeedForward(ConnectiveLayer, ThreeDLayer):
         )
         self.passing_flash_color = passing_flash_color
 
+    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+        return super().construct_layer(input_layer, output_layer, **kwargs)
+
     def make_forward_pass_animation(self, layer_args={}, run_time=1.5, **kwargs):
         """Forward pass animation from conv2d to conv2d"""
         animations = []

manim_ml/neural_network/layers/embedding.py

@@ -16,11 +16,20 @@ class EmbeddingLayer(VGroupNeuralNetworkLayer):
         **kwargs
     ):
         super(VGroupNeuralNetworkLayer, self).__init__(**kwargs)
+        self.mean = mean
+        self.covariance = covariance
         self.gaussian_distributions = VGroup()
         self.add(self.gaussian_distributions)
         self.point_radius = point_radius
         self.dist_theme = dist_theme
         self.paired_query_mode = paired_query_mode
+
+    def construct_layer(
+        self, 
+        input_layer: 'NeuralNetworkLayer', 
+        output_layer: 'NeuralNetworkLayer', 
+        **kwargs
+    ):
         self.axes = Axes(
             tips=False,
             x_length=0.8,
@@ -33,11 +42,14 @@ class EmbeddingLayer(VGroupNeuralNetworkLayer):
         self.add(self.axes)
         self.axes.move_to(self.get_center())
         # Make point cloud
-        self.point_cloud = self.construct_gaussian_point_cloud(mean, covariance)
+        self.point_cloud = self.construct_gaussian_point_cloud(
+            self.mean, 
+            self.covariance
+        )
         self.add(self.point_cloud)
         # Make latent distribution
         self.latent_distribution = GaussianDistribution(
-            self.axes, mean=mean, cov=covariance
+            self.axes, mean=self.mean, cov=self.covariance
         ) # Use defaults
 
     def add_gaussian_distribution(self, gaussian_distribution):

manim_ml/neural_network/layers/embedding_to_feed_forward.py

@@ -30,6 +30,9 @@ class EmbeddingToFeedForward(ConnectiveLayer):
         self.animation_dot_color = animation_dot_color
         self.dot_radius = dot_radius
 
+    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+        return super().construct_layer(input_layer, output_layer, **kwargs)
+
     def make_forward_pass_animation(self, layer_args={}, run_time=1.5, **kwargs):
         """Makes dots diverge from the given location and move the decoder"""
         # Find point to converge on by sampling from gaussian distribution

manim_ml/neural_network/layers/feed_forward_to_embedding.py

@@ -30,6 +30,9 @@ class FeedForwardToEmbedding(ConnectiveLayer):
         self.animation_dot_color = animation_dot_color
         self.dot_radius = dot_radius
 
+    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+        return super().construct_layer(input_layer, output_layer, **kwargs)
+
     def make_forward_pass_animation(self, layer_args={}, run_time=1.5, **kwargs):
         """Makes dots converge on a specific location"""
         # Find point to converge on by sampling from gaussian distribution

manim_ml/neural_network/layers/feed_forward_to_feed_forward.py

@@ -5,7 +5,6 @@ from manim import *
 from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
 from manim_ml.neural_network.layers.parent_layers import ConnectiveLayer
 
-
 class FeedForwardToFeedForward(ConnectiveLayer):
     """Layer for connecting FeedForward layer to FeedForwardLayer"""
 
@@ -37,6 +36,7 @@ class FeedForwardToFeedForward(ConnectiveLayer):
         self.animation_dot_color = animation_dot_color
         self.edge_width = edge_width
 
+    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
         self.edges = self.construct_edges()
         self.add(self.edges)
 

manim_ml/neural_network/layers/feed_forward_to_image.py

@@ -31,6 +31,9 @@ class FeedForwardToImage(ConnectiveLayer):
         self.feed_forward_layer = input_layer
         self.image_layer = output_layer
 
+    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+        return super().construct_layer(input_layer, output_layer, **kwargs)
+
     def make_forward_pass_animation(self, layer_args={}, **kwargs):
         """Makes dots diverge from the given location and move to the feed forward nodes decoder"""
         animations = []

manim_ml/neural_network/layers/feed_forward_to_vector.py

@@ -31,6 +31,9 @@ class FeedForwardToVector(ConnectiveLayer):
         self.feed_forward_layer = input_layer
         self.vector_layer = output_layer
 
+    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+        return super().construct_layer(input_layer, output_layer, **kwargs)
+
     def make_forward_pass_animation(self, layer_args={}, **kwargs):
         """Makes dots diverge from the given location and move to the feed forward nodes decoder"""
         animations = []

manim_ml/neural_network/layers/image.py

@@ -10,6 +10,7 @@ class ImageLayer(NeuralNetworkLayer):
 
     def __init__(self, numpy_image, height=1.5, show_image_on_create=True, **kwargs):
         super().__init__(**kwargs)
+        self.image_height = height
         self.numpy_image = numpy_image
         self.show_image_on_create = show_image_on_create
 
