ebay-ml-lister/testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7eea0d4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  2\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "33d18ebd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 Physical GPU, 3 Logical GPUs\n"
     ]
    }
   ],
   "source": [
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "  # Create 2 virtual GPUs with 1GB memory each\n",
    "  try:\n",
    "    tf.config.set_logical_device_configuration(\n",
    "        gpus[0],\n",
    "        [tf.config.LogicalDeviceConfiguration(memory_limit=1024),\n",
    "         tf.config.LogicalDeviceConfiguration(memory_limit=1024)])\n",
    "    logical_gpus = tf.config.list_logical_devices('GPU')\n",
    "    print(len(gpus), \"Physical GPU,\", len(logical_gpus), \"Logical GPUs\")\n",
    "  except RuntimeError as e:\n",
    "    # Virtual devices must be set before GPUs have been initialized\n",
    "    print(e)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b9ca96e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[22. 28.]\n",
      " [49. 64.]], shape=(2, 2), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "tf.debugging.set_log_device_placement(True)\n",
    "\n",
    "a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])\n",
    "b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])\n",
    "\n",
    "# Run on the GPU\n",
    "c = tf.matmul(a, b)\n",
    "print(c)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "# returns a compiled model\n",
    "# identical to the previous one\n",
    "model = load_model('Model_1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.predict_generator()"
   ]
  }
 ],
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