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AvikantSrivastava/dog_breed_2.ipynb

Created February 11, 2021 11:51
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dog_breed_2.ipynb
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"<a href=\"https://colab.research.google.com/gist/AvikantSrivastava/072d98b5514a8f4ccd352cb49da15180/dog_breed_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "cyKYue_WzVfe",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "dd2ca389-2f40-4432-e6ea-516f4eedd365"
},
"source": [
"import tensorflow as tf\n",
"import tensorflow_datasets as tfds\n",
"tf.__version__"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'2.3.0'"
]
},
"metadata": {
"tags": []
},
"execution_count": 1
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1TPVS6wL4Pjt"
},
"source": [
"import PIL\n",
"from PIL import Image\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from IPython.display import Image as Image1\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "DFwuFWzJzbLW"
},
"source": [
"data = tfds.builder('stanford_dogs')\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "yx27IkLw75Ye"
},
"source": [
"# getting class labels\n",
"# !wget http://vision.stanford.edu/aditya86/ImageNetDogs/lists.tar\n",
"!tar -xf lists.tar "
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-nv8nJRj8QlB"
},
"source": [
"get_name = data.info.features['label'].int2str"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "e9Vl_hCT_cjZ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "6bc5fd88-9c7e-4783-e48d-3e4c0a92ea2e"
},
"source": [
"get_name(0)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'n02085620-chihuahua'"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "PqM4h3aQn0Rm"
},
"source": [
"names = []\n",
"\n",
"for i in range(120):\n",
" name = get_name(i)\n",
" names.append(name)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "0mtSMc2foD5r"
},
"source": [
"with open('your_file.txt', 'w') as f:\n",
" for name in names:\n",
" f.write(\"%s\\n\" % name)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "LYTQkS26zjeU",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "bf115bfe-72cc-4905-e9f0-7e5ae637f297"
},
"source": [
"w to convet print(\n",
" # data.info.features['image'].shape,\n",
"data.info.features['label'].shape)\n",
"# data.info.features['train'].shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"()\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "oUgkHIY7zsLO",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 582,
"referenced_widgets": [
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},
"outputId": "7d0751db-deea-4dbf-f143-2388bb4b5bdb"
},
"source": [
"data.download_and_prepare()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"\u001b[1mDownloading and preparing dataset stanford_dogs/0.2.0 (download: 778.12 MiB, generated: Unknown size, total: 778.12 MiB) to /root/tensorflow_datasets/stanford_dogs/0.2.0...\u001b[0m\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7c1992e29057474c8d21ba601da953c7",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5606ceb410024e62a570cf2a5f794438",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Size...', max=1.0, style=ProgressSty…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n",
"\n",
"\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "63b788f861c24fc283657eecc58183f5",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3d716148ec5c4b649e346cd9399acbc3",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Size...', max=1.0, style=ProgressSty…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fadec0d88ee64762995fa0f4d2e25a8b",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Extraction completed...', max=1.0, styl…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n",
"\n",
"\n",
"\n",
"\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "84645be906e64ac9b30f1367f5c9758e",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\rShuffling and writing examples to /root/tensorflow_datasets/stanford_dogs/0.2.0.incompleteR4AMZR/stanford_dogs-train.tfrecord\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f1d03c6887dd48718c13720e69106f7a",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=12000.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\r"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9e3bceb708664d48877f4d207e06e763",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\rShuffling and writing examples to /root/tensorflow_datasets/stanford_dogs/0.2.0.incompleteR4AMZR/stanford_dogs-test.tfrecord\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "598b2dc527324297852e60b4d2706a85",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=8580.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7c9037420fd648c9bf9ec38574b8a4d1",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Computing statistics...', max=2.0, style=ProgressStyle(de…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\r"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2d43ea7ee71b458cb3eb5d007b39fefa",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"ERROR:absl:Statistics generation doesn't work for nested structures yet\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\r"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1c35dd690b65475c9c67980539b4bfe4",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"ERROR:absl:Statistics generation doesn't work for nested structures yet\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\r\n",
"\u001b[1mDataset stanford_dogs downloaded and prepared to /root/tensorflow_datasets/stanford_dogs/0.2.0. Subsequent calls will reuse this data.\u001b[0m\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "rFEesBF6z7rX"
},
"source": [
"dataset = data.as_dataset()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "HMo7iY6f1Mnf"
