google Colaboratory上で行うkerasを用いたhyperasのエラーの対処法

hyperasを使用してMLPのパラメータチューニングを自動調整したいのですがエラーが出てプログラムを回すことができません.エラーを消すにはどのようにすればよいでしょうか?
以下にソースコードとエラー,参照したurlを添付します.

python
1コード
2"""importするもの"""
3!pip install hyperas
4!pip install hyperopt
5
6from hyperopt import Trials, STATUS_OK, tpe
7from hyperas import optim
8from hyperas.distributions import choice
9
10import tensorflow as tf
11from tensorflow.keras.models import Sequential
12from tensorflow.keras.layers import Activation, Dense, Dropout, LeakyReLU
13from tensorflow.keras import regularizers
14from tensorflow.keras.optimizers import Adagrad
15from tensorflow.keras.optimizers import Adam
16from tensorflow.keras.models import load_model
17from keras.regularizers import l1, l2
18from keras import regularizers
19from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, CSVLogger
20
21from sklearn.model_selection import train_test_split
22import numpy as np
23import os
24import time
25import csv
26import cv2
27import math
28
29#ファイルの入出力
30os.chdir('/content/drive/My Drive/Colab Notebooks/my_drive/deep_learning_google')
31
32X=[]
33Y=[]
34
35#画像(入力データ)
36X = np.load('data/numpy/pictures_120x120.npy')
37#座標(出力データ)
38location = np.loadtxt("data/coordinates_data/sigma_x/max_stress_coordinates_a.csv",delimiter=",",skiprows=0)
39Y.extend(location)
40location = np.loadtxt("data/coordinates_data/sigma_x/max_stress_coordinates_b.csv",delimiter=",",skiprows=0)
41Y.extend(location)
42location = np.loadtxt("data/coordinates_data/sigma_x/max_stress_coordinates_c.csv",delimiter=",",skiprows=0)
43Y.extend(location)
44location = np.loadtxt("data/coordinates_data/sigma_x/max_stress_coordinates_d.csv",delimiter=",",skiprows=0)
45Y.extend(location)
46location = np.loadtxt("data/coordinates_data/sigma_x/max_stress_coordinates_e.csv",delimiter=",",skiprows=0)
47Y.extend(location)
48location = np.loadtxt("data/coordinates_data/sigma_x/max_stress_coordinates_f.csv",delimiter=",",skiprows=0)
49Y.extend(location)
50location = np.loadtxt("data/coordinates_data/sigma_x/max_stress_coordinates_g.csv",delimiter=",",skiprows=0)
51Y.extend(location)
52location = np.loadtxt("data/coordinates_data/sigma_x/max_stress_coordinates_h.csv",delimiter=",",skiprows=0)
53Y.extend(location)
54location = np.loadtxt("data/coordinates_data/sigma_x/max_stress_coordinates_i.csv",delimiter=",",skiprows=0)
55Y.extend(location)
56
57Y = np.array(Y)
58
59def prepare_data():
60  x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.3)
61  return x_train, y_train, x_test, y_test
62
63def create_model(x_train, y_train, x_test, y_test):
64    
65  model = Sequential()
66  model.add(Dense({{choice([1000,2000,3000])}},
67                  input_dim=14400,kernel_initializer='random_normal',bias_initializer='zeros')) 
68  
69  model.add(LeakyReLU())
70  model.add(Dropout({{uniform(0,1)}}))
71
72  model.add(Dense({{choice([100,200,300,400,500,600,700,800,900,1000])}},
73                  kernel_initializer='random_normal',bias_initializer='zeros'))
74  model.add(LeakyReLU())
75  model.add(Dropout({{uniform(0,1)}}))
76
77  model.add(Dense({{choice([100,200,300,400,500,600,700,800,900,1000])}},
78                  kernel_initializer='random_normal',
79                  bias_initializer='zeros'))
80  model.add(LeakyReLU())
81  model.add(Dropout(({{uniform(0,1)}})))
82
83  model.add(Dense({{choice([50,150,200,250,300,350,400,450])}},
84                  kernel_initializer='random_normal',bias_initializer='zeros'))
85  model.add(LeakyReLU())
86  model.add(Dropout(({{uniform(0,1)}})))
87
88  model.add(Dense({{choice([10,20,30,40,50,60,70,80,90,100])}},
89                  kernel_initializer='random_normal',bias_initializer='zeros'))
90  model.add(LeakyReLU())
91  model.add(Dropout(({{uniform(0,1)}})))
92
93  model.add(Dense({{choice([5,10,15,20,25,30,40,45,50])}},
94                  kernel_initializer='random_normal',bias_initializer='zeros'))
95  model.add(LeakyReLU())
96  model.add(Dropout(({{uniform(0,1)}})))
97
98  model.add(Dense(2))
99  model.add(Activation("linear"))
100 
101  opt = Adam(lr=0.0001) 
102  model.compile(loss="mean_absolute_percentage_error",
103                optimizer=opt)
104  history = model.fit(x_train,y_train,
105                      nb_epoch=200,
106                      batch_size=32,
107                      verbose=1,
108                      validation_data=(x_test,y_test)) 
109  return {'loss': val_loss, 'status': STATUS_OK, 'model': model}
110
111if __name__=="__main__":
112
113  best_run,best_model = optim.minimize(model=create_model,
114                                       data=prepare_data,
115                                       algo=tpe.suggest,
116                                       max_evals=200,
117                                       trials=Trials())
118  print(best_model.summary())
119  print(best_run)
120  _, _, x_test, y_test = prepare_data()
121  val_loss, val_acc = best_model.evaluate(x_test, y_test)
122  print("val_loss: ", val_loss)
123
124エラー内容
125FileNotFoundError                         Traceback (most recent call last)
126<ipython-input-10-d5b8b93952c3> in <module>()
127    118                                        algo=tpe.suggest,
128    119                                        max_evals=200,
129--> 120                                        trials=Trials())
130    121   print(best_model.summary())
131    122   print(best_run)
132
1333 frames
134/usr/local/lib/python3.6/dist-packages/hyperas/optim.py in minimize(model, data, algo, max_evals, trials, functions, rseed, notebook_name, verbose, eval_space, return_space, keep_temp)
135     67                                      notebook_name=notebook_name,
136     68                                      verbose=verbose,
137---> 69                                      keep_temp=keep_temp)
138     70 
139     71     best_model = None
140
141/usr/local/lib/python3.6/dist-packages/hyperas/optim.py in base_minimizer(model, data, functions, algo, max_evals, trials, rseed, full_model_string, notebook_name, verbose, stack, keep_temp)
142     96         model_str = full_model_string
143     97     else:
144---> 98         model_str = get_hyperopt_model_string(model, data, functions, notebook_name, verbose, stack)
145     99     temp_file = './temp_model.py'
146    100     write_temp_files(model_str, temp_file)
147
148/usr/local/lib/python3.6/dist-packages/hyperas/optim.py in get_hyperopt_model_string(model, data, functions, notebook_name, verbose, stack)
149    183     else:
150    184         calling_script_file = os.path.abspath(inspect.stack()[stack][1])
151--> 185         with open(calling_script_file, 'r') as f:
152    186             source = f.read()
153    187 
154
155FileNotFoundError: [Errno 2] No such file or directory: '/content/drive/My Drive/Colab Notebooks/my_drive/deep_learning_google/<ipython-input-10-d5b8b93952c3>'
156
157参照url
158https://qiita.com/UpAllNight/items/773396f3070eb9b3382d