-
data.csv
気象庁データ : 2018/1/1~2021/10/25 内の東京都の平均気温データ
-
Weather_Prediction.ipynb
テーマ設定〜学習結果&評価〜考察〜Pythonコード
- 案①
Model(
(lstm): LSTM(365, 180, batch_first=True)
(lstm_): Sequential(
(0): Dropout(p=0.2, inplace=False)
(1): ReLU()
)
(dense): Linear(in_features=180, out_features=1, bias=True)
)
- 案②
Model(
(lstm): LSTM(365, 128, batch_first=True)
(lstm_): Sequential(
(0): Dropout(p=0.2, inplace=False)
(1): ReLU()
)
(dense1): Sequential(
(0): Linear(in_features=128, out_features=64, bias=True)
(1): ReLU()
(2): Dropout(p=0.2, inplace=False)
)
(dense2): Sequential(
(0): Linear(in_features=64, out_features=32, bias=True)
(1): ReLU()
(2): Dropout(p=0.1, inplace=False)
)
(dense3): Sequential(
(0): Linear(in_features=32, out_features=16, bias=True)
(1): ReLU()
(2): Dropout(p=0.1, inplace=False)
)
(dense4): Linear(in_features=16, out_features=1, bias=True)
)
- 案③
Model(
(lstm): LSTM(365, 180, batch_first=True)
(lstm_): Sequential(
(0): Dropout(p=0.2, inplace=False)
(1): ReLU()
)
(dense1): Sequential(
(0): Linear(in_features=180, out_features=90, bias=True)
(1): ReLU()
(2): Dropout(p=0.2, inplace=False)
)
(dense2): Sequential(
(0): Linear(in_features=90, out_features=30, bias=True)
(1): ReLU()
(2): Dropout(p=0.1, inplace=False)
)
(dense3): Sequential(
(0): Linear(in_features=30, out_features=7, bias=True)
(1): ReLU()
(2): Dropout(p=0.1, inplace=False)
)
(dense4): Linear(in_features=7, out_features=1, bias=True)
)
- 案④
Model(
(lstm): LSTM(365, 52, batch_first=True)
(lstm_): Sequential(
(0): Dropout(p=0.2, inplace=False)
(1): ReLU()
)
(dense1): Sequential(
(0): Linear(in_features=52, out_features=24, bias=True)
(1): ReLU()
(2): Dropout(p=0.2, inplace=False)
)
(dense2): Sequential(
(0): Linear(in_features=24, out_features=12, bias=True)
(1): ReLU()
(2): Dropout(p=0.1, inplace=False)
)
(dense3): Sequential(
(0): Linear(in_features=12, out_features=4, bias=True)
(1): ReLU()
(2): Dropout(p=0.1, inplace=False)
)
(dense4): Linear(in_features=4, out_features=1, bias=True)
)