How to use tft.compute_and_apply_vocabulary and tft.tfidf correctly?
来源： https://stackoverflow.com/questions/57447937/how-to-use-tft-compute-and-apply-vocabulary-and-tft-tfidf-correctly
tensorflow-transform
Pre Processing Data for Tensorflow: InvalidArgumentError
问题 When I run my tensorflow model I am receiving this error InvalidArgumentError: Field 4 in record 0 is not a valid float: latency [[Node: DecodeCSV = DecodeCSV[OUT_TYPE=[DT_STRING, DT_STRING, DT_STRING, DT_STRING, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_STRING, DT_STRING, DT_STRING, DT_STRING, DT_STRING, DT_STRING, DT_STRING], field_delim=",", na_value="", use_quote_delim=true](arg0, DecodeCSV/record_defaults_0, DecodeCSV/record_defaults_1, DecodeCSV/record_defaults_2, DecodeCSV/record_defaults_3,
Tensorflow transform on beams with flink runner
问题 It may seem stupid but it is my very first post here. Sorry for doing anything wrong. I am currently building a simple ML pipeline with TFX 0.11 (i.e. tfdv-tft-tfserving) and tensorflow 1.11, using python2.7. I currently have a apache-flink cluster and I want to use that for TFX. I know the framework behind TFX is apache-beams 2.8, and it (apache-beams) supports flink with python SDK currently through a portable runner layer. But the problem is how I can code in TFX (tfdv-tft) using apache
Apply TensorFlow Transform to transform/scale features in production
问题 Overview I followed the following guide to write TF Records, where I used tf.Transform to preprocess my features. Now, I would like to deploy my model, for which I need apply this preprocessing function on real live data. My Approach First, suppose I have 2 features: features = ['amount', 'age'] I have the transform_fn from the Apache Beam, residing in working_dir=gs://path-to-transform-fn/ Then I load the transform function using: tf_transform_output = tft.TFTransformOutput(working_dir) I
Tensorflow transform on beams with flink runner
It may seem stupid but it is my very first post here. Sorry for doing anything wrong. I am currently building a simple ML pipeline with TFX 0.11 (i.e. tfdv-tft-tfserving) and tensorflow 1.11, using python2.7. I currently have a apache-flink cluster and I want to use that for TFX. I know the framework behind TFX is apache-beams 2.8, and it (apache-beams) supports flink with python SDK currently through a portable runner layer. But the problem is how I can code in TFX (tfdv-tft) using apache-beams with flink runner through this portable runner concept, as TFX currently seems to only support
How to use tf.contrib.estimator.forward_features
问题 I'm trying to use forward_features to get instance keys for cloudml, but I always get errors that I'm not sure how to fix. The preprocessing section that uses tf.Transform is a modification of https://github.com/GoogleCloudPlatform/cloudml-samples/tree/master/reddit_tft where the instance key is a string and everything else is a bunch of floats. def gzip_reader_fn(): return tf.TFRecordReader(options=tf.python_io.TFRecordOptions( compression_type=tf.python_io.TFRecordCompressionType.GZIP)) def
Pre Processing Data for Tensorflow: InvalidArgumentError
When I run my tensorflow model I am receiving this error InvalidArgumentError: Field 4 in record 0 is not a valid float: latency [[Node: DecodeCSV = DecodeCSV[OUT_TYPE=[DT_STRING, DT_STRING, DT_STRING, DT_STRING, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_STRING, DT_STRING, DT_STRING, DT_STRING, DT_STRING, DT_STRING, DT_STRING], field_delim=",", na_value="", use_quote_delim=true](arg0, DecodeCSV/record_defaults_0, DecodeCSV/record_defaults_1, DecodeCSV/record_defaults_2, DecodeCSV/record_defaults_3, DecodeCSV/record_defaults_4, DecodeCSV/record_defaults_5, DecodeCSV/record_defaults_6, DecodeCSV/record
Data Normalization with tensorflow tf-transform
问题 I'm doing a neural network prediction with my own datasets using Tensorflow. The first I did was a model that works with a small dataset in my computer. After this, I changed the code a little bit in order to use Google Cloud ML-Engine with bigger datasets to realize in ML-Engine the train and the predictions. I am normalizing the features in the panda dataframe but this introduces skew and I get poor prediction results. What I would really like is use the library tf-transform to normalize
Data Normalization with tensorflow tf-transform
I'm doing a neural network prediction with my own datasets using Tensorflow. The first I did was a model that works with a small dataset in my computer. After this, I changed the code a little bit in order to use Google Cloud ML-Engine with bigger datasets to realize in ML-Engine the train and the predictions. I am normalizing the features in the panda dataframe but this introduces skew and I get poor prediction results. What I would really like is use the library tf-transform to normalize the data in the graph. To do this, I would like to create a function preprocessing_fn and use the ' tft