tf.saved_model.save and predict a single value

In order to save the model, I have added this line after the training loop: tf.saved_model.save(model, os.path.join(FLAGS.output_dir, "1") ) in order to get: assets, saved_model.pb and variables

from there, I am trying to load the model and predict a single value:

loaded = tf.saved_model.load( os.path.join(model_dir, "1") )

tokenizer = tokenization.FullTokenizer(vocab_file=None,spm_model_file=spm_model_file, do_lower_case=True)

text_a = "the movie was not good"
example = classifier_data_lib.InputExample(guid=0, text_a=text_a, text_b=None, label=0)

labels = [0, 1]
max_seq_length = 128

feature = classifier_data_lib.convert_single_example(ex_index=0, example=example, label_list=labels, max_seq_length=max_seq_length, tokenizer=tokenizer)

test_input_word_ids =tf.convert_to_tensor([feature.input_ids], dtype=tf.int32, name='input_word_ids')
test_input_mask     =tf.convert_to_tensor([feature.input_mask], dtype=tf.int32, name='input_mask')
test_input_type_ids =tf.convert_to_tensor([feature.segment_ids], dtype=tf.int32, name='input_type_ids')

logit = loaded.signatures["serving_default"]( input_mask=test_input_mask,input_type_ids=test_input_type_ids,input_word_ids=test_input_word_ids )

pred = tf.argmax(logit['output'], axis=-1, output_type=tf.int32)
prob = tf.nn.softmax(logit['output'], axis=-1)

print(f'Prediction: {pred} Probabilities: {prob}')

This solution works for a single value. Thanks

[birdmw]

We’re in the same boat. We are doing it on DataBricks. Had some extra errors with the callbacks. So we commented them out, borrowed your code, and put it in a custom callback and it works now. So cheers. `class MyCustomCallback(tf.keras.callbacks.Callback):

def on_train_batch_begin(self, batch, logs=None): print(‘Training: batch {} begins at {}’.format(batch, datetime.datetime.now().time()))

def on_train_batch_end(self, batch, logs=None): print(‘Training: batch {} ends at {}’.format(batch, datetime.datetime.now().time())) print(“saving model as per callback to:”, os.path.join(FLAGS.output_dir, “1”)) tf.saved_model.save(model, os.path.join(FLAGS.output_dir, “1”) ) print(“model saved”)

def on_test_batch_begin(self, batch, logs=None): print(‘Evaluating: batch {} begins at {}’.format(batch, datetime.datetime.now().time()))

def on_test_batch_end(self, batch, logs=None): print(‘Evaluating: batch {} ends at {}’.format(batch, datetime.datetime.now().time()))`

a la https://www.tensorflow.org/guide/keras/custom_callback

[008karan]

@birdmw Can tell what changes you did to add your custom callbacks. When I am running training its printing out results at the end of each epoch. I want to see at every step. I am not able to figure it out. can you help?