colab-notebooks/11_VectorizedGDAlgorithms.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/satyajitghana/PadhAI-Course/blob/master/11_VectorizedGDAlgorithms.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_h6hAoQ7CjdU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.colors\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, mean_squared_error, log_loss\n",
        "from tqdm import tqdm_notebook \n",
        "import seaborn as sns\n",
        "import imageio\n",
        "import time\n",
        "from IPython.display import HTML\n",
        "\n",
        "sns.set()\n",
        "\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.datasets import make_blobs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LEfSg5YBCrGI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "my_cmap = 'inferno'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYc221B-CuUb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.random.seed(0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4bALEIu7CwbJ",
        "colab_type": "text"
      },
      "source": [
        "# Generate Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOwxhC82CxkH",
        "colab_type": "code",
        "outputId": "397a5b8e-038f-40bd-e516-710d5185246f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "data, labels = make_blobs(n_samples=1000, centers=4, n_features=2, random_state=0)\n",
        "print(data.shape, labels.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1000, 2) (1000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5NJtP06nC1VL",
        "colab_type": "code",
        "outputId": "52dab8e5-54a6-49aa-8a13-887e5227bcf2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "source": [
        "plt.scatter(data[:,0], data[:,1], c=labels, cmap=my_cmap)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dXsOPMNJVvTIIrorrxEgny3MtPFu5f9SPp7nv9I1mkVIraf5dcd0Idr4R0xyG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\nZDJHfX0EVfV/2LW3Zzllyg0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cf/33uPjifys75777/sbkyZMxDINYLEZDQ4Ov/Rpg6FD/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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hsAikOCrC214",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels_orig = labels\n",
        "labels = np.mod(labels_orig, 2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4BxoRC1MC5S4",
        "colab_type": "code",
        "outputId": "fc04e939-b607-4fac-d545-bcd7884275ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "source": [
        "plt.scatter(data[:,0], data[:,1], c=labels, cmap=my_cmap)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bbe7OcBt+BJGO6pZBcFQcO0IyXppr4drKvaN+XM19q2c0i5RaUfPviOOEsHL1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\nzi9p+GHbNvF4htraEKrq/bB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EIhHq6uoqmtcGDPAOg81mzYpvbYsWLWTWrEfYunUra9eu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\nPEMFdoVCoZhnqMCuUCgU84z/D7Tje+JdyyKvAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zbfIArfiC6xa",
        "colab_type": "text"
      },
      "source": [
        "# MultiClass Classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K7utNo0OC_QY",
        "colab_type": "code",
        "outputId": "ab7322fd-b277-4073-9d02-6f2892a10ff0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "X_train, X_val, Y_train, Y_val = train_test_split(data, labels_orig, stratify=labels_orig, random_state=0)\n",
        "print(X_train.shape, X_val.shape, labels_orig.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(750, 2) (250, 2) (1000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mT4a89B_DA3u",
        "colab_type": "code",
        "outputId": "d03a35a7-c24c-44b7-bd3c-43d0281830c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "source": [
        "enc = OneHotEncoder()\n",
        "# 0 -> (1, 0, 0, 0), 1 -> (0, 1, 0, 0), 2 -> (0, 0, 1, 0), 3 -> (0, 0, 0, 1)\n",
        "y_OH_train = enc.fit_transform(np.expand_dims(Y_train,1)).toarray()\n",
        "y_OH_val = enc.fit_transform(np.expand_dims(Y_val,1)).toarray()\n",
        "print(y_OH_train.shape, y_OH_val.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(750, 4) (250, 4)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
            "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
            "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
            "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
            "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uirOe2jrDDHn",
        "colab_type": "code",
        "outputId": "8ac7bbc9-5585-4ed1-8fd1-a39fccabf769",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "W1 = np.random.randn(2,2)\n",
        "W2 = np.random.randn(2,4)\n",
        "print(W1)\n",
        "print(W2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.76405235 0.40015721]\n",
            " [0.97873798 2.2408932 ]]\n",
            "[[ 1.86755799 -0.97727788  0.95008842 -0.15135721]\n",
            " [-0.10321885  0.4105985   0.14404357  1.45427351]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0U2iZPSoDFFk",
