experiment

app.py

@@ -3,8 +3,57 @@ from tensorflow.keras.models import load_model
 import gradio as gr
 import numpy as np
 import cv2
+import numpy as np
+import os
+import sys
+from tensorflow import keras
+from tensorflow.keras import layers
+
+EPOCHS = 10
+IMG_WIDTH = 30
+IMG_HEIGHT = 30
+NUM_CATEGORIES = 43
+TEST_SIZE = 0.4
+
+def get_model():
+    """
+    Returns a compiled convolutional neural network model. Assume that the
+    `input_shape` of the first layer is `(IMG_WIDTH, IMG_HEIGHT, 3)`.
+    The output layer should have `NUM_CATEGORIES` units, one for each category.
+    """
+    # Experimenting with different architectures of a Sequential model and this is so far best for me
+    # Accepts input with shape of (30, 30, 3)
+    # Convolutional layer with 65 filters and relu activation function
+    # Maxpooled by (2, 2) kernel
+    # Second convolutional layer with higher number of filter i.e 256 and relu activation function
+    # Flattering nd shape and Dense layer with 450 nuerons and relu activation funtion
+    # Output layer with 43 nuerons and softmax activation function
+
+    model = tf.keras.Sequential([
+        layers.Conv2D(64, 5, input_shape = (30, 30, 3), name = "conv1", activation="relu"),
+        layers.MaxPool2D((2, 2), name = "pool1"),
+        layers.Conv2D(256, 3, name = "conv2", activation="relu"),
+        layers.MaxPool2D((2, 2), name = "pool2"),
+        layers.Flatten(),
+        layers.Dense(450, activation = "relu", name = "dense1"),
+        layers.Dense(NUM_CATEGORIES-1, activation = "softmax", name = "output")
+    ])
+
+    # Printing model summary and compiling with adam algorithm, categorical_crossentropy as 43 output neurons
+    model.summary()
+
+    model.compile(
+        optimizer = "adam",
+        loss = "categorical_crossentropy",
+        metrics=["accuracy"]
+    )
+
+    return model
+
+# model = load_model("best_traffic_model.h5")
+# model.compile(optimizer="adam", loss="categorical_crossentropy")
 
-model = load_model("model_1.h5")
+model = get_model()
 
 labels = ["zero","one","two","three","four","five","six","seven","eight","nine","ten","eleven","twelve","thrteen","fourteen","fifteen","sixteen","seventeen","eightteen","nineteen"]