# coding: utf-8
#  	...modified on 2023.12.05
#       by m@echan.xyz
# 
from google.colab import drive
drive.mount('/content/drive',force_remount=True)
bd = 'drive/My Drive/Colab Notebooks/mymodules'
import sys, os
sys.path.append(bd)  # mymodules のファイルをインポートするための設定
import numpy as np
import matplotlib.pyplot as plt
# # for _c : cupy 
# - runtime の変更 (none => GPU)
# !pip install cupy # ←は不要になった
# - padded[original_area_slice] = cupy.asarray(array) in pad.py [in Colab.]
# - and then, reset runtime
from mnist_c import load_mnist
from simple_convnet_c import SimpleConvNet
from trainer_c import Trainer
#  -  -  -  -  -  -  -  -  -  -  -  -  -  -  -  -  -  -  -  -  -  -  -  -
# データの読み込み
(x_train, t_train), (x_test, t_test) = load_mnist(flatten=False)

# 処理に時間のかかる場合はデータを削減 
#x_train, t_train = x_train[:5000], t_train[:5000]
#x_test, t_test = x_test[:1000], t_test[:1000]

max_epochs = 20 # acc= .9894 by 6m4s.

network = SimpleConvNet(input_dim=(1,28,28), 
                        conv_param = {'filter_num': 30, 'filter_size': 5, 'pad': 0, 'stride': 1},
                        hidden_size=100, output_size=10, weight_init_std=0.01)
                        
trainer = Trainer(network, x_train, t_train, x_test, t_test,
                  epochs=max_epochs, mini_batch_size=100,
                  optimizer='Adam', optimizer_param={'lr': 0.001},
                  evaluate_sample_num_per_epoch=1000)
trainer.train()

# パラメータの保存
network.save_params("params.pkl")
print("Saved Network Parameters!")

# グラフの描画
markers = {'train': 'o', 'test': 's'}
x = np.arange(max_epochs)
plt.plot(x, trainer.train_acc_list, marker='o', label='train', markevery=2)
plt.plot(x, trainer.test_acc_list, marker='s', label='test', markevery=2)
plt.xlabel("epochs")
plt.ylabel("accuracy")
plt.ylim(0, 1.0)
plt.legend(loc='lower right')
plt.show()