        "colab_type": "text"
      },
      "source": [
        "# FF Class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uWlI_GHbDHg9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class FFNetwork:\n",
        "  \n",
        "  def __init__(self, W1, W2):\n",
        "        \n",
        "    self.params={}\n",
        "    self.params[\"W1\"]=W1.copy()\n",
        "    self.params[\"W2\"]=W2.copy()\n",
        "    self.params[\"B1\"]=np.zeros((1,2))\n",
        "    self.params[\"B2\"]=np.zeros((1,4))\n",
        "    self.num_layers=2\n",
        "    self.gradients={}\n",
        "    self.update_params={}\n",
        "    self.prev_update_params={}\n",
        "    for i in range(1,self.num_layers+1):\n",
        "      self.update_params[\"v_w\"+str(i)]=0\n",
        "      self.update_params[\"v_b\"+str(i)]=0\n",
        "      self.update_params[\"m_b\"+str(i)]=0\n",
        "      self.update_params[\"m_w\"+str(i)]=0\n",
        "      self.prev_update_params[\"v_w\"+str(i)]=0\n",
        "      self.prev_update_params[\"v_b\"+str(i)]=0\n",
        "  \n",
        "  def forward_activation(self, X): \n",
        "    return 1.0/(1.0 + np.exp(-X))\n",
        "      \n",
        "  def grad_activation(self, X):\n",
        "    return X*(1-X) \n",
        "\n",
        "  def softmax(self, X):\n",
        "    exps = np.exp(X)\n",
        "    return exps / np.sum(exps, axis=1).reshape(-1,1)\n",
        "  \n",
        "  def forward_pass(self, X, params = None):\n",
        "    if params is None:\n",
        "        params = self.params\n",
        "    self.A1 = np.matmul(X, params[\"W1\"]) + params[\"B1\"] # (N, 2) * (2, 2) -> (N, 2)\n",
        "    self.H1 = self.forward_activation(self.A1) # (N, 2)\n",
        "    self.A2 = np.matmul(self.H1, params[\"W2\"]) + params[\"B2\"] # (N, 2) * (2, 4) -> (N, 4)\n",
        "    self.H2 = self.softmax(self.A2) # (N, 4)\n",
        "    return self.H2\n",
        "  \n",
        "  def grad(self, X, Y, params = None):\n",
        "    if params is None:\n",
        "      params = self.params \n",
        "      \n",
        "    self.forward_pass(X, params)\n",
        "    m = X.shape[0]\n",
        "    self.gradients[\"dA2\"] = self.H2 - Y # (N, 4) - (N, 4) -> (N, 4)\n",
        "    self.gradients[\"dW2\"] = np.matmul(self.H1.T, self.gradients[\"dA2\"]) # (2, N) * (N, 4) -> (2, 4)\n",
        "    self.gradients[\"dB2\"] = np.sum(self.gradients[\"dA2\"], axis=0).reshape(1, -1) # (N, 4) -> (1, 4)\n",
        "    self.gradients[\"dH1\"] = np.matmul(self.gradients[\"dA2\"], params[\"W2\"].T) # (N, 4) * (4, 2) -> (N, 2)\n",
        "    self.gradients[\"dA1\"] = np.multiply(self.gradients[\"dH1\"], self.grad_activation(self.H1)) # (N, 2) .* (N, 2) -> (N, 2)\n",
        "    self.gradients[\"dW1\"] = np.matmul(X.T, self.gradients[\"dA1\"]) # (2, N) * (N, 2) -> (2, 2)\n",
        "    self.gradients[\"dB1\"] = np.sum(self.gradients[\"dA1\"], axis=0).reshape(1, -1) # (N, 2) -> (1, 2)\n",
        "    \n",
        "  def fit(self, X, Y, epochs=1, algo= \"GD\", display_loss=False, \n",
        "          eta=1, mini_batch_size=100, eps=1e-8,  \n",
        "          beta=0.9, beta1=0.9, beta2=0.9, gamma=0.9 ):\n",
        "      \n",
        "    if display_loss:\n",
        "      loss = {}\n",
        "    for num_epoch in tqdm_notebook(range(epochs), total=epochs, unit=\"epoch\"):\n",
        "      m = X.shape[0]\n",
        "      \n",
        "      if algo == \"GD\":\n",
        "        self.grad(X, Y)\n",
        "        for i in range(1,self.num_layers+1):\n",
        "          self.params[\"W\"+str(i)] -= eta * (self.gradients[\"dW\"+str(i)]/m)\n",
        "          self.params[\"B\"+str(i)] -= eta * (self.gradients[\"dB\"+str(i)]/m)\n",
        "          \n",
        "      elif algo == \"MiniBatch\":\n",
        "        for k in range(0,m,mini_batch_size):\n",
        "          self.grad(X[k:k+mini_batch_size], Y[k:k+mini_batch_size])\n",
        "          for i in range(1,self.num_layers+1):\n",
        "            self.params[\"W\"+str(i)] -= eta * (self.gradients[\"dW\"+str(i)]/mini_batch_size)\n",
        "            self.params[\"B\"+str(i)] -= eta * (self.gradients[\"dB\"+str(i)]/mini_batch_size)\n",
        "            \n",
        "      elif algo == \"Momentum\":\n",
        "        self.grad(X, Y)\n",
        "        for i in range(1,self.num_layers+1):\n",
        "          self.update_params[\"v_w\"+str(i)] = gamma *self.update_params[\"v_w\"+str(i)] + eta * (self.gradients[\"dW\"+str(i)]/m)\n",
        "          self.update_params[\"v_b\"+str(i)] = gamma *self.update_params[\"v_b\"+str(i)] + eta * (self.gradients[\"dB\"+str(i)]/m)\n",
        "          self.params[\"W\"+str(i)] -= self.update_params[\"v_w\"+str(i)]\n",
        "          self.params[\"B\"+str(i)] -= self.update_params[\"v_b\"+str(i)]  \n",
        "          \n",
        "      elif algo == \"NAG\":\n",
        "        temp_params = {}\n",
