Intro to GAN - DC GAN MNIST.ipynb

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    "colab": {
      "name": "DC GAN - MNIST",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_zeXJKLNesg8",
        "colab_type": "text"
      },
      "source": [
        "# [PART 1] Generative Adverserial Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fxRvtgPeesg-",
        "colab_type": "code",
        "outputId": "9b48ad5c-dae7-4722-cdd0-3847dda378d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 64
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tensorflow.keras.models import Sequential, load_model\n",
        "from tensorflow.keras.layers import Dense, Conv2D, Flatten, BatchNormalization, Dropout\n",
        "from tensorflow.keras.layers import Reshape, UpSampling2D, MaxPooling2D, Activation\n",
        "from tensorflow.keras.datasets import mnist, fashion_mnist\n",
        "from tensorflow.keras.optimizers import SGD, RMSprop\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "from PIL import Image"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oh_ZsU26gPiF",
        "colab_type": "text"
      },
      "source": [
        "## Load MNIST Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "obPzmoTKcdbP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
        "#(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()\n",
        "\n",
        "X_train = (X_train.astype(np.float32) - 127.5)/127.5 # normalization\n",
        "X_train = X_train[:, :, :, None]\n",
        "X_test = X_test[:, :, :, None]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bUx5SvTYnUYx",
        "colab_type": "text"
      },
      "source": [
        "## Define Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vD88W-oreshI",
        "colab_type": "text"
      },
      "source": [
        "### Generator Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "6mxnqAnJd7MB"
      },
      "source": [
        "### <font color='red'>**EXERCISE**: </font>\n",
        "\n",
        "Cobalah untuk mengubah-ubah arsitektur\n",
        "* menambah layer Conv2D,\n",
        "* menambah filter,\n",
        "* dsb"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lz4mo0KeeshJ",
        "colab_type": "code",
        "outputId": "2a48fed7-d768-4e19-9035-0e1fa3faefff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        }
      },
      "source": [
        "def generator_model():\n",
        "    model = Sequential([\n",
        "        Dense(1024, input_dim=100, activation='tanh'),\n",
        "        Dense(128*7*7),\n",
        "        BatchNormalization(),\n",
        "        Activation('tanh'),\n",
        "        Reshape((7, 7, 128)),\n",
        "        UpSampling2D(size=(2, 2)),\n",
        "        Conv2D(64, (5, 5), padding='same', activation='tanh'),\n",
        "        UpSampling2D(size=(2, 2)),\n",
        "        Conv2D(1, (5, 5), padding='same', activation='tanh')\n",
        "    ])\n",
        "    return model\n",
        "\n",
        "generator_model().summary()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 1024)              103424    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 6272)              6428800   \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 6272)              25088     \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "reshape (Reshape)            (None, 7, 7, 128)         0         \n",
            "_________________________________________________________________\n",
            "up_sampling2d (UpSampling2D) (None, 14, 14, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 14, 14, 64)        204864    \n",
            "_________________________________________________________________\n",
            "up_sampling2d_1 (UpSampling2 (None, 28, 28, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 28, 28, 1)         1601      \n",
            "=================================================================\n",
            "Total params: 6,763,777\n",
            "Trainable params: 6,751,233\n",
            "Non-trainable params: 12,544\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sOdQvTIheshO",
        "colab_type": "text"
      },
      "source": [
