YAO/YAO-optimizer
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update

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Newnius 2020-05-02 16:24:53 +08:00
parent 37b670e338
commit 5a71f23db5
2 changed files with 8 additions and 6 deletions
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2
.idea/workspace.xml
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@ -243,7 +243,7 @@
<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="1588407734896" /> <property name="com.android.tools.idea.instantapp.provision.ProvisionBeforeRunTaskProvider.myTimeStamp" value="1588407831519" />
<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" />

12
train.py
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@ -69,8 +69,8 @@ def fit_lstm(train, batch_size2, nb_epoch, neurons):
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):
model.fit(X, y, epochs=1, batch_size=batch_size2, verbose=0, shuffle=False) model.fit(X, y, epochs=1, batch_size=batch_size2, verbose=0, shuffle=False)
#loss = model.evaluate(X, y) # loss = model.evaluate(X, y)
#print("Epoch {}/{}, loss = {}".format(i, nb_epoch, loss)) # print("Epoch {}/{}, loss = {}".format(i, nb_epoch, loss))
model.reset_states() model.reset_states()
return model return model
@ -95,6 +95,8 @@ def experiment(repeats, series, seed):
batch_size = 32 batch_size = 32
if supervised_values.shape[0] < 100: if supervised_values.shape[0] < 100:
batch_size = 16 batch_size = 16
if supervised_values.shape[0] < 60:
batch_size = 8
test_data_num = batch_size test_data_num = batch_size
# split data into train and test-sets # split data into train and test-sets
@ -150,9 +152,9 @@ results = DataFrame()
with_seed = experiment(repeats, series, True) with_seed = experiment(repeats, series, True)
results['with-seed'] = with_seed results['with-seed'] = with_seed
# without seeding # without seeding
without_seed = experiment(repeats, series, False) # without_seed = experiment(repeats, series, False)
results['without-seed'] = without_seed # results['without-seed'] = without_seed
# summarize results # summarize results
print(results.describe()) print(results.describe())
# save boxplot # save boxplot
results.boxplot() # results.boxplot()