RNN_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 16\n",
    "input_dim = 28\n",
    "\n",
    "units = 42\n",
    "output_size = 10\n",
    "\n",
    "# Build the RNN model\n",
    "def build_model():\n",
    "    lstm_layer = keras.layers.RNN(\n",
    "        keras.layers.LSTMCell(units), input_shape=(None, input_dim)\n",
    "    )\n",
    "    model = keras.models.Sequential(\n",
    "        [\n",
    "            lstm_layer,\n",
    "            keras.layers.BatchNormalization(),\n",
    "            keras.layers.Dense(output_size),\n",
    "        ]\n",
    "    )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
      "32768/29515 [=================================] - 0s 3us/step\n",
      "40960/29515 [=========================================] - 0s 3us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "26427392/26421880 [==============================] - 3s 0us/step\n",
      "26435584/26421880 [==============================] - 3s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "16384/5148 [===============================================================================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
      "4423680/4422102 [==============================] - 1s 0us/step\n",
      "4431872/4422102 [==============================] - 1s 0us/step\n"
     ]
    }
   ],
   "source": [
    "mnist = keras.datasets.fashion_mnist\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tensorboard extension is already loaded. To reload it, use:\n",
      "  %reload_ext tensorboard\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " rnn (RNN)                   (None, 42)                11928     \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 42)               168       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " dense (Dense)               (None, 10)                430       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 12,526\n",
      "Trainable params: 12,442\n",
      "Non-trainable params: 84\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-06 10:10:44.087605: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-06 10:10:44.297184: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-06 10:10:44.297791: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-06 10:10:44.302211: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2021-11-06 10:10:44.303666: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-06 10:10:44.304262: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-06 10:10:44.304762: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-06 10:10:45.103928: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-06 10:10:45.104276: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-06 10:10:45.104638: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-06 10:10:45.104909: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1613 MB memory:  -> device: 0, name: NVIDIA GeForce MX150, pci bus id: 0000:01:00.0, compute capability: 6.1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-06 10:10:45.411399: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 188160000 exceeds 10% of free system memory.\n",
      "2021-11-06 10:10:45.696458: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 188160000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "3750/3750 [==============================] - 120s 31ms/step - loss: 0.7612 - accuracy: 0.7265 - val_loss: 0.6195 - val_accuracy: 0.7780\n",
      "Epoch 2/5\n",
      "3750/3750 [==============================] - 115s 31ms/step - loss: 0.5467 - accuracy: 0.8038 - val_loss: 0.5203 - val_accuracy: 0.8114\n",
      "Epoch 3/5\n",
      "3750/3750 [==============================] - 129s 35ms/step - loss: 0.4868 - accuracy: 0.8264 - val_loss: 0.4870 - val_accuracy: 0.8223\n",
      "Epoch 4/5\n",
      "3750/3750 [==============================] - 129s 34ms/step - loss: 0.4572 - accuracy: 0.8371 - val_loss: 0.4563 - val_accuracy: 0.8358\n",
      "Epoch 5/5\n",
      "3750/3750 [==============================] - 154s 41ms/step - loss: 0.4380 - accuracy: 0.8438 - val_loss: 0.4172 - val_accuracy: 0.8490\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7feab83e61f0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load_ext tensorboard\n",
    "\n",
    "model = build_model()\n",
    "display(model.summary())\n",
    "\n",
    "model.compile(\n",
    "    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "    optimizer=\"sgd\",\n",
    "    metrics=[\"accuracy\"],\n",
    ")\n",
    "\n",
    "log_dir = \"logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
    "\n",
    "model.fit(\n",
    "    x_train, y_train, validation_data=(x_test, y_test), batch_size=batch_size, epochs=5, callbacks=[tensorboard_callback]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n",
      "Predicted result is: [8], target result is: 8\n"
     ]
    },
    {
     "data": {
      "image/png": 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adjxjxoyw3tPTE9Y70XDejf+zpKGasSdsTx3IEVfQAZkg7EAmCDuQCcIOZIKwA5kg7EAmrGqf9bgezKx9D9Zk3d3dpbXZs2eHx3Z1xVcKjx07NqynpnpGyxqnpnKmpJaxrjL9NnUNQKoXPm7cuLAeST2nvb29YX3RokUNP3arufuQ85Y5swOZIOxAJgg7kAnCDmSCsAOZIOxAJgg7kIl299m/kTR4zeZJkv7WtgEcn04dW6eOS2JsjWrm2Ka7+9lDFdoa9u89uNm6Tl2brlPH1qnjkhhbo9o1Nl7GA5kg7EAm6g77spofP9KpY+vUcUmMrVFtGVutv7MDaJ+6z+wA2oSwA5moJexmdq2Z/Y+ZfW5m99cxhjJmtsXMPjKzDXXvT1fsoddjZhsH3TbRzNaY2WfFxyH32KtpbEvNbFvx3G0ws/k1je0cM/uTmX1iZh+b2d3F7bU+d8G42vK8tf13djMbIel/Jf2LpK2S1krqdvdP2jqQEma2RVKXu9d+AYaZ/bOkPknPufvs4rZHJe1094eL/ygnuPt9HTK2pZL66t7Gu9itaOrgbcYlLZB0m2p87oJxLVQbnrc6zuxzJX3u7l+4+yFJL0i6sYZxdDx3f0fSzu/cfKOk5cXnyzXwj6XtSsbWEdx9u7u/X3y+R9KxbcZrfe6CcbVFHWGfJumvg77eqs7a790l/dHM1pvZ4roHM4Qp7r69+PxrSVPqHMwQktt4t9N3thnvmOeuke3Pq+INuu+73N3/SdJ1ku4sXq52JB/4HayTeqfD2sa7XYbYZvz/1fncNbr9eVV1hH2bpHMGff2D4raO4O7bio89kl5S521FvePYDrrFx47ZYbCTtvEeaptxdcBzV+f253WEfa2kmWZ2npmNlrRI0soaxvE9ZjaueONEZjZO0tXqvK2oV0q6tfj8Vkmv1DiWv9Mp23iXbTOump+72rc/d/e2/5E0XwPvyG+W9G91jKFkXD+U9N/Fn4/rHpukFRp4WXdYA+9t/FTSWZLekvSZpDclTeygsT0v6SNJH2ogWFNrGtvlGniJ/qGkDcWf+XU/d8G42vK8cbkskAneoAMyQdiBTBB2IBOEHcgEYQcyQdiBTBB2IBP/B6W5B7dFbFmkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_idx = 34\n",
    "sample, sample_label = x_test[sample_idx], y_test[sample_idx]\n",
    "print(sample.shape)\n",
    "\n",
    "result = tf.argmax(model.predict_on_batch(tf.expand_dims(sample, 0)), axis=1)\n",
    "print(\n",
    "    \"Predicted result is: %s, target result is: %s\" % (result.numpy(), sample_label)\n",
    ")\n",
    "plt.imshow(sample, cmap=plt.get_cmap(\"gray\"))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
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