convolutional_network_MNIST.ipynb

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    "colab": {
      "name": "convolutional_network.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/eshnil2000/google-colab/blob/master/convolutional_network_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3jVyyNEPoQDQ",
        "colab_type": "text"
      },
      "source": [
        "## CNN Overview\n",
        "\n",
        "![CNN](http://personal.ie.cuhk.edu.hk/~ccloy/project_target_code/images/fig3.png)\n",
        "\n",
        "## MNIST Dataset Overview\n",
        "\n",
        "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n",
        "\n",
        "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n",
        "\n",
        "More info: http://yann.lecun.com/exdb/mnist/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7C_XpBMeoQDR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#%%capture\n",
        "import warnings\n",
        "warnings.simplefilter('ignore')\n",
        "!git clone https://github.com/eshnil2000/google-colab.git\n",
        "%cd google-colab\n",
        "#Install required modules\n",
        "!pip install tensorflow numpy matplotlib pandas sklearn\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QrGmojJVoQDc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "outputId": "c56d7724-55ec-4fe7-b2ef-4abc64f723b4"
      },
      "source": [
        "from __future__ import division, print_function, absolute_import\n",
        "\n",
        "# Import MNIST data\n",
        "from tensorflow.examples.tutorials.mnist import input_data\n",
        "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False)\n",
        "\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0729 12:09:14.390954 139739089745792 deprecation.py:323] From <ipython-input-3-8f4b81ac1ea3>:5: 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",
            "W0729 12:09:14.392877 139739089745792 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/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",
            "W0729 12:09:14.394231 139739089745792 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/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",
            "W0729 12:09:14.478507 139739089745792 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
            "Extracting /tmp/data/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "W0729 12:09:14.803349 139739089745792 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/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",
            "W0729 12:09:14.892627 139739089745792 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/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": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
            "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
            "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
            "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
            "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
            "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWovSejFoQDh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Training Parameters\n",
        "learning_rate = 0.1\n",
        "num_steps = 100\n",
        "batch_size = 25\n",
        "\n",
        "# Network Parameters\n",
        "num_input = 784 # MNIST data input (img shape: 28*28)\n",
        "num_classes = 10 # MNIST total classes (0-9 digits)\n",
        "dropout = 0.4 # Dropout, probability to drop a unit"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JLT4eZmNoQDl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create the neural network\n",
        "def conv_net(x_dict, n_classes, dropout, reuse, is_training):\n",
        "    \n",
        "    # Define a scope for reusing the variables\n",
        "    with tf.variable_scope('ConvNet', reuse=reuse):\n",
        "        # TF Estimator input is a dict, in case of multiple inputs\n",
        "        x = x_dict['images']\n",
        "\n",
        "        # MNIST data input is a 1-D vector of 784 features (28*28 pixels)\n",
        "        # Reshape to match picture format [Height x Width x Channel]\n",
        "        # Tensor input become 4-D: [Batch Size, Height, Width, Channel]\n",
        "        x = tf.reshape(x, shape=[-1, 28, 28, 1])\n",
        "\n",
        "        # Convolution Layer with 32 filters and a kernel size of 5\n",
        "        conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu)\n",
        "        # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n",
        "        conv1 = tf.layers.max_pooling2d(conv1, 2, 2)\n",
        "\n",
        "        # Convolution Layer with 64 filters and a kernel size of 3\n",
        "        conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)\n",
        "        # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n",
        "        conv2 = tf.layers.max_pooling2d(conv2, 2, 2)\n",
        "\n",
        "        # Flatten the data to a 1-D vector for the fully connected layer\n",
        "        fc1 = tf.contrib.layers.flatten(conv2)\n",