        "        for i in range(1,self.num_layers+1):\n",
        "          self.update_params[\"v_w\"+str(i)]=gamma*self.prev_update_params[\"v_w\"+str(i)]\n",
        "          self.update_params[\"v_b\"+str(i)]=gamma*self.prev_update_params[\"v_b\"+str(i)]\n",
        "          temp_params[\"W\"+str(i)]=self.params[\"W\"+str(i)]-self.update_params[\"v_w\"+str(i)]\n",
        "          temp_params[\"B\"+str(i)]=self.params[\"B\"+str(i)]-self.update_params[\"v_b\"+str(i)]\n",
        "        self.grad(X,Y,temp_params)\n",
        "        for i in range(1,self.num_layers+1):\n",
        "          self.update_params[\"v_w\"+str(i)] = gamma *self.update_params[\"v_w\"+str(i)] + eta * (self.gradients[\"dW\"+str(i)]/m)\n",
        "          self.update_params[\"v_b\"+str(i)] = gamma *self.update_params[\"v_b\"+str(i)] + eta * (self.gradients[\"dB\"+str(i)]/m)\n",
        "          self.params[\"W\"+str(i)] -= eta * (self.update_params[\"v_w\"+str(i)])\n",
        "          self.params[\"B\"+str(i)] -= eta * (self.update_params[\"v_b\"+str(i)]) \n",
        "        self.prev_update_params=self.update_params \n",
        "        \n",
        "      elif algo == \"AdaGrad\":\n",
        "        self.grad(X, Y)\n",
        "        for i in range(1,self.num_layers+1):\n",
        "          self.update_params[\"v_w\"+str(i)] += (self.gradients[\"dW\"+str(i)]/m)**2\n",
        "          self.update_params[\"v_b\"+str(i)] += (self.gradients[\"dB\"+str(i)]/m)**2\n",
        "          self.params[\"W\"+str(i)] -= (eta/(np.sqrt(self.update_params[\"v_w\"+str(i)])+eps)) * (self.gradients[\"dW\"+str(i)]/m)\n",
        "          self.params[\"B\"+str(i)] -= (eta/(np.sqrt(self.update_params[\"v_b\"+str(i)])+eps)) * (self.gradients[\"dB\"+str(i)]/m)\n",
        "      \n",
        "      elif algo == \"RMSProp\":\n",
        "        self.grad(X, Y)\n",
        "        for i in range(1,self.num_layers+1):\n",
        "          self.update_params[\"v_w\"+str(i)] = beta*self.update_params[\"v_w\"+str(i)] +(1-beta)*((self.gradients[\"dW\"+str(i)]/m)**2)\n",
        "          self.update_params[\"v_b\"+str(i)] = beta*self.update_params[\"v_b\"+str(i)] +(1-beta)*((self.gradients[\"dB\"+str(i)]/m)**2)\n",
        "          self.params[\"W\"+str(i)] -= (eta/(np.sqrt(self.update_params[\"v_w\"+str(i)]+eps)))*(self.gradients[\"dW\"+str(i)]/m)\n",
        "          self.params[\"B\"+str(i)] -= (eta/(np.sqrt(self.update_params[\"v_b\"+str(i)]+eps)))*(self.gradients[\"dB\"+str(i)]/m)\n",
        "      \n",
        "      elif algo == \"Adam\":\n",
        "        self.grad(X, Y)\n",
        "        num_updates=0\n",
        "        for i in range(1,self.num_layers+1):\n",
        "          num_updates+=1\n",
        "          self.update_params[\"m_w\"+str(i)]=beta1*self.update_params[\"m_w\"+str(i)]+(1-beta1)*(self.gradients[\"dW\"+str(i)]/m)\n",
        "          self.update_params[\"v_w\"+str(i)]=beta2*self.update_params[\"v_w\"+str(i)]+(1-beta2)*((self.gradients[\"dW\"+str(i)]/m)**2)\n",
        "          m_w_hat=self.update_params[\"m_w\"+str(i)]/(1-np.power(beta1,num_updates))\n",
        "          v_w_hat=self.update_params[\"v_w\"+str(i)]/(1-np.power(beta2,num_updates))\n",
        "          self.params[\"W\"+str(i)] -=(eta/np.sqrt(v_w_hat+eps))*m_w_hat\n",
        "          \n",
        "          self.update_params[\"m_b\"+str(i)]=beta1*self.update_params[\"m_b\"+str(i)]+(1-beta1)*(self.gradients[\"dB\"+str(i)]/m)\n",
        "          self.update_params[\"v_b\"+str(i)]=beta2*self.update_params[\"v_b\"+str(i)]+(1-beta2)*((self.gradients[\"dB\"+str(i)]/m)**2)\n",
        "          m_b_hat=self.update_params[\"m_b\"+str(i)]/(1-np.power(beta1,num_updates))\n",
        "          v_b_hat=self.update_params[\"v_b\"+str(i)]/(1-np.power(beta2,num_updates))\n",
        "          self.params[\"B\"+str(i)] -=(eta/np.sqrt(v_b_hat+eps))*m_b_hat\n",
        "      if display_loss:\n",
        "        Y_pred = self.predict(X)\n",
        "        loss[num_epoch] = log_loss(np.argmax(Y, axis=1), Y_pred)\n",
        "    \n",
        "    if display_loss:\n",
        "      plt.plot(list(loss.values()), '-o', markersize=5)\n",
        "      plt.xlabel('Epochs')\n",
        "      plt.ylabel('Log Loss')\n",
        "      plt.show()\n",
        "      \n",
        "  \n",
        "  def predict(self, X):\n",
        "    Y_pred = self.forward_pass(X)\n",
        "    return np.array(Y_pred).squeeze()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MD-NNUV6DNhH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def print_accuracy():    \n",
        "    Y_pred_train = model.predict(X_train)\n",
        "    Y_pred_train = np.argmax(Y_pred_train,1)\n",
        "    Y_pred_val = model.predict(X_val)\n",
        "    Y_pred_val = np.argmax(Y_pred_val,1)\n",
        "    accuracy_train = accuracy_score(Y_pred_train, Y_train)\n",
        "    accuracy_val = accuracy_score(Y_pred_val, Y_val)\n",
        "    print(\"Training accuracy\", round(accuracy_train, 4))\n",
