CatVsDog.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CatVsDog.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GabrielDeml/CatvsDogClassifier/blob/master/CatVsDog.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7s0M_TNK95O",
        "colab_type": "code",
        "outputId": "4591a732-fd3a-4b0e-f6b7-2226dcbeeb47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "\n",
        "import IPython.display as display\n",
        "import pathlib\n",
        "import tensorflow as tf\n",
        "import os\n",
        "tf.enable_eager_execution()\n",
        "tf.__version__"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.14.0-rc1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o1lO3pQpqSBs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
        "BATCH_SIZE = 32"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ea_vPgqVwz7",
        "colab_type": "code",
        "outputId": "1d40e69b-e88b-4666-b96f-c3ce7892ecea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!mkdir .kaggle"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘.kaggle’: File exists\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6-GbumI5WTjF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import json\n",
        "token = {\"username\":\"username\",\"key\":\"KEY\"}\n",
        "with open('/content/.kaggle/kaggle.json', 'w') as file:\n",
        "    json.dump(token, file)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pnoZ2c8sXB0H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!chmod 600 /content/.kaggle/kaggle.json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "__SmE3GxYS0V",
        "colab_type": "code",
        "outputId": "c8cfce0c-2166-4c45-8c7d-4ad1dc8a76af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mkdir ~/.kaggle/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘/root/.kaggle/’: File exists\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JgDcpDegXLZa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cp /content/.kaggle/kaggle.json ~/.kaggle/kaggle.json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jBM8d1AeYs5C",
        "colab_type": "code",
        "outputId": "2564228d-df68-4e01-b4aa-743ab0c6e116",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!kaggle config set -n path -v{/content}"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "- path is now set to: {/content}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9y_IzH4jY3dZ",
        "colab_type": "code",
        "outputId": "d1b9d851-4906-4ad8-9105-912dd6845fae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "!kaggle competitions download -c dogs-vs-cats -p /content"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sampleSubmission.csv: Skipping, found more recently modified local copy (use --force to force download)\n",
            "test1.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
            "train.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3meovQ4qZcDJ",
        "colab_type": "code",
        "outputId": "b537fdd6-e481-4c5a-adb4-78f85db904d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print (\"train: {}, test1 {}\".format(os.path.exists(\"train\"), os.path.exists(\"test1\")))\n",
        "if not (os.path.exists(\"train\") and os.path.exists(\"test1\")):\n",
        "  !unzip -q train.zip\n",
        "  !unzip -q test1.zip\n",
        "else:\n",
        "  print(\"We already did this\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train: True, test1 True\n",
            "We already did this\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yGMtpwBRMW0V",
        "colab_type": "code",
        "outputId": "e13ae88a-d408-4dc1-c248-351dce361b3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "!mkdir train/cat\n",
        "!mkdir train/dog\n",
        "!mv train/cat* train/cat\n",
        "!mv train/dog* train/dog"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘train/cat’: File exists\n",
            "mkdir: cannot create directory ‘train/dog’: File exists\n",
            "mv: cannot move 'train/cat' to a subdirectory of itself, 'train/cat/cat'\n",
            "mv: cannot move 'train/dog' to a subdirectory of itself, 'train/dog/dog'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yzDr3xxBeIGt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_root = pathlib.Path(\"train\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqsp4b6EeTje",
        "colab_type": "code",
        "outputId": "2e629658-4a94-499a-ab5a-27e53c400daa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "for item in data_root.iterdir():\n",
        "  print(item)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train/cat\n",
            "train/dog\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jNI7i1k_ef-b",
        "colab_type": "code",
        "outputId": "a79b3167-fdd4-4674-d693-075016ae311c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import random\n",
        "all_image_paths = list(data_root.glob('*/*'))\n",
        "all_image_paths = [str(path) for path in all_image_paths]\n",
        "random.shuffle(all_image_paths)\n",
        "\n",
        "image_count = len(all_image_paths)\n",