        "### Discriminator Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "pkEciVyxLqTv"
      },
      "source": [
        "### <font color='red'>**EXERCISE**: </font>\n",
        "\n",
        "Cobalah untuk mengubah-ubah arsitektur\n",
        "* menambah layer Conv2D,\n",
        "* menambah filter,\n",
        "* dsb"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gZ04Wj52eshQ",
        "colab_type": "code",
        "outputId": "f1b86ddd-c13b-4796-8bfd-9e91a8484b17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "source": [
        "def discriminator_model():\n",
        "    model = Sequential([\n",
        "        Conv2D(64, (5, 5), input_shape=(28, 28, 1), padding='same', activation='tanh'),\n",
        "        MaxPooling2D(pool_size=(2, 2)),\n",
        "        Conv2D(128, (5, 5),activation='tanh'),\n",
        "        MaxPooling2D(pool_size=(2, 2)),\n",
        "        Flatten(),\n",
        "        Dense(1024, activation='tanh'),\n",
        "        Dense(1, activation='sigmoid')\n",
        "    ])\n",
        "    return model\n",
        "\n",
        "discriminator_model().summary()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_2 (Conv2D)            (None, 28, 28, 64)        1664      \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 10, 10, 128)       204928    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 3200)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1024)              3277824   \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 1025      \n",
            "=================================================================\n",
            "Total params: 3,485,441\n",
            "Trainable params: 3,485,441\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Go4bbqd9eshZ",
        "colab_type": "text"
      },
      "source": [
        "### GAN = Generator + Discriminator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lakdtO03esha",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def combine_model(g, d):\n",
        "    model = Sequential()\n",
        "    model.add(g)\n",
        "    model.add(d)\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oaa7lbqgeshe",
        "colab_type": "text"
      },
      "source": [
        "## Training Functions\n",
        "untuk melakukan training diperlukan BATCH_SIZE yang merupakan banyaknya gambar yang di train tiap epochs nya. Tiap piksel gambar akan dikonversikan menjadi nilai antara [-1,1). Hasil training akan disimpan pada file discriminator dan generator."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "42dCuaj3n9lk",
        "colab_type": "text"
      },
      "source": [
        "### Combine Generated Image\n",
        "\n",
        "Combine Image adalah fungsi untuk menggabungkan gambar kedalam satu frame."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EB_HZV1Peshh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def combine_images(generated_images):\n",
        "    num = generated_images.shape[0]\n",
        "    width = int(np.sqrt(num))\n",
        "    height = int(np.ceil(float(num)/width))\n",
        "    shape = generated_images.shape[1:3]\n",
        "    image = np.zeros((height*shape[0], width*shape[1]),\n",
        "                     dtype=generated_images.dtype)\n",
        "    for index, img in enumerate(generated_images):\n",
        "        i = int(index/width)\n",
        "        j = index % width\n",
        "        image[i*shape[0]:(i+1)*shape[0], j*shape[1]:(j+1)*shape[1]] = \\\n",
        "            img[:, :, 0]\n",
        "    return image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJ6y9x9xn_3b",
        "colab_type": "text"
      },
      "source": [
        "### Training Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ctJzIyAJeshl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_gan(X_train, Y_train, batch_size, epochs, g, d, save_every=500, print_every=100):\n",
        "    \n",
        "    # ukuran vektor z\n",
        "    z_size = g.layers[0].input_shape[1]\n",
        "    \n",
        "    # gabungkan Discriminator dan Generator\n",
        "    d.trainable = False # set Discriminator tidak bisa dilatih sebelum digabung\n",
        "    d_on_g = combine_model(g, d)    \n",
