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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n",
"WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
"WARNING:absl:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[2], line 41\u001b[0m\n\u001b[0;32m 38\u001b[0m face_image \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mvstack([face_image])\n\u001b[0;32m 40\u001b[0m \u001b[38;5;66;03m# Predict emotion using the loaded model\u001b[39;00m\n\u001b[1;32m---> 41\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_best\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mface_image\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 42\u001b[0m emotion_label \u001b[38;5;241m=\u001b[39m class_names[np\u001b[38;5;241m.\u001b[39margmax(predictions)]\n\u001b[0;32m 44\u001b[0m \u001b[38;5;66;03m# Display the emotion label on the frame\u001b[39;00m\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 117\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 119\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:448\u001b[0m, in \u001b[0;36mTensorFlowTrainer.predict\u001b[1;34m(self, x, batch_size, verbose, steps, callbacks)\u001b[0m\n\u001b[0;32m 443\u001b[0m \u001b[38;5;129m@traceback_utils\u001b[39m\u001b[38;5;241m.\u001b[39mfilter_traceback\n\u001b[0;32m 444\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\n\u001b[0;32m 445\u001b[0m \u001b[38;5;28mself\u001b[39m, x, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto\u001b[39m\u001b[38;5;124m\"\u001b[39m, steps\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, callbacks\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 446\u001b[0m ):\n\u001b[0;32m 447\u001b[0m \u001b[38;5;66;03m# Create an iterator that yields batches of input data.\u001b[39;00m\n\u001b[1;32m--> 448\u001b[0m epoch_iterator \u001b[38;5;241m=\u001b[39m \u001b[43mTFEpochIterator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 449\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 450\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 451\u001b[0m \u001b[43m \u001b[49m\u001b[43msteps_per_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msteps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 452\u001b[0m \u001b[43m \u001b[49m\u001b[43mshuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 453\u001b[0m \u001b[43m \u001b[49m\u001b[43mdistribute_strategy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdistribute_strategy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 454\u001b[0m \u001b[43m \u001b[49m\u001b[43msteps_per_execution\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msteps_per_execution\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 455\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 457\u001b[0m \u001b[38;5;66;03m# Container that configures and calls callbacks.\u001b[39;00m\n\u001b[0;32m 458\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(callbacks, callbacks_module\u001b[38;5;241m.\u001b[39mCallbackList):\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:666\u001b[0m, in \u001b[0;36mTFEpochIterator.__init__\u001b[1;34m(self, distribute_strategy, *args, **kwargs)\u001b[0m\n\u001b[0;32m 664\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 665\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_distribute_strategy \u001b[38;5;241m=\u001b[39m distribute_strategy\n\u001b[1;32m--> 666\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_iterator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 667\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dataset, tf\u001b[38;5;241m.\u001b[39mdistribute\u001b[38;5;241m.\u001b[39mDistributedDataset):\n\u001b[0;32m 668\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_distribute_strategy\u001b[38;5;241m.\u001b[39mexperimental_distribute_dataset(\n\u001b[0;32m 669\u001b[0m dataset\n\u001b[0;32m 670\u001b[0m )\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:675\u001b[0m, in \u001b[0;36mTFEpochIterator._get_iterator\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 674\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_iterator\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 675\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata_adapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_tf_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\array_data_adapter.py:232\u001b[0m, in \u001b[0;36mArrayDataAdapter.get_tf_dataset\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 229\u001b[0m dataset \u001b[38;5;241m=\u001b[39m dataset\u001b[38;5;241m.\u001b[39mwith_options(options)\n\u001b[0;32m 230\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dataset\n\u001b[1;32m--> 232\u001b[0m indices_dataset \u001b[38;5;241m=\u001b[39m \u001b[43mindices_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_map\u001b[49m\u001b[43m(\u001b[49m\u001b[43mslice_batch_indices\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 233\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m shuffle \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbatch\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 234\u001b[0m indices_dataset \u001b[38;5;241m=\u001b[39m indices_dataset\u001b[38;5;241m.