Neural Network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Network\n",
    "\n",
    "by Atmam Al Faruq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/not/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/not/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/not/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/not/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/not/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/not/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/usr/lib/python3/dist-packages/requests/__init__.py:80: RequestsDependencyWarning: urllib3 (1.25.7) or chardet (3.0.4) doesn't match a supported version!\n",
      "  RequestsDependencyWarning)\n",
      "/home/not/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/not/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/not/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/not/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/not/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/not/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "# Memuat seluruh library yang dibutuhkan\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Memanggil dataset yang telah disiapkan oleh keras library. kemudian data dibagi kedalam data training dan testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "(train_image, train_labels),(test_image, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check data pada tiap array ( train_image, train_labels, test_image, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data train : 60000\n",
      "Data train labels : 60000\n",
      "Data test : 10000\n",
      "Data test labels : 60000\n"
     ]
    }
   ],
   "source": [
    "print(\"Data train : \"+str(len(train_image)))\n",
    "print(\"Data train labels : \"+str(len(train_labels)))\n",
    "print(\"Data test : \"+str(len(test_image)))\n",
    "print(\"Data test labels : \"+str(len(train_labels)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mengecek salah satu gambar, kemudian melakukan inspeksi terhadap gambar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f3da75f4da0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train_image[0])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dapat dilihat gambar diatas memiliki rentan warna 0 - 255, dengan melihat indikator pada fungsi colorbar.\n",
    "\n",
    "Selanjutnya, akan dieksplorasi lagi dengan memanggil dua puluh data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "dikarenakan format dalam train_labels berupa integer,\n",
    "dibuatlah class_names untuk menerjemahkan nilai integer.\n",
    "''' \n",
    "\n",
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(20):\n",
    "    plt.subplot(4,5,i+1) #disiapkan plot 4 baris 5 kolom, dengan indeks i + 1.\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.imshow(train_image[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "untuk mempermudah proses training karena nilai yang diperlukan adalah binary 0 - 1, \n",
    "maka nilai warna sebanyak 255 dilakukan normalisasi sehingga menjadi 0.0 - 1.0.\n",
    "\n",
    "Hal ini dapat dilakukan dengan membagi dataset dengan 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_image / 255.0\n",
    "test_images = test_image / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Membuat model dengan inputan gamabr 28x28, \n",
    "kemudian masuk kedalam hidden layer dengan node sebanyak 128 yang diproses dengan relu.\n",
    "node tersebut akan disesuaikan kepada kelasa mana diantara 1 - 10.\n",
    "probabilitas untuk menemukan class tersebut menggunakan softmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/not/.local/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(128, activation='relu'),\n",