        "    dg_optim = RMSprop (lr=0.0005)\n",
        "    g_optim = RMSprop (lr=0.0005)\n",
        "    d_on_g.compile(loss='binary_crossentropy', optimizer=dg_optim)\n",
        "    \n",
        "    g.compile(loss='binary_crossentropy', optimizer=g_optim)\n",
        "    \n",
        "    # set Discriminator agar bisa dilatih kembali\n",
        "    d.trainable = True\n",
        "    d_optim = RMSprop (lr=0.0005)\n",
        "    d.compile(loss='binary_crossentropy', optimizer=d_optim)\n",
        "    \n",
        "    print(\"Number of batches\", int(X_train.shape[0]/batch_size))\n",
        "    \n",
        "    # mulai pelatihan\n",
        "    for epoch in range(epochs):\n",
        "        print(\"\\n-------------------------------\\nEpoch :\", epoch)        \n",
        "        \n",
        "        for index in range(int(X_train.shape[0]/batch_size)):\n",
        "            \n",
        "            # bangkitkan matrix z secara acak\n",
        "            noise = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
        "            \n",
        "            # bangkitkan data gambar palsu dari matrix z\n",
        "            generated_images = g.predict(noise, verbose=0)\n",
        "            \n",
        "            # ambil data gambar asli\n",
        "            image_batch = X_train[index*batch_size:(index+1)*batch_size]\n",
        "            \n",
        "            if index % save_every == 0:\n",
        "                image = combine_images(generated_images)\n",
        "                image = image*127.5+127.5\n",
        "                # Image.fromarray(image.astype(np.uint8)).save(\"train_ep\"+\n",
        "                #     str(epoch)+\"_\"+str(index)+\".png\")\n",
        "                \n",
        "                plt.imshow(image, cmap=plt.get_cmap('gray'))\n",
        "                plt.axis('off')\n",
        "                plt.show()\n",
        "                \n",
        "            # gabungkan data untuk pelatihan Discriminator\n",
        "            X = np.concatenate((image_batch, generated_images))\n",
        "            y = [1] * batch_size + [0] * batch_size\n",
        "            \n",
        "            # latih Discriminator\n",
        "            d_loss = d.train_on_batch(X, y)           \n",
        "            \n",
        "            # bangkitkan matrix z secara acak untuk pelatihan Generator\n",
        "            noise = np.random.uniform(-1, 1, (batch_size, z_size))\n",
        "            \n",
        "            # set Discriminator tidak bisa dilatih sebelum digabung\n",
        "            d.trainable = False            \n",
        "            \n",
        "            # latih Generator\n",
        "            g_loss = d_on_g.train_on_batch(noise, [1] * batch_size)\n",
        "            \n",
        "            # print loss\n",
        "            if index % print_every == 0: \n",
        "                print(\"batch %d, g_loss : %f, d_loss : %f\" % (index, g_loss, d_loss))\n",
        "            \n",
        "            # set Discriminator agar bisa dilatih kembali\n",
        "            d.trainable = True       \n",
        "            \n",
        "        \n",
        "    return g, d"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CkFnvwjQoCOp",
        "colab_type": "text"
      },
      "source": [
        "## Training Process"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HEsyFQUPnJFp",
        "colab_type": "text"
      },
      "source": [
        "### Initialize Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Zb1d61beshU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "z_size = 100\n",
        "g_model = generator_model()\n",
        "d_model = discriminator_model()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "564IaJunwsEZ",
        "colab_type": "text"
      },
      "source": [
        "### Train GAN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "a_zwpKTyesht",
        "colab_type": "code",
        "outputId": "8e185e6d-e9cd-426f-dceb-4860cbf52c98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "source": [
        "batch = 225\n",
        "epochs = 15\n",
