train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-13T19:09:44.023596641Z",
     "start_time": "2023-08-13T19:09:43.977450143Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-15 21:14:21.662840: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-08-15 21:14:22.853637: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-15 21:14:23.910667: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-08-15 21:14:24.175956: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-08-15 21:14:24.176139: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "print(tf.config.list_physical_devices('GPU'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "is_executing": true
   },
   "outputs": [],
   "source": [
    "%pip install datasets\n",
    "%pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.datasets import cifar10\n",
    "from tensorflow.keras import backend as K\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Helper functions\n",
    "def show_min_max(array, i):\n",
    "  random_image = array[i]\n",
    "  print(\"min and max value in image: \", random_image.min(), random_image.max())\n",
    "\n",
    "\n",
    "def plot_image(array, i, labels):\n",
    "  plt.imshow(np.squeeze(array[i]))\n",
    "  plt.title(str(label_names[labels[i]]))\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "  plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_columns = 28\n",
    "num_rows = 28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_classes = 345 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_shape = (num_columns, num_rows, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Student\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"Xenova/quickdraw-small\")\n",
    "\n",
    "\n",
    "train_data = pd.DataFrame(dataset['train'][0:400000])\n",
    "test_data = pd.DataFrame(dataset['test'][0:20000])\n",
    "valid_data = pd.DataFrame(dataset['valid'][0:30000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = np.array([np.asarray(image) for image in train_data[\"image\"]])\n",
    "test_images = np.array([np.asarray(image) for image in test_data[\"image\"]])\n",
    "valid_images = np.array([np.asarray(image) for image in valid_data[\"image\"]])\n",
    "train_labels = np.array([np.asarray(image) for image in train_data[\"label\"]])\n",
    "test_labels = np.array([np.asarray(image) for image in test_data[\"label\"]])\n",
    "valid_labels = np.array([np.asarray(image) for image in valid_data[\"label\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images.reshape(-1, 28, 28, 1)\n",
    "test_images = test_images.reshape(-1, 28, 28, 1)\n",
    "valid_images = valid_images.reshape(-1, 28, 28, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "label_names = requests.get(\"https://raw.githubusercontent.com/googlecreativelab/quickdraw-dataset/master/categories.txt\").text.split(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20000, 28, 28, 1) (400000, 28, 28, 1) (30000, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "print(test_images.shape, train_images.shape, valid_images.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_image(train_images, 100, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min and max value in image:  0 255\n"
     ]
    }
   ],
   "source": [
    "show_min_max(train_images, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images.astype('float32')\n",
    "test_images = test_images.astype('float32')\n",
    "valid_images = valid_images.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images /= 255\n",
    "test_images /= 255\n",
    "valid_images /= 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min and max value in image:  0.0 1.0\n"
     ]
    }
   ],
   "source": [
    "show_min_max(train_images, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = keras.utils.to_categorical(train_labels, num_classes)\n",
    "test_labels = keras.utils.to_categorical(test_labels, num_classes)\n",
    "valid_labels = keras.utils.to_categorical(test_labels, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential \n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, BatchNormalization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 15\n",
    "batch_size = 64\n",
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.pop()\n",
    "model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))\n",
    "model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(rate=0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(BatchNormalization()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(1, 1)))\n",
    "model.add(Dropout(rate=0.3)) \n",
    "model.add(BatchNormalization())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Conv2D(filters=128, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(1, 1)))\n",
    "model.add(Dropout(rate=0.3)) \n",
    "model.add(BatchNormalization())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Flatten())\n",