        "image_count"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ffGgAEc0jf__",
        "colab_type": "code",
        "outputId": "f726f940-19c1-4c1d-8158-143778b9cb29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "label_names = sorted(item.name for item in data_root.glob('*/') if item.is_dir())\n",
        "label_names"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['cat', 'dog']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OfFxKOFgjume",
        "colab_type": "code",
        "outputId": "aef19e5b-47eb-48b2-a92c-a93de92d11f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "label_to_index = dict((name, index) for index,name in enumerate(label_names))\n",
        "label_to_index"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'cat': 0, 'dog': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P2KEAGBuj4w6",
        "colab_type": "code",
        "outputId": "8812577b-bdae-4da0-a578-c7ee26c6005a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "all_image_labels = [label_to_index[pathlib.Path(path).parent.name]\n",
        "                    for path in all_image_paths]\n",
        "\n",
        "print(\"First 10 labels indices: \", all_image_labels[:10])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "First 10 labels indices:  [0, 1, 0, 1, 0, 1, 0, 0, 1, 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nrEbtBpSlRDi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def preprocess_image(image):\n",
        "  image = tf.image.decode_jpeg(image, channels=3)\n",
        "  image = tf.image.resize(image, [192, 192])\n",
        "  image /= 255.0  # normalize to [0,1] range\n",
        "\n",
        "  return image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g3G_nYGJlSOx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_and_preprocess_image(path):\n",
        "  image = tf.read_file(path)\n",
        "  return preprocess_image(image)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D8kzXkpMn3vK",
        "colab_type": "code",
        "outputId": "c7ca5183-d181-4488-9bf0-1d9fa03b314c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ds = tf.data.Dataset.from_tensor_slices((all_image_paths, all_image_labels))\n",
        "\n",
        "# The tuples are unpacked into the positional arguments of the mapped function\n",
        "def load_and_preprocess_from_path_label(path, label):\n",
        "  return load_and_preprocess_image(path), label\n",
        "\n",
        "image_label_ds = ds.map(load_and_preprocess_from_path_label)\n",
        "image_label_ds"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<DatasetV1Adapter shapes: ((192, 192, 3), ()), types: (tf.float32, tf.int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6JRqG5yuqmv0",
        "colab_type": "code",
        "outputId": "1ea2d96e-85cd-4124-e2fa-318cbbd25ec3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "source": [
        "ds = image_label_ds.apply(\n",
        "tf.data.experimental.shuffle_and_repeat(buffer_size=10000))\n",
        "ds = ds.batch(64)\n",
        "ds = ds.prefetch(buffer_size=1000)\n",
        "ds = ds.repeat()\n",
        "ds\n",
        "# ds = image_label_ds.apply(\n",
        "#   tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))\n",
        "# ds = ds.batch(BATCH_SIZE).prefetch(buffer_size=AUTOTUNE)\n",
        "# ds\n",
        "# ds = ds.shuffle(buffer_size=10000) ## 10000: size of sample/record pool for random selection\n",
        "# ds = ds.batch(32) ## 32: number of samples/records per batch (to be read into memory)\n",
        "# ds = ds.repeat() ## None: keep repeating"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0618 02:54:20.973101 139729980331904 deprecation.py:323] From <ipython-input-21-29cd976dcf6a>:2: shuffle_and_repeat (from tensorflow.python.data.experimental.ops.shuffle_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.shuffle(buffer_size, seed)` followed by `tf.data.Dataset.repeat(count)`. Static tf.data optimizations will take care of using the fused implementation.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<DatasetV1Adapter shapes: ((?, 192, 192, 3), (?,)), types: (tf.float32, tf.int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JlKDEh-so5CT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = models.Sequential()\n",
        "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(192, 192, 3)))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(64, activation='relu'))\n",
        "model.add(layers.Dense(2, activation='softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QPRu4ItjpTPt",
        "colab_type": "code",
        "outputId": "96dcb31f-929c-4716-ff25-76f5414c145a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 190, 190, 32)      896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 95, 95, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 93, 93, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 46, 46, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 44, 44, 64)        36928     \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 123904)            0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 64)                7929920   \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 2)                 130       \n",