\u001b[39mmap(tf\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mshuffle)\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\dataset_ops.py:2389\u001b[0m, in \u001b[0;36mDatasetV2.flat_map\u001b[1;34m(self, map_func, name)\u001b[0m\n\u001b[0;32m 2385\u001b[0m \u001b[38;5;66;03m# Loaded lazily due to a circular dependency (dataset_ops -> flat_map_op ->\u001b[39;00m\n\u001b[0;32m 2386\u001b[0m \u001b[38;5;66;03m# dataset_ops).\u001b[39;00m\n\u001b[0;32m 2387\u001b[0m \u001b[38;5;66;03m# pylint: disable=g-import-not-at-top,protected-access\u001b[39;00m\n\u001b[0;32m 2388\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mops\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m flat_map_op\n\u001b[1;32m-> 2389\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mflat_map_op\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_flat_map\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmap_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\flat_map_op.py:24\u001b[0m, in \u001b[0;36m_flat_map\u001b[1;34m(input_dataset, map_func, name)\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_flat_map\u001b[39m(input_dataset, map_func, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m): \u001b[38;5;66;03m# pylint: disable=unused-private-name\u001b[39;00m\n\u001b[0;32m 23\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"See `Dataset.flat_map()` for details.\"\"\"\u001b[39;00m\n\u001b[1;32m---> 24\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_FlatMapDataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmap_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\flat_map_op.py:33\u001b[0m, in \u001b[0;36m_FlatMapDataset.__init__\u001b[1;34m(self, input_dataset, map_func, name)\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, input_dataset, map_func, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m 32\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_input_dataset \u001b[38;5;241m=\u001b[39m input_dataset\n\u001b[1;32m---> 33\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_map_func \u001b[38;5;241m=\u001b[39m \u001b[43mstructured_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mStructuredFunctionWrapper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 34\u001b[0m \u001b[43m \u001b[49m\u001b[43mmap_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_transformation_name\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_dataset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 35\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_map_func\u001b[38;5;241m.\u001b[39moutput_structure, dataset_ops\u001b[38;5;241m.\u001b[39mDatasetSpec):\n\u001b[0;32m 36\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m 37\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe `map_func` argument must return a `Dataset` object. Got \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 38\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdataset_ops\u001b[38;5;241m.\u001b[39mget_type(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_map_func\u001b[38;5;241m.\u001b[39moutput_structure)\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\data\\ops\\structured_function.py:265\u001b[0m, in \u001b[0;36mStructuredFunctionWrapper.__init__\u001b[1;34m(self, func, transformation_name, dataset, input_classes, input_shapes, input_types, input_structure, add_to_graph, use_legacy_function, defun_kwargs)\u001b[0m\n\u001b[0;32m 258\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m 259\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEven though the `tf.config.experimental_run_functions_eagerly` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 260\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moption is set, this option does not apply to tf.data functions. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 261\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTo force eager execution of tf.data functions, please use \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 262\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`tf.data.experimental.enable_debug_mode()`.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 263\u001b[0m fn_factory \u001b[38;5;241m=\u001b[39m trace_tf_function(defun_kwargs)\n\u001b[1;32m--> 265\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_function \u001b[38;5;241m=\u001b[39m \u001b[43mfn_factory\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 266\u001b[0m \u001b[38;5;66;03m# There is no graph to add in eager mode.\u001b[39;00m\n\u001b[0;32m 267\u001b[0m add_to_graph \u001b[38;5;241m&\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m context\u001b[38;5;241m.\u001b[39mexecuting_eagerly()\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:1251\u001b[0m, in \u001b[0;36mFunction.get_concrete_function\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1249\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_concrete_function\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 1250\u001b[0m \u001b[38;5;66;03m# Implements PolymorphicFunction.get_concrete_function.\u001b[39;00m\n\u001b[1;32m-> 1251\u001b[0m concrete \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_concrete_function_garbage_collected\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1252\u001b[0m concrete\u001b[38;5;241m.