        "    print(\"Validation accuracy\", round(accuracy_val, 4))\n",
        "    \n",
        "    if False:\n",
        "      plt.scatter(X_train[:,0], X_train[:,1], c=Y_pred_train, cmap=my_cmap, s=15*(np.abs(np.sign(Y_pred_train-Y_train))+.1))\n",
        "      plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZiV8RPoBDPk2",
        "colab_type": "code",
        "outputId": "e16659b6-b82c-4bc7-a779-18a793f60ad3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "072246ea5df5410898c8006c96809375",
            "66828e3d2a434134800b69bf4d9a16ac",
            "e9c277c31c5742d69e4dc4dd463f6878",
            "adedc387225b4f21bed3bdd8da992812",
            "dc650dff5dc44daa9527210286a71e59",
            "3d9742ab15b94223a538a694af14af69",
            "d7a3f3bbf381438d8b028bbbe43b1920",
            "83ab5d97dee44748b4b39dff57adc752"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=100, eta=1, algo=\"GD\", display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "072246ea5df5410898c8006c96809375",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tW7a4ZGGffPIJRowYgWHDhjn0ebYyYm8iIgL7/LneTIpxd4351eXTcbRci73FZ7G/tA76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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.7053\n",
            "Validation accuracy 0.716\n",
            "CPU times: user 434 ms, sys: 4.03 ms, total: 439 ms\n",
            "Wall time: 449 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fSzhrlV4DRiH",
        "colab_type": "code",
        "outputId": "caeccaeb-93d3-4774-a6b7-a32f9528410a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "185e476280d241aa8aadf22c9d951d32",
            "2f75cf3e8a0a4b9d948d178d99cee28a",
            "411dba11a14d44299352e3bfecc2850a",
            "52a6bfc7c2814bb89bf55ed20cfae1e8",
            "9e4560b8c94a4d3ca21c6e7b93dfe94f",
            "77c03719c24b49f280d857b8aa841d00",
            "589ce39884cc434489469956bfa33867",
            "a9a472142ff544f7b0ec5cd3613733f0"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=100, eta=1, algo=\"MiniBatch\", mini_batch_size=128, display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "185e476280d241aa8aadf22c9d951d32",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mIjY2ttNrdTodHnjgAcydOxfLli2z6X24T8I2cqxbjjUDPavbtO/DUpBYEyrX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\nYG8u/jsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.8867\n",
            "Validation accuracy 0.904\n",
            "CPU times: user 488 ms, sys: 6.54 ms, total: 495 ms\n",
            "Wall time: 506 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rh7iUsFuDTpu",
        "colab_type": "code",
        "outputId": "6648edec-b481-4358-a5c4-f53f430fed84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "f2eb9fef23b6460e80f3852ae3283775",
            "383830ffc72c4cb3ab6670960edd9fac",
            "16a00666f1484e91b448358095365aab",
            "988b5d3d698f4b3784566eb5085ac148",
            "e2a45db766644ef7a761bd13d6ba3c79",
            "da6994ed603c430ea460311821c3aa4c",
            "f50c1c17088c4267b288f54318438f79",
            "906f92a4b8554378bd00fe420defd416"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=100, eta=1, algo=\"MiniBatch\", mini_batch_size=8, display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f2eb9fef23b6460e80f3852ae3283775",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2pKamYurUqdi7d284imgqKz0ZAGBv6YLTa+9qb9IA6SZNSpIEVYVH3wQRUTQK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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.8693\n",
            "Validation accuracy 0.876\n",
            "CPU times: user 1.18 s, sys: 14 ms, total: 1.19 s\n",
            "Wall time: 1.18 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sfj4u5ylDWh-",
        "colab_type": "code",
        "outputId": "a8b6d88d-0c44-47f1-bc6d-a59b181941b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "fa5311f31f444c1c89806c48792f3625",
            "5726ba1cb84e45fca846f02904a52ef1",
            "8358873adc1444c987c1350e2546dfdf",
            "3486748789d64dc29f4bef8ce62a9939",
            "be6dddba019e4a09a3565c90325ff5be",
            "93874278b3ee42c38207fd85411542ed",
            "cba994038798486999721f7291a99d5c",
            "2c56ba0d80854cfcaabc393625fcaaef"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=100, eta=1, algo=\"Momentum\", gamma=0.5, display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fa5311f31f444c1c89806c48792f3625",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MGzcO+/fv98S4SCI6uh+FQiGzHArIrbFEvuFUT8IRNq7JXewdCmgURB4KSORjXQoJmYy/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\nQ4KIiBxiSBARkUMMCSIicuj/A+658xP+H8gyAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.8387\n",
            "Validation accuracy 0.852\n",
            "CPU times: user 430 ms, sys: 3.98 ms, total: 434 ms\n",
            "Wall time: 438 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VL_JJJJuDcd_",
        "colab_type": "code",
