python_study/fromScratch_example_ch4_towlayernet.py at master · 2changhyeon/python_study · GitHub

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# -*- coding: utf-8 -*-
"""
Created on Mon May 24 20:27:58 2021

@author: 이창현
"""

import sys, os
sys.path.append(os.pardir)
from common.functions import *
from common.gradient import numerical_gradient

class TwoLayerNet:
    def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):
        self.params = {}
        self.params['W1'] = weight_init_std * \
                            np.random.randn(input_size, hidden_size)
        self.params['b1'] = np.zeros(hidden_size)
        self.params['W2'] = weight_init_std * \
                            np.random.randn(hidden_size, output_size)
        self.params['b2'] = np.zeros(output_size)

    def predict(self,x):
        W1, W2 = self.params['W1'], self.params['W2']
        b1, b2 = self.params['b1'], self.params['b2']

        a1 = np.dot(x,W1) + b1
        z1 = sigmoid(a1)
        a2 = np.dot(z1,W2) + b2
        y = softmax(a2)

        return y

    def loss(self,x,t):
        y = self.predict(x)

        return cross_entropy_error(y, t)

    def accuracy(self,x,t):
        y = self.predict(x)
        y = np.argmax(y, axis=1)
        t = np.argmax(t, axis=1)

        accuracy = np.sum(y==t) / float(x.shape[0])
        return accuracy

    def numerical_gradient(self,x,t):
        loss_W = lambda W: self.loss(x,t)

        grads = {}
        grads['W1'] = numerical_gradient(loss_W, self.params['W1'])
        grads['b1'] = numerical_gradient(loss_W, self.params['b1'])
        grads['W2'] = numerical_gradient(loss_W, self.params['W2'])
        grads['b2'] = numerical_gradient(loss_W, self.params['b2'])

        return grads