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中略しました。

2021/05/11 09:30

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@@ -30,303 +30,7 @@
 from google.colab import drive
-drive.mount('/content/drive')
-import sys
-sys.path.append('/content/drive/My Drive')
-import numpy as np
-import ActivationFunction as AF
-# 3層ニューラルネットワーク
-class ThreeLayerNetwork:
-    # コンストラクタ
-    def __init__(self, inodes, hnodes, onodes, lr):
-        # 各レイヤーのノード数
-        self.inodes = inodes
-        self.hnodes = hnodes
-        self.onodes = onodes
-        # 学習率
-        self.lr = lr
-        # 重みの初期化
-        self.w_ih = np.random.normal(0.0, 1.0, (self.hnodes, self.inodes))
-        self.w_ho = np.random.normal(0.0, 1.0, (self.onodes, self.hnodes))
-        # 活性化関数
-        self.af = AF.sigmoid
-        self.daf = AF.derivative_sigmoid
-    # 誤差逆伝搬
-    def backprop(self, idata, tdata):
-        # 縦ベクトルに変換
-        o_i = np.array(idata, ndmin=2).T
-        t = np.array(tdata, ndmin=2).T
-        # 隠れ層
-        x_h = np.dot(self.w_ih, o_i)
-        o_h = self.af(x_h)
-        # 出力層
-        x_o = np.dot(self.w_ho, o_h)
-        o_o = self.af(x_o)
-        # 誤差計算
-        e_o = (t - o_o)
-        e_h = np.dot(self.w_ho.T, e_o)
-        # 重みの更新
-        self.w_ho += self.lr * np.dot((e_o * self.daf(o_o)), o_h.T)
-        self.w_ih += self.lr * np.dot((e_h * self.daf(o_h)), o_i.T)
-    # 順伝搬
-    def feedforward(self, idata):
-        # 入力のリストを縦ベクトルに変換
-        o_i = np.array(idata, ndmin=2).T
-        # 隠れ層
-        x_h = np.dot(self.w_ih, o_i)
-        o_h = self.af(x_h)
-        # 出力層
-        x_o = np.dot(self.w_ho, o_h)
-        o_o = self.af(x_o)
-        return o_o
-if __name__=='__main__':
-    # パラメータ
-    inodes = 784
-    hnodes = 100
-    onodes = 10
-    lr = 0.3
-    # ニューラルネットワークの初期化
-    nn = ThreeLayerNetwork(inodes, hnodes, onodes, lr)
-    # トレーニングデータのロード
-    training_data_file = open('drive/My Drive/mnist_dataset/mnist_train.csv', 'r')
-    training_data_list = training_data_file.readlines()
-    training_data_file.close()
-    # テストデータのロード
-    test_data_file = open('drive/My Drive/mnist_dataset/mnist_test.csv')
-    test_data_list = test_data_file.readlines()
-    test_data_file.close()
-    # 学習
-    epoch = 10
-    for e in range(epoch):
-        print('#epoch ', e)
-        data_size = len(training_data_list)
-        for i in range(data_size):
-            if i % 1000 == 0:
-                print('  train: {0:>5d} / {1:>5d}'.format(i, data_size))
-            val = training_data_list[i].split(',')
-            idata = (np.asfarray(val[1:]) / 255.0 * 0.99) + 0.01
-            tdata = np.zeros(onodes) + 0.01
-            tdata[int(val[0])] = 0.99
-            nn.backprop(idata, tdata)
-            pass
-        pass
-    # テスト
-    scoreboard = []
-    for record in test_data_list:
-        val = record.split(',')
-        idata = (np.asfarray(val[1:]) / 255.0 * 0.99) + 0.01
-        tlabel = int(val[0])
-        predict = nn.feedforward(idata)
-        plabel = np.argmax(predict)
-        print(plabel)
-        scoreboard.append(tlabel == plabel)
-        pass
-    scoreboard_array = np.asarray(scoreboard)
-    print('performance: ', scoreboard_array.sum() / scoreboard_array.size)
-```
+中略
-と同じコードを用いて精度をはかった結果が以下
-Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
-#epoch  0
-  train:     0 /     3
-#epoch  1
-  train:     0 /     3
-#epoch  2
-  train:     0 /     3
-#epoch  3
-  train:     0 /     3
-#epoch  4
-  train:     0 /     3
-#epoch  5
-  train:     0 /     3
-#epoch  6
-  train:     0 /     3
-#epoch  7
-  train:     0 /     3
-#epoch  8
-  train:     0 /     3
-#epoch  9
-  train:     0 /     3
-0
-3
-7
-performance:  0.3333333333333333
-となり、
-```python
-        print(plabel)
 ```