        "outputId": "85e0ceca-4ed1-487b-a8db-4422c7381b70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411,
          "referenced_widgets": [
            "92b27bdacb1d48619486d661cf9df642",
            "7e26967c492e49a79b3bfde66d246d81",
            "d0cf281664d64952899765eff780437b",
            "0dd826ea49044baf856b2904c8612826",
            "ef3ae101681f4278817c83d0a0e45d00",
            "045e178fe0ec4650a7211c10a393383e",
            "78f40ffb6000459ca2f8e88784fe0428",
            "6835923c617148b690c7d7265b5e5fc8"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=100, eta=1, algo=\"Momentum\", gamma=0.9, display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "92b27bdacb1d48619486d661cf9df642",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HMSR8jMkk4kprB+qbr+ODbyogM+eB2H2TXVJMiMP7OhERcbjJxxRpDWi62mn5XBR7/gPQ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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.8947\n",
            "Validation accuracy 0.916\n",
            "CPU times: user 450 ms, sys: 11.6 ms, total: 462 ms\n",
            "Wall time: 480 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QUuU5uqDDfoN",
        "colab_type": "code",
        "outputId": "95cc1dd5-d922-4754-bf17-52fbb28ea2c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "0387f1db1a4a408998ba5576e8431ab9",
            "ecc09dcdbc3d4e98b9aef63f30db5906",
            "164da9ec9559441ba7ec1af945d3177a",
            "35467caaec304067974194beb4ae7d40",
            "b1b04f0266464a9d8b27184227ef8a74",
            "788af1c048f240c1956c5791ddc20105",
            "62d1bcb572e3494592e9a1a23ee0288b",
            "3db2c90f4ca54b9bb838ff7ecc74592a"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=100, eta=1, algo=\"Momentum\", gamma=0.99, display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0387f1db1a4a408998ba5576e8431ab9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iIJ/P43Of+9wSr66eULD2tgUDPku1WDs2DsXx/OuTYBjg2Ng8Lt5oLrWNa9IcuYIIWVZw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\n2FSCAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.6347\n",
            "Validation accuracy 0.66\n",
            "CPU times: user 433 ms, sys: 4.57 ms, total: 437 ms\n",
            "Wall time: 457 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uKCaMWaFDhVD",
        "colab_type": "code",
        "outputId": "b51adea2-0a88-4284-900c-c6ddd5759059",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "d5e008770a074742bee7704c88488ed3",
            "de67c43edb13445cade039fff26590ff",
            "e4373f03ea0d4b5c9a593c9ad50e302f",
            "e378d55a57aa4963b324c4d3945ecbd9",
            "704a570e928b41e0a71b3337e9ec9919",
            "449732a39c9c4490bd2b0da890220ffa",
            "047a76057f2f46c9944fd2a7454a4540",
            "44f9440c38954a9bba47ee9643e8b6c5"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=100, eta=1, algo=\"NAG\", gamma=0.99, display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d5e008770a074742bee7704c88488ed3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0u59bfFA4YUgQecA2qwAAQ1VTj41tnldB4YDLTUT9kKVXQ5+i4k6zFLY4kyDq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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.88\n",
            "Validation accuracy 0.872\n",
            "CPU times: user 447 ms, sys: 9.41 ms, total: 456 ms\n",
            "Wall time: 471 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfKJoiK_Dj0O",
        "colab_type": "code",
        "outputId": "0d4bdc87-d5e3-4d63-f510-0f6b9837b01a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "aaac002b76a64003bf3a79d6f2f22940",
            "1be4195d3db0423095406d6dca5414dc",
            "b686c5c390c442bbbcf84c519f32f879",
            "d1c09f08b2a04f2bbd3c819cb7fad347",
            "8acaa0b6365144ce9e1a7586c0a9b0f6",
            "d2acd2ad8ced4af8bdf988c4265ff33e",
            "6309f49259854d959e11f657dae8398a",
            "4eceb63b0adb41709879be938eaebae7"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=100, eta=1, algo=\"NAG\", gamma=0.5, display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aaac002b76a64003bf3a79d6f2f22940",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vX466ujoAQFFREebMmYPp06fj0Ucfhclk8vFo3e/tt99Gamoqzpw5AyDw59za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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.704\n",
            "Validation accuracy 0.728\n",
            "CPU times: user 452 ms, sys: 7.87 ms, total: 460 ms\n",
            "Wall time: 469 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W4g3ZKHeDlku",
        "colab_type": "code",
        "outputId": "d0770716-4c49-4255-dce4-b33cac8759ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "eba6d22a40174ed2bc4d109c274bca8d",
            "7972e0902e564bf6bf85a977fd8ad74d",
            "5a84246676cc4997bcd207810acc34b5",
            "ac45c9b61ae24a95ba1bccd71a8e2d2e",
            "a9ab4b193c9d4656a6e6bd67fd052f6a",
            "d1afa45c640f495a96945cdae5bfa1ba",
            "46a2ef3b9b0c483d9f3f6da4e621455d",
