C2 W2 preprocessing_fn

AI Discussions
1

Hi, i am working on the lab for the 2nd week of the second curse and i am having some problems with the preprocessing function. this is what i have. Any suggestion or correction? Thanks

def preprocessing_fn(inputs):
“”“tf.transform’s callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
“””
outputs = {}

### START CODE HERE

# Scale these features to the z-score.
for key in _DENSE_FLOAT_FEATURE_KEYS:
    # Scale these features to the z-score.
    outputs[_transformed_name(key)] = tft.scale_to_z_score(
        inputs[key])
        

# Scale these feature/s from 0 to 1
for key in _RANGE_FEATURE_KEYS:
    outputs[_transformed_name(key)] = tft.scale_to_0_1(
        inputs[key])
        
# Transform the strings into indices 
# hint: use the VOCAB_SIZE and OOV_SIZE to define the top_k and num_oov parameters
for key in _VOCAB_FEATURE_KEYS:
    outputs[_transformed_name(key)] = tft.compute_and_apply_vocabulary(
        tf.strings.split(inputs[key]), top_k=_VOCAB_SIZE, num_oov_buckets=_OOV_SIZE)
                
# Bucketize the feature
for key in _BUCKET_FEATURE_KEYS:
    outputs[_transformed_name(key)] =  tft.bucketize(
        inputs[key], _FEATURE_BUCKET_COUNT[key])
        

# Keep the features as is. No tft function needed.
for key in _CATEGORICAL_FEATURE_KEYS:
    outputs[_transformed_name(key)] = inputs[key]

# Use `tf.cast` to cast the label key to float32
traffic_volume = tf.cast(inputs[_VOLUME_KEY], dtype=tf.float32)

# Create a feature that shows if the traffic volume is greater than the mean and cast to an int
outputs[_transformed_name(_VOLUME_KEY)] = tf.cast(          
    # Use `tf.greater` to check if the traffic volume in a row is
    # greater than the mean of the entire traffic volumn column
    # Hint: Use a `tft` function to compute the mean.
    tf.greater(traffic_volume, tf.mean(tf.cast(inputs[_VOLUME_KEY], tf.float32))),
    tf.int64) 

### END CODE HERE

return outputs
2

Hey @Helena_Munoz,
Welcome, and we are glad that you could become a part of our community :partying_face:

Can you please specify as to which specialization are you referring to? You have posted this topic in the “General Discussions” category. You can change the category of your post by using the little pencil icon next to the title, so that your query can reach out to the correct mentors.

Cheers,
Elemento