    "model.add(Dense(units=128, activation='relu'))\n",
    "model.add(Dense(units=num_classes,activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 24, 24, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 12, 12, 64)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 12, 12, 64)        0         \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 12, 12, 64)       256       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 10, 10, 64)        36928     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 10, 10, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 10, 10, 64)        0         \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 10, 10, 64)       256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 8, 8, 128)         73856     \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 6, 6, 64)          73792     \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 6, 6, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 6, 6, 64)          0         \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 6, 6, 64)         256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 2304)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               295040    \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 345)               44505     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 543,705\n",
      "Trainable params: 543,321\n",
      "Non-trainable params: 384\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# model.build(input_shape)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "6250/6250 [==============================] - 764s 122ms/step - loss: 2.8115 - accuracy: 0.3788 - val_loss: 2.1239 - val_accuracy: 0.5015\n",
      "Epoch 2/15\n",
      "6250/6250 [==============================] - 666s 107ms/step - loss: 2.0614 - accuracy: 0.5148 - val_loss: 1.8853 - val_accuracy: 0.5508\n",
      "Epoch 3/15\n",
      "6250/6250 [==============================] - 661s 106ms/step - loss: 1.8879 - accuracy: 0.5499 - val_loss: 1.7767 - val_accuracy: 0.5794\n",
      "Epoch 4/15\n",
      "6250/6250 [==============================] - 693s 111ms/step - loss: 1.7942 - accuracy: 0.5689 - val_loss: 1.7023 - val_accuracy: 0.5922\n",
      "Epoch 5/15\n",
      "6250/6250 [==============================] - 662s 106ms/step - loss: 1.7349 - accuracy: 0.5823 - val_loss: 1.6556 - val_accuracy: 0.6033\n",
      "Epoch 6/15\n",
      "6250/6250 [==============================] - 638s 102ms/step - loss: 1.6909 - accuracy: 0.5916 - val_loss: 1.6384 - val_accuracy: 0.6076\n",
      "Epoch 7/15\n",
      "6250/6250 [==============================] - 636s 102ms/step - loss: 1.6538 - accuracy: 0.5991 - val_loss: 1.6249 - val_accuracy: 0.6112\n",
      "Epoch 8/15\n",
      "6250/6250 [==============================] - 638s 102ms/step - loss: 1.6270 - accuracy: 0.6043 - val_loss: 1.6199 - val_accuracy: 0.6088\n",
      "Epoch 9/15\n",
      "6250/6250 [==============================] - 638s 102ms/step - loss: 1.6081 - accuracy: 0.6084 - val_loss: 1.5950 - val_accuracy: 0.6161\n",
      "Epoch 10/15\n",
      "6250/6250 [==============================] - 641s 103ms/step - loss: 1.5877 - accuracy: 0.6128 - val_loss: 1.5888 - val_accuracy: 0.6212\n",
      "Epoch 11/15\n",
      "6250/6250 [==============================] - 690s 110ms/step - loss: 1.5721 - accuracy: 0.6158 - val_loss: 1.5822 - val_accuracy: 0.6193\n",
      "Epoch 12/15\n",
      "6250/6250 [==============================] - 718s 115ms/step - loss: 1.5580 - accuracy: 0.6193 - val_loss: 1.5628 - val_accuracy: 0.6271\n",
      "Epoch 13/15\n",
      "6250/6250 [==============================] - 644s 103ms/step - loss: 1.5466 - accuracy: 0.6212 - val_loss: 1.5554 - val_accuracy: 0.6284\n",
      "Epoch 14/15\n",
      "6250/6250 [==============================] - 656s 105ms/step - loss: 1.5355 - accuracy: 0.6233 - val_loss: 1.5529 - val_accuracy: 0.6281\n",
      "Epoch 15/15\n",
      "6250/6250 [==============================] - 701s 112ms/step - loss: 1.5264 - accuracy: 0.6249 - val_loss: 1.5410 - val_accuracy: 0.6283\n",
      "Test accuracy: 0.628250002861023\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=keras.losses.categorical_crossentropy, optimizer='adam', metrics=['accuracy'])\n",
    "model.fit(train_images, train_labels, batch_size=batch_size, epochs=epochs, validation_data=(test_images, test_labels), shuffle=True)\n",
    "scores=model.evaluate(test_images, test_labels,verbose=0)\n",
    "print('Test accuracy:', scores[1])       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "625/625 [==============================] - 9s 15ms/step - loss: 1.5410 - accuracy: 0.6283\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.5410195589065552, 0.628250002861023]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_images, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('model.h5')"
   ]
  }
 ],
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