mongo?

Zadanie_06_training.py

@@ -7,50 +7,69 @@ import numpy as np
 import sys
 from sklearn.preprocessing import StandardScaler, LabelEncoder
 from tensorflow.keras.optimizers import Adam
-from sacred.observers import FileStorageObserver
+from sacred.observers import FileStorageObserver, MongoObserver
 from sacred import Experiment
 from datetime import datetime
 import os
 import pymongo
 
+ex = Experiment("434788-mongo", interactive=False, save_git_info=False)
+ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name='sacred'))
 
-wine=pd.read_csv('train.csv')
+@ex.config
+def my_config():
+    batch_param = int(sys.argv[1])
+    epoch_param = int(sys.argv[2])
 
-y = wine['quality']
-x = wine.drop('quality', axis=1)
+@ex.capture
+def prepare_model(epoch_param, batch_param, _run):
+    _run.info["prepare_model_ts"] = str(datetime.now())
 
-citricacid = x['fixed acidity'] * x['citric acid']
-citric_acidity = pd.DataFrame(citricacid, columns=['citric_accidity'])
+    wine=pd.read_csv('train.csv')
 
-density_acidity = x['fixed acidity'] * x['density']
-density_acidity = pd.DataFrame(density_acidity, columns=['density_acidity'])
+    y = wine['quality']
+    x = wine.drop('quality', axis=1)
+
+    citricacid = x['fixed acidity'] * x['citric acid']
+    citric_acidity = pd.DataFrame(citricacid, columns=['citric_accidity'])
+
+    density_acidity = x['fixed acidity'] * x['density']
+    density_acidity = pd.DataFrame(density_acidity, columns=['density_acidity'])
 
 
-x = wine.join(citric_acidity).join(density_acidity)
+    x = wine.join(citric_acidity).join(density_acidity)
 
-bins = (2, 5, 8)
-gnames = ['bad', 'nice']
-y = pd.cut(y, bins = bins, labels = gnames)
+    bins = (2, 5, 8)
+    gnames = ['bad', 'nice']
+    y = pd.cut(y, bins = bins, labels = gnames)
 
-enc = LabelEncoder()
-yenc = enc.fit_transform(y)
+    enc = LabelEncoder()
+    yenc = enc.fit_transform(y)
 
-scale = StandardScaler()
-scaled_x = scale.fit_transform(x)
+    scale = StandardScaler()
+    scaled_x = scale.fit_transform(x)
 
-NeuralModel = Sequential([
-                          Dense(128, activation='relu', input_shape=(14,)),
-                          Dense(32, activation='relu'),
-                          Dense(64, activation='relu'),
-                          Dense(64, activation='relu'),
-                          Dense(64, activation='relu'),
-                          Dense(1, activation='sigmoid')
-])
+    NeuralModel = Sequential([
+                            Dense(128, activation='relu', input_shape=(14,)),
+                            Dense(32, activation='relu'),
+                            Dense(64, activation='relu'),
+                            Dense(64, activation='relu'),
+                            Dense(64, activation='relu'),
+                            Dense(1, activation='sigmoid')
+    ])
 
-rms = Adam(lr=0.0003)
+    rms = Adam(lr=0.0003)
 
-NeuralModel.compile(optimizer=rms, loss='binary_crossentropy', metrics=['accuracy'])
+    NeuralModel.compile(optimizer=rms, loss='binary_crossentropy', metrics=['accuracy'])
 
-NeuralModel.fit(scaled_x, yenc, batch_size=int(sys.argv[1]), epochs = int(sys.argv[2])) #verbose = 1
+    NeuralModel.fit(scaled_x, yenc, batch_size= batch_param, epochs = epoch_param) #verbose = 1
 
-NeuralModel.save('wine_model.h5')
+    NeuralModel.save('wine_model.h5')
+
+@ex.main
+def my_main(train_size_param, test_size_param):
+    print(prepare_model())
+
+
+r = ex.run()
+ex.add_artifact("saved_model/saved_model.pb")