From 7ef330e80455eab7dc5d289c57e0e1565ac48506 Mon Sep 17 00:00:00 2001
From: jakubknczny <jakubknczny@gmail.com>
Date: Sun, 16 May 2021 13:01:57 +0200
Subject: [PATCH] revert to what works locally

---
 lab5/eval/eval.py   | 30 +++---------------------------
 lab5/train/train.py |  4 ----
 2 files changed, 3 insertions(+), 31 deletions(-)

diff --git a/lab5/eval/eval.py b/lab5/eval/eval.py
index 706c20d..01112d2 100644
--- a/lab5/eval/eval.py
+++ b/lab5/eval/eval.py
@@ -4,43 +4,19 @@ import seaborn as sns
 import sys
 import tensorflow
 from tensorflow.keras import layers
-# from tensorflow.keras.models import load_model
+from tensorflow.keras.models import load_model
 
 
-# X_test = pd.read_csv('test.csv')
-#
-# Y_test = X_test.pop('stabf')
-# Y_test = pd.get_dummies(Y_test)
-#
-# model = load_model('grid-stability-dense.h5')
-X_train = pd.read_csv('train.csv')
 X_test = pd.read_csv('test.csv')
-X_valid = pd.read_csv('valid.csv')
-
-Y_train = X_train.pop('stabf')
-Y_train = pd.get_dummies(Y_train)
 
 Y_test = X_test.pop('stabf')
 Y_test = pd.get_dummies(Y_test)
 
-Y_valid = X_valid.pop('stabf')
-Y_valid = pd.get_dummies(Y_valid)
+model = load_model('grid-stability-dense.h5')
 
-model = tensorflow.keras.Sequential([
-    layers.Input(shape=(12,)),
-    layers.Dense(32),
-    layers.Dense(16),
-    layers.Dense(2, activation='softmax')
-])
-
-model.compile(
-    loss=tensorflow.keras.losses.BinaryCrossentropy(),
-    optimizer=tensorflow.keras.optimizers.Adam(),
-    metrics=[tensorflow.keras.metrics.BinaryAccuracy()])
-
-history = model.fit(X_train, Y_train, epochs=2, validation_data=(X_valid, Y_valid))
 results = model.evaluate(X_test, Y_test, batch_size=64)
 
 with open('eval.csv', 'a', newline='') as fp:
     wr = csv.writer(fp, dialect='excel')
     wr.writerow(results)
+
diff --git a/lab5/train/train.py b/lab5/train/train.py
index 0f3c519..952c297 100644
--- a/lab5/train/train.py
+++ b/lab5/train/train.py
@@ -4,15 +4,11 @@ import tensorflow
 from tensorflow.keras import layers
 
 X_train = pd.read_csv('train.csv')
-X_test = pd.read_csv('test.csv')
 X_valid = pd.read_csv('valid.csv')
 
 Y_train = X_train.pop('stabf')
 Y_train = pd.get_dummies(Y_train)
 
-Y_test = X_test.pop('stabf')
-Y_test = pd.get_dummies(Y_test)
-
 Y_valid = X_valid.pop('stabf')
 Y_valid = pd.get_dummies(Y_valid)