问题描述
限时送ChatGPT账号..我有一个已保存的模型(一个包含 model.pd
和变量的目录),并且想对 Pandas 数据框进行预测.
I have a saved model (a directory with model.pd
and variables) and wanted to run predictions on a pandas data frame.
我尝试了几种方法都没有成功:
I've unsuccessfully tried a few ways to do this:
尝试 1:从保存的模型中恢复估算器
estimator = tf.estimator.LinearClassifier(
feature_columns=create_feature_cols(),
model_dir=path,
warm_start_from=path)
其中 path 是具有 model.pd
和 variables 文件夹的目录.我有一个错误
Where path is the directory that has a model.pd
and variables folder. I got an error
ValueError: Tensor linear/linear_model/dummy_feature1/weights is not found in
gs://bucket/Trainer/output/2013/20191008T170504.583379-63adee0eaee0/serving_model_dir/export/1570554483/variables/variables
checkpoint {'linear/linear_model/dummy_feature1/weights': [1, 1], 'linear/linear_model/dummy_feature2/weights': [1, 1]
}
尝试 2:通过运行直接从保存的模型运行预测
imported = tf.saved_model.load(path) # path is the directory that has a `model.pd` and variables folder
imported.signatures["predict"](example)
但尚未成功传递参数 - 看起来该函数正在寻找 tf.example
并且我不确定如何将数据框转换为 tf.example
>.我的转换尝试如下,但出现 df[f] 不是张量的错误:
But has not successfully passed the argument - looks like the function is looking for a tf.example
and I am not sure how to convert a data frame to tf.example
.
My attempt to convert is below but got an error that df[f] is not a tensor:
for f in features:
example.features.feature[f].float_list.value.extend(df[f])
我在 StackOverflow 上看到过解决方案,但它们都是 tensorflow 1.14.如果有人可以帮助使用 tensorflow 2.0,将不胜感激.
I've seen solutions on StackOverflow but they are all tensorflow 1.14. Greatly appreciate it if someone can help with tensorflow 2.0.
推荐答案
考虑到您保存的模型如下所示:
Considering you have your saved model present like this:
my_model
assets saved_model.pb variables
您可以使用以下方法加载保存的模型:
You can load your saved model using:
new_model = tf.keras.models.load_model('saved_model/my_model')
# Check its architecture
new_model.summary()
要对 DataFrame 执行预测,您需要:
To perform prediction on a DataFrame you need to:
将标量包装成一个列表,以便有一个批量维度(模型只处理批量数据,而不是单个样本)对每个特征调用convert_to_tensor
示例 1:如果您将第一个测试行的值设为
Example 1: If you have values for the first test row as
sample = {
'Type': 'Cat',
'Age': 3,
'Breed1': 'Tabby',
'Gender': 'Male',
'Color1': 'Black',
'Color2': 'White',
'MaturitySize': 'Small',
'FurLength': 'Short',
'Vaccinated': 'No',
'Sterilized': 'No',
'Health': 'Healthy',
'Fee': 100,
'PhotoAmt': 2,
}
input_dict = {name: tf.convert_to_tensor([value]) for name, value in sample.items()}
predictions = new_model.predict(input_dict)
prob = tf.nn.sigmoid(predictions[0])
print(
"This particular pet had a %.1f percent probability "
"of getting adopted." % (100 * prob)
)
示例 2:或者,如果您有多行与火车数据的顺序相同
Example 2: Or if you have multiple rows present in the same order as the train data
predict_dataset = tf.convert_to_tensor([
[5.1, 3.3, 1.7, 0.5,],
[5.9, 3.0, 4.2, 1.5,],
[6.9, 3.1, 5.4, 2.1]
])
# training=False is needed only if there are layers with different
# behavior during training versus inference (e.g. Dropout).
predictions = new_model(predict_dataset, training=False)
for i, logits in enumerate(predictions):
class_idx = tf.argmax(logits).numpy()
p = tf.nn.softmax(logits)[class_idx]
name = class_names[class_idx]
print("Example {} prediction: {} ({:4.1f}%)".format(i, name, 100*p))
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