            "=================================================================\n",
            "Total params: 7,986,370\n",
            "Trainable params: 7,986,370\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nh-sLctEpjcq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.Adam(),\n",
        "              loss=tf.keras.losses.sparse_categorical_crossentropy,\n",
        "              metrics=[\"accuracy\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "evwpKmi4rDMa",
        "colab_type": "code",
        "outputId": "376107fa-a62c-4c01-e08a-f98dd3b1a2ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 21441
        }
      },
      "source": [
        "model.fit(ds, epochs=600, steps_per_epoch=3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/600\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "W0618 02:54:47.236327 139729980331904 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "3/3 [==============================] - 30s 10s/step - loss: 2.5614 - acc: 0.5208\n",
            "Epoch 2/600\n",
            "3/3 [==============================] - 0s 75ms/step - loss: 0.7420 - acc: 0.5729\n",
            "Epoch 3/600\n",
            "3/3 [==============================] - 0s 75ms/step - loss: 0.6887 - acc: 0.5365\n",
            "Epoch 4/600\n",
            "3/3 [==============================] - 0s 76ms/step - loss: 0.7006 - acc: 0.4948\n",
            "Epoch 5/600\n",
            "3/3 [==============================] - 0s 75ms/step - loss: 0.6863 - acc: 0.5469\n",
            "Epoch 6/600\n",
            "3/3 [==============================] - 0s 74ms/step - loss: 0.6963 - acc: 0.4740\n",
            "Epoch 7/600\n",
            "3/3 [==============================] - 0s 78ms/step - loss: 0.6924 - acc: 0.4896\n",
            "Epoch 8/600\n",
            "3/3 [==============================] - 0s 74ms/step - loss: 0.6926 - acc: 0.5677\n",
            "Epoch 9/600\n",
            "3/3 [==============================] - 0s 73ms/step - loss: 0.6892 - acc: 0.5208\n",
            "Epoch 10/600\n",
            "3/3 [==============================] - 0s 77ms/step - loss: 0.6803 - acc: 0.5625\n",
            "Epoch 11/600\n",
            "3/3 [==============================] - 0s 74ms/step - loss: 0.6782 - acc: 0.5312\n",
            "Epoch 12/600\n",
            "3/3 [==============================] - 0s 73ms/step - loss: 0.6753 - acc: 0.5573\n",
            "Epoch 13/600\n",
            "3/3 [==============================] - 0s 73ms/step - loss: 0.6852 - acc: 0.5625\n",
            "Epoch 14/600\n",
            "3/3 [==============================] - 0s 72ms/step - loss: 0.7034 - acc: 0.5781\n",
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            "Epoch 545/600\n",
            "3/3 [==============================] - 0s 123ms/step - loss: 0.2331 - acc: 0.9167\n",
            "Epoch 546/600\n",
            "3/3 [==============================] - 0s 120ms/step - loss: 0.2504 - acc: 0.8958\n",
            "Epoch 547/600\n",
            "3/3 [==============================] - 0s 126ms/step - loss: 0.1752 - acc: 0.9427\n",
            "Epoch 548/600\n",
            "3/3 [==============================] - 0s 116ms/step - loss: 0.3474 - acc: 0.8385\n",
            "Epoch 549/600\n",
            "3/3 [==============================] - 0s 115ms/step - loss: 0.3334 - acc: 0.8490\n",
            "Epoch 550/600\n",
            "3/3 [==============================] - 0s 128ms/step - loss: 0.2404 - acc: 0.9010\n",
            "Epoch 551/600\n",
            "3/3 [==============================] - 0s 120ms/step - loss: 0.2942 - acc: 0.8906\n",
            "Epoch 552/600\n",
            "3/3 [==============================] - 0s 119ms/step - loss: 0.2900 - acc: 0.8854\n",
            "Epoch 553/600\n",
            "3/3 [==============================] - 0s 125ms/step - loss: 0.2977 - acc: 0.8802\n",
            "Epoch 554/600\n",
            "3/3 [==============================] - 0s 124ms/step - loss: 0.2619 - acc: 0.8802\n",
            "Epoch 555/600\n",
            "3/3 [==============================] - 0s 125ms/step - loss: 0.1790 - acc: 0.9323\n",
            "Epoch 556/600\n",
            "3/3 [==============================] - 0s 119ms/step - loss: 0.2339 - acc: 0.8854\n",
            "Epoch 557/600\n",
            "3/3 [==============================] - 0s 124ms/step - loss: 0.1951 - acc: 0.9271\n",
            "Epoch 558/600\n",
            "3/3 [==============================] - 0s 123ms/step - loss: 0.2876 - acc: 0.8594\n",
            "Epoch 559/600\n",
            "3/3 [==============================] - 0s 121ms/step - loss: 0.2446 - acc: 0.8854\n",
            "Epoch 560/600\n",
            "3/3 [==============================] - 0s 121ms/step - loss: 0.2574 - acc: 0.8698\n",
            "Epoch 561/600\n",
            "3/3 [==============================] - 0s 130ms/step - loss: 0.2110 - acc: 0.9115\n",
            "Epoch 562/600\n",
            "3/3 [==============================] - 0s 122ms/step - loss: 0.2370 - acc: 0.9062\n",
            "Epoch 563/600\n",
