NeuralNetworks-Handwriting2.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MLHW7.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "hKgl30C2Vq1m",
        "colab_type": "code",
        "outputId": "448c468e-c039-41a7-cac3-532f75278e8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8az-Z7bGW9_l",
        "colab_type": "code",
        "outputId": "a5fad3ce-36ee-4ca8-ff05-09b8afd02003",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 64
        }
      },
      "source": [
        "# importing necessary libraries\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix,classification_report"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xemDdRH2W-NG",
        "colab_type": "code",
        "outputId": "1994a840-ea4b-4c6b-9197-e9f05759dd88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        }
      },
      "source": [
        "# Importing data\n",
        "...\n",
        "from tensorflow.examples.tutorials.mnist import input_data\n",
        "\n",
        "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True) "
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-2-c1c43185039d>:4: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please write your own downloading logic.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/base.py:252: _internal_retry.<locals>.wrap.<locals>.wrapped_fn (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use urllib or similar directly.\n",
            "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.data to implement this functionality.\n",
            "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
            "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.data to implement this functionality.\n",
            "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.one_hot on tensors.\n",
            "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
            "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
            "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
            "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uRbPjQV0W-UJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "outputId": "f9941dec-a545-4be1-b3cf-f242aee0838c"
      },
      "source": [
        "...\n",
        "n_train = mnist.train.num_examples \n",
        "n_validation = mnist.validation.num_examples\n",
        "n_test = mnist.test.num_examples\n",
        "\n",
        "print(n_train, n_validation, n_test)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-babce5763b0d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mn_validation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmnist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_examples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mn_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmnist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_examples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mn_Y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmnist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_examples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_validation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'Datasets' object has no attribute 'Y'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3SUE4Z5CXnKq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " # Defining the neural network architecture\n",
        " ...\n",
        "n_input = 784  # input layer (28x28 pixels)\n",
        "n_hidden1 = 512  # 1st hidden layer\n",
        "n_hidden2 = 256  # 2nd hidden layer\n",
        "n_hidden3 = 128  # 3rd hidden layer\n",
        "n_output = 10  # output layer (0-9 digits)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mww5LSUiXnOI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Defining the hyperparameters\n",
        "...\n",
        "learning_rate = 1e-4\n",
        "n_iterations = 1000\n",
        "batch_size = 128\n",
        "dropout = 0.5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNztwrQUXnRp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Initializing placeholder tensors\n",
        "...\n",
        "X = tf.placeholder(\"float\", [None, n_input])\n",
        "Y = tf.placeholder(\"float\", [None, n_output])\n",
        "keep_prob = tf.placeholder(tf.float32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_UQL3LEXnVQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Setting initital weights\n",
        "...\n",
        "weights = {\n",
        "    'w1': tf.Variable(tf.truncated_normal([n_input, n_hidden1], stddev=0.1)),\n",
