{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-11T18:25:46.889303Z",
"iopub.status.busy": "2024-01-11T18:25:46.888957Z",
"iopub.status.idle": "2024-01-11T18:25:46.892796Z",
"shell.execute_reply": "2024-01-11T18:25:46.892108Z"
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"id": "vtNtfcHHoHNP"
},
"outputs": [],
"source": [
"# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# http://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License.\n",
"# =============================================================================="
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jZwnHZ70oUIM"
},
"source": [
"# CropNet: Cassava Disease Detection"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6sg9wHP9oR3q"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "grEgSWu2iTxm"
},
"source": [
"このノートブックでは、**TensorFlow Hub** の CropNet [キャッサバの病気の分類](https://tfhub.dev/google/cropnet/classifier/cassava_disease_V1/2)モデルの使用方法を説明します。このモデルはキャッサバの葉の画像を 6 つのクラスのいずれかに分類します。クラスは*斑点細菌病、褐色条斑病、緑ダニ、モザイク病、健康、不明*です。\n",
"\n",
"この Colab では、以下の方法を実演します。\n",
"\n",
"- **TensorFlow Hub** からモデル https://tfhub.dev/google/cropnet/classifier/cassava_disease_V1/2 を読み込む。\n",
"- **TensorFlow Datasets (TFDS)** から[キャッサバ](https://www.tensorflow.org/datasets/catalog/cassava)データセットを読み込む。\n",
"- キャッサバの葉の画像を、4 つの異なるキャッサバの病気のカテゴリ、あるいは健康または不明として分類する。\n",
"- 分類器の*精度*を評価し、ドメイン外の画像を適用した際のモデルの*ロバスト性*を検査する。"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bKn4Fiq2OD7u"
},
"source": [
"## インポートとセットアップ"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-11T18:25:46.896732Z",
"iopub.status.busy": "2024-01-11T18:25:46.896163Z",
"iopub.status.idle": "2024-01-11T18:26:50.785325Z",
"shell.execute_reply": "2024-01-11T18:26:50.784510Z"
},
"id": "LMgjpSoYqJIz"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting matplotlib==3.2.2\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Downloading matplotlib-3.2.2.tar.gz (40.3 MB)\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Preparing metadata (setup.py) ... \u001b[?25l-"
]
},
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"output_type": "stream",
"text": [
"\b \b\\"
]
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"\b \b|"
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"text": [
"\b \bdone\r\n",
"\u001b[?25hRequirement already satisfied: cycler>=0.10 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from matplotlib==3.2.2) (0.12.1)\r\n",
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"Building wheels for collected packages: matplotlib\r\n"
]
},
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"text": [
" Building wheel for matplotlib (setup.py) ... \u001b[?25l-"
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"\b \bdone\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[?25h Created wheel for matplotlib: filename=matplotlib-3.2.2-cp39-cp39-linux_x86_64.whl size=12283270 sha256=31effce7c144b88209146cc9982f6ec6a899de0d0cf7692b606fa4502b20c15d\r\n",
" Stored in directory: /home/kbuilder/.cache/pip/wheels/eb/ab/a7/4319da29f630cb41caf52c8bdfac311c21e774532fd6e101e4\r\n"
]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"Successfully built matplotlib\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Installing collected packages: matplotlib\r\n",
" Attempting uninstall: matplotlib\r\n",
" Found existing installation: matplotlib 3.8.2\r\n",
" Uninstalling matplotlib-3.8.2:\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Successfully uninstalled matplotlib-3.8.2\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
"seaborn 0.13.1 requires matplotlib!=3.6.1,>=3.4, but you have matplotlib 3.2.2 which is incompatible.\u001b[0m\u001b[31m\r\n",
"\u001b[0mSuccessfully installed matplotlib-3.2.2\r\n"
]
}
],
"source": [
"!pip install matplotlib==3.2.2"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"cellView": "both",
"execution": {
"iopub.execute_input": "2024-01-11T18:26:50.789677Z",
"iopub.status.busy": "2024-01-11T18:26:50.789373Z",
"iopub.status.idle": "2024-01-11T18:26:54.115418Z",
"shell.execute_reply": "2024-01-11T18:26:54.114688Z"
},
"id": "FIP4rkjp45MG"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-11 18:26:51.613353: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2024-01-11 18:26:51.613403: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2024-01-11 18:26:51.614982: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
