YAO/YAO-optimizer
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Newnius
2020-05-02 10:23:57 +08:00
parent ac607fc170
commit 9d2d0635c3
2 changed files with 8 additions and 2 deletions
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8
train.py
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@@ -70,6 +70,7 @@ def fit_lstm(train, batch_size, nb_epoch, neurons):
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
for i in range(nb_epoch):
print("Epoch {}/{}".format(i, nb_epoch))
model.fit(X, y, epochs=1, batch_size=batch_size, verbose=0, shuffle=False)
model.reset_states()
return model
@@ -99,8 +100,13 @@ def experiment(repeats, series, seed):
for r in range(repeats):
# fit the model
batch_size = 4
t = train.shape[0] % batch_size
train = train[train.shape[0] - t * batch_size:]
test = test.shape[0] % batch_size
test = test[test.shape[0] - t * batch_size:]
train_trimmed = train_scaled[2:, :]
lstm_model = fit_lstm(train_trimmed, batch_size, 3000, 4)
lstm_model = fit_lstm(train_trimmed, batch_size, 30, 4)
# forecast the entire training dataset to build up state for forecasting
if seed:
train_reshaped = train_trimmed[:, 0].reshape(len(train_trimmed), 1, 1)