YAO/YAO-optimizer
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update

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Newnius
2020-05-02 02:25:02 +08:00
parent 6e27af40ea
commit e60dc18eb8
2 changed files with 66 additions and 64 deletions
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32
serve.py
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@@ -21,7 +21,7 @@ class Config:
predict_day = 1
input_size = len(feature_columns) - 1
input_size = len(feature_columns)
output_size = len(label_columns)
hidden_size = 128
@@ -35,7 +35,7 @@ class Config:
shuffle_train_data = True
# train_data_rate = 0.95 #comment yqy
train_data_rate = 0.95 # add yqy
train_data_rate = 1 # add yqy
valid_data_rate = 0.15
batch_size = 64
@@ -115,8 +115,8 @@ class Data:
time_step_size = feature_data.shape[0] // self.config.time_step
test_x = [feature_data[self.start_num_in_test + i * self.config.time_step: self.start_num_in_test + (
i + 1) * self.config.time_step] for i in range(time_step_size)]
i + 1) * self.config.time_step]
for i in range(time_step_size)]
if return_label_data:
label_data = self.norm_data[self.train_num + self.start_num_in_test:, self.config.label_in_feature_columns]
return np.array(test_x), label_data
@@ -128,9 +128,7 @@ class Data:
test_data_yqy = []
# test_data_yqy=test_data_yqy[1:21]
feature_data = (test_data_yqy - self.mean) / self.std
print(feature_data[:, :1])
test_x = [feature_data[:, :1]]
print(feature_data)
test_x = [feature_data]
return np.array(test_x)
@@ -142,6 +140,7 @@ def draw_yqy(config2, origin_data, predict_norm_data, mean_yqy, std_yqy):
assert label_norm_data.shape[0] == predict_norm_data.shape[
0], "The element number in origin and predicted data is different"
print("dsa")
# label_norm_data=label_norm_data[:,1]
label_name = 'high'
label_column_num = 3
@@ -153,14 +152,16 @@ def draw_yqy(config2, origin_data, predict_norm_data, mean_yqy, std_yqy):
# label_X = range(origin_data.data_num - origin_data.train_num - origin_data.start_num_in_test)
# predict_X = [x + config.predict_day for x in label_X]
# print(label_norm_data[:, 1:2])
label_data = label_norm_data[:, 1:2] * std_yqy[1:2] + mean_yqy[1:2]
# print(label_data)
print("2")
# print(predict_norm_data)
print(label_norm_data[:, 1:2])
label_data = label_norm_data[:, 1:2] * std_yqy[1:2] + mean_yqy[1:2]
print(label_data)
print(predict_norm_data)
predict_data = predict_norm_data * std_yqy[config.label_in_feature_columns] + mean_yqy[
config.label_in_feature_columns]
# print(predict_data)
print(predict_data)
print(label_data[:, -1])
print(predict_data[:, -1])
@@ -178,13 +179,14 @@ def train_models():
train_X, valid_X, train_Y, valid_Y = data_gainer.get_train_and_valid_data()
print(train_X, valid_X, train_Y, valid_Y)
print(train_X.shape[0])
if train_X.shape[0] < 500:
config.batch_size = 32
if train_X.shape[0] < 200:
config.batch_size = 16
print(train_X[:, :, :1], valid_X[:, :, :1], train_Y, valid_Y)
train(config, train_X[:, :, :1], train_Y, valid_X[:, :, :1], valid_Y)
train(config, train_X, train_Y, valid_X, valid_Y)
lock.release()
@@ -328,7 +330,7 @@ if __name__ == '__main__':
csvfile, delimiter=',',
quotechar='|', quoting=csv.QUOTE_MINIMAL
)
# spamwriter.writerow(["job", "model", "time", "utilGPU", "utilCPU", "pre", "main", "post"])
#spamwriter.writerow(["job", "model", "time", "utilGPU", "utilCPU", "pre", "main", "post"])
spamwriter.writerow(["seq", "value"])
# Wait forever for incoming http requests