},
"source": [
"train_dataset, test_dataset = dataset['train'], dataset['test']\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "YEro2OiaVpJz"
},
"source": [
"def get_images(features):\n",
" image = features['image']\n",
" image = tf.image.resize(\n",
" image, (), \n",
" method=ResizeMethod.BILINEAR, \n",
" preserve_aspect_ratio=False)\n",
" return image\n",
"\n",
"def get_labels(features):\n",
" # print(features['label'])\n",
" one_hotted = tf.one_hot(features['label'], 120)\n",
" return one_hotted\n",
"\n",
"def get_data(features):\n",
" # print(features['label'])\n",
" one_hotted = tf.one_hot(features['label'], 120)\n",
" image = features['image']\n",
" image = tf.image.resize(\n",
" image, [224,224], \n",
" # method=ResizeMethod.BILINEAR, \n",
" preserve_aspect_ratio=False)\n",
" return image,one_hotted\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "YC9GMdx4Zeyj",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "b437e1a6-a353-4e3a-d190-d6ba8a231b36"
},
"source": [
"for example in train_dataset.take(1): # Only take a single example\n",
" image, label = example[\"image\"], example[\"label\"]\n",
" print(label)\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"tf.Tensor(36, shape=(), dtype=int64)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "lUVowRNnVjgk",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 408
},
"outputId": "877ab313-d9c0-4787-b1a3-9799e54f7c5b"
},
"source": [
"x_train = train_dataset.map(get_images).batch(32)\n",
"y_train = train_dataset.map(get_labels).batch(32)"
],
"execution_count": null,
"outputs": [
{
"output_type": "error",
"ename": "NameError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-bc5b5ce9477a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_images\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0my_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, map_func, num_parallel_calls, deterministic)\u001b[0m\n\u001b[1;32m 2507\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnum_parallel_calls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2508\u001b[0m return DatasetV1Adapter(\n\u001b[0;32m-> 2509\u001b[0;31m MapDataset(self, map_func, preserve_cardinality=False))\n\u001b[0m\u001b[1;32m 2510\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2511\u001b[0m return DatasetV1Adapter(\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, input_dataset, map_func, use_inter_op_parallelism, preserve_cardinality, use_legacy_function)\u001b[0m\n\u001b[1;32m 4043\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transformation_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4044\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4045\u001b[0;31m use_legacy_function=use_legacy_function)\n\u001b[0m\u001b[1;32m 4046\u001b[0m variant_tensor = gen_dataset_ops.map_dataset(\n\u001b[1;32m 4047\u001b[0m \u001b[0minput_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variant_tensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, func, transformation_name, dataset, input_classes, input_shapes, input_types, input_structure, add_to_graph, use_legacy_function, defun_kwargs)\u001b[0m\n\u001b[1;32m 3369\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtracking\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresource_tracker_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresource_tracker\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3370\u001b[0m \u001b[0;31m# TODO(b/141462134): Switch to using garbage collection.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3371\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwrapper_fn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_concrete_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3372\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0madd_to_graph\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3373\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_to_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mget_concrete_function\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2937\u001b[0m \"\"\"\n\u001b[1;32m 2938\u001b[0m graph_function = self._get_concrete_function_garbage_collected(\n\u001b[0;32m-> 2939\u001b[0;31m *args, **kwargs)\n\u001b[0m\u001b[1;32m 2940\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_garbage_collector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2941\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[0;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m 3073\u001b[0m \u001b[0marg_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg_names\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3074\u001b[0m \u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3075\u001b[0;31m capture_by_value=self._capture_by_value),\n\u001b[0m\u001b[1;32m 3076\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_attributes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3077\u001b[0m \u001b[0mfunction_spec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction_spec\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[0;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m 984\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moriginal_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_decorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munwrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpython_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 985\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 986\u001b[0;31m \u001b[0mfunc_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 987\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 988\u001b[0m \u001b[0;31m# invariant: `func_outputs` contains only Tensors, CompositeTensors,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py\u001b[0m in \u001b[0;36mwrapper_fn\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 3362\u001b[0m attributes=defun_kwargs)\n\u001b[1;32m 3363\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mwrapper_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint: disable=missing-docstring\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3364\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_wrapper_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3365\u001b[0m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstructure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_tensor_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output_structure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3366\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py\u001b[0m in \u001b[0;36m_wrapper_helper\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 3297\u001b[0m \u001b[0mnested_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnested_args\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3299\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mautograph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf_convert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mag_ctx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnested_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3300\u001b[0m \u001b[0;31m# If `func` returns a list of tensors, `nest.flatten()` and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3301\u001b[0m \u001b[0;31m# `ops.convert_to_tensor()` would conspire to attempt to stack\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/impl/api.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 256\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint:disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 257\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ag_error_metadata'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 258\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 259\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 260\u001b[0m \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: in user code:\n\n <ipython-input-8-6064e0495a43>:3 get_images *\n image = tf.image.resize(\n\n NameError: name 'ResizeMethod' is not defined\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "NujO5rN9ej7r"
},
"source": [
"train_data = train_dataset.map(get_data).batch(64)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "uyWTJb_6xAJk"
},
"source": [
"test_data = test_dataset.map(get_data).batch(64)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "hEcU9WcIxkru",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "67f353b2-6f28-4283-94be-e541b8074c2c"
},
"source": [
"train_data"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<DatasetV1Adapter shapes: ((None, 224, 224, 3), (None, 120)), types: (tf.float32, tf.float32)>"
]
},
"metadata": {
"tags": []
},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6r4WRvDn1PQP",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "a9aca9b4-84ea-46a5-f799-e6b231570e02"
},
"source": [
"mobile_net_base_model = tf.keras.applications.MobileNetV2(\n",
" input_shape=(224, 224, 3), alpha=1.0, include_top=0, weights='imagenet',\n",
" classes= 120 , classifier_activation='softmax')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
"9412608/9406464 [==============================] - 0s 0us/step\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "cuUYCKaw1mRs",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "9ac079bf-17b8-4e9b-b699-47ce4a250879"
},
"source": [
"mobile_net_base_model.summary()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Model: \"mobilenetv2_1.00_224\"\n",
"__________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
"input_1 (InputLayer) [(None, 224, 224, 3) 0 \n",
"__________________________________________________________________________________________________\n",
"Conv1_pad (ZeroPadding2D) (None, 225, 225, 3) 0 input_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"Conv1 (Conv2D) (None, 112, 112, 32) 864 Conv1_pad[0][0] \n",
"__________________________________________________________________________________________________\n",
"bn_Conv1 (BatchNormalization) (None, 112, 112, 32) 128 Conv1[0][0] \n",
"__________________________________________________________________________________________________\n",
"Conv1_relu (ReLU) (None, 112, 112, 32) 0 bn_Conv1[0][0] \n",
"__________________________________________________________________________________________________\n",
"expanded_conv_depthwise (Depthw (None, 112, 112, 32) 288 Conv1_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"expanded_conv_depthwise_BN (Bat (None, 112, 112, 32) 128 expanded_conv_depthwise[0][0] \n",
"__________________________________________________________________________________________________\n",
"expanded_conv_depthwise_relu (R (None, 112, 112, 32) 0 expanded_conv_depthwise_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"expanded_conv_project (Conv2D) (None, 112, 112, 16) 512 expanded_conv_depthwise_relu[0][0\n",
"__________________________________________________________________________________________________\n",
