From a51a6ab4890e50f00a076ce53e548d149c52732c Mon Sep 17 00:00:00 2001 From: Grant Sanderson Date: Thu, 5 Dec 2019 12:41:00 -0800 Subject: [PATCH] Reorganized files for 3b1b videos into a 3b1b_projects folder --- .../active}/diffyq/all_part1_scenes.py | 0 .../active}/diffyq/all_part2_scenes.py | 0 .../active}/diffyq/all_part3_scenes.py | 0 .../active}/diffyq/all_part4_scenes.py | 0 .../active}/diffyq/all_part5_scenes.py | 0 .../active}/diffyq/fourier_montage_scenes.py | 0 .../active}/diffyq/part1/pendulum.py | 0 .../active}/diffyq/part1/phase_space.py | 0 .../active}/diffyq/part1/pi_scenes.py | 0 .../active}/diffyq/part1/shared_constructs.py | 0 .../active}/diffyq/part1/staging.py | 0 .../active}/diffyq/part1/wordy_scenes.py | 0 .../active}/diffyq/part2/fourier_series.py | 0 .../active}/diffyq/part2/heat_equation.py | 0 .../active}/diffyq/part2/pi_scenes.py | 0 .../active}/diffyq/part2/shared_constructs.py | 0 .../active}/diffyq/part2/staging.py | 0 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from active_projects/eop/reusables/sick_pi_creature.py rename to 3b1b_projects/on_hold/eop/reusables/sick_pi_creature.py diff --git a/active_projects/eop/reusables/upright_coins.py b/3b1b_projects/on_hold/eop/reusables/upright_coins.py similarity index 100% rename from active_projects/eop/reusables/upright_coins.py rename to 3b1b_projects/on_hold/eop/reusables/upright_coins.py diff --git a/active_projects/eop/what_does_probability_mean.py b/3b1b_projects/on_hold/eop/what_does_probability_mean.py similarity index 100% rename from active_projects/eop/what_does_probability_mean.py rename to 3b1b_projects/on_hold/eop/what_does_probability_mean.py diff --git a/active_projects/holomorphic.py b/3b1b_projects/on_hold/holomorphic.py similarity index 100% rename from active_projects/holomorphic.py rename to 3b1b_projects/on_hold/holomorphic.py diff --git a/active_projects/moduli.py b/3b1b_projects/on_hold/moduli.py similarity index 100% rename from active_projects/moduli.py rename to 3b1b_projects/on_hold/moduli.py diff --git a/active_projects/shadows.py b/3b1b_projects/on_hold/shadows.py similarity index 100% rename from active_projects/shadows.py rename to 3b1b_projects/on_hold/shadows.py diff --git a/LICENSE b/LICENSE index 9a09471e..cfe5a25d 100644 --- a/LICENSE +++ b/LICENSE @@ -1,5 +1,4 @@ -All files of this project under the directories active_projects and old_projects -are copyright 3Blue1Brown LLC and used by permission for this project only. +All files of this project under the director "projects" are copyright 3Blue1Brown LLC and used by permission for this project only. Any other file of this project is available under the MIT license as follow: diff --git a/old_projects/nn/playground.py b/old_projects/nn/playground.py deleted file mode 100644 index b10066d4..00000000 --- a/old_projects/nn/playground.py +++ /dev/null @@ -1,178 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -import sys -import os.path -from functools import reduce - -sys.path.append(os.path.join(os.path.dirname(__file__), '..')) -from constants import * - -from manimlib.imports import * - -from nn.network import * -from nn.part1 import * - - -class Test(Scene): - def construct(self): - network = get_pretrained_network() - training_data, validation_data, test_data = load_data_wrapper() - self.show_weight_rows(network, index = 0) - # self.show_maximizing_inputs(network) - # self.show_all_activation_images(network, test_data) - - # group = Group() - # for k in range(10): - # v = np.zeros((10, 1)) - # v[k] = 1 - # h_group = Group() - # for W, b in reversed(zip(network.weights, network.biases)): - # h_group.add(MNistMobject(v)) - # v = np.dot(W.T, sigmoid_inverse(v) - b) - # v = sigmoid(v) - # h_group.add(MNistMobject(v)) - # h_group.arrange(LEFT) - # group.add(h_group) - # group.arrange(DOWN) - # group.set_height(FRAME_HEIGHT - 1) - # self.add(group) - - - def show_random_results(self): - group = Group(*[ - Group(*[ - MNistMobject(a) - for a in network.get_activation_of_all_layers( - np.random.randn(784, 1) - ) - ]).arrange(RIGHT) - for x in range(10) - ]).arrange(DOWN) - group.set_height(FRAME_HEIGHT - 1) - self.add(group) - - def show_weight_rows(self, network, index): - group = VGroup() - for row in network.weights[index]: - mob = PixelsFromVect(np.zeros(row.size)) - for n, pixel in zip(row, mob): - color = GREEN if n > 0 else RED - opacity = 2*(sigmoid(abs(n)) - 0.5) - pixel.set_fill(color, opacity = opacity) - group.add(mob) - group.arrange_in_grid() - group.set_height(FRAME_HEIGHT - 1) - self.add(group) - - def show_all_activation_images(self, network, test_data): - image_samples = Group(*[ - self.get_activation_images(digit, network, test_data) - for digit in range(10) - ]) - image_samples.arrange_in_grid( - n_rows = 2, buff = LARGE_BUFF - ) - image_samples.set_height(FRAME_HEIGHT - 1) - self.add(image_samples) - - def get_activation_images(self, digit, network, test_data, n_examples = 8): - input_vectors = [ - data[0] - for data in test_data - if data[1] == digit - ] - activation_iamges = Group(*[ - Group(*[ - MNistMobject(a) - for a in network.get_activation_of_all_layers(vect) - ]).arrange(RIGHT) - for vect in input_vectors[:n_examples] - ]).arrange(DOWN) - activation_iamges.set_height(FRAME_HEIGHT - 1) - return activation_iamges - - def show_two_blend(self): - training_data, validation_data, test_data = load_data_wrapper() - vects = [ - data[0] - for data in training_data[:30] - if np.argmax(data[1]) == 2 - ] - mean_vect = reduce(op.add, vects)/len(vects) - self.add(MNistMobject(mean_vect)) - - def show_maximizing_inputs(self, network): - training_data, validation_data, test_data = load_data_wrapper() - layer = 1 - n_neurons = DEFAULT_LAYER_SIZES[layer] - groups = Group() - for k in range(n_neurons): - out = np.zeros(n_neurons) - out[k] = 1 - in_vect = maximizing_input(network, layer, out) - new_out = network.get_activation_of_all_layers(in_vect)[layer] - group = Group(*list(map(MNistMobject, [in_vect, new_out]))) - group.arrange(DOWN+RIGHT, SMALL_BUFF) - groups.add(group) - groups.arrange_in_grid() - groups.set_height(FRAME_HEIGHT - 1) - self.add(groups) - - def show_test_input(self, network): - training_data, validation_data, test_data = load_data_wrapper() - group = Group(*[ - self.get_set(network, test) - for test in test_data[3:20] - if test[1] in [4, 9] - ]) - group.arrange(DOWN, buff = MED_LARGE_BUFF) - group.set_height(FRAME_HEIGHT - 1) - self.play(FadeIn(group)) - - def get_set(self, network, test): - test_in, test_out = test - activations = network.get_activation_of_all_layers(test_in) - group = Group(*list(map(MNistMobject, activations))) - group.arrange(RIGHT, buff = LARGE_BUFF) - return group - - # def show_frame(self): - # pass - - -if __name__ == "__main__": - save_pretrained_network() - test_network() - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -