MNIST/Main.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import tensorflow.keras as keras\n",
        "import keras.datasets as kds\n",
        "import keras.backend as K"
      ],
      "metadata": {
        "id": "gEEXok5ibeoh"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading Fashion MNIST data"
      ],
      "metadata": {
        "id": "3WDdhLOjacDc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
        "x_train = x_train / 255.0\n",
        "x_test = x_test / 255.0\n",
        "x_train.shape, y_train.shape, x_test.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1TDjdlMfbfAv",
        "outputId": "73d0c6f9-d926-40a8-ff68-98a935d5228d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 1s 0us/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 28, 28), (60000,), (10000, 28, 28), (10000,))"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Function for merging label and input:"
      ],
      "metadata": {
        "id": "HTWu40rDaflA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def edit_data(x, y, method=\"edit\"):\n",
        "    is_batch = x.ndim == 3\n",
        "    if method == \"edit\":\n",
        "        if is_batch:\n",
        "            x[:, 0, :10] = 0.0\n",
        "            for i in range(x.shape[0]):\n",
        "                x[i, 0, y[i]] = 1.0\n",
        "        else:\n",
        "            x[0, :10] = 0.0\n",
        "            x[0, y] = 1.0"
      ],
      "metadata": {
        "id": "6HV0PWRKbglC"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Function for creating a label for negative data:"
      ],
      "metadata": {
        "id": "3jPdzv46am4l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def random_label(y):\n",
        "    if type(y) != np.ndarray:\n",
        "        label = list(range(10))\n",
        "        del label[y]\n",
        "        return np.random.choice(label)\n",
        "    else:\n",
        "        label = np.copy(y)\n",
        "        for i in range(y.shape[0]):\n",
        "            label[i] = random_label(y[i])\n",
        "        return label"
      ],
      "metadata": {
        "id": "YBee8dp_bi3C"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Constructing positive and negative data:"
      ],
      "metadata": {
        "id": "8EmGoFelausj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pos = np.copy(x_train)\n",
        "neg = np.copy(x_train)\n",
        "edit_data(pos, y_train)\n",
        "edit_data(neg, random_label(y_train))\n",
        "_, ax = plt.subplots(1, 2)\n",
        "ax[0].imshow(pos[0], cmap=\"gray\")\n",
        "ax[1].imshow(neg[0], cmap=\"gray\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "id": "yO4Oz_VBbl2D",
        "outputId": "8aed06f6-efc5-4864-a711-bd529ed4cab6"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loss function:\n",
        "`y_true` can be 1 and -1. -1 for positive data and 1 for negative data. by this method we maximize goodness for positive data $-g+threshold$ and minimize goodness for negative data $g-thhreshold$"
      ],
      "metadata": {
        "id": "DGpzrt-Qa1hG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def myLoss_with_threshold(threshold):\n",
        "    def myLoss(y_true, y_pred):\n",
        "        g = K.pow(y_pred, 2)\n",
        "        g = K.mean(g, axis=1)\n",
        "        g = g - threshold\n",
        "        g = g * y_true\n",
        "        loss = K.log(1 + K.exp(g))\n",
        "        loss = K.mean(loss)\n",
        "        return loss\n",
        "    return myLoss"
      ],
      "metadata": {
        "id": "w0_1IZakbnaG"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pos = pos.reshape(pos.shape[0], -1)\n",
        "neg = neg.reshape(neg.shape[0], -1)\n",
        "pos.shape, neg.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v-C_HR60b3h2",
        "outputId": "f3ab1f65-213d-464c-f791-723eb272be30"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 784), (60000, 784))"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Constructing Neural network:\n",
        "In order to implement forward-forward correctly, we have to set an optimizer for each layer"
      ],
      "metadata": {
        "id": "aBu_c_paa8DM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def normalize_(x_):\n",
        "    return x_ / (tf.norm(x_, ord=2, axis=1, keepdims=True) + 1e-4)\n",
        "layer1 = keras.Sequential([\n",
        "    keras.layers.Dense(500, activation=\"relu\", input_shape=[784])\n",
        "])\n",
        "layer2 = keras.Sequential([\n",
        "    keras.layers.Dense(500, activation=\"relu\", input_shape=[500])\n",
        "])"
      ],
      "metadata": {
        "id": "LZQwPWp2cQ7e"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "layer1.compile(loss=myLoss_with_threshold(2), optimizer=keras.optimizers.Adam())\n",
        "layer2.compile(loss=myLoss_with_threshold(2), optimizer=keras.optimizers.Adam())"
      ],
      "metadata": {
        "id": "Gc3j9JMLcTOI"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm"
      ],
      "metadata": {
        "id": "Hl2LB-g4cVzg"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training layer 1:"
      ],
      "metadata": {
        "id": "igEh7fwha_fV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 1000\n",
        "y_pos = np.ones((batch_size)) * -1\n",
        "y_neg = np.ones((batch_size))\n",
        "for ep in tqdm(range(1000)):\n",
        "  for b in range(pos.shape[0] // batch_size):\n",
        "    x = pos[b * batch_size: (b + 1) * batch_size]\n",
        "    layer1.train_on_batch(x, y_pos)\n",
        "    x = neg[b * batch_size: (b + 1) * batch_size]\n",
        "    layer1.train_on_batch(x, y_neg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "7bab5f2fb8b2429c9954943a7a2f6792",
            "96100996199a430f9548a9ff07fae283",
            "1feeb33f274644eaba18693587165cfd",
            "049134f1e4ef423d92369c2b6c40bcd8",
            "993ea038afe74b5eb0c0faaab19de846",
            "83f63d44077e45bd805f53158de313bd",
            "28945cf3392247169f990493468820f8",
            "cb7a8d33ac394dfe983242393e002e6b",
            "e5c27a6fc1b0450ea75c5882b28efa5e",
            "db92437069df43879a0b92c1c4ff542c",
            "f48db490280140e2a1cf3159d5d94973"
          ]
        },
        "id": "KCV0wfo_cYLk",
        "outputId": "7f8776f2-e805-46ae-faba-7164934de880"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7bab5f2fb8b2429c9954943a7a2f6792"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pos1 = layer1.predict(pos)\n",
        "neg1 = layer1.predict(neg)\n",
        "pos1 = normalize_(pos1)\n",
        "neg1 = normalize_(neg1)\n",
        "pos1.shape, neg1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UyuspW09chUN",
        "outputId": "93b58bc5-9150-4832-d04f-3907ffc4e4a0"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1875/1875 [==============================] - 3s 1ms/step\n",
            "1875/1875 [==============================] - 3s 1ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(TensorShape([60000, 500]), TensorShape([60000, 500]))"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training layer 2:"
      ],
      "metadata": {
        "id": "wI82wEYxbA_6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 1000\n",
        "for ep in tqdm(range(1000)):\n",
        "  for b in range(pos.shape[0] // batch_size):\n",
        "    x = pos1[b * batch_size: (b + 1) * batch_size]\n",
        "    layer2.train_on_batch(x, y_pos)\n",
        "    x = neg1[b * batch_size: (b + 1) * batch_size]\n",
        "    layer2.train_on_batch(x, y_neg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "048de146065747af8b25d05ebc521c2c",
            "9b73f78a582a486aaa77daf659f633d3",
            "7a5ba4509142458d84fd32312129899e",
            "d7a1a0e021b243bd8f88cea124501951",
            "555a558f02d04b71b71d989d58b874fc",
            "bb1288bfac5c406e850e318614e4439d",
            "09a87471426c4bb7af466ea498015f5b",
            "f4b7d4ba21a4455b9153161feaa00b95",
            "74fe162400aa43f78da9eeb4ba667251",
            "0f5555e5a7264bc6a552c2649507590c",
            "03e663201d714452820c026213433fab"
          ]
        },
        "id": "r068RkLXcnE7",
        "outputId": "d7ed4538-6c69-48ec-8239-fa8130702202"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "048de146065747af8b25d05ebc521c2c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test method for inference:"
      ],
      "metadata": {
        "id": "dhPKJH0KbFih"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def test(z):\n",
        "  z = z.reshape(1, 784)\n",
        "  zs = [np.copy(z) for _ in range(10)]\n",
        "  for i in range(10):\n",
        "    edit_data(zs[i], i)\n",
        "  zs1 = [layer1.predict(zs[i], verbose=0) for i in range(10)]\n",
        "  zsnorm = [normalize_(zs1[i]) for i in range(10)]\n",
        "  zs2 = [layer2.predict(zsnorm[i], verbose=0) for i in range(10)]\n",
        "  ans = np.array([np.mean(np.power(zs1[i], 2)) + np.mean(np.power(zs2[i], 2)) for i in range(10)])\n",
        "  return np.argmax(ans)"
      ],
      "metadata": {
        "id": "vCGR3vpBcvj5"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z = x_train[7]\n",
        "print(test(z))\n",
        "plt.imshow(z, cmap=\"gray\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "MpGCorcXc4cx",
        "outputId": "e12172ba-e466-4051-aa49-84a11df3e387"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f1ab0144940>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = 0\n",
        "test_acc = 0\n",
        "for i in range(100):\n",
