Coming soon: We’re working on a brand new, revamped Community experience. Want to receive updates? Sign up now!
To use the tensorflow in the custom python model, the code needs to provide the methods fit() and predict(), like SK-Learn.
The code below is the code that I think I need to use.
import tensorflow as tf
# Specify that all features have real-value data
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]
# Build 3 layer DNN with 10, 20, 10 units respectively.
classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
hidden_units=[10, 20, 10],
# Define the training inputs
x = tf.constant(training_set.data)
y = tf.constant(training_set.target)
return x, y
# Fit model.
What I think the problem is, I need to change the input into tf.constant and send them to the fit method.
But I have no idea how the data is retrieved or the variable name that is used in the fit method.
Does anyone have a sample code, or know the walk away round?
I am new to python, ML, DDS everything so please help.