Run prediction from saved model in tensorflow 2.0 - tensorflow

Question

I have a saved model (a directory with model.pd and variables) and wanted to run predictions on a pandas data frame.
I've unsuccessfully tried a few ways to do this:
Attempt 1: Restore the estimator from the saved model
estimator = tf.estimator.LinearClassifier(
feature_columns=create_feature_cols(),
model_dir=path,
warm_start_from=path)
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]
}
Attempt 2: Run prediction directly from the saved model by running
imported = tf.saved_model.load(path) # path is the directory that has a `model.pd` and variables folder
imported.signatures["predict"](example)
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])
I've seen solutions on StackOverflow but they are all tensorflow 1.14. Greatly appreciate it if someone can help with tensorflow 2.0.

Answer 1

Considering you have your saved model present like this:
my_model
assets saved_model.pb variables
You can load your saved model using:
new_model = tf.keras.models.load_model('saved_model/my_model')
# Check its architecture
new_model.summary()
To perform prediction on a DataFrame you need to:
Wrap scalars into a list so as to have a batch dimension (models only process batches of data, not single samples)
Call convert_to_tensor on each feature
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)
)
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))