Create more models and picked the best

src/SubprojectMaksymilianKierski/PlateRecognition.py

@@ -24,17 +24,20 @@ relative_path = os.path.join(current_path, 'Data/')
 DATADIR = relative_path + 'LearningData'
 CATEGORIES = ['Full', 'Empty']  # labels
 IMG_SIZE = 150  # specify on which size we want to transform the photos for example 50x50px
-NAME = 'plate-cnn-64x2-{}'.format(int(time.time()))
+NAME = 'plate-cnn-64x3x1-{}'.format(int(time.time()))
 
 training_data = []
 X = []  # feature set
 y = []  # label set
 
 '''
-layer size | conv leyer |
+layer size | conv layer | Dense layer |
-     64    |     1      | loss: 0.0443 - accuracy: 0.9942 - val_loss: 0.3614 - val_accuracy: 0.7692
+     64    |     1      |      0      | loss: 0.0443 - accuracy: 0.9942 - val_loss: 0.3614 - val_accuracy: 0.7692
-     64    |     2      | loss: 0.0931 - accuracy: 0.9625 - val_loss: 0.4772 - val_accuracy: 0.8462
+     64    |     2      |      0      | loss: 0.0931 - accuracy: 0.9625 - val_loss: 0.4772 - val_accuracy: 0.8462 
-     64    |     3      |
+     64    |     3      |      0      | loss: 0.2491 - accuracy: 0.9020 - val_loss: 0.3762 - val_accuracy: 0.7949
+     64    |     1      |      1      | loss: 0.0531 - accuracy: 0.9971 - val_loss: 0.4176 - val_accuracy: 0.8205 ->
+     64    |     2      |      1      | loss: 0.0644 - accuracy: 0.9798 - val_loss: 0.5606 - val_accuracy: 0.8462
+     64    |     3      |      1      | loss: 0.1126 - accuracy: 0.9625 - val_loss: 0.5916 - val_accuracy: 0.8205
 '''
 
 
@@ -110,6 +113,10 @@ def creat_model():
     model.add(Activation('relu'))
     model.add(MaxPooling2D(pool_size=(2, 2)))
 
+    # model.add(Conv2D(64, (3, 3)))
+    # model.add(Activation('relu'))
+    # model.add(MaxPooling2D(pool_size=(2, 2)))
+
     model.add(Flatten())  # converts our 3D feature maps to 1D feature vectors
 
     model.add(Dense(1))  # output layer 1 output
@@ -135,11 +142,12 @@ def use_model_to_predict(name):
         plt.show()
         return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1)
 
-    model = tf.keras.models.load_model(relative_path + 'SavedModels/plate-cnn-64x1-1590497461.model')
+    model = tf.keras.models.load_model(relative_path + 'SavedModels/plate-64x2-cnn.model')
+    #model = tf.keras.models.load_model(relative_path + 'SavedModels/plate-cnn-64x1-1590497461.model')
     prediction = model.predict([prepare(relative_path + 'TestData/' + name + '.jpg')])
 
-    print(prediction)
+    #print(prediction)
-    print(CATEGORIES[int(prediction[0][0])])
+    #print(CATEGORIES[int(prediction[0][0])])
     return int(prediction[0][0])
 
 
@@ -164,5 +172,8 @@ def text_speech(font: str, size: int, text: str, color, background, x, y, bold:
 
 
 # save_dataset()
-#use_model_to_predict('test-0')
+# for x in range(10):
-creat_model()
+#     print(CATEGORIES[use_model_to_predict('test-{}'.format(x))])
+#creat_model()
+
+#python -m tensorboard.main --logdir=Data/logs