train into eval

lab5/eval/eval.py

@@ -1,17 +1,50 @@
 import csv
 import pandas as pd
-from tensorflow.keras.models import load_model
+import seaborn as sns
+import sys
+import tensorflow
+from tensorflow.keras import layers
+# 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)
 
-model = load_model('grid-stability-dense.h5')
+Y_valid = X_valid.pop('stabf')
+Y_valid = pd.get_dummies(Y_valid)
+
+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(lr=float(sys.argv[1])),
+    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)
 
+sns.set_theme(style="darkgrid")
+df = pd.read_csv('eval.csv')
+sns.lineplot(x='build', y='score', data=df.iloc[1])

lab5/eval/requirements.txt

@@ -1,4 +1,5 @@
 numpy~=1.19.2
 pandas
 tensorflow
-keras==2.4.3
+keras
+seaborn

lab5/train/train.py

@@ -16,18 +16,12 @@ Y_test = pd.get_dummies(Y_test)
 Y_valid = X_valid.pop('stabf')
 Y_valid = pd.get_dummies(Y_valid)
 
-# model = tensorflow.keras.Sequential([
-#                           layers.Input(shape=(12,)),
-#                           layers.Dense(32),
-#                           layers.Dense(16),
-#                           layers.Dense(2, activation='softmax')
-# ])
-
-model = tensorflow.keras.Sequential()
-model.add(layers.Input(shape=(12,)))
-model.add(layers.Dense(32))
-model.add(layers.Dense(16))
-model.add(layers.Dense(2, activation='softmax'))
+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(),