            "6a78a23a02ef4ae9b308171aed0980fd"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=100, eta=1, algo=\"NAG\", gamma=0.9, display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "eba6d22a40174ed2bc4d109c274bca8d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gEenkGgwJIhdyZnjKkR7G5cbOfo/pDQJqtL2rs1R1reg2WAeIvd4Hj0inwWBI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\nrv8PuYLMZTNp7nIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.8773\n",
            "Validation accuracy 0.9\n",
            "CPU times: user 424 ms, sys: 8.06 ms, total: 432 ms\n",
            "Wall time: 445 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vF71oBECDnoN",
        "colab_type": "code",
        "outputId": "2734ebf7-bf85-40ee-fba1-a22bdcf85f27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "9c75c5977e1244498c194b2354534108",
            "289afff05e58470f87b2aa5fed8dece8",
            "cf271a60d54c4f10a95dad0de77b2339",
            "f98b03de5f914469a87c9b351c04d151",
            "33165cf71d694d5ea481c8e10f5a601e",
            "8ef24223eea44f7bb21b3688660cfdaf",
            "7014ced7c3a844be9a378b20cf93b1ee",
            "4a3674702ddd43b899f0dceee8e8ca1a"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=100, eta=1, algo=\"AdaGrad\", display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9c75c5977e1244498c194b2354534108",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MiMi72nV1U6ASBAUWTUnAyRwTtu7PRm2diEVTEgLy6iYiIkc4krBDEBRI6NMVP+8fhWuV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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.88\n",
            "Validation accuracy 0.904\n",
            "CPU times: user 432 ms, sys: 5.14 ms, total: 437 ms\n",
            "Wall time: 453 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tnB1KAakDrEp",
        "colab_type": "code",
        "outputId": "02674fdc-515b-4560-e1de-9349b9557926",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410,
          "referenced_widgets": [
            "6392ec41ae754d06aa510bc50ebe5b07",
            "e190307338c44308b15adf681b978adc",
            "89003304b30a42989d23949796c534cf",
            "99dab697c3c44780b761f24e11310d93",
            "1bfa9d8ffa2541bbb6744b81aa60b9b2",
            "8b92e15a08f942cabf0e465a0cb35d19",
            "1d751d9105ab4b96984c9d99cc107f2d",
            "3acb95407e6545dabd8495c57e9c24a2"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=100, eta=.1, algo=\"AdaGrad\", display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6392ec41ae754d06aa510bc50ebe5b07",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/+lXXKyOHaTur+OZEPi7k1bR7ng1tIuXSCIK129O4LvYk7GNv3WcuVeLDPeet9ikAoJe7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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.644\n",
            "Validation accuracy 0.636\n",
            "CPU times: user 441 ms, sys: 10.9 ms, total: 452 ms\n",
            "Wall time: 474 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x_QNLsS9Dsx1",
        "colab_type": "code",
        "outputId": "eee83ea6-4c8a-4c45-a7f8-fc3431c63dd2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "a6c8ed3483234dce88aa08f1690423ad",
            "8d35525d576f4e5f9cc2c96e45fec69b",
            "ee373805eb014d8eadc0ef643dcf2959",
            "6d9aebb2142a422b9ca6cba2a964cb79",
            "c0331a1be9de4628ab84376339082b0d",
            "5728b98083df40cf8dbb2178feaf7f98",
            "945a998aabbc4955b40c38d4d5be749d",
            "015d06e71a1d4344b71ebc3a8a10824e"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=100, eta=.1, algo=\"RMSProp\", beta=0.9, display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a6c8ed3483234dce88aa08f1690423ad",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DSz7kWDPgWnVbao47gumyXE+dVbBxTURuraMd5fZcTcVZhXUYEkTkVF1ZTWWJ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\nov8PB0dEmXVGJXUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.8827\n",
            "Validation accuracy 0.908\n",
            "CPU times: user 427 ms, sys: 3.01 ms, total: 430 ms\n",
            "Wall time: 440 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RdfzCForDuA4",
        "colab_type": "code",
        "outputId": "77a76c48-11d8-4302-a19a-f02e7e35b3d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "c30a4e02317042eca33c17ec6db8f164",
            "e2a9893b44514428beba4bc393c26799",
            "7e939409ec684f50ba7806a88856f448",
            "d39b9c4292524fdc9648c1522515de68",
            "090357432ded4c5b8f09147f7aaa28f2",
            "e589227fff1546c5b6f142fb2c6f0cf4",
            "9c661aad815647cfa113e48136f78602",
            "0fb9ecfd1711470d866ca195c31dc245"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=100, eta=.9, algo=\"RMSProp\", beta=0.9, display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c30a4e02317042eca33c17ec6db8f164",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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utz9lncz4wuhjhONzD+Qzdxn8EUiNdQamXm9P4eHvlNJQC8dn7S0A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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.8453\n",
