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Bug fixes and linting for the activation functions addition.

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This commit is contained in:
Alec Helbling
2023-01-25 08:40:32 -05:00
parent ce184af78e
commit f56620f047
42 changed files with 1275 additions and 387 deletions
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56
manim_ml/neural_network/layers/max_pooling_2d.py
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@ -1,27 +1,31 @@
from manim import *
from manim_ml.gridded_rectangle import GriddedRectangle
from manim_ml.neural_network.layers.parent_layers import ThreeDLayer, VGroupNeuralNetworkLayer
from manim_ml.neural_network.layers.parent_layers import (
ThreeDLayer,
VGroupNeuralNetworkLayer,
)
class MaxPooling2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
"""Max pooling layer for Convolutional2DLayer
Note: This is for a Convolutional2DLayer even though
it is called MaxPooling2DLayer because the 2D corresponds
to the 2 spatial dimensions of the convolution.
to the 2 spatial dimensions of the convolution.
"""
def __init__(
self,
kernel_size=2,
stride=1,
cell_highlight_color=ORANGE,
cell_width=0.2,
filter_spacing=0.1,
color=BLUE,
show_grid_lines=False,
stroke_width=2.0,
**kwargs
self,
kernel_size=2,
stride=1,
cell_highlight_color=ORANGE,
cell_width=0.2,
filter_spacing=0.1,
color=BLUE,
show_grid_lines=False,
stroke_width=2.0,
**kwargs
):
"""Layer object for animating 2D Convolution Max Pooling
@ -42,11 +46,15 @@ class MaxPooling2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
self.show_grid_lines = show_grid_lines
self.stroke_width = stroke_width
def construct_layer(self, input_layer: 'NeuralNetworkLayer', output_layer: 'NeuralNetworkLayer', **kwargs):
def construct_layer(
self,
input_layer: "NeuralNetworkLayer",
output_layer: "NeuralNetworkLayer",
**kwargs
):
# Make the output feature maps
self.feature_maps = self._make_output_feature_maps(
input_layer.num_feature_maps,
input_layer.feature_map_size
input_layer.num_feature_maps, input_layer.feature_map_size
)
self.add(self.feature_maps)
self.rotate(
@ -58,17 +66,13 @@ class MaxPooling2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
input_layer.feature_map_size[0] / self.kernel_size,
input_layer.feature_map_size[1] / self.kernel_size,
)
def _make_output_feature_maps(
self,
num_input_feature_maps,
input_feature_map_size
):
def _make_output_feature_maps(self, num_input_feature_maps, input_feature_map_size):
"""Makes a set of output feature maps"""
# Compute the size of the feature maps
# Compute the size of the feature maps
output_feature_map_size = (
input_feature_map_size[0] / self.kernel_size,
input_feature_map_size[1] / self.kernel_size
input_feature_map_size[1] / self.kernel_size,
)
# Draw rectangles that are filled in with opacity
feature_maps = []
@ -92,12 +96,10 @@ class MaxPooling2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
# rectangle.set_z_index(4)
feature_maps.append(rectangle)
return VGroup(
*feature_maps
)
return VGroup(*feature_maps)
def make_forward_pass_animation(self, layer_args={}, **kwargs):
"""Makes forward pass of Max Pooling Layer.
"""Makes forward pass of Max Pooling Layer.
Parameters
----------