sacred training added

Dockerfile

@@ -19,11 +19,13 @@ COPY ./requirements.txt ./
 COPY ./avocado-preprocessing.py ./
 COPY ./avocado-training.py ./
 COPY ./avocado-evaluation.py ./
+COPY ./sacred-training.py ./
 
 RUN chmod +x ./requirements.txt
 RUN chmod +x ./avocado-preprocessing.py 
 RUN chmod +x ./avocado-training.py
 RUN chmod +x ./avocado-evaluation.py
+RUN chmod +x ./sacred-training.py
 
 RUN pip3 install -r ./requirements.txt
 # CMD python3 avocado-preprocessing.py

evaluation.Jenkinsfile

@@ -45,7 +45,7 @@ pipeline {
                     sh 'chmod +x avocado-evaluation.py'
                     sh "echo ${env.BUILD_ID}"
                     sh "python3 avocado-evaluation.py ${env.BUILD_ID}"
-
+                    sh 'python3 sacred-training.py'
                 
                 }
 			}

requirements.txt

@@ -5,4 +5,5 @@ sklearn
 tensorflow==2.4.1
 keras
 jinja2==2.11.3
-matplotlib
+matplotlib
+sacred==0.8.2

sacred-training.py

@@ -0,0 +1,75 @@
+import sys
+from keras.backend import mean
+import pandas as pd
+import numpy as np
+from sklearn import preprocessing
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.layers import Input, Dense, Activation,Dropout
+from tensorflow.keras.models import Model
+from tensorflow.keras.callbacks import EarlyStopping
+from keras.models import Sequential
+from sacred import Experiment
+from sacred.observers import FileStorageObserver
+
+ex = Experiment("file_observer")
+ex.observers.append(FileStorageObserver('my_runs'))
+
+@ex.config
+def my_config():
+    epochs = 10
+    batch_size = 16
+
+@ex.capture
+def prepare_model():
+    # odczytanie danych z plików
+    avocado_train = pd.read_csv('avocado_train.csv')
+    avocado_test = pd.read_csv('avocado_test.csv')
+    avocado_validate = pd.read_csv('avocado_validate.csv')
+
+
+    # podzial na X i y
+    X_train = avocado_train[['average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags']]
+    y_train = avocado_train[['type']]
+    X_test = avocado_test[['average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags']]
+    y_test = avocado_test[['type']]
+
+    print(X_train.shape[1])
+    # keras model
+    model = Sequential()
+    model.add(Dense(9, input_dim = X_train.shape[1], kernel_initializer='normal', activation='relu'))
+    model.add(Dense(1,kernel_initializer='normal', activation='sigmoid'))
+
+    early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)
+
+    # kompilacja
+    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+    # model fit
+    epochs = int(sys.argv[1])
+    batch_size = int(sys.argv[2])
+
+    # trenowanie modelu
+    model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test))
+
+    # predykcja
+    prediction = model.predict(X_test)
+
+    # ewaluacja
+    rmse = mean_squared_error(y_test, prediction)
+
+    # zapisanie modelu
+    model.save('avocado_model.h5')
+
+    return rmse
+
+
+
+@ex.main
+def my_main():
+    print(prepare_model())
+
+ex.run()
+ex.add_artifact('avocado_model.h5')