极简主义︱使用Turicreate进行快速图像分类迁移训练与预测（六）

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.

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)

# 训练文件夹
    # 分门别类存放
# Load images from the downloaded data
reference_data  = tc.image_analysis.load_images(train_file)
reference_data = reference_data.add_row_number()

#reference_data
reference_data["y"] = reference_data["path"].apply(lambda path: "pos" if "pos" in path else 'neg')

dataBuffer = reference_data
trainingBuffers, testingBuffers = dataBuffer.random_split(0.9)

# 模型训练
model = turicreate.image_classifier.create(trainingBuffers, target="y", model="resnet-50")

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):

# 读入方式一：url
img = turicreate.Image('http://img5.cache.netease.com/house/2014/1/7/2014010711263691ea9_550.jpg')

# 读入方式二：本地文件
img = turicreate.Image('train/pos/p89.jpg')

# 读入方式三：加载本地文件
image_data = tc.image_analysis.load_images('train/pos/p89.jpg')

# 预测
predictions = loaded_model.predict(image_data, output_type='class', batch_size=64)

# 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.

# Evaluate the model and print the results
metrics = model.evaluate(testingBuffers)
print(metrics['accuracy'])

---------------------------------------------------------------------------
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'

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']))