google Colaboratory上で行うkerasを用いたhyperasのエラーを直したい

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"""importするもの"""
2!pip install hyperas
3!pip install hyperopt
4!pip install h5py
5!pip install -U -q PyDrive
6
7from hyperopt import Trials, STATUS_OK, tpe
8from hyperas import optim
9from hyperas.distributions import choice
10
11import tensorflow as tf
12from tensorflow.keras.models import Sequential
13from tensorflow.keras.layers import Activation, Dense, Dropout, LeakyReLU
14from tensorflow.keras import regularizers
15from tensorflow.keras.optimizers import Adagrad
16from tensorflow.keras.optimizers import Adam
17from tensorflow.keras.models import load_model
18from keras.regularizers import l1, l2
19from keras import regularizers
20from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, CSVLogger
21
22from sklearn.model_selection import train_test_split
23import numpy as np
24import os
25import time
26import csv
27import cv2
28import math
29
30from pydrive.auth import GoogleAuth
31from pydrive.drive import GoogleDrive
32from google.colab import auth
33from oauth2client.client import GoogleCredentials
34
35auth.authenticate_user()
36gauth = GoogleAuth()
37gauth.credentials = GoogleCredentials.get_application_default()
38drive = GoogleDrive(gauth)
39
40#hyperasファイル
41fid = drive.ListFile({'q':"title='hyperas.ipynb'"}).GetList()[0]['id']
42f = drive.CreateFile({'id': fid})
43f.GetContentFile('hyperas.ipynb')
44
45#ファイルの入出力
46os.chdir('/content/drive/My Drive/Colab Notebooks/my_drive/deep_learning_google')
47
48X=[]
49Y=[]
50
51#def prepare_data():
52#画像(入力データ)
53X = np.load('data/numpy/pictures_120x120.npy')
54#座標(出力データ)
55location = np.loadtxt("data/coordinates_data/sigma_x/max_stress_coordinates_a.csv",delimiter=",",skiprows=0)
56Y.extend(location)
57location = np.loadtxt("data/coordinates_data/sigma_x/max_stress_coordinates_b.csv",delimiter=",",skiprows=0)
58Y.extend(location)
59location = np.loadtxt("data/coordinates_data/sigma_x/max_stress_coordinates_c.csv",delimiter=",",skiprows=0)
60Y.extend(location)
61location = np.loadtxt("data/coordinates_data/sigma_x/max_stress_coordinates_d.csv",delimiter=",",skiprows=0)
62Y.extend(location)
63location = np.loadtxt("data/coordinates_data/sigma_x/max_stress_coordinates_e.csv",delimiter=",",skiprows=0)
64Y.extend(location)
65location = np.loadtxt("data/coordinates_data/sigma_x/max_stress_coordinates_f.csv",delimiter=",",skiprows=0)
66Y.extend(location)
67location = np.loadtxt("data/coordinates_data/sigma_x/max_stress_coordinates_g.csv",delimiter=",",skiprows=0)
68Y.extend(location)
69location = np.loadtxt("data/coordinates_data/sigma_x/max_stress_coordinates_h.csv",delimiter=",",skiprows=0)
70Y.extend(location)
71location = np.loadtxt("data/coordinates_data/sigma_x/max_stress_coordinates_i.csv",delimiter=",",skiprows=0)
72Y.extend(location)
73
74Y = np.array(Y)
75
76def prepare_data():
77  x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.3)
78  return x_train, y_train, x_test, y_test
79
80def create_model(x_train, y_train, x_test, y_test):
81    
82  model = Sequential()
83  model.add(Dense({{choice([1000,2000,3000])}},
84                  input_dim=14400,kernel_initializer='random_normal',bias_initializer='zeros')) 
85  
86  model.add(LeakyReLU())
87  model.add(Dropout({{uniform(0,1)}}))
88
89  model.add(Dense({{choice([100,200,300,400,500,600,700,800,900,1000])}},
90                  kernel_initializer='random_normal',bias_initializer='zeros'))
91  model.add(LeakyReLU())
92  model.add(Dropout({{uniform(0,1)}}))
93
94  model.add(Dense({{choice([100,200,300,400,500,600,700,800,900,1000])}},
95                  kernel_initializer='random_normal',
96                  bias_initializer='zeros'))
97  model.add(LeakyReLU())
98  model.add(Dropout(({{uniform(0,1)}})))
99
100  model.add(Dense({{choice([50,150,200,250,300,350,400,450])}},
101                  kernel_initializer='random_normal',bias_initializer='zeros'))
102  model.add(LeakyReLU())
103  model.add(Dropout(({{uniform(0,1)}})))
104
105  model.add(Dense({{choice([10,20,30,40,50,60,70,80,90,100])}},
106                  kernel_initializer='random_normal',bias_initializer='zeros'))
107  model.add(LeakyReLU())
108  model.add(Dropout(({{uniform(0,1)}})))
109
110  model.add(Dense({{choice([5,10,15,20,25,30,40,45,50])}},
111                  kernel_initializer='random_normal',bias_initializer='zeros'))
112  model.add(LeakyReLU())
113  model.add(Dropout(({{uniform(0,1)}})))
114
115  model.add(Dense(2))
116  model.add(Activation("linear"))
117 
118  opt = Adam(lr=0.0001) 
119  model.compile(loss="mean_absolute_percentage_error",
120                optimizer=opt)
121  history = model.fit(x_train,y_train,
122                      nb_epoch=200,
123                      batch_size=32,
124                      verbose=1,
125                      validation_data=(x_test,y_test)) 
126  return {'loss': val_loss, 'status': STATUS_OK, 'model': model}
127
128if __name__=="__main__":
129
130  best_run,best_model = optim.minimize(model=create_model,
131                                       data=prepare_data,
132                                       algo=tpe.suggest,
133                                       max_evals=200,
134                                       trials=Trials())
135
136  print(best_model.summary())
137  print(best_run)
138  _, _, x_test, y_test = prepare_data()
139  val_loss, val_acc = best_model.evaluate(x_test, y_test)
140  print("val_loss: ", val_loss)

error
1---------------------------------------------------------------------------
2FileNotFoundError                         Traceback (most recent call last)
3<ipython-input-5-379c67ff7b07> in <module>()
4    132                                        algo=tpe.suggest,
5    133                                        max_evals=200,
6--> 134                                        trials=Trials())
7    135 
8    136   print(best_model.summary())
9
103 frames
11/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)
12     67                                      notebook_name=notebook_name,
13     68                                      verbose=verbose,
14---> 69                                      keep_temp=keep_temp)
15     70 
16     71     best_model = None
17
18/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)
19     96         model_str = full_model_string
20     97     else:
21---> 98         model_str = get_hyperopt_model_string(model, data, functions, notebook_name, verbose, stack)
22     99     temp_file = './temp_model.py'
23    100     write_temp_files(model_str, temp_file)
24
25/usr/local/lib/python3.6/dist-packages/hyperas/optim.py in get_hyperopt_model_string(model, data, functions, notebook_name, verbose, stack)
26    183     else:
27    184         calling_script_file = os.path.abspath(inspect.stack()[stack][1])
28--> 185         with open(calling_script_file, 'r') as f:
29    186             source = f.read()
30    187 
31
32FileNotFoundError: [Errno 2] No such file or directory: '/content/drive/My Drive/Colab Notebooks/my_drive/deep_learning_google/<ipython-input-5-379c67ff7b07>'