]
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import tensorflow as tf\n",
"import tensorflow_datasets as tfds\n",
"import tensorflow_hub as hub"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"cellView": "form",
"execution": {
"iopub.execute_input": "2024-01-11T18:26:54.119967Z",
"iopub.status.busy": "2024-01-11T18:26:54.119141Z",
"iopub.status.idle": "2024-01-11T18:26:54.125791Z",
"shell.execute_reply": "2024-01-11T18:26:54.125207Z"
},
"id": "mIqmq_qmWw78"
},
"outputs": [],
"source": [
"#@title Helper function for displaying examples\n",
"def plot(examples, predictions=None):\n",
" # Get the images, labels, and optionally predictions\n",
" images = examples['image']\n",
" labels = examples['label']\n",
" batch_size = len(images)\n",
" if predictions is None:\n",
" predictions = batch_size * [None]\n",
"\n",
" # Configure the layout of the grid\n",
" x = np.ceil(np.sqrt(batch_size))\n",
" y = np.ceil(batch_size / x)\n",
" fig = plt.figure(figsize=(x * 6, y * 7))\n",
"\n",
" for i, (image, label, prediction) in enumerate(zip(images, labels, predictions)):\n",
" # Render the image\n",
" ax = fig.add_subplot(x, y, i+1)\n",
" ax.imshow(image, aspect='auto')\n",
" ax.grid(False)\n",
" ax.set_xticks([])\n",
" ax.set_yticks([])\n",
"\n",
" # Display the label and optionally prediction\n",
" x_label = 'Label: ' + name_map[class_names[label]]\n",
" if prediction is not None:\n",
" x_label = 'Prediction: ' + name_map[class_names[prediction]] + '\\n' + x_label\n",
" ax.xaxis.label.set_color('green' if label == prediction else 'red')\n",
" ax.set_xlabel(x_label)\n",
"\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kwrg9yIlaUSb"
},
"source": [
"## データセット\n",
"\n",
"TFDS から*キャッサバ*データセットを読み込みます。"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-11T18:26:54.129312Z",
"iopub.status.busy": "2024-01-11T18:26:54.128690Z",
"iopub.status.idle": "2024-01-11T18:26:57.246268Z",
"shell.execute_reply": "2024-01-11T18:26:57.245457Z"
},
"id": "0rTcnxoSkp31"
},
"outputs": [],
"source": [
"dataset, info = tfds.load('cassava', with_info=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GpC71TFDhJFO"
},
"source": [
"データセットの情報を見て、説明や引用、例の数などの詳細情報を確認しましょう。"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-11T18:26:57.250847Z",
"iopub.status.busy": "2024-01-11T18:26:57.250188Z",
"iopub.status.idle": "2024-01-11T18:26:57.256044Z",
"shell.execute_reply": "2024-01-11T18:26:57.255381Z"
},
"id": "btJBMovmbYtR"
},
"outputs": [
{
"data": {
"text/plain": [
"tfds.core.DatasetInfo(\n",
" name='cassava',\n",
" full_name='cassava/0.1.0',\n",
" description=\"\"\"\n",
" Cassava consists of leaf images for the cassava plant depicting healthy and\n",
" four (4) disease conditions; Cassava Mosaic Disease (CMD), Cassava Bacterial\n",
" Blight (CBB), Cassava Greem Mite (CGM) and Cassava Brown Streak Disease (CBSD).\n",
" Dataset consists of a total of 9430 labelled images.\n",
" The 9430 labelled images are split into a training set (5656), a test set(1885)\n",
" and a validation set (1889). The number of images per class are unbalanced with\n",
" the two disease classes CMD and CBSD having 72% of the images.\n",
" \"\"\",\n",
" homepage='https://www.kaggle.com/c/cassava-disease/overview',\n",
" data_dir='gs://tensorflow-datasets/datasets/cassava/0.1.0',\n",
" file_format=tfrecord,\n",
" download_size=1.26 GiB,\n",
" dataset_size=Unknown size,\n",
" features=FeaturesDict({\n",
" 'image': Image(shape=(None, None, 3), dtype=uint8),\n",
" 'image/filename': Text(shape=(), dtype=string),\n",
" 'label': ClassLabel(shape=(), dtype=int64, num_classes=5),\n",
" }),\n",
" supervised_keys=('image', 'label'),\n",
" disable_shuffling=False,\n",
" splits={\n",
" 'test': ,\n",
" 'train': ,\n",
" 'validation': ![](\"https://www.tensorflow.org/images/tf_logo_32px.png\"){kind=logo}
TensorFlow.org で表示\n",
"![](\"https://www.tensorflow.org/images/colab_logo_32px.png\"){kind=logo}
Google Colabで実行\n",
"![](\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\")
GitHub でソースを表示![](\"https://www.tensorflow.org/images/download_logo_32px.png\"){kind=logo}
ノートブックをダウンロード![](\"https://www.tensorflow.org/images/hub_logo_32px.png\"){kind=logo}
TFハブモデルを参照してください