        "  acc += test(x_train[i]) == y_train[i]\n",
        "for i in range(100, 200):\n",
        "  test_acc += test(x_test[i]) == y_test[i]\n",
        "acc / 100, test_acc / 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gUICZLOlcznN",
        "outputId": "e4435ad4-e0bf-427b-ad7b-345e30953fa1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.97, 0.98)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Save and Load Model:"
      ],
      "metadata": {
        "id": "0IPMBf03bLUL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "layer1.save(\"/content/layer1.h5\")\n",
        "layer2.save(\"/content/layer2.h5\")"
      ],
      "metadata": {
        "id": "Ba2C8tW1kSx6"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "L1 = keras.models.load_model(\"/content/layer1.h5\", custom_objects={'myLoss' : myLoss_with_threshold(2)})\n",
        "L2 = keras.models.load_model(\"/content/layer2.h5\", custom_objects={'myLoss' : myLoss_with_threshold(2)})"
      ],
      "metadata": {
        "id": "nC2_SPU4lYtz"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Accuracy for train and test:"
      ],
      "metadata": {
        "id": "i4QVjn9-bOcF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def test_loaded_model(z, layer1, layer2):\n",
        "  anses = []\n",
        "  for i in tqdm(range(10)):\n",
        "    tmp = np.copy(z)\n",
        "    edit_data(tmp, np.ones((tmp.shape[0]), dtype=int) * i)\n",
        "    tmp = tmp.reshape(tmp.shape[0], -1)\n",
        "    ans1 = layer1.predict(tmp, verbose=0)\n",
        "    norm_ans1 = normalize_(ans1)\n",
        "    ans2 = layer2.predict(norm_ans1, verbose=0)\n",
        "    ans = np.mean(np.power(ans1, 2), axis=1) + np.mean(np.power(ans2, 2), axis=1)\n",
        "    anses.append(ans.reshape(-1, 1))\n",
        "  ans = np.concatenate(anses, axis=1)\n",
        "  return np.argmax(ans, axis=1)"
      ],
      "metadata": {
        "id": "kDSlSJfElkNI"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "acc = 0\n",
        "ans = test_loaded_model(x_test, L1, L2)\n",
        "acc = np.sum(ans == y_test)\n",
        "acc = acc / y_test.shape[0] * 100\n",
        "print(f\"test acc : {acc:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "76342bdf9ead4aefa015097fbc700095",
            "7942540a5e704c828d69a577025f35fd",
            "92707231d486456bae1fead50f75c8e3",
            "fb7ac82f1037465f9f7a17a6a3937ba9",
            "e8910eacc591426db2f66c55e7770478",
            "7828d7cdf74246dca58e3d576e3bf134",
            "71e9d781c8fc420592a50b92dc2b9365",
            "20655b01c44f47259aa932462fcd1c6a",
            "31e7b7710b61439688e5ac43e6164ce4",
            "3756469ef8ba4aca8045791053401310",
            "15e49a200a274dcda5202f09374cb931"
          ]
        },
        "id": "SRIXP_dllt-i",
        "outputId": "e617cc62-e8e0-472d-aff1-c3311cdf6d27"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "76342bdf9ead4aefa015097fbc700095"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test acc : 96.83%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = 0\n",
        "ans = test_loaded_model(x_train, L1, L2)\n",
        "acc = np.sum(ans == y_train)\n",
        "acc = acc / y_train.shape[0] * 100\n",
        "print(f\"train acc : {acc:.2f}%\")"
      ],
      "metadata": {
        "id": "je7ycPj6MqP_",
        "outputId": "9d077b29-8cb2-439b-9660-6d1cc9b28acb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "04418d37fd994423a7987099767f5ea5",
            "6e4df50a83864c629d949aa2f1c4c586",
            "55726d9a8d074fe7892cb18e98cafae8",
            "26f5222ff2884196ab5bb480739a45dd",
            "b27ac689ff644ad290d35ccdfd2003aa",
            "ba47e53aa1ea4e2facbf32b10ae19079",
            "7f44bcef34bd44c4a2cab00d6455db67",
            "fd713f6c83594d7d9f3a1cee4c1953b3",
            "104469d493184a23b71aa12554d19e4d",
            "e97b37a3181940ad94d0baa5b5108f7b",
            "c425a13c544545aaa1d8be5eca1dfab1"
          ]
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "04418d37fd994423a7987099767f5ea5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train acc : 98.24%\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Welcome To Colaboratory",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU",
    "gpuClass": "standard",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "7bab5f2fb8b2429c9954943a7a2f6792": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_96100996199a430f9548a9ff07fae283",
              "IPY_MODEL_1feeb33f274644eaba18693587165cfd",
              "IPY_MODEL_049134f1e4ef423d92369c2b6c40bcd8"
            ],
            "layout": "IPY_MODEL_993ea038afe74b5eb0c0faaab19de846"
          }
        },
        "96100996199a430f9548a9ff07fae283": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
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