小编Mad*_*dhi的帖子

使用ResNet50预训练的权重我正在尝试构建一个分类器.代码库完全在Keras高级Tensorflow API中实现.完整的代码发布在下面的GitHub链接中.

源代码:使用RestNet50架构进行分类

预训练模型的文件大小为94.7mb.

我加载了预先训练好的文件

new_model = Sequential()

new_model.add(ResNet50(include_top=False,
                pooling='avg',
                weights=resnet_weight_paths))

并适合模型

train_generator = data_generator.flow_from_directory(
    'path_to_the_training_set',
    target_size = (IMG_SIZE,IMG_SIZE),
    batch_size = 12,
    class_mode = 'categorical'
    )

validation_generator = data_generator.flow_from_directory(
    'path_to_the_validation_set',
    target_size = (IMG_SIZE,IMG_SIZE),
    class_mode = 'categorical'
    )

#compile the model

new_model.fit_generator(
    train_generator,
    steps_per_epoch = 3,
    validation_data = validation_generator,
    validation_steps = 1
)

在训练数据集中,我有两个文件夹狗和猫,每个持有近10,000张图像.当我编译脚本时,我收到以下错误

Epoch 1/1 2018-05-12 13:04:45.847298:W tensorflow/core/framework/allocator.cc:101] 38535168的分配超过系统内存的10%.2018-05-12 13:04:46.845021:W tensorflow/core/framework/allocator.cc:101] 37171200的分配超过系统内存的10%.2018-05-12 13:04:47.552176:W tensorflow/core/framework/allocator.cc:101] 37171200的分配超过系统内存的10%.2018-05-12 13:04:48.199240:W tensorflow/core/framework/allocator.cc:101] 37171200的分配超过系统内存的10%.2018-05-12 13:04:48.918930:W tensorflow/core/framework/allocator.cc:101] 37171200的分配超过系统内存的10%.2018-05-12 13:04:49.274137:W tensorflow/core/framework/allocator.cc:101] 19267584的分配超过系统内存的10%.2018-05-12 13:04:49.647061:W tensorflow/core/framework/allocator.cc:101] 19267584的分配超过系统内存的10%.2018-05-12 …

我需要获取存储在Google Bucket中的文件信息。信息，如文件大小，存储类，最后修改，类型。我搜索了google文档，但是可以通过curl或console方法来完成。我需要从Python API获得该信息，例如下载blob，将blob上传到存储桶。示例代码或任何帮助表示赞赏！

我正在做文档分类并获得了高达 76% 的准确率。在预测文档类别时，我遵循了一个

doc_clf.predict(tf_idf.transform((count_vect.transform([r'document']))))

我收到以下错误：

File "/usr/local/lib/python3.5/dist- packages/sklearn/utils/metaestimators.py", line 115, in <lambda>
  out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/sklearn/pipeline.py", line 306, in predict
  Xt = transform.transform(Xt)
File "/usr/local/lib/python3.5/dist-packages/sklearn/feature_extraction/text.py", line 923, in transform
  _, X = self._count_vocab(raw_documents, fixed_vocab=True)
File "/usr/local/lib/python3.5/dist-packages/sklearn/feature_extraction/text.py", line 792, in _count_vocab
  for feature in analyze(doc):
File "/usr/local/lib/python3.5/dist-packages/sklearn/feature_extraction/text.py", line 266, in <lambda>
  tokenize(preprocess(self.decode(doc))), stop_words)
File "/usr/local/lib/python3.5/dist-packages/sklearn/feature_extraction/text.py", line 232, in <lambda>
  return lambda x: strip_accents(x.lower())
File "/usr/local/lib/python3.5/dist-packages/scipy/sparse/base.py", line 647, in __getattr__
  raise AttributeError(attr + " not found") …

我正在做图像分类，我训练了一个模型并保存了一个模型。当我尝试预测模型时，它显示输入错误。我正在使用 ResNet 架构构建一个分类器，最初将 input_size 声明为224 x 224。现在我需要预测测试图像的类别。

我将图像转换为224x224 numpy array。当我尝试以下代码时

#plot the figure
fig = plt.figure()

for num,data in enumerate(test_data):

    img_num = data[1]
    img_data = data[0]

    y = fig.add_subplot(9,3,num+1)
    orig = img_data
    data = img_data.reshape(1,IMG_SIZ,IMG_SIZ,3)

    #predict the model
    model_out = model.predict_classes([orig])[0]

    if np.argmax(model_out) == 1: str_label='Dog'
    else: str_label='Cat'

    y.imshow(orig,cmap = 'gray')
    plt.title(str_label)
    y.axes.get_xaxis().set_visible(False)
    y.axes.get_yaxis().set_visible(False)


plt.show()
plt.savefig('test_labeled.jpg')

它向我显示以下错误

ValueError：无法将大小为 50176 的数组重塑为形状 (1,224,224,3)

我必须以什么尺寸重塑正确的尺寸？

谢谢！