"expanded_conv_project_BN (Batch (None, 112, 112, 16) 64 expanded_conv_project[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_1_expand (Conv2D) (None, 112, 112, 96) 1536 expanded_conv_project_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_1_expand_BN (BatchNormali (None, 112, 112, 96) 384 block_1_expand[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_1_expand_relu (ReLU) (None, 112, 112, 96) 0 block_1_expand_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_1_pad (ZeroPadding2D) (None, 113, 113, 96) 0 block_1_expand_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_1_depthwise (DepthwiseCon (None, 56, 56, 96) 864 block_1_pad[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_1_depthwise_BN (BatchNorm (None, 56, 56, 96) 384 block_1_depthwise[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_1_depthwise_relu (ReLU) (None, 56, 56, 96) 0 block_1_depthwise_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_1_project (Conv2D) (None, 56, 56, 24) 2304 block_1_depthwise_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_1_project_BN (BatchNormal (None, 56, 56, 24) 96 block_1_project[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_2_expand (Conv2D) (None, 56, 56, 144) 3456 block_1_project_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_2_expand_BN (BatchNormali (None, 56, 56, 144) 576 block_2_expand[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_2_expand_relu (ReLU) (None, 56, 56, 144) 0 block_2_expand_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_2_depthwise (DepthwiseCon (None, 56, 56, 144) 1296 block_2_expand_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_2_depthwise_BN (BatchNorm (None, 56, 56, 144) 576 block_2_depthwise[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_2_depthwise_relu (ReLU) (None, 56, 56, 144) 0 block_2_depthwise_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_2_project (Conv2D) (None, 56, 56, 24) 3456 block_2_depthwise_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_2_project_BN (BatchNormal (None, 56, 56, 24) 96 block_2_project[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_2_add (Add) (None, 56, 56, 24) 0 block_1_project_BN[0][0] \n",
" block_2_project_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_3_expand (Conv2D) (None, 56, 56, 144) 3456 block_2_add[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_3_expand_BN (BatchNormali (None, 56, 56, 144) 576 block_3_expand[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_3_expand_relu (ReLU) (None, 56, 56, 144) 0 block_3_expand_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_3_pad (ZeroPadding2D) (None, 57, 57, 144) 0 block_3_expand_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_3_depthwise (DepthwiseCon (None, 28, 28, 144) 1296 block_3_pad[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_3_depthwise_BN (BatchNorm (None, 28, 28, 144) 576 block_3_depthwise[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_3_depthwise_relu (ReLU) (None, 28, 28, 144) 0 block_3_depthwise_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_3_project (Conv2D) (None, 28, 28, 32) 4608 block_3_depthwise_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_3_project_BN (BatchNormal (None, 28, 28, 32) 128 block_3_project[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_4_expand (Conv2D) (None, 28, 28, 192) 6144 block_3_project_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_4_expand_BN (BatchNormali (None, 28, 28, 192) 768 block_4_expand[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_4_expand_relu (ReLU) (None, 28, 28, 192) 0 block_4_expand_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_4_depthwise (DepthwiseCon (None, 28, 28, 192) 1728 block_4_expand_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_4_depthwise_BN (BatchNorm (None, 28, 28, 192) 768 block_4_depthwise[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_4_depthwise_relu (ReLU) (None, 28, 28, 192) 0 block_4_depthwise_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_4_project (Conv2D) (None, 28, 28, 32) 6144 block_4_depthwise_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_4_project_BN (BatchNormal (None, 28, 28, 32) 128 block_4_project[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_4_add (Add) (None, 28, 28, 32) 0 block_3_project_BN[0][0] \n",
" block_4_project_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_5_expand (Conv2D) (None, 28, 28, 192) 6144 block_4_add[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_5_expand_BN (BatchNormali (None, 28, 28, 192) 768 block_5_expand[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_5_expand_relu (ReLU) (None, 28, 28, 192) 0 block_5_expand_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_5_depthwise (DepthwiseCon (None, 28, 28, 192) 1728 block_5_expand_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_5_depthwise_BN (BatchNorm (None, 28, 28, 192) 768 block_5_depthwise[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_5_depthwise_relu (ReLU) (None, 28, 28, 192) 0 block_5_depthwise_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_5_project (Conv2D) (None, 28, 28, 32) 6144 block_5_depthwise_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_5_project_BN (BatchNormal (None, 28, 28, 32) 128 block_5_project[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_5_add (Add) (None, 28, 28, 32) 0 block_4_add[0][0] \n",
" block_5_project_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_6_expand (Conv2D) (None, 28, 28, 192) 6144 block_5_add[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_6_expand_BN (BatchNormali (None, 28, 28, 192) 768 block_6_expand[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_6_expand_relu (ReLU) (None, 28, 28, 192) 0 block_6_expand_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_6_pad (ZeroPadding2D) (None, 29, 29, 192) 0 block_6_expand_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_6_depthwise (DepthwiseCon (None, 14, 14, 192) 1728 block_6_pad[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_6_depthwise_BN (BatchNorm (None, 14, 14, 192) 768 block_6_depthwise[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_6_depthwise_relu (ReLU) (None, 14, 14, 192) 0 block_6_depthwise_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_6_project (Conv2D) (None, 14, 14, 64) 12288 block_6_depthwise_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_6_project_BN (BatchNormal (None, 14, 14, 64) 256 block_6_project[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_7_expand (Conv2D) (None, 14, 14, 384) 24576 block_6_project_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_7_expand_BN (BatchNormali (None, 14, 14, 384) 1536 block_7_expand[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_7_expand_relu (ReLU) (None, 14, 14, 384) 0 block_7_expand_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_7_depthwise (DepthwiseCon (None, 14, 14, 384) 3456 block_7_expand_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_7_depthwise_BN (BatchNorm (None, 14, 14, 384) 1536 block_7_depthwise[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_7_depthwise_relu (ReLU) (None, 14, 14, 384) 0 block_7_depthwise_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_7_project (Conv2D) (None, 14, 14, 64) 24576 block_7_depthwise_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_7_project_BN (BatchNormal (None, 14, 14, 64) 256 block_7_project[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_7_add (Add) (None, 14, 14, 64) 0 block_6_project_BN[0][0] \n",
" block_7_project_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_8_expand (Conv2D) (None, 14, 14, 384) 24576 block_7_add[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_8_expand_BN (BatchNormali (None, 14, 14, 384) 1536 block_8_expand[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_8_expand_relu (ReLU) (None, 14, 14, 384) 0 block_8_expand_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_8_depthwise (DepthwiseCon (None, 14, 14, 384) 3456 block_8_expand_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_8_depthwise_BN (BatchNorm (None, 14, 14, 384) 1536 block_8_depthwise[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_8_depthwise_relu (ReLU) (None, 14, 14, 384) 0 block_8_depthwise_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_8_project (Conv2D) (None, 14, 14, 64) 24576 block_8_depthwise_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_8_project_BN (BatchNormal (None, 14, 14, 64) 256 block_8_project[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_8_add (Add) (None, 14, 14, 64) 0 block_7_add[0][0] \n",
" block_8_project_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_9_expand (Conv2D) (None, 14, 14, 384) 24576 block_8_add[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_9_expand_BN (BatchNormali (None, 14, 14, 384) 1536 block_9_expand[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_9_expand_relu (ReLU) (None, 14, 14, 384) 0 block_9_expand_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_9_depthwise (DepthwiseCon (None, 14, 14, 384) 3456 block_9_expand_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_9_depthwise_BN (BatchNorm (None, 14, 14, 384) 1536 block_9_depthwise[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_9_depthwise_relu (ReLU) (None, 14, 14, 384) 0 block_9_depthwise_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_9_project (Conv2D) (None, 14, 14, 64) 24576 block_9_depthwise_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_9_project_BN (BatchNormal (None, 14, 14, 64) 256 block_9_project[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_9_add (Add) (None, 14, 14, 64) 0 block_8_add[0][0] \n",
" block_9_project_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_10_expand (Conv2D) (None, 14, 14, 384) 24576 block_9_add[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_10_expand_BN (BatchNormal (None, 14, 14, 384) 1536 block_10_expand[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_10_expand_relu (ReLU) (None, 14, 14, 384) 0 block_10_expand_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_10_depthwise (DepthwiseCo (None, 14, 14, 384) 3456 block_10_expand_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_10_depthwise_BN (BatchNor (None, 14, 14, 384) 1536 block_10_depthwise[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_10_depthwise_relu (ReLU) (None, 14, 14, 384) 0 block_10_depthwise_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_10_project (Conv2D) (None, 14, 14, 96) 36864 block_10_depthwise_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_10_project_BN (BatchNorma (None, 14, 14, 96) 384 block_10_project[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_11_expand (Conv2D) (None, 14, 14, 576) 55296 block_10_project_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_11_expand_BN (BatchNormal (None, 14, 14, 576) 2304 block_11_expand[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_11_expand_relu (ReLU) (None, 14, 14, 576) 0 block_11_expand_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_11_depthwise (DepthwiseCo (None, 14, 14, 576) 5184 block_11_expand_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_11_depthwise_BN (BatchNor (None, 14, 14, 576) 2304 block_11_depthwise[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_11_depthwise_relu (ReLU) (None, 14, 14, 576) 0 block_11_depthwise_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_11_project (Conv2D) (None, 14, 14, 96) 55296 block_11_depthwise_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_11_project_BN (BatchNorma (None, 14, 14, 96) 384 block_11_project[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_11_add (Add) (None, 14, 14, 96) 0 block_10_project_BN[0][0] \n",
" block_11_project_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_12_expand (Conv2D) (None, 14, 14, 576) 55296 block_11_add[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_12_expand_BN (BatchNormal (None, 14, 14, 576) 2304 block_12_expand[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_12_expand_relu (ReLU) (None, 14, 14, 576) 0 block_12_expand_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_12_depthwise (DepthwiseCo (None, 14, 14, 576) 5184 block_12_expand_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_12_depthwise_BN (BatchNor (None, 14, 14, 576) 2304 block_12_depthwise[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_12_depthwise_relu (ReLU) (None, 14, 14, 576) 0 block_12_depthwise_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_12_project (Conv2D) (None, 14, 14, 96) 55296 block_12_depthwise_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_12_project_BN (BatchNorma (None, 14, 14, 96) 384 block_12_project[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_12_add (Add) (None, 14, 14, 96) 0 block_11_add[0][0] \n",
