




from manim import *
class 		UpperCAmelCase   (				UpperCAmelCase__			):







						'''simple docstring'''





						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






							snake_case_		    =     Rectangle(height=0.5       ,	width=0.5						)
							snake_case_		    =     Rectangle(height=0.46       ,	width=0.46						).set_stroke(width=0						)

							snake_case_		    =     [mem.copy() for i in range(6						)]
							snake_case_		    =     [mem.copy() for i in range(6						)]
							snake_case_		    =     VGroup(*__lowercase						).arrange(__lowercase       ,	buff=0						)
							snake_case_		    =     VGroup(*__lowercase						).arrange(__lowercase       ,	buff=0						)
							snake_case_		    =     VGroup(__lowercase       ,	__lowercase						).arrange(__lowercase       ,	buff=0						)
							snake_case_		    =     Text("CPU"       ,	font_size=24						)
							snake_case_		    =     Group(__lowercase       ,	__lowercase						).arrange(__lowercase       ,	buff=0.5       ,	aligned_edge=__lowercase						)
							cpu.move_to([-2.5, -0.5, 0]						)
							self.add(__lowercase						)

							snake_case_		    =     [mem.copy() for i in range(1						)]
							snake_case_		    =     VGroup(*__lowercase						).arrange(__lowercase       ,	buff=0						)
							snake_case_		    =     Text("GPU"       ,	font_size=24						)
							snake_case_		    =     Group(__lowercase       ,	__lowercase						).arrange(__lowercase       ,	buff=0.5       ,	aligned_edge=__lowercase						)
							gpu.align_to(__lowercase       ,	__lowercase						)
							gpu.set_x(gpu.get_x() - 1						)

							self.add(__lowercase						)

							snake_case_		    =     [mem.copy() for i in range(6						)]
							snake_case_		    =     VGroup(*__lowercase						).arrange(__lowercase       ,	buff=0						)

							snake_case_		    =     Text("Model"       ,	font_size=24						)
							snake_case_		    =     Group(__lowercase       ,	__lowercase						).arrange(__lowercase       ,	buff=0.5       ,	aligned_edge=__lowercase						)
							model.move_to([3, -1.0, 0]						)

							self.play(
							    Create(__lowercase       ,	run_time=1						)       ,	Create(__lowercase       ,	run_time=1						)       ,	Create(__lowercase       ,	run_time=1						)       ,	)

							snake_case_		    =     MarkupText(
							    f"First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM."       ,	font_size=24       ,	)

							snake_case_		    =     Square(side_length=2.2						)
							key.move_to([-5, 2, 0]						)

							snake_case_		    =     MarkupText(
							    f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"       ,	font_size=18       ,	)

							key_text.move_to([-5, 2.4, 0]						)

							step_a.move_to([2, 2, 0]						)
							self.play(Write(__lowercase       ,	run_time=2.5						)       ,	Write(__lowercase						)       ,	Write(__lowercase						)						)

							self.add(__lowercase						)

							snake_case_		    =     []
							snake_case_		    =     []
							snake_case_		    =     []
							for i, rect in enumerate(__lowercase						):
								snake_case_		    =     Rectangle(height=0.46       ,	width=0.46						).set_stroke(width=0.0						).set_fill(__lowercase       ,	opacity=0.7						)
								cpu_target.move_to(__lowercase						)
								cpu_target.generate_target()
								snake_case_		    =     0.46 / 4
								snake_case_		    =     0.46 / 3

								if i == 0:
									cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT						)       ,	buff=0.02       ,	direction=__lowercase						)
									cpu_target.target.set_x(cpu_target.target.get_x() + 0.1						)
								elif i == 3:
									cpu_target.target.next_to(cpu_targs[0].target       ,	direction=__lowercase       ,	buff=0.0						)
								else:
									cpu_target.target.next_to(cpu_targs[i - 1].target       ,	direction=__lowercase       ,	buff=0.0						)
								cpu_targs.append(__lowercase						)

								first_animations.append(rect.animate(run_time=0.5						).set_stroke(__lowercase						)						)
								second_animations.append(MoveToTarget(__lowercase       ,	run_time=1.5						)						)

							self.play(*__lowercase						)
							self.play(*__lowercase						)

							self.wait()






import numpy
class 		UpperCAmelCase   :







						'''simple docstring'''





						def __init__(						self							:     Union[str, Any]       ,	__lowercase							:     numpy.ndarray       ,	__lowercase							:     numpy.ndarray						):






							"""simple docstring"""






							snake_case_		    =     input_array

							# Random initial weights are assigned where first argument is the
							# number of nodes in previous layer and second argument is the
							# number of nodes in the next layer.