        "g_model, d_model = train_gan(X_train,y_train, batch, epochs, g_model, d_model)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "Number of batches 266\n",
            "\n",
            "-------------------------------\n",
            "Epoch : 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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h9/upVqt0Oh2MRiNvvfUW4XCYdrvNyckJw+FQBCXr6+t0u102NjbY29tjuVxS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\n+hO9Pj6cH6+P15/o9f8A/Iup8f+gAkgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "batch 0, g_loss : 0.005041, d_loss : 0.657468\n",
            "batch 100, g_loss : 1.713865, d_loss : 0.699898\n",
            "batch 200, g_loss : 0.406236, d_loss : 0.621468\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OKsn2emweshy",
        "colab_type": "text"
      },
      "source": [
        "## Generate gambar\n",
        "pada fungsi ini gambar bisa digenerate dengan input BATCH_SIZE yang menunjukan jumlah gambar yang akan dibangkitkan. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YsBrii5KesiB",
        "colab_type": "code",
        "outputId": "64c3e4de-a794-4fd7-ff07-b20a47aa5820",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "source": [
        "seed = np.random.uniform(-1, 1, (4, 100))\n",
        "images = g_model.predict(seed)\n",
        "\n",
        "for i in range(4):\n",
        "    plt.subplot(2,2,1+i)\n",
        "    plt.imshow(np.reshape(images[i], (28,28,)),cmap=plt.get_cmap('gray'))\n",
        "    plt.axis('off')\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OE67yilv233zzjdOhz4mbsuLxo48+6nTz5s0r8I53Dj8TVq7t3bu3061atSrI\n8+fPd7q//e1vQZ4zZ47TYcoMv3exVDBsPpo35EIrJyFEkmhyEkIkSdSs4wjptWvXZn42tj1YCnPp\n1FNPdceLFy8OMhfEwiUrm58LFy50x7gdK7Pu5wNvw6N5xlnz+B6wDk0UfgcwmhzHZCHEKmVwlDtH\nXqPpyCE16PrAECEzHz6A75mZf5/4u8BMDY5ex3vLW+FDKychRJJochJCJIkmJyFEkkR9Tmwb4nGs\namWpwJB33t7FZgucboC2MDciYNtffqafJ7ztjln7XOUiFm6C51m2bJnTYXoHnxPTZ/he8HqcZhNL\nA2HfEfpX+R3B5+B7w8+y3xmvyf+H5+T0FayoqUqYQoiqRpOTECJJCooQz6srFWiSTZo0Kff/xaLV\neUlZ7hCIYlGDg/LCvztukbO5gr9FzJRh8wxNG95ax36KHNaAIQLcFw/fiZg5yPfGYxmfiU0wdN/E\nQhliuth7xs0lstAbIIRIEk1OQogk0eQkhEiSGm2lCyFSRCsnIUSSaHISQiSJJichRJJochJCJIkm\nJyFEkmhyEkIkyf8BBhebMYfKSw4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iyagPNWysqHa",
        "colab_type": "text"
      },
      "source": [
        "### Generate Gambar dari Generator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "izeeWEhIeshy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generate_images(g, batch_size):\n",
        "    z_size = g.layers[0].input_shape[1]\n",
        "    noise = np.random.uniform(-1, 1, (batch_size, z_size))\n",
        "    generated_images = g.predict(noise, verbose=1)\n",
        "    image = combine_images(generated_images)\n",
        "    filename = \"generated_image.png\"\n",
        "    image = image*127.5+127.5\n",
        "    Image.fromarray(image.astype(np.uint8)).save(filename)\n",
        "    return image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "20IGMkKGesh3",
        "colab_type": "code",
        "outputId": "c3508e48-0ace-4c67-c02e-1af165dbc76f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "source": [
        "images = generate_images(g_model, 100)\n",
        "plt.imshow(images, cmap=plt.get_cmap('gray'))\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100/100 [==============================] - 0s 213us/sample\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GzZYQ/Yi0Lb/fj9tvvx3bt29HQkICLBZLWNt3330X6enpaGlpgc1mw2233QaN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\naX7Ho7q6mhQKBdXU1JDFYiGtVksNDQ20d+9eKisro8OHD9Px48epubmZ7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jT8x1nzyiqKKigjo7O6mhoUE1Hvi5556juro6amxs5GUj7xezs7NcCRqNxnm36LW1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\nPzHouqYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CCcO6WjRsV5p",
        "colab_type": "text"
      },
      "source": [
        "### Generate Gambar dengan pengecekan Diskriminator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pCdIPuwhesh6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generate_best_images(g, d, batch_size):\n",
        "    z_size = g.layers[0].input_shape[1]\n",
        "    noise = np.random.uniform(-1, 1, (batch_size*20, z_size))\n",