        "\n",
        "        # Fully connected layer (in tf contrib folder for now)\n",
        "        fc1 = tf.layers.dense(fc1, 1024)\n",
        "        # Apply Dropout (if is_training is False, dropout is not applied)\n",
        "        fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)\n",
        "\n",
        "        # Output layer, class prediction\n",
        "        out = tf.layers.dense(fc1, n_classes)\n",
        "\n",
        "    return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUP86CZKoQDo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define the model function (following TF Estimator Template)\n",
        "def model_fn(features, labels, mode):\n",
        "    \n",
        "    # Build the neural network\n",
        "    # Because Dropout have different behavior at training and prediction time, we\n",
        "    # need to create 2 distinct computation graphs that still share the same weights.\n",
        "    logits_train = conv_net(features, num_classes, dropout, reuse=False, is_training=True)\n",
        "    logits_test = conv_net(features, num_classes, dropout, reuse=True, is_training=False)\n",
        "    \n",
        "    # Predictions\n",
        "    pred_classes = tf.argmax(logits_test, axis=1)\n",
        "    pred_probas = tf.nn.softmax(logits_test)\n",
        "    \n",
        "    # If prediction mode, early return\n",
        "    if mode == tf.estimator.ModeKeys.PREDICT:\n",
        "        return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) \n",
        "        \n",
        "    # Define loss and optimizer\n",
        "    loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n",
        "        logits=logits_train, labels=tf.cast(labels, dtype=tf.int32)))\n",
        "    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
        "    train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step())\n",
        "    \n",
        "    # Evaluate the accuracy of the model\n",
        "    acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes)\n",
        "    \n",
        "    # TF Estimators requires to return a EstimatorSpec, that specify\n",
        "    # the different ops for training, evaluating, ...\n",
        "    estim_specs = tf.estimator.EstimatorSpec(\n",
        "      mode=mode,\n",
        "      predictions=pred_classes,\n",
        "      loss=loss_op,\n",
        "      train_op=train_op,\n",
        "      eval_metric_ops={'accuracy': acc_op})\n",
        "\n",
        "    return estim_specs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jozkl4FDoQDq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5f4896c1-a034-4e2c-aadf-8f4796dcdf4f"
      },
      "source": [
        "# Build the Estimator\n",
        "model = tf.estimator.Estimator(model_fn)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0729 12:09:32.943925 139739089745792 estimator.py:1811] Using temporary folder as model directory: /tmp/tmp03skv439\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "34k6F5GgoQDu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "outputId": "2fd06f45-952b-4f26-d434-185d94b2d70a"
      },
      "source": [
        "# Define the input function for training\n",
        "input_fn = tf.estimator.inputs.numpy_input_fn(\n",
        "    x={'images': mnist.train.images}, y=mnist.train.labels,\n",
        "    batch_size=batch_size, num_epochs=None, shuffle=True)\n",
        "# Train the Model\n",
        "model.train(input_fn, steps=num_steps)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0729 12:09:35.526695 139739089745792 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
            "W0729 12:09:35.548512 139739089745792 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_queue_runner.py:62: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "To construct input pipelines, use the `tf.data` module.\n",
            "W0729 12:09:35.553842 139739089745792 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_functions.py:500: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "To construct input pipelines, use the `tf.data` module.\n",
            "W0729 12:09:35.568846 139739089745792 deprecation.py:323] From <ipython-input-5-7d17c018e158>:14: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.Conv2D` instead.\n",
            "W0729 12:09:35.572911 139739089745792 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "W0729 12:09:35.835268 139739089745792 deprecation.py:323] From <ipython-input-5-7d17c018e158>:16: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.MaxPooling2D instead.\n",
            "W0729 12:09:36.003292 139739089745792 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/layers/python/layers/layers.py:1634: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.flatten instead.\n",
            "W0729 12:09:36.248863 139739089745792 deprecation.py:323] From <ipython-input-5-7d17c018e158>:27: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.dense instead.\n",
            "W0729 12:09:36.736410 139739089745792 deprecation.py:323] From <ipython-input-5-7d17c018e158>:29: dropout (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.dropout instead.\n",