        "    'w2': tf.Variable(tf.truncated_normal([n_hidden1, n_hidden2], stddev=0.1)),\n",
        "    'w3': tf.Variable(tf.truncated_normal([n_hidden2, n_hidden3], stddev=0.1)),\n",
        "    'out': tf.Variable(tf.truncated_normal([n_hidden3, n_output], stddev=0.1)),\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i11PqmyhYUf4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Defining biases\n",
        "...\n",
        "biases = {\n",
        "    'b1': tf.Variable(tf.constant(0.1, shape=[n_hidden1])),\n",
        "    'b2': tf.Variable(tf.constant(0.1, shape=[n_hidden2])),\n",
        "    'b3': tf.Variable(tf.constant(0.1, shape=[n_hidden3])),\n",
        "    'out': tf.Variable(tf.constant(0.1, shape=[n_output]))\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TlWo4sXcYUid",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "0c3f7c73-41b8-40dd-a29a-e141b5258b7b"
      },
      "source": [
        "# Setting up layers of the network\n",
        "...\n",
        "layer_1 = tf.add(tf.matmul(X, weights['w1']), biases['b1'])\n",
        "layer_2 = tf.add(tf.matmul(layer_1, weights['w2']), biases['b2'])\n",
        "layer_3 = tf.add(tf.matmul(layer_2, weights['w3']), biases['b3'])\n",
        "layer_drop = tf.nn.dropout(layer_3, keep_prob)\n",
        "output_layer = tf.matmul(layer_drop, weights['out']) + biases['out']"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-11-bc64fa5524e5>:5: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5bXqxtoQYUlb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "a46a573a-a510-4e18-b2df-5cfcf977d423"
      },
      "source": [
        "# Addding optimization method to minimize the loss function\n",
        "...\n",
        "cross_entropy = tf.reduce_mean(\n",
        "    tf.nn.softmax_cross_entropy_with_logits(\n",
        "        labels=Y, logits=output_layer\n",
        "        ))\n",
        "train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-12-eb5244c5a0fe>:4: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "\n",
            "Future major versions of TensorFlow will allow gradients to flow\n",
            "into the labels input on backprop by default.\n",
            "\n",
            "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ge-LMyJvYUo4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Training and Testing\n",
        "...\n",
        "correct_pred = tf.equal(tf.argmax(output_layer, 1), tf.argmax(Y, 1))\n",
        "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mVc1VklnYhjm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Feeding the network with training examples\n",
        "...\n",
        "init = tf.global_variables_initializer()\n",
        "sess = tf.Session()\n",
        "sess.run(init)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hp3IjdnZYhn3",
        "colab_type": "code",
        "outputId": "aa459228-55af-44b4-db58-5ffc1b11ceaa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        }
      },
      "source": [
        "...\n",
        "# train on mini batches\n",
        "for i in range(n_iterations):\n",
        "    batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
        "    sess.run(train_step, feed_dict={\n",
        "        X: batch_x, Y: batch_y, keep_prob: dropout\n",
        "        })\n",
        "\n",
        "    # print loss and accuracy (per minibatch)\n",
        "    if i % 100 == 0:\n",
        "        minibatch_loss, minibatch_accuracy = sess.run(\n",
        "            [cross_entropy, accuracy],\n",
        "            feed_dict={X: batch_x, Y: batch_y, keep_prob: 1.0}\n",
        "            )\n",
        "        print(\n",
        "            \"Iteration\",\n",
        "            str(i),\n",
        "            \"\\t| Loss =\",\n",
        "            str(minibatch_loss),\n",
        "            \"\\t| Accuracy =\",\n",
        "            str(minibatch_accuracy)\n",
        "            )"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration 0 \t| Loss = 3.7238853 \t| Accuracy = 0.1796875\n",
            "Iteration 100 \t| Loss = 0.47182506 \t| Accuracy = 0.84375\n",
            "Iteration 200 \t| Loss = 0.47069433 \t| Accuracy = 0.8203125\n",
            "Iteration 300 \t| Loss = 0.48407793 \t| Accuracy = 0.8828125\n",
            "Iteration 400 \t| Loss = 0.3004863 \t| Accuracy = 0.90625\n",
            "Iteration 500 \t| Loss = 0.41401246 \t| Accuracy = 0.890625\n",
            "Iteration 600 \t| Loss = 0.27579126 \t| Accuracy = 0.9296875\n",
            "Iteration 700 \t| Loss = 0.6134044 \t| Accuracy = 0.875\n",
            "Iteration 800 \t| Loss = 0.22307369 \t| Accuracy = 0.9609375\n",
