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# layer.py
# From Classic Computer Science Problems in Python Chapter 7
# Copyright 2018 David Kopec
#
# 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, Optional
from random import random
from neuron import Neuron
from util import dot_product
class Layer:
def __init__(self, previous_layer: Optional[Layer], num_neurons: int, learning_rate: float, activation_function: Callable[[float], float], derivative_activation_function: Callable[[float], float]) -> None:
self.previous_layer: Optional[Layer] = previous_layer
self.neurons: List[Neuron] = []
# the following could all be one large list comprehension, but gets a bit long that way
for i in range(num_neurons):
if previous_layer is None:
random_weights: List[float] = []
else:
random_weights = [random() for _ in range(len(previous_layer.neurons))]
neuron: Neuron = Neuron(random_weights, learning_rate, activation_function, derivative_activation_function)
self.neurons.append(neuron)
self.output_cache: List[float] = [0.0 for _ in range(num_neurons)]
def outputs(self, inputs: List[float]) -> List[float]:
if self.previous_layer is None:
self.output_cache = inputs
else:
self.output_cache = [n.output(inputs) for n in self.neurons]
return self.output_cache
# should only be called on output layer
def calculate_deltas_for_output_layer(self, expected: List[float]) -> None:
for n in range(len(self.neurons)):
self.neurons[n].delta = self.neurons[n].derivative_activation_function(self.neurons[n].output_cache) * (expected[n] - self.output_cache[n])
# should not be called on output layer
def calculate_deltas_for_hidden_layer(self, next_layer: Layer) -> None:
for index, neuron in enumerate(self.neurons):
next_weights: List[float] = [n.weights[index] for n in next_layer.neurons]
next_deltas: List[float] = [n.delta for n in next_layer.neurons]
sum_weights_and_deltas: float = dot_product(next_weights, next_deltas)
neuron.delta = neuron.derivative_activation_function(neuron.output_cache) * sum_weights_and_deltas