小编Yus*_*stx的帖子

有没有一种方法可以使用GridSearchCV或任何其他内置的sklearn函数来为OneClassSVM分类器找到最佳的超参数？

我目前要做的是使用训练/测试拆分自己执行搜索，如下所示：

Gamma和nu值定义为：

gammas = np.logspace(-9, 3, 13)
nus = np.linspace(0.01, 0.99, 99)

探索所有可能的超参数并找到最佳参数的函数：

clf = OneClassSVM()

results = []

train_x = vectorizer.fit_transform(train_contents)
test_x = vectorizer.transform(test_contents)

for gamma in gammas:
    for nu in nus:
        clf.set_params(gamma=gamma, nu=nu)

        clf.fit(train_x)

        y_pred = clf.predict(test_x)

        if 1. in y_pred:  # Check if at least 1 review is predicted to be in the class
            results.append(((gamma, nu), (accuracy_score(y_true, y_pred),
                                              precision_score(y_true, y_pred),
                                              recall_score(y_true, y_pred),
                                              f1_score(y_true, y_pred),
                                              roc_auc_score(y_true, y_pred),
                                              ))
                               )

    # Determine and print the best parameter settings …

我有几个文件与不同的文件:

• main.py
• watch.py
• read.py
• detect.py < - 使用darkflow依赖于图模式的基于张量流的库
• translate.py < - 使用tf eager执行

在darkflow的TFNet初始化期间,我收到此错误:

Traceback (most recent call last):
  File "/home/justin/Projects/comp3931/main.py", line 6, in <module>
    watcher = Watcher('res/vid/planet_earth_s01e01/video.mp4', 'res/vid/planet_earth_s01e01/english.srt')
  File "/home/justin/Projects/comp3931/watch.py", line 9, in __init__
    self.detector = Detector()
  File "/home/justin/Projects/comp3931/detect.py", line 6, in __init__
    self.tfnet = TFNet(self.options)
  File "/usr/local/lib64/python3.6/site-packages/darkflow/net/build.py", line 75, in __init__
    self.build_forward()
  File "/usr/local/lib64/python3.6/site-packages/darkflow/net/build.py", line 105, in build_forward
    self.inp = tf.placeholder(tf.float32, inp_size, 'input')
  File "/usr/local/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py", line 1677, in placeholder
    raise RuntimeError("tf.placeholder() is not compatible with " …