YAO/YAO-optimizer
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Code Issues Releases Wiki Activity

update

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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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2
.idea/workspace.xml generated
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@@ -221,7 +221,7 @@
<component name="PropertiesComponent"> <component name="PropertiesComponent">
<property name="WebServerToolWindowFactoryState" value="false" /> <property name="WebServerToolWindowFactoryState" value="false" />
<property name="aspect.path.notification.shown" value="true" /> <property name="aspect.path.notification.shown" value="true" />
<property name="com.android.tools.idea.instantapp.provision.ProvisionBeforeRunTaskProvider.myTimeStamp" value="1588384828302" /> <property name="com.android.tools.idea.instantapp.provision.ProvisionBeforeRunTaskProvider.myTimeStamp" value="1588385985992" />
<property name="go.gopath.indexing.explicitly.defined" value="true" /> <property name="go.gopath.indexing.explicitly.defined" value="true" />
<property name="nodejs_interpreter_path.stuck_in_default_project" value="undefined stuck path" /> <property name="nodejs_interpreter_path.stuck_in_default_project" value="undefined stuck path" />
<property name="nodejs_npm_path_reset_for_default_project" value="true" /> <property name="nodejs_npm_path_reset_for_default_project" value="true" />

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.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam') model.compile(loss='mean_squared_error', optimizer='adam')
for i in range(nb_epoch): 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.fit(X, y, epochs=1, batch_size=batch_size, verbose=0, shuffle=False)
model.reset_states() model.reset_states()
return model return model
@@ -99,8 +100,13 @@ def experiment(repeats, series, seed):
for r in range(repeats): for r in range(repeats):
# fit the model # fit the model
batch_size = 4 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:, :] 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 # forecast the entire training dataset to build up state for forecasting
if seed: if seed:
train_reshaped = train_trimmed[:, 0].reshape(len(train_trimmed), 1, 1) train_reshaped = train_trimmed[:, 0].reshape(len(train_trimmed), 1, 1)