apple开源机器学习框架turicreate内容非常广阔，本篇介绍冰山一角的图像相似，极简主义的代表！！！！ github:https://apple.github.io/turicreate/docs/api/generated/turicreate.SFrame.html
首篇博客：python︱apple开源机器学习框架turicreate中的SFrame——新形态pd.DataFrame
其中，Turicreate的后台是mxnet框架，turicreate不太适合使用GPU图像训练，因为现在的mxnet已经cuda10 - 1.4.0+ 而turicreate还支持很老版本的 mxnet - 1.1.0，因为版本问题会出现很多问题，一种比较合适的方式是使用他们官方内部的docker启动。 如果要启用GPU之前，需要了解：（linuxGPU.md）
Turi Create does not require a GPU, but certain models can be accelerated by the use of a GPU. To enable GPU support in linux after installation of the turicreate package, please perform the following steps: Install CUDA 8.0 (instructions) Install cuDNN 5 for CUDA 8.0 (instructions) Make sure to add the CUDA library path to your LD_LIBRARY_PATH environment variable. In the typical case, this means adding the following line to your ~/.bashrc file: export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH If you installed the cuDNN files into a separate directory, make sure to separately add it as well. Next step is to uninstall mxnet and install the CUDA-enabled mxnet-cu80 package: (venv) pip uninstall -y mxnet (venv) pip install mxnet-cu80==1.1.0 Make sure you install the same version of MXNet as the one turicreate recommends (currently 1.1.0). If you have trouble setting up the GPU, the MXNet installation instructions may offer additional help.确实会报错：
Downloading https://docs-assets.developer.apple.com/turicreate/models/resnet-50-symbol.json Download completed: /var/tmp/model_cache/resnet-50-symbol.json [13:44:53] src/nnvm/legacy_json_util.cc:190: Loading symbol saved by previous version v0.8.0. Attempting to upgrade... [13:44:53] src/nnvm/legacy_json_util.cc:198: Symbol successfully upgraded! ERROR: Incomplete installation for leveraging GPUs for computations. Please make sure you have CUDA installed and run the following line in your terminal and try again: pip uninstall -y mxnet && pip install mxnet-cu90==1.1.0 Adjust 'cu90' depending on your CUDA version ('cu75' and 'cu80' are also available). You can also disable GPU usage altogether by invoking turicreate.config.set_num_gpus(0)只要把不同的分类的图像，像这样放在不同文件夹即可。
其中tc.image_analysis.load_images可以读入整个文件夹，也可以读入本地单张图片。 dataBuffer.random_split(0.9)，把数据集随机拆分，按照 9/1 比例; .image_classifier.create，是进行创建模型，target是选择因变量，model目前有以下几种：
Uses a pretrained model to bootstrap an image classifier: - "resnet-50" : Uses a pretrained resnet model. Exported Core ML model will be ~90M. - "squeezenet_v1.1" : Uses a pretrained squeezenet model. Exported Core ML model will be ~4.7M. - "VisionFeaturePrint_Scene": Uses an OS internal feature extractor. Only on available on iOS 12.0+, macOS 10.14+ and tvOS 12.0+. Exported Core ML model will be ~41K.create函数详解（image_classifier.py）：
def create(dataset, target, feature=None, model = 'resnet-50', l2_penalty=0.01, l1_penalty=0.0, solver='auto', feature_rescaling=True, convergence_threshold = _DEFAULT_SOLVER_OPTIONS['convergence_threshold'], step_size = _DEFAULT_SOLVER_OPTIONS['step_size'], lbfgs_memory_level = _DEFAULT_SOLVER_OPTIONS['lbfgs_memory_level'], max_iterations = _DEFAULT_SOLVER_OPTIONS['max_iterations'], class_weights = None, validation_set = 'auto', verbose=True, seed=None, batch_size=64):其中predictions的output_type参数有，可以返回，probability - 概率（1的概率），rank - 排序，class - 分类名称：
# predictions # output_type：{'probability', 'margin', 'class', 'probability_vector'} # - `probability`: Probability associated with each label in the prediction. # - `rank` : Rank associated with each label in the prediction. # - `margin` : Margin associated with each label in the prediction.目前最近版本的评估是错误的，会报错：
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-11-77aa635d24e6> in <module> 1 # Evaluate the model and print the results ----> 2 metrics = model.evaluate(testingBuffers[:10]) 3 print(metrics['accuracy']) /usr/local/lib/python3.6/dist-packages/turicreate/toolkits/image_classifier/image_classifier.py in evaluate(self, dataset, metric, verbose, batch_size) 798 vectors = map(lambda l: {'name': l, 'pos':list(sf_conf_mat[sf_conf_mat['target_label']==l].sort('predicted_label')['norm_prob'])}, 799 labels) --> 800 evaluation_result['sorted_labels'] = hclusterSort(vectors, l2Dist)[0]['name'].split("|") 801 802 # Get recall and precision per label /usr/local/lib/python3.6/dist-packages/turicreate/toolkits/image_classifier/image_classifier.py in hclusterSort(vectors, dist_fn) 752 distances.append({'from': v, 'to': new_vec, 'dist': total/len(v.get('members', [v]))/len(new_vec['members'])}) 753 --> 754 vecs.append(new_vec) 755 distances = sorted(distances, key=lambda d: d['dist']) 756 AttributeError: 'filter' object has no attribute 'append'那么就可以自己通过sklearn写：
from sklearn.metrics import classification_report,accuracy_score,recall_score,f1_score f1_score(test_data['y'], test_data['pre_y']) accuracy_score(test_data['y'], test_data['pre_y']) recall_score(y_true, y_pred, average='micro') print(classification_report(test_data['y'], test_data['pre_y']))相关的文档： 1 极简主义︱利用apple机器学习平台Turicreate实现图像相似性检索（二） 2 Classify Image Example-ResNet50.ipynb 3 示范文档：image_classifier