        "\n",
        "    generated_images = g.predict(noise, verbose=1)\n",
        "    d_pret = d.predict(generated_images, verbose=1)\n",
        "\n",
        "    index = np.arange(0, batch_size*20)\n",
        "    index.resize((batch_size*20, 1))\n",
        "\n",
        "    pre_with_index = list(np.append(d_pret, index, axis=1))\n",
        "    pre_with_index.sort(key=lambda x: x[0], reverse=True)\n",
        "\n",
        "    nice_images = np.zeros((batch_size,) + generated_images.shape[1:3], dtype=np.float32)\n",
        "    nice_images = nice_images[:, :, :, None]\n",
        "\n",
        "    for i in range(batch_size):\n",
        "        idx = int(pre_with_index[i][1])\n",
        "        nice_images[i, :, :, 0] = generated_images[idx, :, :, 0]\n",
        "\n",
        "    image = combine_images(nice_images)\n",
        "    filename = \"generated_image_best.png\"\n",
        "    image = image*127.5+127.5\n",
        "    Image.fromarray(image.astype(np.uint8)).save(filename)\n",
        "    return image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y_tIkXNzesh-",
        "colab_type": "code",
        "outputId": "a85cb4fe-f522-4e5f-c1aa-3fac275f2c7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "images = generate_best_images(g_model, d_model, 100)\n",
        "plt.imshow(images, cmap=plt.get_cmap('gray'))\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2000/2000 [==============================] - 0s 158us/sample\n",
            "2000/2000 [==============================] - 0s 139us/sample\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UU1NDOTk5dOHCBXK73eT3+zkEi81moz179ijG84c//IGMRiNlZ2fTnj17+O8M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\nvyn9P1UQdmOymf0UAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "CG91Npafcr0l"
      },
      "source": [
        "---\n",
        "## <font color='red'>**EXERCISE**: </font>\n",
        "\n",
        "<font size=5>Try Fashion MNIST on PART 1\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UVqSnZ-qtMJp",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "# [PART 2] Latent Space Testing]\n",
        "It's a bonus for you"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y4grLSCeetmi",
        "colab_type": "text"
      },
      "source": [
        "## Train MNIST Classifier\n",
        "\n",
        "This function will classify mnist image with size of (1,28,28,1) using CNN. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5mRr-qNetmj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def mnist_classifier():\n",
        "    model = Sequential([\n",
        "        Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28,28,1)),\n",
        "        # keras.layers.Conv2D(filters, \n",
        "        # kernel_size, strides=(1, 1), \n",
        "        # padding='valid', \n",
        "        # data_format=None, \n",
        "        # dilation_rate=(1, 1), \n",
        "        # activation=None, \n",
        "        # use_bias=True, \n",
        "        # kernel_initializer='glorot_uniform', \n",
        "        # bias_initializer='zeros', \n",
        "        # kernel_regularizer=None, \n",
        "        # bias_regularizer=None, \n",
        "        # activity_regularizer=None, \n",
        "        # kernel_constraint=None, \n",
        "        # bias_constraint=None)\n",
        "    \n",
        "        Conv2D(64, (3, 3), activation='relu'),\n",
        "        MaxPooling2D(pool_size=(2, 2)),\n",
        "        Dropout(0.25),\n",
        "        Flatten(),\n",
        "        Dense(128, activation='relu'),\n",
        "        Dropout(0.5),\n",
        "        Dense(10, activation='softmax')\n",
        "    ])\n",
        "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QpEizZ-metmn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_classifier(model, batch_size, epochs):\n",
        "    (X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
        "    #(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()\n",
        "    \n",
        "    X_train = X_train.astype('float32')\n",
        "    X_test = X_test.astype('float32')\n",
        "    \n",
        "    X_train /= 255\n",