            "Validation accuracy 0.844\n",
            "CPU times: user 533 ms, sys: 9.69 ms, total: 543 ms\n",
            "Wall time: 569 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GeFD_sWxDwpe",
        "colab_type": "code",
        "outputId": "ea94de13-cfeb-4e5c-d66a-086607830b75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "9b740912a9fc438b9955c8e7de845297",
            "6ad7e534a0284097aacffe294977bf3e",
            "2b18c67355f14bf6901501ea68880238",
            "d285e0678c694ed99dd9d57963d2a4f3",
            "5cc843d3149e45aa8a270c6a0169ed8e",
            "1c5fb62e4ad74618a3a7d286aa098f8b",
            "133a85aac55e42d880db1863404c039e",
            "e525cad2b4b9422bbe8d54402c90eb4c"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=100, eta=.9, algo=\"Adam\", beta=0.9, display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9b740912a9fc438b9955c8e7de845297",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HkbR5JUkolUqkpaVh7969WLhwIfbu3Yu0tDSboSYASExMxKFDh5Ceng69Xo+v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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.8973\n",
            "Validation accuracy 0.884\n",
            "CPU times: user 437 ms, sys: 3.01 ms, total: 440 ms\n",
            "Wall time: 473 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fipJ1Hn7DznY",
        "colab_type": "code",
        "outputId": "75a0f06c-97e1-4c94-ce56-ed7d63c2a137",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "5509a6190d2141c7aa5070fb6607b3c3",
            "95f526d561a340a283eb4d1970535eca",
            "6dd29319e48143458061d4bdea88aec7",
            "7e3899d0f26143a68731b9adafd733df",
            "245aa5755bdc439989752ad0dea345b1",
            "f40c8bd6d3db4b25a05845cef28bfc4c",
            "e2d2ad927da14cf188f6ca333070c4b9",
            "953463f86b82467e916c89ddc212dfb2"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=100, eta=.1, algo=\"Adam\", beta=0.9, display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5509a6190d2141c7aa5070fb6607b3c3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Y2ieZ+0aRVDAkSPJsBUaZoQHDwwfj3ycuQ6fvemktp6HInzEkiKxYBwYATLlt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\niMguhgQREdnFkCAiIrsYEkREZNf/B3yEH0clZGORAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.9227\n",
            "Validation accuracy 0.94\n",
            "CPU times: user 457 ms, sys: 5.35 ms, total: 462 ms\n",
            "Wall time: 475 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6APks0aLD1CG",
        "colab_type": "text"
      },
      "source": [
        "# Good Configuration for each Algorithm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEC-WhSaD6No",
        "colab_type": "code",
        "outputId": "bc2d1a95-ade9-4c98-9118-a127f8866125",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "5dd7cbe8c8b1436aaa7d2bcc2f5e9e9c",
            "7a4432ccf89e4f338f7658a53529574c",
            "95fa13b9ce994c86a209a658d388db8b",
            "e7d87495957d4f85ad4a7f27af0e1366",
            "ab4b00b63d3e4872a8251d162ea9267c",
            "899cc88b035c4612868185239c3cfd60",
            "abc95c2ea5ae449396aebc5de46eeb9d",
            "7b84110617984d6697d4774d37d47cc1"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=10000, eta=0.5, algo=\"GD\", display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5dd7cbe8c8b1436aaa7d2bcc2f5e9e9c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=10000), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PRYXxtttmyXN89Y4yf/xsO8I2iyf2ykKysJg4cSKeeOIJZGRkYPPmzfj000+x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\nDAsiInLofwHzpCPeFyu+tgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.9293\n",
            "Validation accuracy 0.936\n",
            "CPU times: user 15 s, sys: 68.3 ms, total: 15.1 s\n",
            "Wall time: 15.1 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RskQqWND7-6",
        "colab_type": "code",
        "outputId": "4f8a5289-aee3-4b40-d90b-2521787560ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "c6e8a0ecaf304c07a13d0ac1b5b84128",
            "6282d8488cc64df7a3c2e80d69fc93a4",
            "9f871d446a664db889f8dd4ddf03825f",
            "3352d3fbaf0f4689a74f9145a17efbbf",
            "d4dbff89342641299b4f1fb4a22eb5f5",
            "41532d49e027485b84725470e44a7580",
            "2cd00f6835884e9899c6ee8f96de6462",
            "50081aa0994c4d98a9f33192497db00d"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=1000, eta=0.5, algo=\"Momentum\", gamma=0.9, display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c6e8a0ecaf304c07a13d0ac1b5b84128",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IwKJFiwAAX3zxBYYMGWJ3QHISExOxdetWmM1mVFdX49///jcSEhI67P3bq3lPiXv6B0Lj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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.9267\n",
            "Validation accuracy 0.944\n",
            "CPU times: user 1.85 s, sys: 18.2 ms, total: 1.87 s\n",
            "Wall time: 1.85 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EuByelEXD9ug",
