kannada-mnist.ipynb

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     "text": [
      "/kaggle/input/Kannada-MNIST/sample_submission.csv\n",
      "/kaggle/input/Kannada-MNIST/Dig-MNIST.csv\n",
      "/kaggle/input/Kannada-MNIST/train.csv\n",
      "/kaggle/input/Kannada-MNIST/test.csv\n"
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import tensorflow as tf"
   ]
  },
  {
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   "source": [
    "train = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')\n",
    "test  = pd.read_csv('/kaggle/input/Kannada-MNIST/test.csv')"
   ]
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   "source": [
    "X = train.values[:, 1:].reshape((-1,28,28,1)) / 255.\n",
    "y = tf.keras.utils.to_categorical(train.values[:, 0], 10)\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebee2382",
   "metadata": {
    "papermill": {
     "duration": 0.004615,
     "end_time": "2022-08-29T13:00:23.815925",
     "exception": false,
     "start_time": "2022-08-29T13:00:23.811310",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Training and Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cc8a57f1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-29T13:00:23.828615Z",
     "iopub.status.busy": "2022-08-29T13:00:23.826533Z",
     "iopub.status.idle": "2022-08-29T13:00:24.710487Z",
     "shell.execute_reply": "2022-08-29T13:00:24.709140Z"
    },
    "papermill": {
     "duration": 0.892634,
     "end_time": "2022-08-29T13:00:24.713277",
     "exception": false,
     "start_time": "2022-08-29T13:00:23.820643",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(9):\n",
    "    plt.subplot(331+i)\n",
    "    plt.imshow(X[i])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "287c9837",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-29T13:00:24.726114Z",
     "iopub.status.busy": "2022-08-29T13:00:24.725728Z",
     "iopub.status.idle": "2022-08-29T13:01:51.869044Z",
     "shell.execute_reply": "2022-08-29T13:01:51.867611Z"
    },
    "papermill": {
     "duration": 87.15344,
     "end_time": "2022-08-29T13:01:51.872236",
     "exception": false,
     "start_time": "2022-08-29T13:00:24.718796",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-29 13:00:24.861225: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
      "2022-08-29 13:00:25.028559: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
      "2022-08-29 13:00:25.032797: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
      "2022-08-29 13:00:25.038254: I tensorflow/core/platform/cpu_feature_guard.cc:142] 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",
      "2022-08-29 13:00:25.038698: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
      "2022-08-29 13:00:25.042923: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
      "2022-08-29 13:00:25.046935: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
      "2022-08-29 13:00:27.902690: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
      "2022-08-29 13:00:27.906929: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
      "2022-08-29 13:00:27.910898: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
      "2022-08-29 13:00:27.914925: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15401 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n",
      "/opt/conda/lib/python3.7/site-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n",
      "2022-08-29 13:00:29.122180: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-29 13:00:31.284388: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1500/1500 [==============================] - 18s 6ms/step - loss: 0.0697 - accuracy: 0.9794 - val_loss: 0.0358 - val_accuracy: 0.9881\n",
      "Epoch 2/5\n",
      "1500/1500 [==============================] - 8s 5ms/step - loss: 0.0198 - accuracy: 0.9940 - val_loss: 0.0551 - val_accuracy: 0.9825\n",
      "Epoch 3/5\n",
      "1500/1500 [==============================] - 8s 5ms/step - loss: 0.0110 - accuracy: 0.9969 - val_loss: 0.0168 - val_accuracy: 0.9946\n",
      "Epoch 4/5\n",
      "1500/1500 [==============================] - 8s 5ms/step - loss: 0.0092 - accuracy: 0.9973 - val_loss: 0.0262 - val_accuracy: 0.9927\n",
      "Epoch 5/5\n",
      "1500/1500 [==============================] - 8s 5ms/step - loss: 0.0044 - accuracy: 0.9990 - val_loss: 0.0180 - val_accuracy: 0.9947\n"
     ]
    }
   ],
   "source": [
    "model=tf.keras.Sequential([\n",
    "                           tf.keras.layers.Conv2D(32,(3,3),activation=\"relu\",input_shape=(28,28,1)),\n",
    "                           tf.keras.layers.BatchNormalization(),\n",
    "                           tf.keras.layers.Conv2D(64,(3,3),activation=\"relu\"),\n",
    "                           tf.keras.layers.MaxPool2D(2,2),\n",
    "                           tf.keras.layers.BatchNormalization(),\n",
    "                           tf.keras.layers.Flatten(),\n",
    "                           tf.keras.layers.Dense(100,activation=\"relu\"),\n",
    "                           tf.keras.layers.BatchNormalization(),\n",
    "                           tf.keras.layers.Dense(10,activation=\"softmax\")]) \n",
    "\n",
    "model.compile(optimizer=tf.keras.optimizers.SGD(lr=0.01,momentum=0.9),loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])\n",
    "history=model.fit(X, y, validation_split=0.2 ,batch_size=32,epochs=5,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "87e83f10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-29T13:01:51.967738Z",
     "iopub.status.busy": "2022-08-29T13:01:51.967344Z",
     "iopub.status.idle": "2022-08-29T13:01:51.977886Z",
     "shell.execute_reply": "2022-08-29T13:01:51.975084Z"
    },
    "papermill": {
     "duration": 0.061201,
     "end_time": "2022-08-29T13:01:51.980878",
     "exception": false,
     "start_time": "2022-08-29T13:01:51.919677",
     "status": "completed"
    },
    "tags": []
   },
   "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",
      "batch_normalization (BatchNo (None, 26, 26, 32)        128       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 24, 24, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 12, 12, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 12, 12, 64)        256       \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 9216)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 100)               921700    \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 100)               400       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                1010      \n",
      "=================================================================\n",
      "Total params: 942,310\n",
      "Trainable params: 941,918\n",
      "Non-trainable params: 392\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4ba259e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-29T13:01:52.073970Z",
     "iopub.status.busy": "2022-08-29T13:01:52.073558Z",
     "iopub.status.idle": "2022-08-29T13:01:52.609750Z",
     "shell.execute_reply": "2022-08-29T13:01:52.608431Z"
    },
    "papermill": {
     "duration": 0.586568,
     "end_time": "2022-08-29T13:01:52.612891",
     "exception": false,
     "start_time": "2022-08-29T13:01:52.026323",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "plt.plot(history.history[\"accuracy\"])\n",
    "plt.plot(history.history[\"val_accuracy\"])\n",
    "plt.title(\"Modelaccuracy\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.legend([\"Train\",\"Test\"],loc=\"upper left\")\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history.history[\"loss\"])\n",
    "plt.plot(history.history[\"val_loss\"])\n",
    "plt.title(\"Model loss\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.legend([\"Train\",\"Test\"],loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09002241",
   "metadata": {
    "papermill": {
     "duration": 0.047857,
     "end_time": "2022-08-29T13:01:52.708486",
     "exception": false,
     "start_time": "2022-08-29T13:01:52.660629",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "38002d48",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-29T13:01:52.803968Z",
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