Ready Model

main.py

@@ -0,0 +1,57 @@
+from classes.DataLoader import DataSetLoader
+import tensorflow as tf
+import matplotlib.pyplot as plt
+
+def create_model(image_size):
+    model = tf.keras.Sequential()
+    
+    if image_size <= 10:
+        model.add(tf.keras.layers.Conv2D(32, (2, 2), activation='relu', input_shape=(image_size, image_size, 3)))
+        model.add(tf.keras.layers.MaxPooling2D((2, 2), padding='same')) 
+    else:
+        model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(image_size, image_size, 3)))
+        model.add(tf.keras.layers.MaxPooling2D((2, 2)))
+
+    if image_size > 10:
+        model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu'))
+        model.add(tf.keras.layers.MaxPooling2D((2, 2)))
+    
+    if image_size > 20:
+        model.add(tf.keras.layers.Conv2D(128, (3, 3), activation='relu'))
+        model.add(tf.keras.layers.MaxPooling2D((2, 2)))
+
+    model.add(tf.keras.layers.GlobalAveragePooling2D())
+    
+    model.add(tf.keras.layers.Dense(128, activation='relu'))
+    model.add(tf.keras.layers.Dense(7, activation='softmax')) 
+
+    return model
+
+if __name__ == '__main__':
+    #Create dataset from image folder
+
+    # loader = DataSetLoader(p='/Users/denysseredenko/Desktop/ML-cropped/processed', x=50, y=50)
+    # ds = loader.create_dataset_from_csv('../all_data_cropped.csv')
+    # ds.save('/Users/denysseredenko/Desktop/privat/StreetSignRecognitionTensor/dataset_cropped_50x50')
+
+    ds = tf.data.Dataset.load('../dataset/13802249122632658895/00000000.shard/00000000.snapshot')
+    ds = ds.shuffle(buffer_size=43349)
+
+    ds.save('/Users/denysseredenko/Desktop/privat/StreetSignRecognitionTensor/dataset_cropped_50x50')
+
+    train_ds, test_ds = tf.keras.utils.split_dataset(ds, left_size=0.9)
+
+    train_data = train_ds.batch(5).prefetch(tf.data.AUTOTUNE)
+    test_data = test_ds.batch(5).prefetch(tf.data.AUTOTUNE)
+        
+
+    model = create_model(50)
+
+    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
+                  loss=tf.keras.losses.SparseCategoricalCrossentropy(),
+                  metrics=['accuracy'])
+
+    model.fit(train_data, epochs=7)
+
+    test_loss, test_acc = model.evaluate(test_data, verbose=2)
+    print('\nTest accuracy:', test_acc)