\u001b[39m_garbage_collector\u001b[38;5;241m.\u001b[39mrelease() \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[0;32m 1253\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m concrete\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:1221\u001b[0m, in \u001b[0;36mFunction._get_concrete_function_garbage_collected\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1219\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_config \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 1220\u001b[0m initializers \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m-> 1221\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_initialize\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43madd_initializers_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitializers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1222\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_initialize_uninitialized_variables(initializers)\n\u001b[0;32m 1224\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[0;32m 1225\u001b[0m \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[0;32m 1226\u001b[0m \u001b[38;5;66;03m# version which is guaranteed to never create variables.\u001b[39;00m\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:696\u001b[0m, in \u001b[0;36mFunction._initialize\u001b[1;34m(self, args, kwds, add_initializers_to)\u001b[0m\n\u001b[0;32m 691\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_config \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate_scoped_tracing_options(\n\u001b[0;32m 692\u001b[0m variable_capturing_scope,\n\u001b[0;32m 693\u001b[0m tracing_compilation\u001b[38;5;241m.\u001b[39mScopeType\u001b[38;5;241m.\u001b[39mVARIABLE_CREATION,\n\u001b[0;32m 694\u001b[0m )\n\u001b[0;32m 695\u001b[0m \u001b[38;5;66;03m# Force the definition of the function for these arguments\u001b[39;00m\n\u001b[1;32m--> 696\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_concrete_variable_creation_fn \u001b[38;5;241m=\u001b[39m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrace_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 697\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable_creation_config\u001b[49m\n\u001b[0;32m 698\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 700\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvalid_creator_scope\u001b[39m(\u001b[38;5;241m*\u001b[39munused_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39munused_kwds):\n\u001b[0;32m 701\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Disables variable creation.\"\"\"\u001b[39;00m\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compilation.py:178\u001b[0m, in \u001b[0;36mtrace_function\u001b[1;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[0;32m 175\u001b[0m args \u001b[38;5;241m=\u001b[39m tracing_options\u001b[38;5;241m.\u001b[39minput_signature\n\u001b[0;32m 176\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m--> 178\u001b[0m concrete_function \u001b[38;5;241m=\u001b[39m \u001b[43m_maybe_define_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 179\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtracing_options\u001b[49m\n\u001b[0;32m 180\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m tracing_options\u001b[38;5;241m.\u001b[39mbind_graph_to_function:\n\u001b[0;32m 183\u001b[0m concrete_function\u001b[38;5;241m.\u001b[39m_garbage_collector\u001b[38;5;241m.\u001b[39mrelease() \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compilation.py:283\u001b[0m, in \u001b[0;36m_maybe_define_function\u001b[1;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[0;32m 281\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 282\u001b[0m target_func_type \u001b[38;5;241m=\u001b[39m lookup_func_type\n\u001b[1;32m--> 283\u001b[0m concrete_function \u001b[38;5;241m=\u001b[39m \u001b[43m_create_concrete_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 284\u001b[0m \u001b[43m \u001b[49m\u001b[43mtarget_func_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlookup_func_context\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtracing_options\u001b[49m\n\u001b[0;32m 285\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 287\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tracing_options\u001b[38;5;241m.\u001b[39mfunction_cache \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 288\u001b[0m tracing_options\u001b[38;5;241m.\u001b[39mfunction_cache\u001b[38;5;241m.\u001b[39madd(\n\u001b[0;32m 289\u001b[0m concrete_function, current_func_context\n\u001b[0;32m 290\u001b[0m )\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compilation.py:310\u001b[0m, in \u001b[0;36m_create_concrete_function\u001b[1;34m(function_type, type_context, func_graph, tracing_options)\u001b[0m\n\u001b[0;32m 303\u001b[0m placeholder_bound_args \u001b[38;5;241m=\u001b[39m function_type\u001b[38;5;241m.\u001b[39mplaceholder_arguments(\n\u001b[0;32m 304\u001b[0m placeholder_context\n\u001b[0;32m 305\u001b[0m )\n\u001b[0;32m 307\u001b[0m disable_acd \u001b[38;5;241m=\u001b[39m tracing_options\u001b[38;5;241m.\u001b[39mattributes \u001b[38;5;129;01mand\u001b[39;00m tracing_options\u001b[38;5;241m.