Apply adjustments to run MNIST verification (#172)

Author: aobolensk

README.md

@@ -120,7 +120,8 @@ To start the testing process locally, you need to go to the directory
 You need to download [Alexnet-model.h5](https://github.com/moizahmed97/Convolutional-Neural-Net-Designer/blob/master/AlexNet-model.h5) to the folder *docs*
 
 # **How do I launch the inference?**
-* You need to run the script *parcer.py* that is located in app/AlexNet to read weights from a model *Alexnet-model.h5* and the json file with the weights will be stored in the *docs* folder.
+* Make sure you install the project dependencies by running: *pip install -r requirements.txt*
+* You need to run the script *parser.py* that is located in app/AlexNet to read weights from a model *Alexnet-model.h5* and the json file with the weights will be stored in the *docs* folder.
 * Then put the test images in png format in the folder *docs/input*
 
 # **Accuracy validation**

app/AlexNet/parser.py

@@ -1,16 +1,27 @@
-import tensorflow as tf
-from tensorflow.keras.models import load_model
 import json
-import numpy as np
 import os
 
+import tensorflow as tf
+from tensorflow.keras.initializers import GlorotUniform as OriginalGlorotUniform, Zeros as OriginalZeros
+from tensorflow.keras.models import load_model
+
+class CustomGlorotUniform(OriginalGlorotUniform):
+    def __init__(self, seed=None, **kwargs):
+        kwargs.pop('dtype', None)  # Remove the unexpected dtype keyword if present
+        super().__init__(seed=seed, **kwargs)
+
+class CustomZeros(OriginalZeros):
+    def __init__(self, **kwargs):
+        kwargs.pop('dtype', None)  # Remove the unexpected dtype keyword if present
+        super().__init__(**kwargs)
+
 # Пути к модели и JSON файлу
 BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 MODEL_PATH = os.path.join(BASE_DIR, 'docs', 'AlexNet-model.h5')
 MODEL_DATA_PATH = os.path.join(BASE_DIR, 'docs', 'model_data_alexnet_1.json')
 
 # Загрузка модели
-model = load_model(MODEL_PATH)
+model = load_model(MODEL_PATH, custom_objects={'GlorotUniform': CustomGlorotUniform, 'Zeros': CustomZeros})
 
 # Получение весов модели и информации о порядке слоев
 layer_info = []

app/AlexNet/requirements.txt

@@ -0,0 +1 @@
+tensorflow==2.19.0