@@ -26,7 +27,10 @@ class ImageLayer(NeuralNetworkLayer):
         if len(np.shape(self.numpy_image)) == 2:
             # Assumed Grayscale
             self.num_channels = 1
-            self.image_mobject = GrayscaleImageMobject(self.numpy_image, height=height)
+            self.image_mobject = GrayscaleImageMobject(
+                self.numpy_image, 
+                height=self.image_height
+            )
         elif len(np.shape(self.numpy_image)) == 3:
             # Assumed RGB
             self.num_channels = 3

manim_ml/neural_network/layers/image_to_convolutional_2d.py

@@ -23,6 +23,9 @@ class ImageToConvolutional2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
         self.input_layer = input_layer
         self.output_layer = output_layer
 
+    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+        return super().construct_layer(input_layer, output_layer, **kwargs)
+
     def make_forward_pass_animation(self, run_time=5, layer_args={}, **kwargs):
         """Maps image to convolutional layer"""
         # Transform the image from the input layer to the

manim_ml/neural_network/layers/image_to_feed_forward.py

@@ -31,6 +31,9 @@ class ImageToFeedForward(ConnectiveLayer):
         self.feed_forward_layer = output_layer
         self.image_layer = input_layer
 
+    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+        return super().construct_layer(input_layer, output_layer, **kwargs)
+
     def make_forward_pass_animation(self, layer_args={}, **kwargs):
         """Makes dots diverge from the given location and move to the feed forward nodes decoder"""
         animations = []

manim_ml/neural_network/layers/max_pooling_2d.py

@@ -26,14 +26,12 @@ class MaxPooling2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
         self.kernel_size = kernel_size
         self.stride = stride
         self.cell_highlight_color = cell_highlight_color
+
+    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
         # Make the output feature maps
         feature_maps = self._make_output_feature_maps()
         self.add(feature_maps)
     
-    def construct_layer(self, input_layer, output_layer):
-        """Constructs the layer in the context of adjacent layers"""
-        pass
-    
     def _make_output_feature_maps(self):
         """Makes a set of output feature maps"""
         # Compute the size of the feature maps 

manim_ml/neural_network/layers/paired_query.py

@@ -22,6 +22,9 @@ class PairedQueryLayer(NeuralNetworkLayer):
         self.add(self.assets)
         self.add(self.title)
 
+    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+        return super().construct_layer(input_layer, output_layer, **kwargs)
+
     @classmethod
     def from_paths(cls, positive_path, negative_path, grayscale=True, **kwargs):
         """Creates a query using the paths"""

manim_ml/neural_network/layers/paired_query_to_feed_forward.py

@@ -31,6 +31,9 @@ class PairedQueryToFeedForward(ConnectiveLayer):
         self.paired_query_layer = input_layer
         self.feed_forward_layer = output_layer
 
+    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+        return super().construct_layer(input_layer, output_layer, **kwargs)
+
     def make_forward_pass_animation(self, layer_args={}, **kwargs):
         """Makes dots diverge from the given location and move to the feed forward nodes decoder"""
         animations = []

manim_ml/neural_network/layers/triplet.py

@@ -25,6 +25,8 @@ class TripletLayer(NeuralNetworkLayer):
 
         self.stroke_width = stroke_width
         self.font_size = font_size
+
+    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
         # Make the assets
         self.assets = self.make_assets()
         self.add(self.assets)

manim_ml/neural_network/layers/triplet_to_feed_forward.py

@@ -31,6 +31,9 @@ class TripletToFeedForward(ConnectiveLayer):
         self.feed_forward_layer = output_layer
         self.triplet_layer = input_layer
 
+    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
+        return super().construct_layer(input_layer, output_layer, **kwargs)
+
     def make_forward_pass_animation(self, layer_args={}, **kwargs):
         """Makes dots diverge from the given location and move to the feed forward nodes decoder"""
         animations = []

manim_ml/neural_network/layers/util.py

@@ -4,7 +4,6 @@ from manim import *
 from manim_ml.neural_network.layers.parent_layers import BlankConnective, ThreeDLayer
 from ..layers import connective_layers_list
 
-
 def get_connective_layer(input_layer, output_layer):
     """
     Deduces the relevant connective layer
@@ -24,7 +23,6 @@ def get_connective_layer(input_layer, output_layer):
         warnings.warn(
             f"Unrecognized input/output class pair: {input_class} and {output_class}"
         )
-
     # Make the instance now
     connective_layer = connective_layer_class(input_layer, output_layer)
 

manim_ml/neural_network/layers/vector.py

@@ -11,6 +11,8 @@ class VectorLayer(VGroupNeuralNetworkLayer):
         super().__init__(**kwargs)
         self.num_values = num_values
         self.value_func = value_func
+
+    def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
         # Make the vector
         self.vector_label = self.make_vector()
         self.add(self.vector_label)