\u001b[39mattributes\u001b[38;5;241m.\u001b[39mget(\n\u001b[0;32m 308\u001b[0m attributes_lib\u001b[38;5;241m.\u001b[39mDISABLE_ACD, \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 309\u001b[0m )\n\u001b[1;32m--> 310\u001b[0m traced_func_graph \u001b[38;5;241m=\u001b[39m \u001b[43mfunc_graph_module\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc_graph_from_py_func\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 311\u001b[0m \u001b[43m \u001b[49m\u001b[43mtracing_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 312\u001b[0m \u001b[43m \u001b[49m\u001b[43mtracing_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpython_function\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 313\u001b[0m \u001b[43m \u001b[49m\u001b[43mplaceholder_bound_args\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 314\u001b[0m \u001b[43m \u001b[49m\u001b[43mplaceholder_bound_args\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 315\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 316\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc_graph\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunc_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 317\u001b[0m \u001b[43m \u001b[49m\u001b[43madd_control_dependencies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdisable_acd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 318\u001b[0m \u001b[43m \u001b[49m\u001b[43marg_names\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction_type_utils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_arg_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunction_type\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 319\u001b[0m \u001b[43m \u001b[49m\u001b[43mcreate_placeholders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 320\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 322\u001b[0m transform\u001b[38;5;241m.\u001b[39mapply_func_graph_transforms(traced_func_graph)\n\u001b[0;32m 324\u001b[0m graph_capture_container \u001b[38;5;241m=\u001b[39m traced_func_graph\u001b[38;5;241m.\u001b[39mfunction_captures\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py:987\u001b[0m, in \u001b[0;36mfunc_graph_from_py_func\u001b[1;34m(name, python_func, args, kwargs, signature, func_graph, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, create_placeholders)\u001b[0m\n\u001b[0;32m 984\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 985\u001b[0m deps_control_manager \u001b[38;5;241m=\u001b[39m ops\u001b[38;5;241m.\u001b[39mNullContextmanager()\n\u001b[1;32m--> 987\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfunc_graph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_default\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdeps_control_manager\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mas\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdeps_ctx\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m 988\u001b[0m \u001b[43m \u001b[49m\u001b[43mcurrent_scope\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mvariable_scope\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_variable_scope\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 989\u001b[0m \u001b[43m \u001b[49m\u001b[43mdefault_use_resource\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mcurrent_scope\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43muse_resource\u001b[49m\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\framework\\auto_control_deps.py:533\u001b[0m, in \u001b[0;36mAutomaticControlDependencies.__exit__\u001b[1;34m(self, unused_type, unused_value, unused_traceback)\u001b[0m\n\u001b[0;32m 526\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m r\u001b[38;5;241m.\u001b[39mgraph\u001b[38;5;241m.\u001b[39mbuilding_function:\n\u001b[0;32m 527\u001b[0m \u001b[38;5;66;03m# There may be many stateful ops in the graph. Adding them as\u001b[39;00m\n\u001b[0;32m 528\u001b[0m \u001b[38;5;66;03m# control inputs to each function output could create excessive\u001b[39;00m\n\u001b[0;32m 529\u001b[0m \u001b[38;5;66;03m# control edges in the graph. Thus we create an intermediate No-op to\u001b[39;00m\n\u001b[0;32m 530\u001b[0m \u001b[38;5;66;03m# chain the control dependencies between stateful ops and function\u001b[39;00m\n\u001b[0;32m 531\u001b[0m \u001b[38;5;66;03m# outputs.\u001b[39;00m\n\u001b[0;32m 532\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m idx \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 533\u001b[0m control_output_op \u001b[38;5;241m=\u001b[39m \u001b[43mcontrol_flow_ops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mno_op\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 534\u001b[0m control_output_op\u001b[38;5;241m.\u001b[39m_add_control_inputs(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mops_which_must_run)\n\u001b[0;32m 535\u001b[0m updated_ops_which_must_run \u001b[38;5;241m=\u001b[39m [control_output_op]\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\ops\\gen_control_flow_ops.py:531\u001b[0m, in \u001b[0;36mno_op\u001b[1;34m(name)\u001b[0m\n\u001b[0;32m 529\u001b[0m \u001b[38;5;66;03m# Add nodes to the TensorFlow graph.