YAO/YAO-optimizer
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Newnius
2020-05-02 11:09:41 +08:00
parent 580c4716b9
commit e7437e5a98
2 changed files with 16 additions and 14 deletions
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10
train.py
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@@ -104,14 +104,14 @@ def experiment(repeats, series, seed):
t2 = test.shape[0] % batch_size
train_trimmed = train_scaled[train_scaled.shape[0] - t1 * batch_size:, :]
lstm_model = fit_lstm(train_trimmed, batch_size, 30, 4)
lstm_model = fit_lstm(train_trimmed, batch_size, 300, 4)
# forecast the entire training dataset to build up state for forecasting
print(train_trimmed)
print(train_trimmed[:, 0])
print(train_trimmed[:, :-1])
if seed:
train_reshaped = train_trimmed[:, :-1].reshape(len(train_trimmed), 1, lag2)
lstm_model.predict(train_reshaped, batch_size=batch_size)
# if seed:
# train_reshaped = train_trimmed[:, :-1].reshape(len(train_trimmed), 1, lag2)
# lstm_model.predict(train_reshaped, batch_size=batch_size)
# forecast test dataset
test_reshaped = test_scaled[:, 0:-1]
test_reshaped = test_reshaped.reshape(len(test_reshaped), 1, lag2)
@@ -137,7 +137,7 @@ def experiment(repeats, series, seed):
# load dataset
series = read_csv('data.csv', header=0, index_col=0, squeeze=True)
# experiment
repeats = 30
repeats = 1
results = DataFrame()
# with seeding
with_seed = experiment(repeats, series, True)