diff --git a/.idea/misc.xml b/.idea/misc.xml
index ea01f0b..8d93904 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -1,4 +1,4 @@
-
+
\ No newline at end of file
diff --git a/.idea/smieciara.iml b/.idea/smieciara.iml
index c67b363..d9e6024 100644
--- a/.idea/smieciara.iml
+++ b/.idea/smieciara.iml
@@ -2,7 +2,7 @@
-
+
\ No newline at end of file
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index 686b5de..bfcb7f8 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -1,7 +1,12 @@
-
+
+
+
+
+
+
@@ -42,7 +47,7 @@
-
+
@@ -92,7 +97,7 @@
-
+
@@ -106,11 +111,16 @@
+
+
+
+
+
+
-
@@ -138,6 +148,29 @@
1615920190869
+
+ 1624381365660
+
+
+
+ 1624381365660
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/neural_network.py b/neural_network.py
index 83f7379..de2ce5a 100644
--- a/neural_network.py
+++ b/neural_network.py
@@ -48,6 +48,6 @@ history = model.fit(x=X_train, y=y_train, validation_split=0.1, epochs=100, batc
#model.evaluate(X_test, y_test)
-model.save('C:/Users/Natalia/Desktop/lsm04/smieciara/saved_model')
+model.save('./saved_model')
diff --git a/neural_network2.py b/neural_network2.py
index eb80cba..67db075 100644
--- a/neural_network2.py
+++ b/neural_network2.py
@@ -14,7 +14,7 @@ def b():
a = dff.sample()
pred(a)
-model = tf.keras.models.load_model('C:/Users/Natalia/Desktop/lsm04/smieciara/saved_model')
+model = tf.keras.models.load_model('./saved_model')
def pred(a):
diff --git a/neural_network_vers2.py b/neural_network_vers2.py
index 1541381..65ae25d 100644
--- a/neural_network_vers2.py
+++ b/neural_network_vers2.py
@@ -8,15 +8,15 @@ from PIL import Image
img_height = 180
img_width = 180
class_names=['glass','metal','paper','plastic']
-model = tf.keras.models.load_model('C:/Users/Natalia/Desktop/lsm04/smieciara/saved_model_vers2')
+model = tf.keras.models.load_model('./saved_model_vers2')
def predict():
- path="C:/Users/Natalia/Documents/dane_testowe"
+ path="./dane_testowe"
files=os.listdir(path)
d=random.choice(files)
- im = Image.open("C:/Users/Natalia/Documents/dane_testowe/" + d)
+ im = Image.open("./dane_testowe/" + d)
im.show()
img = keras.preprocessing.image.load_img(
- "C:/Users/Natalia/Documents/dane_testowe/" + d, target_size=(img_height, img_width)
+ "./dane_testowe/" + d, target_size=(img_height, img_width)
)
img_array = keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0) # Create a batch