\u001b[39;00m\n\u001b[0;32m 530\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 531\u001b[0m _, _, _op, _outputs \u001b[38;5;241m=\u001b[39m \u001b[43m_op_def_library\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_apply_op_helper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 532\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mNoOp\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 533\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mValueError\u001b[39;00m):\n\u001b[0;32m 534\u001b[0m _result \u001b[38;5;241m=\u001b[39m _dispatch\u001b[38;5;241m.\u001b[39mdispatch(\n\u001b[0;32m 535\u001b[0m no_op, (), \u001b[38;5;28mdict\u001b[39m(name\u001b[38;5;241m=\u001b[39mname)\n\u001b[0;32m 536\u001b[0m )\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:796\u001b[0m, in \u001b[0;36m_apply_op_helper\u001b[1;34m(op_type_name, name, **keywords)\u001b[0m\n\u001b[0;32m 791\u001b[0m must_colocate_inputs \u001b[38;5;241m=\u001b[39m [val \u001b[38;5;28;01mfor\u001b[39;00m arg, val \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(op_def\u001b[38;5;241m.\u001b[39minput_arg, inputs)\n\u001b[0;32m 792\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mis_ref]\n\u001b[0;32m 793\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _MaybeColocateWith(must_colocate_inputs):\n\u001b[0;32m 794\u001b[0m \u001b[38;5;66;03m# Add Op to graph\u001b[39;00m\n\u001b[0;32m 795\u001b[0m \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m--> 796\u001b[0m op \u001b[38;5;241m=\u001b[39m \u001b[43mg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_op_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mop_type_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtypes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 797\u001b[0m \u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscope\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_types\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_types\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 798\u001b[0m \u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattr_protos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_def\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mop_def\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 800\u001b[0m \u001b[38;5;66;03m# `outputs` is returned as a separate return value so that the output\u001b[39;00m\n\u001b[0;32m 801\u001b[0m \u001b[38;5;66;03m# tensors can the `op` per se can be decoupled so that the\u001b[39;00m\n\u001b[0;32m 802\u001b[0m \u001b[38;5;66;03m# `op_callbacks` can function properly. See framework/op_callbacks.py\u001b[39;00m\n\u001b[0;32m 803\u001b[0m \u001b[38;5;66;03m# for more details.\u001b[39;00m\n\u001b[0;32m 804\u001b[0m outputs \u001b[38;5;241m=\u001b[39m op\u001b[38;5;241m.\u001b[39moutputs\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py:670\u001b[0m, in \u001b[0;36mFuncGraph._create_op_internal\u001b[1;34m(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)\u001b[0m\n\u001b[0;32m 668\u001b[0m inp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcapture(inp)\n\u001b[0;32m 669\u001b[0m captured_inputs\u001b[38;5;241m.\u001b[39mappend(inp)\n\u001b[1;32m--> 670\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_op_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[0;32m 671\u001b[0m \u001b[43m \u001b[49m\u001b[43mop_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_types\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_def\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 672\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompute_device\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\framework\\ops.py:2701\u001b[0m, in \u001b[0;36mGraph._create_op_internal\u001b[1;34m(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)\u001b[0m\n\u001b[0;32m 2698\u001b[0m \u001b[38;5;66;03m# _create_op_helper mutates the new Operation. `_mutation_lock` ensures a\u001b[39;00m\n\u001b[0;32m 2699\u001b[0m \u001b[38;5;66;03m# Session.run call cannot occur between creating and mutating the op.\u001b[39;00m\n\u001b[0;32m 2700\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mutation_lock():\n\u001b[1;32m-> 2701\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mOperation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_node_def\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2702\u001b[0m \u001b[43m \u001b[49m\u001b[43mnode_def\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2703\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2704\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2705\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_types\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtypes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2706\u001b[0m \u001b[43m \u001b[49m\u001b[43mcontrol_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontrol_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2707\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_types\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_types\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2708\u001b[0m \u001b[43m \u001b[49m\u001b[43moriginal_op\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_default_original_op\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2709\u001b[0m \u001b[43m \u001b[49m\u001b[43mop_def\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mop_def\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2710\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2711\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_op_helper(ret, compute_device\u001b[38;5;241m=\u001b[39mcompute_device)\n\u001b[0;32m 2712\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\framework\\ops.py:1196\u001b[0m, in \u001b[0;36mOperation.from_node_def\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m 1193\u001b[0m control_input_ops\u001b[38;5;241m.