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nn.py
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import numpy as np
class NeuralNetwork:
def __init__(self, layer_sizes):
"""
Neural Network initialization.
Given layer_sizes as an input, you have to design a Fully Connected Neural Network architecture here.
:param layer_sizes: A list containing neuron numbers in each layers. For example [3, 10, 2] means that there are
3 neurons in the input layer, 10 neurons in the hidden layer, and 2 neurons in the output layer.
"""
self.weights = {}
self.biases = {}
for i in range(len(layer_sizes) - 1):
self.weights[i] = np.random.normal(size=(layer_sizes[i],layer_sizes[i+1]))
self.biases[i] = np.zeros(layer_sizes[i+1])
def activation(self, x):
"""
The activation function of our neural network, e.g., Sigmoid, ReLU.
:param x: Vector of a layer in our network.
:return: Vector after applying activation function.
"""
# return 1/(1 + np.exp(-x))
output = []
for i in range(len(x)):
output.append(max(0, x[i]))
# print("hi")
return output
def forward(self, x):
"""
Receives input vector as a parameter and calculates the output vector based on weights and biases.
:param x: Input vector which is a numpy array.
:return: Output vector
"""
# TODO (Implement forward function here)
for i in range(len(self.weights.keys())):
results = (x @ self.weights[i]) + self.biases[i]
results = self.activation(results)
x = results
return results