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ShowAveragingCost of nn/part3
This commit is contained in:
Grant Sanderson
parent
8a5d63917f
commit
20318cb9d2
1 changed files with 180 additions and 12 deletions
192
nn/part3.py
192
nn/part3.py
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@ -114,8 +114,10 @@ class InterpretGradientComponents(GradientNudging):
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cost = self.cost
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for x in range(self.n_steps):
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self.move_grad_terms_into_position(grad_terms.copy())
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self.play(*self.get_weight_adjustment_anims(edges, cost))
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self.move_grad_terms_into_position(
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grad_terms.copy(),
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*self.get_weight_adjustment_anims(edges, cost)
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)
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self.play(*self.get_decimal_change_anims(decimals))
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def ask_about_high_dimensions(self):
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@ -149,7 +151,7 @@ class InterpretGradientComponents(GradientNudging):
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def circle_magnitudes(self):
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rects = VGroup()
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for decimal in self.grad_vect.decimals:
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rects.add(SurroundingRectangle(VGroup(*decimal[-4:])))
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rects.add(SurroundingRectangle(VGroup(*decimal[-5:])))
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rects.highlight(WHITE)
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self.play(LaggedStart(ShowCreation, rects))
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@ -239,6 +241,9 @@ class InterpretGradientComponents(GradientNudging):
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term.dot.save_state()
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term.dot.move_to(term)
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term.dot.set_fill(opacity = 0)
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term.words = TextMobject("Nudge this weight")
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term.words.scale(0.7)
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term.words.next_to(term.number_line, UP, MED_SMALL_BUFF)
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groups = [
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VGroup(d, d.arrow, edge, w)
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@ -265,6 +270,7 @@ class InterpretGradientComponents(GradientNudging):
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GrowFromCenter(w.brace),
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ShowCreation(w.number_line),
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w.dot.restore,
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Write(w.words, run_time = 1),
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*added_anims
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)
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for x in range(2):
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@ -280,12 +286,14 @@ class InterpretGradientComponents(GradientNudging):
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run_time = 2,
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)
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self.dither()
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self.play(*map(FadeOut, [w.dot, w.brace, w.number_line]))
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self.play(*map(FadeOut, [
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w.dot, w.brace, w.number_line, w.words
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]))
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######
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def move_grad_terms_into_position(self, grad_terms):
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def move_grad_terms_into_position(self, grad_terms, *added_anims):
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cost_expression = self.cost_expression
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w_terms = self.cost_expression[1]
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points = VGroup(*[
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@ -316,7 +324,8 @@ class InterpretGradientComponents(GradientNudging):
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submobject_mode = "lagged_start",
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run_time = 1
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),
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FadeOut(words)
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FadeOut(words),
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*added_anims
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)
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def get_weight_adjustment_anims(self, edges, cost):
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@ -406,17 +415,176 @@ class GetLostInNotation(PiCreatureScene):
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)
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self.dither()
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class TODOInsertPreviewLearning(TODOStub):
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CONFIG = {
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"message" : "Insert PreviewLearning"
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}
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class ShowAveragingCost(PreviewLearning):
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CONFIG = {
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"network_scale_val" : 0.8,
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"stroke_width_exp" : 1,
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"start_examples_time" : 5,
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"examples_per_adjustment_time" : 2,
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"n_adjustments" : 5,
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"time_per_example" : 1./15,
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"image_height" : 1.2,
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}
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def construct(self):
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self.setup_network()
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self.setup_diff_words()
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self.show_many_examples()
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def setup_network(self):
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self.network_mob.scale(self.network_scale_val)
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self.network_mob.to_edge(DOWN)
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self.network_mob.shift(LEFT)
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self.color_network_edges()
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def setup_diff_words(self):
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last_layer_copy = self.network_mob.layers[-1].deepcopy()
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last_layer_copy.add(self.network_mob.output_labels.copy())
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last_layer_copy.shift(1.5*RIGHT)
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double_arrow = DoubleArrow(
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self.network_mob.output_labels,
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last_layer_copy,
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color = RED
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)
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brace = Brace(
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VGroup(self.network_mob.layers[-1], last_layer_copy),
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UP
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)
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cost_words = brace.get_text("Cost of \\\\ one example")
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cost_words.highlight(RED)
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self.add(last_layer_copy, double_arrow, brace, cost_words)
