YAO/YAO-optimizer
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Newnius 2020-04-29 18:59:40 +08:00
parent c11466c958
commit 8fbce50cf8
3 changed files with 9 additions and 9 deletions
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2
.idea/workspace.xml
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@ -151,7 +151,7 @@
<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="1588157839783" /> <property name="com.android.tools.idea.instantapp.provision.ProvisionBeforeRunTaskProvider.myTimeStamp" value="1588157865997" />
<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" />

4
main.py
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@ -164,11 +164,11 @@ def main(config):
if config.do_train: if config.do_train:
train_X, valid_X, train_Y, valid_Y = data_gainer.get_train_and_valid_data() train_X, valid_X, train_Y, valid_Y = data_gainer.get_train_and_valid_data()
train(config, train_X, train_Y, valid_X, valid_Y) model = train(config, train_X, train_Y, valid_X, valid_Y)
if config.do_predict: if config.do_predict:
test_X, test_Y = data_gainer.get_test_data(return_label_data=True) test_X, test_Y = data_gainer.get_test_data(return_label_data=True)
pred_result = predict(config, test_X) pred_result = predict(config, test_X, model)
draw(config, data_gainer, pred_result) draw(config, data_gainer, pred_result)

12
model/model_tensorflow.py
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@ -76,19 +76,19 @@ def train(config, train_X, train_Y, valid_X, valid_Y):
if bad_epoch >= config.patience: if bad_epoch >= config.patience:
print(" The training stops early in epoch {}".format(epoch)) print(" The training stops early in epoch {}".format(epoch))
break break
return model
def predict(config, test_X): def predict(config, test_X, model):
config.dropout_rate = 0.1 config.dropout_rate = 0.1
tf.reset_default_graph()
with tf.variable_scope("stock_predict", reuse=tf.AUTO_REUSE): #with tf.variable_scope("stock_predict", reuse=tf.AUTO_REUSE):
model = Model(config) # model = Model(config)
test_len = len(test_X) test_len = len(test_X)
with tf.Session() as sess: with tf.Session() as sess:
module_file = tf.train.latest_checkpoint(config.model_save_path) #module_file = tf.train.latest_checkpoint(config.model_save_path)
model.saver.restore(sess, module_file) #model.saver.restore(sess, module_file)
result = np.zeros((test_len * config.time_step, config.output_size)) result = np.zeros((test_len * config.time_step, config.output_size))
for step in range(test_len): for step in range(test_len):