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# coding: utf-8 | |
import sys, os | |
sys.path.append(os.pardir) # 親ディレクトリのファイルをインポートするための設定 | |
import numpy as np | |
from common.layers import * | |
from common.gradient import numerical_gradient | |
from collections import OrderedDict | |
class TwoLayerNet: | |
def __init__(self, input_size, hidden_size, output_size, weight_init_std = 0.01): | |
# 重みの初期化 | |
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) | |
# レイヤの生成 | |
self.layers = OrderedDict() | |
self.layers['Affine1'] = Affine(self.params['W1'], self.params['b1']) | |
self.layers['Relu1'] = Relu() | |
self.layers['Affine2'] = Affine(self.params['W2'], self.params['b2']) | |
self.lastLayer = SoftmaxWithLoss() | |
def predict(self, x): | |
for layer in self.layers.values(): | |
x = layer.forward(x) | |
return x | |
# x:入力データ, t:教師データ | |
def loss(self, x, t): | |
y = self.predict(x) | |
return self.lastLayer.forward(y, t) | |
def accuracy(self, x, t): | |
y = self.predict(x) | |
y = np.argmax(y, axis=1) | |
if t.ndim != 1 : t = np.argmax(t, axis=1) | |
accuracy = np.sum(y == t) / float(x.shape[0]) | |
return accuracy | |
# x:入力データ, t:教師データ | |
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 | |
def gradient(self, x, t): | |
# forward | |
self.loss(x, t) | |
# backward | |
dout = 1 | |
dout = self.lastLayer.backward(dout) | |
layers = list(self.layers.values()) | |
layers.reverse() | |
for layer in layers: | |
dout = layer.backward(dout) | |
# 設定 | |
grads = {} | |
grads['W1'], grads['b1'] = self.layers['Affine1'].dW, self.layers['Affine1'].db | |
grads['W2'], grads['b2'] = self.layers['Affine2'].dW, self.layers['Affine2'].db | |
return grads |