        "    X_test /= 255\n",
        "    \n",
        "    X_train = X_train[:, :, :, None]\n",
        "    X_test = X_test[:, :, :, None]\n",
        "    \n",
        "    y_train = to_categorical(y_train, 10)\n",
        "    y_test = to_categorical(y_test, 10)\n",
        "    \n",
        "    model.fit(X_train, y_train, batch_size=batch_size,\n",
        "          epochs=epochs,verbose=2,validation_data=(X_test, y_test))\n",
        "    \n",
        "    score = model.evaluate(X_test, y_test, verbose=0)\n",
        "    print('Test accuracy:', score[1])\n",
        "    \n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "s5SvM3A1etmr",
        "colab_type": "code",
        "outputId": "539414af-f8a2-49c5-e7b5-57a3189e612c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "source": [
        "batch = 512\n",
        "mnist_model = mnist_classifier()\n",
        "mnist_model = train_classifier(mnist_model, batch, 10)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "60000/60000 - 6s - loss: 0.3735 - acc: 0.8878 - val_loss: 0.0782 - val_acc: 0.9752\n",
            "Epoch 2/10\n",
            "60000/60000 - 5s - loss: 0.1070 - acc: 0.9691 - val_loss: 0.0498 - val_acc: 0.9836\n",
            "Epoch 3/10\n",
            "60000/60000 - 5s - loss: 0.0760 - acc: 0.9772 - val_loss: 0.0374 - val_acc: 0.9878\n",
            "Epoch 4/10\n",
            "60000/60000 - 5s - loss: 0.0595 - acc: 0.9821 - val_loss: 0.0324 - val_acc: 0.9883\n",
            "Epoch 5/10\n",
            "60000/60000 - 5s - loss: 0.0500 - acc: 0.9856 - val_loss: 0.0322 - val_acc: 0.9886\n",
            "Epoch 6/10\n",
            "60000/60000 - 5s - loss: 0.0447 - acc: 0.9862 - val_loss: 0.0284 - val_acc: 0.9907\n",
            "Epoch 7/10\n",
            "60000/60000 - 5s - loss: 0.0398 - acc: 0.9879 - val_loss: 0.0285 - val_acc: 0.9908\n",
            "Epoch 8/10\n",
            "60000/60000 - 5s - loss: 0.0346 - acc: 0.9892 - val_loss: 0.0297 - val_acc: 0.9894\n",
            "Epoch 9/10\n",
            "60000/60000 - 5s - loss: 0.0330 - acc: 0.9898 - val_loss: 0.0269 - val_acc: 0.9908\n",
            "Epoch 10/10\n",
            "60000/60000 - 5s - loss: 0.0302 - acc: 0.9904 - val_loss: 0.0273 - val_acc: 0.9911\n",
            "Test accuracy: 0.9911\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YGuWSvvnetmt",
        "colab_type": "text"
      },
      "source": [
        "## Test MNIST Classifier GAN Images\n",
        "input: random seed with size (1,100) with value in range of [-1,1) "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A5tKxN0Oetmv",
        "colab_type": "code",
        "outputId": "f189327d-d307-4e7c-e9d0-dcc53a8fdcbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "source": [
        "seed = np.random.uniform(-1, 1, (1, 100))\n",
        "\n",
        "image = g_model.predict(seed)\n",
        "plt.imshow(np.reshape(image, (28,28,)),cmap=plt.get_cmap('gray'))\n",
        "plt.axis('off')\n",
        "plt.show()\n",
        "\n",
        "pred = mnist_model.predict(image)\n",
        "label = np.argmax(pred, axis=1)\n",
        "print('Predicted label is %s' % label)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEwiKcgJBUU4gKMoJBPUPg7Ip2YUae4AAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Predicted label is [2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3GOYLcEaetmy",
        "colab_type": "text"
      },
      "source": [
        "## Test MNIST GAN in Batch \n",
        "generate 1000 image, classify, then arrange based on its class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SAF2oMpeetm0",
        "colab_type": "code",
        "outputId": "741ae28b-c310-45eb-e3a8-1e60a5d1c00b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "seed = np.random.uniform(-1, 1, (5000, 100))\n",
        "print(seed.shape)\n",
        "\n",
        "images = g_model.predict(seed)\n",
        "print(images.shape)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(5000, 100)\n",
            "(5000, 28, 28, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IK6m3V4tetm3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Predict label\n",
        "pred = mnist_model.predict(images)\n",
        "y_preds = np.argmax(pred, axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iCWt2P34t1Zl",