            "Iteration 900 \t| Loss = 0.2874374 \t| Accuracy = 0.90625\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ox4-XhHWYhuj",
        "colab_type": "code",
        "outputId": "7d5fcc25-70cd-43eb-e792-ef142ed0da35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "# Testing the accuracy of the model with dropout\n",
        "...\n",
        "test_accuracy = sess.run(accuracy, feed_dict={X: mnist.test.images[:3300], Y: mnist.test.labels[:3300], keep_prob: 1.0})\n",
        "print(\"\\nAccuracy on test set:\", test_accuracy)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy on test set: 0.8909091\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-KlTOSsPYhx5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Recalculating the accuracy without dropout layer\n",
        "output_layer = tf.matmul(layer_3, weights['out']) + biases['out']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KExJk3z_pzdH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Addding optimization method to minimize the loss function\n",
        "...\n",
        "cross_entropy = tf.reduce_mean(\n",
        "    tf.nn.softmax_cross_entropy_with_logits(\n",
        "        labels=Y, logits=output_layer\n",
        "        ))\n",
        "train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nanYcfFHpzfI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Training and Testing\n",
        "...\n",
        "correct_pred = tf.equal(tf.argmax(output_layer, 1), tf.argmax(Y, 1))\n",
        "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JCHXPxtepzg2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Feeding the network with training examples\n",
        "...\n",
        "init = tf.global_variables_initializer()\n",
        "sess = tf.Session()\n",
        "sess.run(init)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0SqlvXvpzis",
        "colab_type": "code",
        "outputId": "a0557e04-6d2f-4ae5-9a64-162ecbaf63b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        }
      },
      "source": [
        "...\n",
        "# train on mini batches\n",
        "for i in range(n_iterations):\n",
        "    batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
        "    sess.run(train_step, feed_dict={\n",
        "        X: batch_x, Y: batch_y, keep_prob: dropout\n",
        "        })\n",
        "\n",
        "    # print loss and accuracy (per minibatch)\n",
        "    if i % 100 == 0:\n",
        "        minibatch_loss, minibatch_accuracy = sess.run(\n",
        "            [cross_entropy, accuracy],\n",
        "            feed_dict={X: batch_x, Y: batch_y, keep_prob: 1.0}\n",
        "            )\n",
        "        print(\n",
        "            \"Iteration\",\n",
        "            str(i),\n",
        "            \"\\t| Loss =\",\n",
        "            str(minibatch_loss),\n",
        "            \"\\t| Accuracy =\",\n",
        "            str(minibatch_accuracy)\n",
        "            )"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration 0 \t| Loss = 4.478079 \t| Accuracy = 0.1171875\n",
            "Iteration 100 \t| Loss = 0.4533025 \t| Accuracy = 0.875\n",
            "Iteration 200 \t| Loss = 0.3440833 \t| Accuracy = 0.8984375\n",
            "Iteration 300 \t| Loss = 0.2844262 \t| Accuracy = 0.9140625\n",
            "Iteration 400 \t| Loss = 0.362429 \t| Accuracy = 0.8828125\n",
            "Iteration 500 \t| Loss = 0.30470306 \t| Accuracy = 0.921875\n",
            "Iteration 600 \t| Loss = 0.3550515 \t| Accuracy = 0.8984375\n",
            "Iteration 700 \t| Loss = 0.2951797 \t| Accuracy = 0.9375\n",
            "Iteration 800 \t| Loss = 0.2640472 \t| Accuracy = 0.921875\n",
            "Iteration 900 \t| Loss = 0.29906905 \t| Accuracy = 0.921875\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XvcJbin9pzkp",
        "colab_type": "code",
        "outputId": "54dc48ff-d015-45eb-bf70-ec5fa709be48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "# Testing the accuracy of the model without dropout\n",
        "...\n",
        "test_accuracy = sess.run(accuracy, feed_dict={X: mnist.test.images[:3300], Y: mnist.test.labels[:3300], keep_prob: 1.0})\n",
        "print(\"\\nAccuracy on test set:\", test_accuracy)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy on test set: 0.89363635\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjAblAcRpzp9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1noXMNahpz7O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D0FoTYt9pzbh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}