Keras - 06 MNIST image classifier with LSTM.ipynb

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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_mnist_csv(path, one_hot = False, shape = None):\n",
    "    df_train = pd.read_csv(path + \"mnist_train.csv\", header=None)\n",
    "    df_test = pd.read_csv(path + \"mnist_test.csv\", header=None)\n",
    "    \n",
    "    X_train = df_train.iloc[:, 1:].values/255\n",
    "    X_test = df_test.iloc[:, 1:].values/255\n",
    "    y_train = df_train.iloc[:, 0].values\n",
    "    y_test = df_test.iloc[:, 0].values\n",
    "    \n",
    "    if shape == \"2D\":\n",
    "        X_train = X_train.reshape(-1, 28, 28)\n",
    "        X_test = X_test.reshape(-1, 28, 28)\n",
    "        \n",
    "    if shape == \"3D\":\n",
    "        X_train = X_train.reshape(-1, 28, 28, 1)\n",
    "        X_test = X_test.reshape(-1, 28, 28, 1)\n",
    "    \n",
    "    if one_hot:\n",
    "        eye = np.eye(len(np.unique(y_train)))\n",
    "        y_train, y_test = eye[y_train], eye[y_test]\n",
    "        \n",
    "    return X_train, X_test, y_train, y_test\n",
    "\n",
    "X_train, X_test, y_train, y_test = load_mnist_csv(\"/Users/abasar/data/MNIST/\", shape=\"2D\", one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28), (10000, 28, 28), (60000, 10), (10000, 10))"
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     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_3d = X_train.reshape((-1, 28, 28))\n",
    "X_test_3d = X_test.reshape((-1, 28, 28))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 100)               51600     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                1010      \n",
      "=================================================================\n",
      "Total params: 52,610\n",
      "Trainable params: 52,610\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 15s 8ms/step - loss: 0.3569 - accuracy: 0.8874\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 16s 9ms/step - loss: 0.1138 - accuracy: 0.9648\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 18s 9ms/step - loss: 0.0782 - accuracy: 0.9760\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 19s 10ms/step - loss: 0.0604 - accuracy: 0.9819\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 19s 10ms/step - loss: 0.0496 - accuracy: 0.9847\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 19s 10ms/step - loss: 0.0412 - accuracy: 0.9869\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 33s 17ms/step - loss: 0.0371 - accuracy: 0.9888\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 39s 21ms/step - loss: 0.0315 - accuracy: 0.9902\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 29s 15ms/step - loss: 0.0280 - accuracy: 0.9911\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 38s 20ms/step - loss: 0.0248 - accuracy: 0.9923\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f95e7dad850>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.layers import Dense, LSTM, InputLayer\n",
    "from tensorflow.keras.losses import categorical_crossentropy\n",
    "\n",
    "def build_model():\n",
    "    model = keras.Sequential()\n",
    "    model.add(InputLayer(input_shape = (28, 28)))\n",
    "    model.add(LSTM(units = 100, activation=\"tanh\"))\n",
    "    model.add(Dense(10, activation=\"softmax\"))\n",
    "    model.compile(loss=categorical_crossentropy, metrics=[\"accuracy\"], optimizer=\"adam\")\n",
    "    return model\n",
    "\n",
    "model = build_model()\n",
    "model.summary()\n",
    "model.fit(X_train, y_train,epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 3ms/step - loss: 0.0454 - accuracy: 0.9865\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.04541502520442009, 0.9865000247955322]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from tensorflow.keras.layers import Dense, LSTM, InputLayer\n",
    "# from tensorflow.keras.losses import categorical_crossentropy\n",
    "\n",
    "# def build_model():\n",
    "#     model = keras.Sequential()\n",
    "#     model.add(InputLayer(input_shape = (28, 28, 1)))\n",
    "#     model.add(LSTM(units = 100, activation=\"tanh\"))\n",
    "#     model.add(Dense(10, activation=\"softmax\"))\n",
    "#     model.compile(loss=categorical_crossentropy, metrics=[\"accuracy\"], optimizer=\"adam\")\n",
    "#     return model\n",
    "\n",
    "# model = build_model()\n",
    "# model.summary()\n",
    "# model.fit(X_train_3d, y_train,epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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