        "colab_type": "text"
      },
      "source": [
        "### Take a look at the result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sBemP-ueetm7",
        "colab_type": "code",
        "outputId": "f8126fb1-5a7e-4e25-8dda-1a4dc2547610",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "source": [
        "id = 0\n",
        "\n",
        "plt.imshow(np.reshape(images[id], (28,28,)),cmap=plt.get_cmap('gray'))\n",
        "plt.axis('off')\n",
        "plt.show()\n",
        "print('Predicted label is %s' % y_preds[id])"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Predicted label is 7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QpL1TN-BuBeC",
        "colab_type": "text"
      },
      "source": [
        "## Collect Latent Space for each classes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VgyJPV53etm9",
        "colab_type": "code",
        "outputId": "d9133c03-4d7a-4975-f3f1-2d9fbe10d96a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "# variabel untuk menyimpan nilai seed dari tiap label\n",
        "labels =  {i: [] for i in range(10)}\n",
        "\n",
        "for idx, l in enumerate(y_preds):\n",
        "    labels[l].append(seed[idx])\n",
        "    \n",
        "for i in range(10):\n",
        "    print('jumlah kelas',i,':',np.array(labels[i]).shape)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "jumlah kelas 0 : (69, 100)\n",
            "jumlah kelas 1 : (348, 100)\n",
            "jumlah kelas 2 : (699, 100)\n",
            "jumlah kelas 3 : (1288, 100)\n",
            "jumlah kelas 4 : (110, 100)\n",
            "jumlah kelas 5 : (808, 100)\n",
            "jumlah kelas 6 : (38, 100)\n",
            "jumlah kelas 7 : (1598, 100)\n",
            "jumlah kelas 8 : (7, 100)\n",
            "jumlah kelas 9 : (35, 100)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Py-_zHsXubKc",
        "colab_type": "text"
      },
      "source": [
        "### average the Latent Space"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rE7sCD_euaRS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "means =  {i: np.mean(np.array(labels[i]), axis=0) for i in range(10)}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5TlDc0uVetnN",
        "colab_type": "text"
      },
      "source": [
        "### Generate Gambar Dari Rata-rata Latent Space\n",
        "dengan menggunakan rata-rata dari seed sebelumnya, nilai rata-rata tersebut digunakan untuk membuat gambar baru. hasil rata-rata dikalingan dengan sebuah bilangan untuk menghasilkan gambar lainya"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mSCzkbFTetnP",
        "colab_type": "code",
        "outputId": "36cc4167-0950-436a-ecf9-85f826247266",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "source": [
        "num = 6\n",
        "mean_seed = np.reshape(means[num], (1, 100))\n",
        "img = g_model.predict(mean_seed)\n",
        "\n",
        "plt.imshow(np.reshape(img, (28,28,)),cmap=plt.get_cmap('gray'))\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3uBWLVoDetnR",
        "colab_type": "code",
        "outputId": "6e1aed63-3d7a-4296-e215-cc0b1906be2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        }
      },
      "source": [
        "for i in range(10):\n",
        "    mean_seed = np.reshape(means[i], (1, 100))\n",
        "    img = g_model.predict(mean_seed)\n",
        "    plt.subplot(2,5,1+i)\n",
        "    plt.imshow(np.reshape(img, (28,28,)),cmap=plt.get_cmap('gray'))\n",
        "    plt.title(str(i))\n",
        "    plt.axis('off')\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yiWcPmgXpVfWn+v3www/r/s4y2O4/bmeKaNlpp50A+MpXvlL397PPPgvA8ccf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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQPPNwZeu3lJ",
        "colab_type": "text"
      },
      "source": [
        "## Kumpulkan Latent Space untuk Kelas yang sama [Balanced]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "reuHZECeu9tx",
        "colab_type": "code",
        "outputId": "3b70b13f-aef0-4787-9fbe-0d97f9c0f038",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "batas = 25\n",
        "\n",
        "labels_balanced = {}\n",
