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# network.py

# From Classic Computer Science Problems in Python Chapter 7

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

from __future__ import annotations

from typing import List, Callable, TypeVar, Tuple

from functools import reduce

from layer import Layer

from util import sigmoid, derivative_sigmoid

T = TypeVar('T') # output type of interpretation of neural network

class Network:

def __init__(self, layer_structure: List[int], learning_rate: float, activation_function: Callable[[float], float] = sigmoid, derivative_activation_function: Callable[[float], float] = derivative_sigmoid) -> None:

if len(layer_structure) < 3:

raise ValueError("Error: Should be at least 3 layers (1 input, 1 hidden, 1 output)")

self.layers: List[Layer] = []

# input layer

input_layer: Layer = Layer(None, layer_structure[0], learning_rate, activation_function, derivative_activation_function)

self.layers.append(input_layer)

# hidden layers and output layer

for previous, num_neurons in enumerate(layer_structure[1::]):

next_layer = Layer(self.layers[previous], num_neurons, learning_rate, activation_function, derivative_activation_function)

self.layers.append(next_layer)

# Pushes input data to the first layer, then output from the first

# as input to the second, second to the third, etc.

def outputs(self, input: List[float]) -> List[float]:

return reduce(lambda inputs, layer: layer.outputs(inputs), self.layers, input)

# Figure out each neuron's changes based on the errors of the output

# versus the expected outcome

def backpropagate(self, expected: List[float]) -> None:

# calculate delta for output layer neurons

last_layer: int = len(self.layers) - 1

self.layers[last_layer].calculate_deltas_for_output_layer(expected)

# calculate delta for hidden layers in reverse order

for l in range(last_layer - 1, 0, -1):

self.layers[l].calculate_deltas_for_hidden_layer(self.layers[l + 1])

# backpropagate() doesn't actually change any weights

# this function uses the deltas calculated in backpropagate() to

# actually make changes to the weights

def update_weights(self) -> None:

for layer in self.layers[1:]: # skip input layer

for neuron in layer.neurons:

for w in range(len(neuron.weights)):

neuron.weights[w] = neuron.weights[w] + (neuron.learning_rate * (layer.previous_layer.output_cache[w]) * neuron.delta)

# train() uses the results of outputs() run over many inputs and compared

# against expecteds to feed backpropagate() and update_weights()

def train(self, inputs: List[List[float]], expecteds: List[List[float]]) -> None:

for location, xs in enumerate(inputs):

ys: List[float] = expecteds[location]

outs: List[float] = self.outputs(xs)

self.backpropagate(ys)

self.update_weights()

# for generalized results that require classification this function will return

# the correct number of trials and the percentage correct out of the total

def validate(self, inputs: List[List[float]], expecteds: List[T], interpret_output: Callable[[List[float]], T]) -> Tuple[int, int, float]:

correct: int = 0

for input, expected in zip(inputs, expecteds):

result: T = interpret_output(self.outputs(input))

if result == expected:

correct += 1

percentage: float = correct / len(inputs)

return correct, len(inputs), percentage