" block_12_project_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_13_expand (Conv2D) (None, 14, 14, 576) 55296 block_12_add[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_13_expand_BN (BatchNormal (None, 14, 14, 576) 2304 block_13_expand[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_13_expand_relu (ReLU) (None, 14, 14, 576) 0 block_13_expand_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_13_pad (ZeroPadding2D) (None, 15, 15, 576) 0 block_13_expand_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_13_depthwise (DepthwiseCo (None, 7, 7, 576) 5184 block_13_pad[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_13_depthwise_BN (BatchNor (None, 7, 7, 576) 2304 block_13_depthwise[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_13_depthwise_relu (ReLU) (None, 7, 7, 576) 0 block_13_depthwise_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_13_project (Conv2D) (None, 7, 7, 160) 92160 block_13_depthwise_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_13_project_BN (BatchNorma (None, 7, 7, 160) 640 block_13_project[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_14_expand (Conv2D) (None, 7, 7, 960) 153600 block_13_project_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_14_expand_BN (BatchNormal (None, 7, 7, 960) 3840 block_14_expand[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_14_expand_relu (ReLU) (None, 7, 7, 960) 0 block_14_expand_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_14_depthwise (DepthwiseCo (None, 7, 7, 960) 8640 block_14_expand_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_14_depthwise_BN (BatchNor (None, 7, 7, 960) 3840 block_14_depthwise[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_14_depthwise_relu (ReLU) (None, 7, 7, 960) 0 block_14_depthwise_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_14_project (Conv2D) (None, 7, 7, 160) 153600 block_14_depthwise_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_14_project_BN (BatchNorma (None, 7, 7, 160) 640 block_14_project[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_14_add (Add) (None, 7, 7, 160) 0 block_13_project_BN[0][0] \n",
" block_14_project_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_15_expand (Conv2D) (None, 7, 7, 960) 153600 block_14_add[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_15_expand_BN (BatchNormal (None, 7, 7, 960) 3840 block_15_expand[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_15_expand_relu (ReLU) (None, 7, 7, 960) 0 block_15_expand_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_15_depthwise (DepthwiseCo (None, 7, 7, 960) 8640 block_15_expand_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_15_depthwise_BN (BatchNor (None, 7, 7, 960) 3840 block_15_depthwise[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_15_depthwise_relu (ReLU) (None, 7, 7, 960) 0 block_15_depthwise_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_15_project (Conv2D) (None, 7, 7, 160) 153600 block_15_depthwise_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_15_project_BN (BatchNorma (None, 7, 7, 160) 640 block_15_project[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_15_add (Add) (None, 7, 7, 160) 0 block_14_add[0][0] \n",
" block_15_project_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_16_expand (Conv2D) (None, 7, 7, 960) 153600 block_15_add[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_16_expand_BN (BatchNormal (None, 7, 7, 960) 3840 block_16_expand[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_16_expand_relu (ReLU) (None, 7, 7, 960) 0 block_16_expand_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_16_depthwise (DepthwiseCo (None, 7, 7, 960) 8640 block_16_expand_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_16_depthwise_BN (BatchNor (None, 7, 7, 960) 3840 block_16_depthwise[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_16_depthwise_relu (ReLU) (None, 7, 7, 960) 0 block_16_depthwise_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_16_project (Conv2D) (None, 7, 7, 320) 307200 block_16_depthwise_relu[0][0] \n",
"__________________________________________________________________________________________________\n",
"block_16_project_BN (BatchNorma (None, 7, 7, 320) 1280 block_16_project[0][0] \n",
"__________________________________________________________________________________________________\n",
"Conv_1 (Conv2D) (None, 7, 7, 1280) 409600 block_16_project_BN[0][0] \n",
"__________________________________________________________________________________________________\n",
"Conv_1_bn (BatchNormalization) (None, 7, 7, 1280) 5120 Conv_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"out_relu (ReLU) (None, 7, 7, 1280) 0 Conv_1_bn[0][0] \n",
"==================================================================================================\n",
"Total params: 2,257,984\n",
"Trainable params: 2,223,872\n",
"Non-trainable params: 34,112\n",
"__________________________________________________________________________________________________\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "bzYq-TJVz4nV"
},
"source": [
"mobile_net_base_model.trainable = False"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "TWntvWCG5lZp"
},
"source": [
"preprocess_input = tf.keras.applications.mobilenet_v2.preprocess_input\n",