        "for i in range(10):\n",
        "    labels_balanced[i] = []\n",
        "    while len(labels_balanced[i])<batas:\n",
        "        seed = np.random.uniform(-1, 1, (1, 100))\n",
        "        images = g_model.predict(seed)\n",
        "        pred = mnist_model.predict(images)\n",
        "        y_preds = np.argmax(pred, axis=1)\n",
        "        if(y_preds==i):\n",
        "            labels_balanced[i].append(seed)\n",
        "\n",
        "\n",
        "for i in range(10):\n",
        "    print('jumlah kelas',i,':',np.array(labels_balanced[i]).shape)\n",
        "    \n",
        "    \n",
        "means_balanced =  {i: np.mean(np.array(labels_balanced[i]), axis=0) for i in range(10)}"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "jumlah kelas 0 : (25, 1, 100)\n",
            "jumlah kelas 1 : (25, 1, 100)\n",
            "jumlah kelas 2 : (25, 1, 100)\n",
            "jumlah kelas 3 : (25, 1, 100)\n",
            "jumlah kelas 4 : (25, 1, 100)\n",
            "jumlah kelas 5 : (25, 1, 100)\n",
            "jumlah kelas 6 : (25, 1, 100)\n",
            "jumlah kelas 7 : (25, 1, 100)\n",
            "jumlah kelas 8 : (25, 1, 100)\n",
            "jumlah kelas 9 : (25, 1, 100)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pcHsOZQIvWif",
        "colab_type": "text"
      },
      "source": [
        "### Generate Gambar"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uIlPnWwSetnW",
        "colab_type": "code",
        "outputId": "020a2131-62b9-4bb9-916b-d1f44466b8b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        }
      },
      "source": [
        "for i in range(10):\n",
        "    mean_seed = np.reshape(means_balanced[i], (1, 100))\n",
        "    img = g_model.predict(mean_seed)\n",
        "    plt.subplot(2,5,1+i)\n",
        "    plt.imshow(np.reshape(img, (28,28,)),cmap=plt.get_cmap('gray'))\n",
        "    plt.title(str(i))\n",
        "    plt.axis('off')\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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d2BaqniGl3S5Ptcb3lgrM/hbbbpVu2VQHk34p3LhHk3KcaqpaEKO1114bgLnm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\n",
            "text/plain": [
              "<Figure size 432x288 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kJzFcWdqvd5b",
        "colab_type": "text"
      },
      "source": [
        "# Latent Space Interpolation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8S-GbhggetnZ",
        "colab_type": "text"
      },
      "source": [
        "Let's do some mathematical operatin in latent space"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J4XIlEi2etnZ",
        "colab_type": "code",
        "outputId": "b7ffbbb4-1ecd-430e-9364-de1210bf4091",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "source": [
        "new_d = means[6] - means[1]\n",
        "img = g_model.predict(np.reshape(new_d, (1, 100)))\n",
        "plt.imshow(np.reshape(img, (28,28,)),cmap=plt.get_cmap('gray'))\n",
        "plt.axis('off')\n",
        "plt.show()\n",
        "\n",
        "pred = mnist_model.predict(img)\n",
        "label = np.argmax(pred, axis=1)\n",
        "print('Predicted label is %s' % label) "
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwBTlBEz9A6w8bxwWgkjxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Predicted label is [6]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "62jHiKG5viKM",
        "colab_type": "code",
        "outputId": "e6eda194-d131-46aa-8462-fedd21939c25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "source": [
        "new_d = means[2] + means[5]\n",
        "img = g_model.predict(np.reshape(new_d, (1, 100)))\n",
        "plt.imshow(np.reshape(img, (28,28,)),cmap=plt.get_cmap('gray'))\n",
        "plt.axis('off')\n",
        "plt.show()\n",
        "\n",
        "pred = mnist_model.predict(img)\n",
        "label = np.argmax(pred, axis=1)\n",
        "print('Predicted label is %s' % label) "
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUU7AFOUETFFOwBTlBExRTsDUX4ooWQx4hETzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Predicted label is [2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vG8JDVI-cPci"
      },
      "source": [
        ""
      ]
    }
  ]
}