Using sparse matrices for Tensorflow's linear models
I've been recently working with some Tensorflow models, namely Large-scale Linear Models. My input consists of the output of:
TfidfVectorizer(stop_words="english",strip_accents="ascii",analyzer="word,max_features=max_features, ngram_range=ngram_range)
hence is a sparse (e.g., coo) matrix. The classes are a single (dense) array. I was wondering, how one would use data in this format with simple linear models, so that something like:
e = tf.estimator.LinearClassifier(
feature_columns=[all_columns],
model_dir=YOUR_MODEL_DIRECTORY)
e.train(input_fn=input_fn_train, steps=200)
would be possible?
The only alternative, I currently see, is that the linear model is coded up in low level API, yet this would loose some flexibility in the initial testing of the many available models by tensorflow.
python tensorflow
asked Nov 11 at 8:41
sdgaw erzswer
702620
add a comment |
I've been recently working with some Tensorflow models, namely Large-scale Linear Models. My input consists of the output of:
TfidfVectorizer(stop_words="english",strip_accents="ascii",analyzer="word,max_features=max_features, ngram_range=ngram_range)
hence is a sparse (e.g., coo) matrix. The classes are a single (dense) array. I was wondering, how one would use data in this format with simple linear models, so that something like:
e = tf.estimator.LinearClassifier(
feature_columns=[all_columns],
model_dir=YOUR_MODEL_DIRECTORY)
e.train(input_fn=input_fn_train, steps=200)
would be possible?
The only alternative, I currently see, is that the linear model is coded up in low level API, yet this would loose some flexibility in the initial testing of the many available models by tensorflow.
python tensorflow
asked Nov 11 at 8:41
sdgaw erzswer
702620
add a comment |
I've been recently working with some Tensorflow models, namely Large-scale Linear Models. My input consists of the output of:
TfidfVectorizer(stop_words="english",strip_accents="ascii",analyzer="word,max_features=max_features, ngram_range=ngram_range)
hence is a sparse (e.g., coo) matrix. The classes are a single (dense) array. I was wondering, how one would use data in this format with simple linear models, so that something like:
e = tf.estimator.LinearClassifier(
feature_columns=[all_columns],
model_dir=YOUR_MODEL_DIRECTORY)
e.train(input_fn=input_fn_train, steps=200)
would be possible?
The only alternative, I currently see, is that the linear model is coded up in low level API, yet this would loose some flexibility in the initial testing of the many available models by tensorflow.
python tensorflow
asked Nov 11 at 8:41
sdgaw erzswer
702620
I've been recently working with some Tensorflow models, namely Large-scale Linear Models. My input consists of the output of:
TfidfVectorizer(stop_words="english",strip_accents="ascii",analyzer="word,max_features=max_features, ngram_range=ngram_range)
hence is a sparse (e.g., coo) matrix. The classes are a single (dense) array. I was wondering, how one would use data in this format with simple linear models, so that something like:
e = tf.estimator.LinearClassifier(
feature_columns=[all_columns],
model_dir=YOUR_MODEL_DIRECTORY)
e.train(input_fn=input_fn_train, steps=200)
would be possible?
The only alternative, I currently see, is that the linear model is coded up in low level API, yet this would loose some flexibility in the initial testing of the many available models by tensorflow.
python tensorflow
python tensorflow
asked Nov 11 at 8:41
sdgaw erzswer
702620
asked Nov 11 at 8:41
sdgaw erzswer
702620
asked Nov 11 at 8:41
sdgaw erzswer
702620
asked Nov 11 at 8:41
sdgaw erzswer
702620
asked Nov 11 at 8:41
sdgaw erzswer
702620
702620
add a comment |
add a comment |
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