            "3/3 [==============================] - 0s 120ms/step - loss: 0.3203 - acc: 0.8750\n",
            "Epoch 564/600\n",
            "3/3 [==============================] - 0s 122ms/step - loss: 0.2607 - acc: 0.8958\n",
            "Epoch 565/600\n",
            "3/3 [==============================] - 0s 117ms/step - loss: 0.3045 - acc: 0.8542\n",
            "Epoch 566/600\n",
            "3/3 [==============================] - 0s 121ms/step - loss: 0.2380 - acc: 0.9115\n",
            "Epoch 567/600\n",
            "3/3 [==============================] - 0s 123ms/step - loss: 0.2448 - acc: 0.9010\n",
            "Epoch 568/600\n",
            "3/3 [==============================] - 0s 121ms/step - loss: 0.2453 - acc: 0.8802\n",
            "Epoch 569/600\n",
            "3/3 [==============================] - 0s 129ms/step - loss: 0.2206 - acc: 0.8802\n",
            "Epoch 570/600\n",
            "3/3 [==============================] - 0s 118ms/step - loss: 0.1794 - acc: 0.9427\n",
            "Epoch 571/600\n",
            "3/3 [==============================] - 0s 120ms/step - loss: 0.1917 - acc: 0.9427\n",
            "Epoch 572/600\n",
            "3/3 [==============================] - 0s 122ms/step - loss: 0.1928 - acc: 0.9219\n",
            "Epoch 573/600\n",
            "3/3 [==============================] - 0s 121ms/step - loss: 0.1975 - acc: 0.9115\n",
            "Epoch 574/600\n",
            "3/3 [==============================] - 0s 119ms/step - loss: 0.2417 - acc: 0.9219\n",
            "Epoch 575/600\n",
            "3/3 [==============================] - 0s 128ms/step - loss: 0.2647 - acc: 0.8854\n",
            "Epoch 576/600\n",
            "3/3 [==============================] - 0s 121ms/step - loss: 0.2359 - acc: 0.8958\n",
            "Epoch 577/600\n",
            "3/3 [==============================] - 0s 118ms/step - loss: 0.2919 - acc: 0.8750\n",
            "Epoch 578/600\n",
            "3/3 [==============================] - 0s 120ms/step - loss: 0.2690 - acc: 0.8750\n",
            "Epoch 579/600\n",
            "3/3 [==============================] - 0s 119ms/step - loss: 0.2738 - acc: 0.8854\n",
            "Epoch 580/600\n",
            "3/3 [==============================] - 0s 125ms/step - loss: 0.2633 - acc: 0.8854\n",
            "Epoch 581/600\n",
            "3/3 [==============================] - 0s 122ms/step - loss: 0.3057 - acc: 0.8542\n",
            "Epoch 582/600\n",
            "3/3 [==============================] - 0s 122ms/step - loss: 0.1689 - acc: 0.9427\n",
            "Epoch 583/600\n",
            "3/3 [==============================] - 0s 123ms/step - loss: 0.2228 - acc: 0.9323\n",
            "Epoch 584/600\n",
            "3/3 [==============================] - 0s 120ms/step - loss: 0.2272 - acc: 0.9167\n",
            "Epoch 585/600\n",
            "3/3 [==============================] - 0s 117ms/step - loss: 0.2194 - acc: 0.9167\n",
            "Epoch 586/600\n",
            "3/3 [==============================] - 0s 121ms/step - loss: 0.2679 - acc: 0.8802\n",
            "Epoch 587/600\n",
            "3/3 [==============================] - 0s 123ms/step - loss: 0.2276 - acc: 0.9062\n",
            "Epoch 588/600\n",
            "3/3 [==============================] - 0s 124ms/step - loss: 0.2063 - acc: 0.9323\n",
            "Epoch 589/600\n",
            "3/3 [==============================] - 0s 123ms/step - loss: 0.2782 - acc: 0.8646\n",
            "Epoch 590/600\n",
            "3/3 [==============================] - 0s 121ms/step - loss: 0.2305 - acc: 0.8854\n",
            "Epoch 591/600\n",
            "3/3 [==============================] - 0s 125ms/step - loss: 0.2201 - acc: 0.9167\n",
            "Epoch 592/600\n",
            "3/3 [==============================] - 0s 118ms/step - loss: 0.2152 - acc: 0.9010\n",
            "Epoch 593/600\n",
            "3/3 [==============================] - 0s 124ms/step - loss: 0.2138 - acc: 0.9219\n",
            "Epoch 594/600\n",
            "3/3 [==============================] - 0s 119ms/step - loss: 0.2226 - acc: 0.9115\n",
            "Epoch 595/600\n",
            "3/3 [==============================] - 0s 123ms/step - loss: 0.1792 - acc: 0.9323\n",
            "Epoch 596/600\n",
            "3/3 [==============================] - 0s 118ms/step - loss: 0.1975 - acc: 0.9219\n",
            "Epoch 597/600\n",
            "3/3 [==============================] - 0s 123ms/step - loss: 0.2155 - acc: 0.9115\n",
            "Epoch 598/600\n",
            "3/3 [==============================] - 0s 122ms/step - loss: 0.2809 - acc: 0.8594\n",
            "Epoch 599/600\n",
            "3/3 [==============================] - 0s 121ms/step - loss: 0.2149 - acc: 0.9115\n",
            "Epoch 600/600\n",
            "3/3 [==============================] - 0s 120ms/step - loss: 0.2503 - acc: 0.8958\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f15230e7da0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VxZO3b-BKzKn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf.keras.models.save_model(model, \"savedModel\", overwrite=True, include_optimizer=True)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}