    "    keras.layers.Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               100480    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 101,770\n",
      "Trainable params: 101,770\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "model kemudian dicompile dan fit dengan data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "             loss='sparse_categorical_crossentropy',\n",
    "             metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 12s 201us/sample - loss: 3.4402 - acc: 0.6833\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 12s 200us/sample - loss: 0.7591 - acc: 0.7303\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 12s 201us/sample - loss: 0.6124 - acc: 0.7767\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 12s 202us/sample - loss: 0.5621 - acc: 0.8021\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 12s 202us/sample - loss: 0.5355 - acc: 0.8179\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 12s 202us/sample - loss: 0.5227 - acc: 0.8255\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 12s 201us/sample - loss: 0.5115 - acc: 0.8288\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 12s 202us/sample - loss: 0.5055 - acc: 0.8352\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 12s 202us/sample - loss: 0.4992 - acc: 0.8363\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 12s 200us/sample - loss: 0.4941 - acc: 0.8379\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f3da6d11550>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_image,train_labels, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 - 1s - loss: 0.5633 - acc: 0.8086\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5633088676452637, 0.8086]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_image, test_labels, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(test_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setelah selesai, data test akan diprediksi dan melihat nilai kecenderungan tertinggi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.5151965e-23, 7.8514429e-17, 3.9038599e-34, 5.3765128e-15,\n",
       "       6.4671068e-37, 2.0560464e-02, 3.1687412e-30, 4.1638814e-02,\n",
       "       8.0759562e-18, 9.3780077e-01], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dapat dicek pada gambar dan melihat nilai distribusinya ke class berapa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Ankle boot')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOsAAAD1CAYAAACx1gI+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAKq0lEQVR4nO3dX2yeZRnH8d+1FtjoytZujLCxrDHpsmi2bKHZiUiMIUQ4QEk4EI2RaDxFT8jOZvTERA88MpkxoKIwTOaI8UA7MJGwsc01yv6BjIO1i4w/W4G2K6PAdntATQr0ue62b7vyY99Psqx5rvd+37vP21+etlfv+4lSigB8+i1Z7AkAmBnCCpggrIAJwgqYIKyAifbZPHj16tWlp6dngaYCYHBwUOfPn4/parMKa09PjwYGBuZnVgA+oa+vr7HGt8GACcIKmCCsgAnCCpggrIAJwgqYIKyACcIKmCCsgAnCCpggrIAJwgqYIKyACcIKmCCsgAnCCpggrIAJwgqYIKyACcIKmCCsgAnCCpggrIAJwgqYIKyACcIKmCCsgAnCCpggrIAJwgqYIKyACcIKmCCsgAnCCpggrIAJwgqYIKyACcIKmCCsgAnCCpggrIAJwgqYIKyACcIKmCCsgAnCCpggrIAJwgqYIKyACcIKmCCsgAnCCphoX+wJ4Op16dKltL5kSfO1JCJaeu2JiYm0ft1116X1l19+ubHW29s7pznVcGUFTBBWwARhBUwQVsAEYQVMEFbABGEFTNBnNVdKaame9TIl6ZVXXmmsHTx4MB171113pfWOjo60vpBqfdSavXv3NtZ27NjR0nM34coKmCCsgAnCCpggrIAJwgqYIKyACcIKmKDP+hlX66PWPPvss421w4cPp2PPnj2b1h988ME5zWk+vPHGG2m9v78/rXd2ds7ndGaEKytggrACJggrYIKwAiYIK2CCsAImCCtggj6rudreu+3t+Vt85MiRtP7iiy821m666aZ0bLa3riTde++9ab2rq6ux9u6776ZjN2zYkNaHh4fT+ujoaFpft25dWl8IXFkBE4QVMEFYAROEFTBBWAEThBUwQVgBE/RZP+UuX76c1mt91PHx8bS+Z8+etJ7tr1vrdY6NjaX1VvY8ro09efJkWr/lllvSetbjler97YXAlRUwQVgBE4QVMEFYAROEFTBBWAETV03rJvtVf0SkY2vtk9r4Wj1rA7S1taVja3bt2pXWa8vcli5d2lgbGhpKx9ZaO7XX/uCDDxprtXNau51k7ZaPIyMjaX1iYqKxVmuXzfVWl1xZAROEFTBBWAEThBUwQVgBE4QVMEFYARM2fdbakqhWe52ZVm+bWFtO1Uovdffu3Wn9tddeS+vbtm1L61mv8+23307Hdnd3p/VVq1al9fPnzzfWLly4kI7N5j0Tta+3d955p7FW24J169atc5oTV1bABGEFTBBWwARhBUwQVsAEYQVMEFbAhE2ftZU+qZSvSa2tV631QWtza6WP+sgjj6T1U6dOpfX169en9dqtD7N+48WLF9Oxtdsi1rYqzc7r9ddfn46traVttW+f6e/vT+v0WYHPOMIKmCCsgAnCCpggrIAJwgqYIKyAiSvaZ631MzO1vletb5atSW11vWrN2bNn0/revXsba7VeZm9vb1qvrfvM9r+V8j7sNddck46tvWfZmtCa2ntW2xe4Nr62t2/2uR04cCAdO1dcWQEThBUwQVgBE4QVMEFYAROEFTBBWAETs+6ztnIv0YXsZ7ay/vDcuXNpfXBwMK2/9NJLaf3VV19N69dee21j7YYbbkjH1vbuHR0dTevvv/9+Ws/6sLX3u3beanv7rly5srGWnTOpvldzrS+/bNmyOT//8uXL07EnTpxorGV9da6sgAnCCpggrIAJwgqYIKyACcIKmJh166aVbTVff/31xtrQ0FA6dnx8vKV69ivx06dPp2NrS7na2/PT2NnZmdazpYMjIyPp2NoSutrcap9b1sKoLUN777330vrNN9+c1rO2U23eXV1dab22dPDNN99M61l7pnabzey5s5YQV1bABGEFTBBWwARhBUwQVsAEYQVMEFbAxLxuRfr000+n9WxLzlo/sLaMrbYkKusPt9onrfXsan23bLlWbavQWj+xtv1rbe7Zea1t11lbKpYtgZPq73krauettpwz62/X+su1r7fGOc1pFIArjrACJggrYIKwAiYIK2CCsAImCCtgYlYNn9HRUe3bt6+x/vDDD6fjN23a1FirrW1sZU2olG9d2eq2lbW51fpuWU9vbGwsHVubW229a20L1+zc1PrH2fplSXrhhRfSenbeau9ZTa0HXFsfvXTp0jk/95o1axpr2W00ubICJggrYIKwAiYIK2CCsAImCCtggrACJmbVZ+3o6ND27dsb64cOHUrHHz9+vLG2f//+2UzlE7L+lJT3Qru7u9OxtfqKFSvSeq3PmvVKh4eH07G1203W9tet3RIy68MePXo0Hbtly5a03tPTk9afeuqpxlptnW+rtxetrTldu3ZtY612m86sd86+wcBnAGEFTBBWwARhBUwQVsAEYQVMzKp109bWlm4fuXPnzjlPpLYl5uHDh9N6rYXx3HPPNdYGBwfTsceOHUvrteVUtWVsWXuk1oKotZU2b96c1u+44460fvfddzfWsmVi8+Gee+5prJ05cyYdu2rVqrRea6/Ulj1mrZ3arTA3btzYWMvOKVdWwARhBUwQVsAEYQVMEFbABGEFTBBWwETUeoBT9fX1lYGBgQWcDnB16+vr08DAwLSNd66sgAnCCpggrIAJwgqYIKyACcIKmCCsgAnCCpggrIAJwgqYIKyACcIKmCCsgAnCCpggrIAJwgqYIKyACcIKmCCsgAnCCpggrIAJwgqYIKyACcIKmCCsgAnCCpggrIAJwgqYIKyACcIKmCCsgAnCCpggrIAJwgqYIKyACcIKmCCsgAnCCpggrIAJwgqYIKyACcIKmCCsgAnCCpggrIAJwgqYiFLKzB8ccU7S0MJNB7jqbSil3DhdYVZhBbB4+DYYMEFYAROEFTBBWBdRRHw9IkpEbJrh4wcjYvU0xy/M8nVn9fjkeR6IiLXz8VyoI6yL635J+yf/d/SAJMJ6hRDWRRIRyyXdJul7kr4x5fiXI+IfEbEnIv4TEY9FRHxs7LKI+GtEfH+a530oIo5ExLGI+HHy+r+IiJMR8feIuHHy2NaIODQ59smI6Go6HhH3SeqT9FhEPB8Ry+blxKARYV08X5P0t1LKKUnDEXHrlNo2ST+U9HlJn5P0xSm15ZL+Iml3KeXXU58wIu6U1Ctpu6Stkm6NiNunee0OSQOllC9IekbSjyaPPyppRylli6Tj2fFSyh5JA5K+VUrZWkq5OJeTgJkjrIvnfklPTH78hD76rfA/Syn/LaVclvS8pJ4ptT9L+k0p5dFpnvPOyX//lvQvSZv0YXg/7rKkP05+/AdJt0XECkkrSynPTB7/naTbm47P+LPEvGlf7AlcjSKiW9JXJG2OiCKpTVKJiIcmHzIx5eGX9NH36YCkr0bE4+WTf9ESkn5aSvnVLKfEX8YY4Mq6OO6T9PtSyoZSSk8pZb2k05K+NIOxOyW9JemX09T6JX138udhRcS6iFgzzeOWTM5Bkr4paX8pZUTSWxHx/zl8W9IzTccnPx6T1DmDOWMeENbFcb+kJz927E+a+W+FfyBpWUT8bOrBUso+SY9LOhgRxyXt0fRhGpe0PSJO6MMr/E8mj39H0s8j4pg+/Jm3dvy3knbxC6Yrg78NBkxwZQVMEFbABGEFTBBWwARhBUwQVsAEYQVM/A/BIaIo+mKN7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_image[0], cmap=plt.cm.binary)\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.xlabel(class_names[np.argmax(predictions[0])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.xticks(range(10))\n",
    "plt.yticks([])\n",
    "plt.bar(range(10),predictions[0],color=\"blue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pada test_image[0] dapat dilihat nilai distribusinya mengarah pada class 9,\n",
    "dimana 9 merupakan ankle boot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Shirt')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_image[4], cmap=plt.cm.binary)\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.xlabel(class_names[np.argmax(predictions[4])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.xticks(range(10))\n",
    "plt.yticks([])\n",
    "plt.bar(range(10),predictions[4],color=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,3))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(test_image[10], cmap=plt.cm.binary)\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[np.argmax(predictions[10])],\n",
    "                                100*np.max(predictions[10]),\n",
    "                                class_names[test_labels[10]]))\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.xticks(range(10))\n",
    "plt.yticks([])\n",
    "plt.bar(range(10),predictions[10],color=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}