\u001b[39mappend(control_op)\n\u001b[0;32m 1195\u001b[0m \u001b[38;5;66;03m# Initialize c_op from node_def and other inputs\u001b[39;00m\n\u001b[1;32m-> 1196\u001b[0m c_op \u001b[38;5;241m=\u001b[39m \u001b[43m_create_c_op\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode_def\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontrol_input_ops\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_def\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mop_def\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1197\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m Operation(c_op, SymbolicTensor)\n\u001b[0;32m 1198\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init(g)\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 152\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
"File \u001b[1;32mc:\\Users\\Aryan\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\framework\\ops.py:1026\u001b[0m, in \u001b[0;36m_create_c_op\u001b[1;34m(graph, node_def, inputs, control_inputs, op_def, extract_traceback)\u001b[0m\n\u001b[0;32m 1024\u001b[0m \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[0;32m 1025\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m graph\u001b[38;5;241m.\u001b[39m_c_graph\u001b[38;5;241m.\u001b[39mget() \u001b[38;5;28;01mas\u001b[39;00m c_graph:\n\u001b[1;32m-> 1026\u001b[0m op_desc \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tf_session\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTF_NewOperation\u001b[49m\u001b[43m(\u001b[49m\u001b[43mc_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1027\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_str\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode_def\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mop\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1028\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_str\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode_def\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1029\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m node_def\u001b[38;5;241m.\u001b[39mdevice:\n\u001b[0;32m 1030\u001b[0m pywrap_tf_session\u001b[38;5;241m.\u001b[39mTF_SetDevice(op_desc, compat\u001b[38;5;241m.\u001b[39mas_str(node_def\u001b[38;5;241m.\u001b[39mdevice))\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"import cv2\n",
"import numpy as np\n",
"from tensorflow.keras.models import load_model\n",
"from tensorflow.keras.preprocessing import image\n",
"\n",
"# Load the trained model\n",
"model_best = load_model('./model/face_modelCNN.h5') # set your machine model file path here\n",
"\n",
"# Classes 7 emotional states\n",
"class_names = ['Angry', 'Disgusted', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']\n",
"\n",
"# Load the pre-trained face cascade\n",
"face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n",
"\n",
"# Open a connection to the webcam (0 is usually the default camera)\n",
"cap = cv2.VideoCapture(0)\n",
"\n",
"while True:\n",
" # Capture frame-by-frame\n",
" ret, frame = cap.read()\n",
"\n",
" # Convert the frame to grayscale for face detection\n",
" gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
"\n",
" # Detect faces in the frame\n",
" faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5, minSize=(30, 30))\n",
"\n",
" # Process each detected face\n",
" for (x, y, w, h) in faces:\n",
" # Extract the face region\n",
" face_roi = frame[y:y + h, x:x + w]\n",
"\n",
" # Resize the face image to the required input size for the model\n",
" face_image = cv2.resize(face_roi, (48, 48))\n",
" face_image = cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY)\n",
" face_image = image.img_to_array(face_image)\n",
" face_image = np.expand_dims(face_image, axis=0)\n",
" face_image = np.vstack([face_image])\n",
"\n",
" # Predict emotion using the loaded model\n",
" predictions = model_best.predict(face_image)\n",
" emotion_label = class_names[np.argmax(predictions)]\n",
"\n",
" # Display the emotion label on the frame\n",
" cv2.putText(frame, f'Emotion: {emotion_label}', (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX,\n",
" 0.9, (0, 0, 255), 2)\n",
"\n",
" # Draw a rectangle around the face\n",
" cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)\n",
"\n",
" # Display the resulting frame\n",
" cv2.imshow('Emotion Detection', frame)\n",
"\n",
" # Break the loop if 'q' key is pressed\n",
" if cv2.waitKey(1) & 0xFF == ord('q'):\n",
" break\n",
"\n",
"# Release the webcam and close the window\n",
"cap.release()\n",
"cv2.destroyAllWindows()"
]
}
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