IUM_06

Jenkinsfile

@@ -2,7 +2,7 @@ pipeline {
   agent { 
     dockerfile true 
   }
-  
+
   triggers {
     upstream(upstreamProjects: 'z-s464913-create-dataset', threshold: hudson.model.Result.SUCCESS)
   }
@@ -13,8 +13,8 @@ pipeline {
       description: 'Which build to use for copying artifacts',
       name: 'BUILD_SELECTOR'
     )
-    string(name: 'learning_rate', defaultValue: '0.001', description: 'Learning rate')
-    string(name: 'epochs', defaultValue: '5', description: 'Number of epochs')
+    string(name: 'LEARNING_RATE', defaultValue: '0.001', description: 'Learning rate')
+    string(name: 'EPOCHS', defaultValue: '5', description: 'Number of epochs')
   }
 
   stages {
@@ -33,7 +33,7 @@ pipeline {
     stage('Run train_model script') {
       steps {
         sh 'chmod +x train_model.py'
-        sh 'python3 ./train_model.py'
+        sh 'python3 ./train_model.py ${params.LEARNING_RATE} ${params.EPOCHS}'
       }
     }
 

train_model.py

@@ -6,6 +6,7 @@ from keras.models import Sequential
 from keras.layers import BatchNormalization, Dropout, Dense, Flatten, Conv1D
 from keras.optimizers import Adam
 import pandas as pd
+import sys
 
 
 def main():
@@ -22,6 +23,9 @@ def main():
     X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)
     X_val = X_val.reshape(X_val.shape[0], X_val.shape[1], 1)
 
+    learning_rate = float(sys.argv[1])
+    epochs = int(sys.argv[2])
+
     model = Sequential(
         [
             Conv1D(32, 2, activation="relu", input_shape=X_train[0].shape),
@@ -38,7 +42,7 @@ def main():
     )
 
     model.compile(
-        optimizer=Adam(learning_rate=1e-3),
+        optimizer=Adam(learning_rate=learning_rate),
         loss="binary_crossentropy",
         metrics=["accuracy"],
     )
@@ -47,7 +51,7 @@ def main():
         X_train,
         y_train,
         validation_data=(X_val, y_val),
-        epochs=5,
+        epochs=epochs,
         verbose=1,
     )