Convolutional Layers

manim_ml/neural_network/neural_network.py

@@ -9,9 +9,7 @@ Example:
     # Create the object with default style settings
     NeuralNetwork(layer_node_count)
 """
-from cv2 import AGAST_FEATURE_DETECTOR_NONMAX_SUPPRESSION
 from manim import *
-import warnings
 import textwrap
 from manim_ml.neural_network.layers.embedding import EmbeddingLayer
 
@@ -104,6 +102,7 @@ class NeuralNetwork(Group):
 
     def replace_layer(self, old_layer, new_layer):
         """Replaces given layer object"""
+        raise NotImplementedError()
         remove_animation = self.remove_layer(insert_index)
         insert_animation = self.insert_layer(layer, insert_index)
         # Make the animation
@@ -119,6 +118,7 @@ class NeuralNetwork(Group):
                                     **kwargs):
         """Generates an animation for feed forward propagation"""
         all_animations = []
+        per_layer_runtime = run_time/len(self.all_layers)
         for layer_index, layer in enumerate(self.all_layers):
             # Get the layer args
             if isinstance(layer, ConnectiveLayer):
@@ -139,10 +139,18 @@ class NeuralNetwork(Group):
                 if layer in layer_args:
                     current_layer_args = layer_args[layer]
             # Perform the forward pass of the current layer
-            layer_forward_pass = layer.make_forward_pass_animation(layer_args=current_layer_args, **kwargs)
+            layer_forward_pass = layer.make_forward_pass_animation(
+                layer_args=current_layer_args, 
+                run_time=per_layer_runtime, 
+                **kwargs
+            )
             all_animations.append(layer_forward_pass)
         # Make the animation group
-        animation_group = AnimationGroup(*all_animations, run_time=run_time, lag_ratio=1.0)
+        animation_group = Succession(
+            *all_animations, 
+            run_time=run_time, 
+            lag_ratio=1.0
+        )
 
         return animation_group
 
@@ -176,7 +184,15 @@ class NeuralNetwork(Group):
     def set_z_index(self, z_index_value: float, family=False):
         """Overriden set_z_index"""
         # Setting family=False stops sub-neural networks from inheriting parent z_index
-        return super().set_z_index(z_index_value, family=False)
+        for layer in self.all_layers:
+            if not isinstance(NeuralNetwork):
+                layer.set_z_index(z_index_value)
+
+    def scale(self, scale_factor, **kwargs):
+        """Overriden scale"""
+        for layer in self.all_layers:
+            layer.scale(scale_factor, **kwargs)
+        # super().scale(scale_factor)
 
     def __repr__(self, metadata=["z_index", "title_text"]):
         """Print string representation of layers"""