def preprocessing_fn(inputs):
no2 = inputs['no2']
pm10 = inputs['pm10']
so2 = inputs['so2']
soot = inputs['soot']
no2_normalized = no2 - tftmean(no2)
so2_normalized = so2 - tft.mean(so2)
pm10_normalized = tft.scale_to_0_1(pm10)
soot_normalized = tft.scale_by_min_max(soot)
return {
'no2_normalized':no2_normalized,
'so2_normalized':so2_normalized,
'pm10_normalized':pm10__normalized,
'soot_normalized':soot_normalized
}
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 = {}
# The input float values for the image encoding are in the range [-0.5, 0.5].
# So scale_by_min_max is a identity operation, since the range is preserved.
outputs[transformed_name(IMAGE_KEY)] = (
tft.scale_by_min_max(inputs[IMAGE_KEY], -0.5, 0.5))
outputs[transformed_name(LABEL_KEY)] = inputs[LABEL_KEY]
return outputs
def preprocessing_fn(inputs):
no2 = inputs["no2"]
pm10 = inputs["pm10"]
so2 = inputs["so2"]
soot = inputs["soot"]
no2_normalized = no2 - tft.mean(no2)
so2_normalized = so2 - tft.mean(so2)
pm10_normalized = tft.scale_to_0_1(pm10)
soot_normalized = tft.scale_by_min_max(soot)
return {
"no2_normalized": no2_normalized,
"so2_normalized": so2_normalized,
"pm10_normalized": pm10_normalized,
"sott_normalized": soot_normalized
}
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 = {}
# The input float values for the image encoding are in the range [-0.5, 0.5].
# So scale_by_min_max is a identity operation, since the range is preserved.
outputs[transformed_name(IMAGE_KEY)] = (tft.scale_by_min_max(
inputs[IMAGE_KEY], -0.5, 0.5))
# TODO(b/157064428): Support label transformation for Keras.
# Do not apply label transformation as it will result in wrong evaluation.
outputs[transformed_name(LABEL_KEY)] = inputs[LABEL_KEY]
return outputs
def preprocessing_fn(inputs):
# Define each column manually
no2 = inputs['no2']
pm10 = inputs['pm10']
so2 = inputs['so2']
soot = inputs['soot']
# Normalize columns in preprocessing
no2_normalized = no2 - tft.mean(no2)
so2_normalized = so2 - tft.mean(so2)
pm10_normalized = tft.scale_to_0_1(pm10)
soot_normalized = tft.scale_by_min_max(soot)
# Return the normalized columns in a dictionary
return {
"no2_normalized": no2_normalized,
"so2_normalized": so2_normalized,
"pm10_normalized": pm10_normalized,
"soot_normalized": soot_normalized
}