Converting from Pandas dataframe to TensorFlow tensor object

Question

I\'m still new to Python, Machine Learning and TensorFlow, but doing my best to jump right in head-first. I could use some help though.

My data is currently in a Pan

Answer 1

The following works easily based on numpy array input data:

import tensorflow as tf
import numpy as np
a = np.array([1,2,3])
with tf.Session() as sess:
    tf.global_variables_initializer().run()

    dataVar = tf.constant(a)
    print(dataVar.eval())

-> [1 2 3]

Don't forget to start the session and run() or eval() your tensor object to see its content; otherwise it will just give you its generic description.

I suspect that since your data is in the DataFrame rather than a simply array, you need to experiment with the shape parameter of tf.constant(), which you are currently not specifying, in order to help it understand the dimensionality of the DataFrame and deal with its indices, etc.?