-load trained weights and predict single image

predict.py

@@ -0,0 +1,51 @@
+import tensorflow as tf
+import config
+import numpy as np
+import pathlib
+from models.resnet import resnet_18, resnet_34, resnet_50, resnet_101, resnet_152
+from config import image_height, image_width, channels, train_dir
+
+
+def get_model():
+    model = resnet_50()
+    if config.model == "resnet18":
+        model = resnet_18()
+    if config.model == "resnet34":
+        model = resnet_34()
+    if config.model == "resnet101":
+        model = resnet_101()
+    if config.model == "resnet152":
+        model = resnet_152()
+    model.build(input_shape=(None, config.image_height, config.image_width, config.channels))
+    model.load_weights(config.save_model_dir)
+    model.summary()
+    return model
+
+def load_image_input(img_path):
+    #load image
+    img = tf.keras.preprocessing.image.load_img(img_path, target_size=(image_height, image_width))
+    img_arr = tf.keras.preprocessing.image.img_to_array(img)
+    input_img = np.expand_dims(img_arr, axis=0)
+    input_img = tf.keras.utils.normalize(input_img)
+    return input_img
+
+def get_label(prediction):
+    data_root = pathlib.Path(train_dir)
+    label_names = sorted(item.name for item in data_root.glob('*/'))
+    return label_names[prediction]
+
+
+if __name__ == '__main__':
+    # GPU settings
+    gpus = tf.config.experimental.list_physical_devices('GPU')
+    if gpus:
+        for gpu in gpus:
+            tf.config.experimental.set_memory_growth(gpu, True)
+
+    # create model
+    model = get_model()
+    # change path to your image
+    # make prediction
+    prediction = model.predict(load_image_input(path))
+    # print prediction
+    print("Prediction:",get_label(np.argmax(prediction[0])), "\nAccuracy:", prediction[0][np.argmax(prediction)])