Closed borisRa closed 7 years ago
def my_model(X, y):
# X - is [batch_size, n_features], where features split into n_cat + n_cont
Xcat = tf.cast(tf.slice(X, [0, 0], [X.get_shape()[0], n_cat]), np.int64)
Xcont = tf.slice(X, [0, n_cat], X.get_shape())
This way Xcat can be passed into categorical_variable
and then combined with continues features.
Stay tuned for a better way to do it!
estimator.predict_proba
which will return probabilities per class instead of predicted class.Let me know if this responds your questions!
Thanks for the quick response !
Is there a solution for this problem in Skflow ?
Thanks again ! Boris
FeatureColumns are the way to do this now. Please use recent version for Tensorflow to do this. Thanks!
Hi,
I need assistance in three issues :
Thanks, Boris