"# this will ensure that the images are in [-1,1] range"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ZUU_D6bq6FCQ"
},
"source": [
"global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
"# GAP layer after the base model"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "kBp8ypKm6SZz"
},
"source": [
"softmax = tf.keras.layers.Softmax(\n",
" axis=-1\n",
")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "OU4Q0iQG70SP"
},
"source": [
"data_augmentation = tf.keras.Sequential([\n",
" tf.keras.layers.experimental.preprocessing.RandomFlip('horizontal'),\n",
" tf.keras.layers.experimental.preprocessing.RandomRotation(0.2),\n",
"])\n",
"# for data augmentation"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Y1IB-BLM8B9E"
},
"source": [
"# stitching the model\n",
"inputs = tf.keras.Input(shape = (224, 224, 3))\n",
"x = data_augmentation(inputs)\n",
"x = preprocess_input(x)\n",
"# x = tf.keras.layers.Reshape((None, None , None , 3))(x)\n",
"x = mobile_net_base_model(x)\n",
"x = global_average_layer(x)\n",
"x = tf.keras.layers.Dropout(0.2)(x)\n",
"outputs = tf.keras.layers.Dense(120, activation='softmax')(x) \n",
"# outputs = softmax(x)\n",
"model = tf.keras.Model(inputs, outputs)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ZhenQC_X9PxR",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 425
},
"outputId": "9fefbef3-2c08-4cf6-e62a-346c786e0194"
},
"source": [
"model.summary()\n",
"model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Model: \"functional_1\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"input_2 (InputLayer) [(None, 224, 224, 3)] 0 \n",
"_________________________________________________________________\n",
"sequential (Sequential) (None, 224, 224, 3) 0 \n",
"_________________________________________________________________\n",
"tf_op_layer_RealDiv (TensorF [(None, 224, 224, 3)] 0 \n",
"_________________________________________________________________\n",
"tf_op_layer_Sub (TensorFlowO [(None, 224, 224, 3)] 0 \n",
"_________________________________________________________________\n",
"mobilenetv2_1.00_224 (Functi (None, 7, 7, 1280) 2257984 \n",
"_________________________________________________________________\n",
"global_average_pooling2d (Gl (None, 1280) 0 \n",
"_________________________________________________________________\n",
"dropout (Dropout) (None, 1280) 0 \n",
"_________________________________________________________________\n",
"dense (Dense) (None, 120) 153720 \n",
"=================================================================\n",
"Total params: 2,411,704\n",
"Trainable params: 153,720\n",
"Non-trainable params: 2,257,984\n",
"_________________________________________________________________\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "YZUQypzPzBTK"
},
"source": [
"callback = tf.keras.callbacks.EarlyStopping(\n",
" monitor='val_loss', verbose = True, patience = 5\n",
")\n",
"\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "n9jUbRuP9-Zt",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "7a147135-5507-4c21-b0af-d3ca2e2286a6"
},
"source": [
"BATCH_SIZE=64\n",
"EPOCHS=50\n",
"\n",
"# data = tf.keras.layers.Reshape(\n",
"# (None,None,None 3) )\n",
"\n",
"history = model.fit(\n",
" train_data, \n",
" \n",
" batch_size=BATCH_SIZE, \n",
" epochs=1, verbose=1, \n",
" # validation_split = 0.2,\n",
" validation_data = test_data,\n",
" callbacks = callback\n",
"\n",
")"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"188/188 [==============================] - 55s 295ms/step - loss: 0.6068 - accuracy: 0.8075 - val_loss: 0.6043 - val_accuracy: 0.8143\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QQDp89Ly_2fw",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "b01f043a-fcbf-432f-b503-e471bce26867"
},
"source": [
"model.evaluate(test_data)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"135/135 [==============================] - 21s 153ms/step - loss: 0.6043 - accuracy: 0.8143\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[0.6043247580528259, 0.8143356442451477]"
]
},
"metadata": {
"tags": []
},
"execution_count": 36
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "fWNYTCnf2bH2"
},
"source": [
"\n",
"def final_process(path):\n",
" img = Image.open(path).convert(\"L\")\n",
" model = imported_model\n",
" pil_img = Image1(filename=path)\n",
" display(pil_img)\n",
"\n",
" img = np.resize(img, (224,224,3))\n",
" im2arr = np.array(img)\n",
" im2arr = im2arr.reshape(1,224,224,3)\n",
" y_pred = model.predict(im2arr)\n",
" probabilities = tf.nn.softmax(y_pred)\n",
" predicted_indices = tf.argmax(probabilities, 1)\n",
" # predicted_class = tf.gather(TARGET_LABELS, predicted_indices)\n",
" name = get_name(tf.cast(predicted_indices[0] , tf.int32))\n",
"\n",
" print(name)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "lQRAckSL3-Ts",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 235
},
"outputId": "e90c099a-a976-4daa-947c-294c852da07e"
},
"source": [
"final_process('bulldog.jpeg')"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/jpeg": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"n02108915-french_bulldog\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "vKfUkslV0aTg"
},
"source": [
"def upload_files(message):\n",
" print(message)\n",
" from google.colab import files\n",
" uploaded = files.upload()\n",
" for k, v in uploaded.items():\n",
" open(k, 'wb').write(v)\n",
"\n",
"\n",
" path = uploaded.keys()\n",
" print(path)\n",
" final_process(path)\n",
"\n",
"\n",
"\n",
" # path_list = list(uploaded.keys())\n",
"\n",
" # for path in path_list:\n",
" # final_process(path_list)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "off2xym41UzM",
"colab": {
"resources": {
"http://localhost:8080/nbextensions/google.colab/files.js": {
"data": "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"ok": true,