        "colab_type": "code",
        "outputId": "2332b78a-d245-4a31-9afe-8ea51a5b79d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "d7eed90aff6943259e650ad053a02c3f",
            "f85cbf5ea9de4130b9be0e8d6bc89399",
            "3b936fd34bc2482d91c29c0894e573e3",
            "979058596fc746599844c182ebcf91ca",
            "8e5dfe085c5743e3ace867ffe1e3fe26",
            "e01ece5e92784df3a71db267d96b3f54",
            "4e559ba72f324b3aa5a0485654ecab15",
            "6fa933be55b74bc393a98613f56cfee7"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=1000, eta=0.5, algo=\"NAG\", gamma=0.9, display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d7eed90aff6943259e650ad053a02c3f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vXm3BvSqtaiNXq31QXFxh8eucmZQ637hRi2UbD8La0a+arl6YMioYD/RXQxCk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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.9213\n",
            "Validation accuracy 0.94\n",
            "CPU times: user 1.9 s, sys: 11.1 ms, total: 1.91 s\n",
            "Wall time: 1.93 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ca_fNlg3D_AO",
        "colab_type": "code",
        "outputId": "25ae3663-6760-45c4-c0a0-cfd77413042a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "0473370af8824f799ff03027bc37f8ad",
            "80608a84b29f49ee9e53a077242fc797",
            "1d34d0ce7af240e8b1d1c8579c01db79",
            "51463234737743bc8c2d703878cdcbd7",
            "c46fe7e65405400e91eb9a6f6441713d",
            "0cccbd8bfb47431191ebb0f7ea2dd469",
            "7934b240b65d4669b16b21519ea9a134",
            "d9af20e19fe14e14ac19f99f3487241f"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=500, eta=1, algo=\"AdaGrad\", display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0473370af8824f799ff03027bc37f8ad",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=500), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jttjDiYhIyRwKiejoaIwaNQpPPPGE+dg//vEP5OXlYcOGDfjwww+xcuVKbNy40WmFOpvR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\nEBGRTQwJIiKyiSFBREQ2/T/IQl8xqHA32wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.912\n",
            "Validation accuracy 0.936\n",
            "CPU times: user 1.09 s, sys: 13.9 ms, total: 1.1 s\n",
            "Wall time: 1.11 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-A5ZXwXEANa",
        "colab_type": "code",
        "outputId": "2e0976a2-e089-4f19-95ab-139b626aea94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "79baee8f67c6413bb6564a8e4d5efc42",
            "6dc5ce44ef2b4ca183562cf411301c60",
            "d4b065147838494881753e52a19004cd",
            "f8139bc38d814f459c0c9a47e4f889d6",
            "905f27ec10aa4200a891e241837c2711",
            "61ca3d1df36c46beb50999e8fef59351",
            "7c5bbb9a288e40fea377479652f9a0e4",
            "d0f9658afbff4f58b2acf112e25878c3"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=2000, eta=.01, algo=\"RMSProp\", beta=0.9, display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "79baee8f67c6413bb6564a8e4d5efc42",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=2000), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Sae0pXnc5LkFEzsaiK4lPP/0Ua9euxeOPP96w7ejRo1iyZAkiIyObfP3SpUuxb98+FBcX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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.9253\n",
            "Validation accuracy 0.94\n",
            "CPU times: user 3.45 s, sys: 13.8 ms, total: 3.47 s\n",
            "Wall time: 3.5 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SdFnJzKwEBdG",
        "colab_type": "code",
        "outputId": "19ca45af-b356-4f34-f0ab-8f7345d25ba5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "d8d3dc68317745d0b917dc37015469ae",
            "189ed9ffa05e4faabf857881cbf41629",
            "0478f370a3f14ed596c7446239d4b495",
            "06c3578c292e4471932ef832618ec727",
            "6be447cb3b4e46798c7d511cc3d5de35",
            "7fb16ae716c7428188ef3c704c482a50",
            "714a8d75348142d0bbc03b7a0bb35ece",
            "3bae700ddb5049d19269bbeba893c26c"
          ]
        }
      },
      "source": [
        "%%time\n",
        "model = FFNetwork(W1, W2)\n",
        "model.fit(X_train, y_OH_train, epochs=200, eta=.1, algo=\"Adam\", beta=0.9, display_loss=True)\n",
        "print_accuracy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d8d3dc68317745d0b917dc37015469ae",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=200), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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p8min9QnOnyDyT6qZcW0ymfCHP/wBr7zyijWZdIVW27/L79XpQrv8Xm/zdWzJ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\nkAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Training accuracy 0.9293\n",
            "Validation accuracy 0.952\n",
            "CPU times: user 629 ms, sys: 5 ms, total: 634 ms\n",
            "Wall time: 649 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TBU-ZTQiECnM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}