GAN_MNIST

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    "colab": {
      "name": "GAN-MNIST.ipynb",
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    "kernelspec": {
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        "outputId": "7083f784-8907-419e-d047-385c2690a301",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        }
      },
      "source": [
        "# Thêm thư viện\n",
        "from keras.datasets import mnist\n",
        "from keras.utils import np_utils\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Input, Dense, Dropout, Activation, Flatten\n",
        "from keras.layers.advanced_activations import LeakyReLU\n",
        "from keras.optimizers import Adam, RMSprop\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "from tqdm import tqdm_notebook\n",
        "\n",
        "# Lấy dữ liệu từ bộ MNIST\n",
        "(X_train, Y_train), (X_test, Y_test) = mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
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        {
          "output_type": "display_data",
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              "<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"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "sxkvgUE3BTj1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Tiền xử lý dữ liệu, reshape từ ảnh xám 28*28 thành vector 784 chiều và đưa dữ liệu từ scale [0, 255] về [0, 1]\n",
        "X_train = X_train.reshape(60000, 784)\n",
        "X_test = X_test.reshape(10000, 784)\n",
        "X_train = X_train.astype('float32')/255\n",
        "X_test = X_test.astype('float32')/255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wry_0KQvBwYY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Số chiều noise vector\n",
        "z_dim = 100"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-0kpJvGBzSJ",
        "colab_type": "code",
        "outputId": "d7923a0c-2e99-4deb-d350-a9550a9e2dfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        }
      },
      "source": [
        "# Optimizer\n",
        "adam = Adam(lr=0.0002, beta_1=0.5)\n",
        "\n",
        "# Mô hình Generator\n",
        "g = Sequential()\n",
        "g.add(Dense(256, input_dim=z_dim, activation=LeakyReLU(alpha=0.2)))\n",
        "g.add(Dense(512, activation=LeakyReLU(alpha=0.2)))\n",
        "g.add(Dense(1024, activation=LeakyReLU(alpha=0.2)))\n",
        "# Vì dữ liệu ảnh MNIST đã chuẩn hóa về [0, 1] nên hàm G khi sinh ảnh ra cũng cần sinh ra ảnh có pixel value trong khoảng [0, 1] => hàm sigmoid được chọn\n",
        "g.add(Dense(784, activation='sigmoid'))  \n",
        "g.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
        "\n",
        "# Mô hình Discriminator\n",
        "d = Sequential()\n",
        "d.add(Dense(1024, input_dim=784, activation=LeakyReLU(alpha=0.2)))\n",
        "d.add(Dropout(0.3))\n",
        "d.add(Dense(512, activation=LeakyReLU(alpha=0.2)))\n",
        "d.add(Dropout(0.3))\n",
        "d.add(Dense(256, activation=LeakyReLU(alpha=0.2)))\n",
        "d.add(Dropout(0.3))\n",
        "# Hàm sigmoid cho bài toán binary classification \n",
        "d.add(Dense(1, activation='sigmoid'))\n",
        "d.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
        "\n",
        "d.trainable = False\n",
        "inputs = Input(shape=(z_dim, ))\n",
        "hidden = g(inputs)\n",
        "output = d(hidden)\n",
        "gan = Model(inputs, output)\n",
        "gan.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3657: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "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",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/activations.py:235: UserWarning: Do not pass a layer instance (such as LeakyReLU) as the activation argument of another layer. Instead, advanced activation layers should be used just like any other layer in a model.\n",
            "  identifier=identifier.__class__.__name__))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nt_xTBUuB2oN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Hàm vẽ loss function\n",
        "def plot_loss(losses):\n",
        "    d_loss = [v[0] for v in losses[\"D\"]]\n",
        "    g_loss = [v[0] for v in losses[\"G\"]]\n",
        "    \n",
        "    plt.figure(figsize=(10,8))\n",
        "    plt.plot(d_loss, label=\"Discriminator loss\")\n",
        "    plt.plot(g_loss, label=\"Generator loss\")\n",
        "    \n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "# Hàm vẽ sample từ Generator\n",
        "def plot_generated(n_ex=10, dim=(1, 10), figsize=(12, 2)):\n",
        "    noise = np.random.normal(0, 1, size=(n_ex, z_dim))\n",
        "    generated_images = g.predict(noise)\n",
        "    generated_images = generated_images.reshape(n_ex, 28, 28)\n",
        "\n",
        "    plt.figure(figsize=figsize)\n",
        "    for i in range(generated_images.shape[0]):\n",
        "        plt.subplot(dim[0], dim[1], i+1)\n",
        "        plt.imshow(generated_images[i], interpolation='nearest', cmap='gray_r')\n",
        "        plt.axis('off')\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7L5H0lUMCFwl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Lưu giá trị loss và accuracy của Discriminator và Generator\n",
        "losses = {\"D\":[], \"G\":[]}\n",
        "\n",
        "def train(epochs=1, plt_frq=1, BATCH_SIZE=128):\n",
        "    # Tính số lần chạy trong mỗi epoch\n",