"headers": [
[
"content-type",
"application/javascript"
]
],
"status": 200,
"status_text": ""
}
},
"base_uri": "https://localhost:8080/",
"height": 514
},
"outputId": "7cf68cc9-b415-44cf-cf4c-4810febc5163"
},
"source": [
"upload_files('Upload a dog picture')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Upload a dog picture\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <input type=\"file\" id=\"files-734b4eb7-98c6-4f78-80c2-1f30278586fa\" name=\"files[]\" multiple disabled\n",
" style=\"border:none\" />\n",
" <output id=\"result-734b4eb7-98c6-4f78-80c2-1f30278586fa\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script src=\"/nbextensions/google.colab/files.js\"></script> "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Saving bulldog.jpeg to bulldog.jpeg\n",
"dict_keys(['bulldog.jpeg'])\n"
],
"name": "stdout"
},
{
"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/usr/local/lib/python3.6/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode)\u001b[0m\n\u001b[1;32m 2812\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2813\u001b[0;31m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2814\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mAttributeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUnsupportedOperation\u001b[0m\u001b[0;34m)\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: 'dict_keys' object has no attribute 'seek'",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-36-41ccb3dc98db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mupload_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Upload a dog picture'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-9-78838a30e51e>\u001b[0m in \u001b[0;36mupload_files\u001b[0;34m(message)\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muploaded\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mfinal_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-32-91639a7db9d0>\u001b[0m in \u001b[0;36mfinal_process\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfinal_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"L\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimported_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mpil_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode)\u001b[0m\n\u001b[1;32m 2813\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2814\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mAttributeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUnsupportedOperation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2815\u001b[0;31m \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2816\u001b[0m \u001b[0mexclusive_fp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'dict_keys' object has no attribute 'read'"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "o3VHJWWt1V1E",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "332c165f-ce35-408f-e5ef-b69057ac8e62"
},
"source": [
"from google.colab import drive\n",
"drive.mount(\"/content/drive\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Mounted at /content/drive\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7aHyvWCkDSrh"
},
"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "sCu6AVugG-j-"
},
"source": [
"model.save('dog_model.h5')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "PEMvDiY7HGmQ"
},
"source": [
"!cp /content/drive/My\\ Drive/dog_model.h5 dog_model.h5"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Xx5V8H3-I44D"
},
"source": [
"imported_model = tf.keras.models.load_model('dog_model.h5')\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "1widrkpGD-R3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 425
},
"outputId": "6c0cf835-fac4-406b-c424-29d333105b2c"
},
"source": [
"imported_model.summary()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Model: \"functional_1\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"input_2 (InputLayer) [(None, 224, 224, 3)] 0 \n",
"_________________________________________________________________\n",
"sequential (Sequential) (None, 224, 224, 3) 0 \n",
"_________________________________________________________________\n",
"tf_op_layer_RealDiv (TensorF (None, 224, 224, 3) 0 \n",
"_________________________________________________________________\n",
"tf_op_layer_Sub (TensorFlowO (None, 224, 224, 3) 0 \n",
"_________________________________________________________________\n",
"mobilenetv2_1.00_224 (Functi (None, 7, 7, 1280) 2257984 \n",
"_________________________________________________________________\n",
"global_average_pooling2d (Gl (None, 1280) 0 \n",
"_________________________________________________________________\n",
"dropout (Dropout) (None, 1280) 0 \n",
"_________________________________________________________________\n",
"dense (Dense) (None, 120) 153720 \n",
"=================================================================\n",
"Total params: 2,411,704\n",
"Trainable params: 153,720\n",
"Non-trainable params: 2,257,984\n",
"_________________________________________________________________\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "3MyJpHjPEDoK",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 275
},
"outputId": "18ed2aa0-e14d-410b-9650-939619e51be4"
},
"source": [
"converter = tf.lite.TFLiteConverter.from_keras_model(imported_model)\n",
"tflite_model = converter.convert()\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: /tmp/tmpc6aunngm/assets\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: /tmp/tmpc6aunngm/assets\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "g2m-wnqmJe-m"
},
"source": [
"with open('model.tflite', 'wb') as f:\n",
" f.write(tflite_model)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "dGcakNzLJlgr"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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