Skip to main content
unilu-dstoolkits
Projects
ds_toolkits_project
Workspace
Overview
Workspace
Runs
Automat.
Sweeps
Reports
Artifacts

Tobiasuruali's workspace

Charts
7
02468101214Step0.9780.980.9820.9840.9860.9880.990.992
lively-field-15
fancy-salad-8
stellar-frog-7
02468101214Step0.050.10.150.20.250.30.35
lively-field-15
fancy-salad-8
stellar-frog-7
02468101214Step02468101214
lively-field-15
fancy-salad-8
stellar-frog-7
02468101214Step0.90.920.940.960.98
lively-field-15
fancy-salad-8
stellar-frog-7
meta
"codepy/main.py"
"main.py"
"codepy/main.py"
"codepy/main.py"
{"remote":"https://github.com/tobiasuruali/DS_ToolKits_Project.git","commit":"1828d9ef2237f691ed3f5231d411f14d66e96a90","__typename":"GitInfo"}
Linux-5.11.0-44-generic-x86_64-with-glibc2.29
Linux-5.11.0-41-generic-x86_64-with-glibc2.29
Linux-5.11.0-41-generic-x86_64-with-glibc2.29
Linux-5.11.0-40-generic-x86_64-with-glibc2.29
main.py
/app/main.py
/app/codepy/main.py
/app/codepy/main.py
40s
1m 51s
7m 59s
12m 7s
summary
_wandb
36
107
474
723
-
108
473
720
-
1
15
14
-
1638998047
1638805354
1638742175
-
0.89024
0.98911
0.98904
-
0
14
14
-
0.086507
0.028527
0.028255
-
0
14
14
-
graph-file
graph-file
graph-file
-
0.35956
0.034002
0.033719
-
0.97817
0.99233
0.99283
1
-
import 
d
ata_preparation 
1
+
import 
co
d
epy.d
ata_preparation 
as data_preparation 
2
-
import 
build_model as build
2
+
import 
codepy.
build_model as build
3
-
import 
model_inspection as inspection
3
+
import 
codepy.
model_inspection as inspection
4
-
import 
p
redictions
4
+
import 
code
p
y.p
redictions
 as predictions
5
-
import 
d
atabase_pg
5
+
import 
co
d
epy.d
atabase_pg
 as database_pg
6
6
7
7
if __name__ == "__main__":
8
8
    data_preparation.initialize_wandb()
9
9
    num_classes, input_shape, x_train, y_train, x_test, y_test = data_preparation.prepare_data()
10
10
    #Comment form here..
11
-
    
# 
model = build.build_model(num_classes, input_shape)
11
+
    
model = build.build_model(num_classes, input_shape)
12
-
    
# 
build.train_model(x_train, y_train, x_test, y_test, model)
12
+
    
build.train_model(x_train, y_train, x_test, y_test, model)
13
-
    
# 
inspection.save_model(model)
13
+
    
inspection.save_model(model)
14
14
    #..To here for testing purposes 
15
15
    loaded_model = inspection.load_model()
16
16
    inspection.evaluate_loaded_model(x_test, y_test, loaded_model)
17
17
    # predictions.predict_on_data(x_test, y_test, loaded_model)
18
18
    database_pg.create_milestone3_db()
19
19
    database_pg.create_input_pred_db()
20
20
    random_img_x, squeezed_random_img_x, img_from_db = database_pg.insert_load_random_image()
21
21
    database_pg.predict_and_persist(img_from_db, loaded_model)