



import numpy






class    SCREAMING_SNAKE_CASE			:





							def __init__(   self		:		Any      ,				__lowercase		:		numpy.ndarray      ,				__lowercase		:		numpy.ndarray	):

											'''simple docstring'''
											__a       =		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.
											__a       =		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.
											__a       =		numpy.random.rand(
											    4      ,				3	)

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

											# Real output values provided.
											__a       =		output_array

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





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											__a       =		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.
											__a       =		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.
											__a       =		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 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		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	)      ,				)
											__a       =		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	)      ,				)
											__a       =		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 						UpperCamelCase_					(   self		:		str      ,				__lowercase		:		numpy.ndarray      ,				__lowercase		:		int      ,				__lowercase		:		bool	):

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







							def 						UpperCamelCase_					(   self		:		str      ,				__lowercase		:		numpy.ndarray	):

											'''simple docstring'''
											__a       =		input_arr

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

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

											__a       =		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__						(							_SCREAMING_SNAKE_CASE       :			numpy.ndarray    ):
				"""simple docstring"""







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

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			numpy.ndarray    ):
				"""simple docstring"""







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

def   lowerCAmelCase__						(							):
				"""simple docstring"""







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

				# Calling neural network class.
				__a       =		TwoHiddenLayerNeuralNetwork(
				    input_array=_SCREAMING_SNAKE_CASE				,     output_array=_SCREAMING_SNAKE_CASE    )

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

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


if __name__ == "__main__":
						example()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


lowerCamelCase__			= {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= ["""SpeechEncoderDecoderModel"""]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= ["""FlaxSpeechEncoderDecoderModel"""]

if TYPE_CHECKING:
						from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
						import sys

						lowerCamelCase__			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