        "    batchCount = int(X_train.shape[0] / BATCH_SIZE)\n",
        "    print('Epochs:', epochs)\n",
        "    print('Batch size:', BATCH_SIZE)\n",
        "    print('Batches per epoch:', batchCount)\n",
        "    \n",
        "    for e in tqdm_notebook(range(1, epochs+1)):\n",
        "        if e == 1 or e%plt_frq == 0:\n",
        "            print('-'*15, 'Epoch %d' % e, '-'*15)\n",
        "        for _ in range(batchCount):\n",
        "            # Lấy ngẫu nhiên các ảnh từ MNIST dataset (ảnh thật)\n",
        "            image_batch = X_train[np.random.randint(0, X_train.shape[0], size=BATCH_SIZE)]\n",
        "            # Sinh ra noise ngẫu nhiên\n",
        "            noise = np.random.normal(0, 1, size=(BATCH_SIZE, z_dim))\n",
        "            \n",
        "            # Dùng Generator sinh ra ảnh từ noise\n",
        "            generated_images = g.predict(noise)\n",
        "            X = np.concatenate((image_batch, generated_images))\n",
        "            # Tạo label\n",
        "            y = np.zeros(2*BATCH_SIZE)\n",
        "            y[:BATCH_SIZE] = 0.9  # gán label bằng 1 cho những ảnh từ MNIST dataset và 0 cho ảnh sinh ra bởi Generator\n",
        "\n",
        "            # Train discriminator\n",
        "            d.trainable = True\n",
        "            d_loss = d.train_on_batch(X, y)\n",
        "\n",
        "            # Train generator\n",
        "            noise = np.random.normal(0, 1, size=(BATCH_SIZE, z_dim))\n",
        "            # Khi train Generator gán label bằng 1 cho những ảnh sinh ra bởi Generator -> cố gắng lừa Discriminator. \n",
        "            y2 = np.ones(BATCH_SIZE)\n",
        "            # Khi train Generator thì không cập nhật hệ số của Discriminator.\n",
        "            d.trainable = False\n",
        "            g_loss = gan.train_on_batch(noise, y2)\n",
        "\n",
        "        # Lưu loss function\n",
        "        losses[\"D\"].append(d_loss)\n",
        "        losses[\"G\"].append(g_loss)\n",
        "\n",
        "        # Vẽ các số được sinh ra để kiểm tra kết quả\n",
        "        if e == 1 or e%plt_frq == 0:\n",
        "            plot_generated()\n",
        "    plot_loss(losses)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ZRyZstFCHti",
        "colab_type": "code",
        "outputId": "8fd4811e-7450-46e8-8887-0da301ad8d16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "train(epochs=200, plt_frq=20, BATCH_SIZE=128)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epochs: 200\n",
            "Batch size: 128\n",
            "Batches per epoch: 468\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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          "text": [
            "--------------- Epoch 1 ---------------\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+2SbJt81o09pRCql37IJzLuWIlmBRNsd5xbWnnABG6tSm0hjs6jmMvsq1yMcq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\nIYQQQkfkB1YIIYQQQgghdMT/A1nwUysQR3oHAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x144 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 20 ---------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 864x144 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 40 ---------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 864x144 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 60 ---------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6Q506darRz62UcqWVcFqlVRzjfeG2227zWFXd/mP9wBWYuErg3Xff7bGNdd5POF1gxIgR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\niIiIiETkf7TYta7TDU+bAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x144 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 80 ---------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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REQmkvBRBERERERERqSCtYImIiIiIiASiGywREREREZFAdIMlIiIiIiISiG6w\nREREREREAtENloiIiIiISCC6wRIREREREQnkf6fuD+RIWsTHAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x144 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 100 ---------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 864x144 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 120 ---------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 864x144 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 140 ---------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 864x144 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 160 ---------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 864x144 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 180 ---------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 864x144 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "--------------- Epoch 200 ---------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bNsx93JOw0qhfv77bcbLWBQsWuK9Tp05uW+W2hg0buo8lgCwR/vjjjwFExynH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\nYII=\n",
            "text/plain": [
              "<Figure size 864x144 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8eLEOPvhgee+VkZFR1F9VnpSUFI0ZM0bDhg3Thg0blJeXp+uvv15dunQpc9uRI0dq8ODB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\nmLTkS2nxF/b1rWulH9+Tup0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n7jhbmFPO+sbCmZ4EAm5XwYvlJADERmgNK5j0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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q5t8R1uLCJ5D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}