From 9aa2b7d4ad7201f5fc2ed519e55a1c31b1147971 Mon Sep 17 00:00:00 2001
From: Wojciech Jarmosz <wojciech@jarmosz.com>
Date: Sat, 15 May 2021 03:50:19 +0200
Subject: [PATCH] Fixes for lab5, lab6

---
 Jenkinsfile          |  2 +-
 linear_regression.py | 27 +++++++++++----------------
 training.py          | 29 +++++++++++++----------------
 3 files changed, 25 insertions(+), 33 deletions(-)

diff --git a/Jenkinsfile b/Jenkinsfile
index eb34a15..16ab74f 100644
--- a/Jenkinsfile
+++ b/Jenkinsfile
@@ -18,7 +18,7 @@ stages {
                 // sh "chmod 777 ./data_download.sh"
                 // sh "./data_download.sh"
                 // Zapisanie artefaktów
-                archiveArtifacts "results.txt"
+                archiveArtifacts "results_lab5.txt"
                 archiveArtifacts "MoviesOnStreamingPlatforms_updated.dev"
                 archiveArtifacts "MoviesOnStreamingPlatforms_updated.test"
                 archiveArtifacts "MoviesOnStreamingPlatforms_updated.train"
diff --git a/linear_regression.py b/linear_regression.py
index 15335e5..75e019c 100644
--- a/linear_regression.py
+++ b/linear_regression.py
@@ -23,24 +23,19 @@ test_Y = tf.convert_to_tensor(test_data[["IMDb"]])
 normalizer = preprocessing.Normalization(input_shape=[3,])
 normalizer.adapt(train_X)
 
-if os.path.isdir('linear_regression'):
-    model = keras.models.load_model('linear_regression')
-else:
-    model = keras.Sequential([
-        keras.Input(shape=(len(columns_to_use),)),
-        normalizer,
-        layers.Dense(30, activation='relu'),
-        layers.Dense(10, activation='relu'),
-        layers.Dense(25, activation='relu'),
-        layers.Dense(1)
-    ])
+model = keras.Sequential([
+    keras.Input(shape=(len(columns_to_use),)),
+    normalizer,
+    layers.Dense(30, activation='relu'),
+    layers.Dense(10, activation='relu'),
+    layers.Dense(25, activation='relu'),
+    layers.Dense(1)
+])
 
-    model.compile(loss='mean_absolute_error',
-                    optimizer=tf.keras.optimizers.Adam(0.001))
+model.compile(loss='mean_absolute_error',
+                optimizer=tf.keras.optimizers.Adam(0.001))
 
-    model.fit(train_X, train_Y, verbose=0, epochs=100)
-
-    model.save('linear_regression')
+model.fit(train_X, train_Y, verbose=0, epochs=100)
 
 # Predykcja na danych testowych
 results = model.predict(test_X)
diff --git a/training.py b/training.py
index f3b3e23..68e91fe 100644
--- a/training.py
+++ b/training.py
@@ -27,22 +27,19 @@ train_Y = tf.convert_to_tensor(train_data[["IMDb"]])
 normalizer = preprocessing.Normalization(input_shape=[3,])
 normalizer.adapt(train_X)
 
-if os.path.isdir('linear_regression'):
-    model = keras.models.load_model('linear_regression')
-else:
-    model = keras.Sequential([
-        keras.Input(shape=(len(columns_to_use),)),
-        normalizer,
-        layers.Dense(30, activation='relu'),
-        layers.Dense(10, activation='relu'),
-        layers.Dense(25, activation='relu'),
-        layers.Dense(1)
-    ])
+model = keras.Sequential([
+    keras.Input(shape=(len(columns_to_use),)),
+    normalizer,
+    layers.Dense(30, activation='relu'),
+    layers.Dense(10, activation='relu'),
+    layers.Dense(25, activation='relu'),
+    layers.Dense(1)
+])
 
-    model.compile(loss='mean_absolute_error',
-                    optimizer=tf.keras.optimizers.Adam(0.001),
-                    metrics=[tf.keras.metrics.RootMeanSquaredError()])
+model.compile(loss='mean_absolute_error',
+                optimizer=tf.keras.optimizers.Adam(0.001),
+                metrics=[tf.keras.metrics.RootMeanSquaredError()])
 
-    model.fit(train_X, train_Y, verbose=verbose, epochs=epochs)
+model.fit(train_X, train_Y, verbose=verbose, epochs=epochs)
 
-    model.save('linear_regression.h5')
\ No newline at end of file
+model.save('linear_regression.h5')
\ No newline at end of file