How to convert numpy arrays to standard TensorFlow format?

Question

I have two numpy arrays:

• One that contains captcha images
• Another that contains the corresponding labels (in one-hot vector format)

I want to load these into TensorFlow so I can classify them using a neural network. How can this be done?

What shape do the numpy arrays need to have?

Additional Info - My images are 60 (height) by 160 (width) pixels each and each of them have 5 alphanumeric characters. Here is a sample image:

Each label is a 5 by 62 array.

Answer 1

You can use tf.convert_to_tensor():

import tensorflow as tf import numpy as np  data = [[1,2,3],[4,5,6]] data_np = np.asarray(data, np.float32)  data_tf = tf.convert_to_tensor(data_np, np.float32)  sess = tf.InteractiveSession()   print(data_tf.eval())  sess.close() 

Here's a link to the documentation for this method:

https://www.tensorflow.org/api_docs/python/tf/convert_to_tensor