            "W0729 12:09:37.175894 139739089745792 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/array_ops.py:1354: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "W0729 12:09:37.433428 139739089745792 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/monitored_session.py:875: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "To construct input pipelines, use the `tf.data` module.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow_estimator.python.estimator.estimator.Estimator at 0x7f17451256a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DnhXK0wDoQDz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "27328c6b-b8ea-4736-d100-caf9e0b9fe40"
      },
      "source": [
        "# Evaluate the Model\n",
        "# Define the input function for evaluating\n",
        "input_fn = tf.estimator.inputs.numpy_input_fn(\n",
        "    x={'images': mnist.test.images}, y=mnist.test.labels,\n",
        "    batch_size=batch_size, shuffle=False)\n",
        "# Use the Estimator 'evaluate' method\n",
        "model.evaluate(input_fn)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0729 12:09:42.257099 139739089745792 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py:1276: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use standard file APIs to check for files with this prefix.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 0.0892, 'global_step': 100, 'loss': 4.209432}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GAgQwn_8oQD2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cda07769-f537-4d0a-e67e-779925cebeb4"
      },
      "source": [
        "# Predict single images\n",
        "n_images = 50\n",
        "# Get images from test set\n",
        "test_images = mnist.test.images[:n_images]\n",
        "# Prepare the input data\n",
        "input_fn = tf.estimator.inputs.numpy_input_fn(\n",
        "    x={'images': test_images}, shuffle=False)\n",
        "# Use the model to predict the images class\n",
        "preds = list(model.predict(input_fn))\n",
        "\n",
        "# Display\n",
        "for i in range(n_images):\n",
        "    plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')\n",
        "    plt.show()\n",
        "    print(\"Model prediction:\", preds[i])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWv8QfFP9AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FAfaK+yOWZZUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/FJRhl8qyvBLRf0f7V4JFFPw3M8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IPxDUfwEJEYHZ+iI4owAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CxPsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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b/iNpvka+8f9fSf9WRg85ff2dpLeyn71l9yZpo0Y2Az/TyHcj35J0maStkt6V9F+SOirU\n2/OS9kjarZGgzSipt1s0skm/W9Kb2c/8sj+7RF+lfG6c4QcExRd+QFCEHwiK8ANBEX4gKMIPBEX4\ngaAIPxAU4QeC+j9mg5DCHVPKAgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cRWGLAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JD5fRQ52+/lbSm9nPnrJ7k/S8aqeBp1V7b2SlpG9K2iLpgKRXJV1aod6eVW0257dUC1pv\nSb0tUO2U/i1Jf85+FpX93CX6KuV54xN+QFC84QcERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+IKj/\nBXeMlEQP+sZNAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1doEGD+nfkvRG9ufKsl+7RF+lvG58wg8Iijf8gKAIPxAU4QeCIvxAUIQfCIrwA0ERfiAo\nwg8E9T++rI2l9L+0EgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/EBThB4Ii/EBQhB8I6v8AD5x+HL6Dan4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nICnCDyT1/ztkW+1mFoEKAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9wt1fyxaXvu9G6qus/VZG+PdImm1m3zCzCZIWS9paQh9fYWbXZx/EyMyul/RdVW/24a2S\nlma3l0raUmIvl6jKzM21ZpZWyfuucjNeu3vLfyTN19An/v8j6V/K6KFGXzdJej/7OVR2b5I2auhl\n4Bca+mzkB5K+JmmnpI8kvSWprUK9/ZeGZnM+oKGgdZTUW5eGXtIfkLQ/+5lf9r5L9FXKfuMMPyAo\nPvADgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaAIPxDU/wG0AMyoVd4U0gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD8paEvCJ7QOmAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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,
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSlkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7JO3O/nVXve4SfVWy3rjCDwiKH/yAoAg/EBThB4Ii/EBQhB8IivADQRF+ICjCDwT1v2fe\nhleh0HeKAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD/GyO6KTbtuuAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyvBLRRl+qaj/B1rx1ZslzezvAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nB2VOVwGQPz9JAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUv8HGMJP7yr2A18AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBcXXVeFREqdAAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVnl8MjRe2AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGUviNOGdhwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+ICjCDwRF+IGgCD8Q1P8AQbmAzON6sb0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4QeC+h8dUEcYm48OHgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model prediction: 5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AtsI3qLioQD6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}