							# Random initial weights are assigned.
							# self.input_array.shape[1] is used to represent number of nodes in input layer.
							# First hidden layer consists of 4 nodes.
							snake_case_		    =     numpy.random.rand(
							    self.input_array.shape[1]       ,	4						)

							# Random initial values for the first hidden layer.
							# First hidden layer has 4 nodes.
							# Second hidden layer has 3 nodes.
							snake_case_		    =     numpy.random.rand(
							    4       ,	3						)

							# Random initial values for the second hidden layer.
							# Second hidden layer has 3 nodes.
							# Output layer has 1 node.
							snake_case_		    =     numpy.random.rand(3       ,	1						)

							# Real output values provided.
							snake_case_		    =     output_array

							# Predicted output values by the neural network.
							# Predicted_output array initially consists of zeroes.
							snake_case_		    =     numpy.zeros(output_array.shape						)






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     sigmoid(
							    numpy.dot(self.input_array       ,	self.input_layer_and_first_hidden_layer_weights						)						)

							# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
							# connecting the first hidden set of nodes with the second hidden set of nodes.
							snake_case_		    =     sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer       ,	self.first_hidden_layer_and_second_hidden_layer_weights       ,	)						)

							# layer_between_second_hidden_layer_and_output is the layer connecting
							# second hidden layer with the output node.
							snake_case_		    =     sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer       ,	self.second_hidden_layer_and_output_layer_weights       ,	)						)

							return self.layer_between_second_hidden_layer_and_output






						def        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							snake_case_		    =     numpy.dot(
							    self.layer_between_first_hidden_layer_and_second_hidden_layer.T       ,	2
							    * (self.output_array - self.predicted_output)
							    * sigmoid_derivative(self.predicted_output						)       ,	)
							snake_case_		    =     numpy.dot(
							    self.layer_between_input_and_first_hidden_layer.T       ,	numpy.dot(
							        2
							        * (self.output_array - self.predicted_output)
							        * sigmoid_derivative(self.predicted_output						)       ,	self.second_hidden_layer_and_output_layer_weights.T       ,	)
							    * sigmoid_derivative(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer						)       ,	)
							snake_case_		    =     numpy.dot(
							    self.input_array.T       ,	numpy.dot(
							        numpy.dot(
							            2
							            * (self.output_array - self.predicted_output)
							            * sigmoid_derivative(self.predicted_output						)       ,	self.second_hidden_layer_and_output_layer_weights.T       ,	)
							        * sigmoid_derivative(
							            self.layer_between_first_hidden_layer_and_second_hidden_layer						)       ,	self.first_hidden_layer_and_second_hidden_layer_weights.T       ,	)
							    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer						)       ,	)

							self.input_layer_and_first_hidden_layer_weights += (
							    updated_input_layer_and_first_hidden_layer_weights
							)
							self.first_hidden_layer_and_second_hidden_layer_weights += (
							    updated_first_hidden_layer_and_second_hidden_layer_weights
							)
							self.second_hidden_layer_and_output_layer_weights += (
							    updated_second_hidden_layer_and_output_layer_weights
							)






						def        snake_case__						(						self							:     Optional[Any]       ,	__lowercase							:     numpy.ndarray       ,	__lowercase							:     int       ,	__lowercase							:     bool						):






							"""simple docstring"""






							for iteration in range(1       ,	iterations + 1						):
								snake_case_		    =     self.feedforward()
								self.back_propagation()
								if give_loss:
									snake_case_		    =     numpy.mean(numpy.square(output - self.feedforward()						)						)
									print(f"Iteration {iteration} Loss: {loss}"						)






						def        snake_case__						(						self							:     Union[str, Any]       ,	__lowercase							:     numpy.ndarray						):






							"""simple docstring"""






							snake_case_		    =     input_arr

							snake_case_		    =     sigmoid(
							    numpy.dot(self.array       ,	self.input_layer_and_first_hidden_layer_weights						)						)

							snake_case_		    =     sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer       ,	self.first_hidden_layer_and_second_hidden_layer_weights       ,	)						)

							snake_case_		    =     sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer       ,	self.second_hidden_layer_and_output_layer_weights       ,	)						)

							return int(self.layer_between_second_hidden_layer_and_output > 0.6						)







def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return 1 / (1 + numpy.exp(-value							))


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return (value) * (1 - (value))


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     numpy.array(
	    (
	        [0, 0, 0],
	        [0, 0, 1],
	        [0, 1, 0],
	        [0, 1, 1],
	        [1, 0, 0],
	        [1, 0, 1],
	        [1, 1, 0],
	        [1, 1, 1],
	    )     ,	dtype=numpy.floataa     ,	)

	# True output values for the given input values.
	snake_case_		    =     numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])     ,	dtype=numpy.floataa							)

	# Calling neural network class.
	snake_case_		    =     TwoHiddenLayerNeuralNetwork(
	    input_array=_A     ,	output_array=_A							)

	# Calling training function.
	# Set give_loss to True if you want to see loss in every iteration.
	neural_network.train(output=_A     ,	iterations=10     ,	give_loss=_A							)

	return neural_network.predict(numpy.array(([1, 1, 1])     ,	dtype=numpy.floataa							)							)


if __name__ == "__main__":
		example()