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self.set_variables_as_attrs(
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last_layer_copy, double_arrow, brace, cost_words
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)
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self.last_layer_copy = last_layer_copy
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def show_many_examples(self):
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training_data, validation_data, test_data = load_data_wrapper()
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training_data_iter = iter(training_data)
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average_words = TextMobject("Average over all training examples")
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average_words.next_to(LEFT, RIGHT)
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average_words.to_edge(UP)
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self.add(average_words)
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for x in xrange(int(self.start_examples_time/self.time_per_example)):
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train_in, train_out = training_data_iter.next()
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self.show_one_example(train_in, train_out)
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self.dither(self.time_per_example)
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#Wiggle all edges
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edges = VGroup(*it.chain(*self.network_mob.edge_groups))
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reversed_edges = VGroup(*reversed(edges))
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self.play(LaggedStart(
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ApplyFunction, edges,
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lambda edge : (
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lambda m : m.rotate_in_place(np.pi/12).highlight(YELLOW),
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edge,
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),
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rate_func = lambda t : wiggle(t, 4),
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run_time = 3,
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))
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#Show all, then adjust
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words = TextMobject(
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"Each step \\\\ uses every \\\\ example\\\\",
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"$\\dots$theoretically",
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alignment = ""
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)
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words.highlight(YELLOW)
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words.scale(0.8)
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words.to_corner(UP+LEFT)
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for x in xrange(self.n_adjustments):
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for y in xrange(int(self.examples_per_adjustment_time/self.time_per_example)):
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train_in, train_out = training_data_iter.next()
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self.show_one_example(train_in, train_out)
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self.dither(self.time_per_example)
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self.play(LaggedStart(
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ApplyMethod, reversed_edges,
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lambda m : (m.rotate_in_place, np.pi),
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run_time = 1,
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lag_ratio = 0.2,
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))
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if x < 2:
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self.play(FadeIn(words[x]))
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else:
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self.dither()
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####
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def show_one_example(self, train_in, train_out):
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if hasattr(self, "curr_image"):
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self.remove(self.curr_image)
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image = MNistMobject(train_in)
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image.scale_to_fit_height(self.image_height)
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image.next_to(
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self.network_mob.layers[0].neurons, UP,
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aligned_edge = LEFT
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)
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self.add(image)
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self.network_mob.activate_layers(train_in)
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index = np.argmax(train_out)
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self.last_layer_copy.neurons.set_fill(opacity = 0)
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self.last_layer_copy.neurons[index].set_fill(WHITE, opacity = 1)
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self.add(self.last_layer_copy)
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self.curr_iamge = image
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class WalkThroughTwoExample(ShowAveragingCost):
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def construct(self):
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self.force_skipping()
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self.setup_network()
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self.setup_diff_words()
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self.show_single_example()
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self.expand_last_layer()
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self.cannot_directly_affect_activations()
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self.show_desired_activation_nudges()
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self.focus_on_one_neuron()
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self.show_activation_formula()
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self.three_ways_to_increase()
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self.note_connections_to_brightest_neurons()
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def show_single_example(self):
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two_vect = get_organized_images()[2][0]
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two_out = np.zeroes(10)
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two_out[2] = 1.0
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self.show_one_example(two_vect, two_out)
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for layer in self.network_mob.layers:
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layer.neurons.set_fill(opacity = 0)
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self.revert_to_original_skipping_status()
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self.feed_forward(two_vect)
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def expand_last_layer(self):
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pass
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def cannot_directly_affect_activations(self):
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pass
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def show_desired_activation_nudges(self):
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pass
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def focus_on_one_neuron(self):
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pass
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def show_activation_formula(self):
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pass
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def three_ways_to_increase(self):
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pass
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def note_connections_to_brightest_neurons(self):
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pass
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