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

User · Answer

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

User · Answer

You can use tf pack  tf stack in TensorFlow 1 0 0  method for this purpose  Here is how to pack a random image of type numpy ndarray into a Tensor   import numpy as np import tensorflow as tf random image   np random randint 0 256   300 400 3   random image tensor   tf pack random image  tf InteractiveSession   evaluated tensor   random image tensor eval     UPDATE  to convert a Python object to a Tensor you can use tf convert to tensor function

User · Answer

You can use placeholders and feed dict   Suppose we have numpy arrays like these   trX   np linspace -1  1  101   trY   2   trX   np random randn  trX shape    0 33    You can declare two placeholders   X   tf placeholder  float    Y   tf placeholder  float     Then  use these placeholders  X  and Y  in your model  cost  etc       model   tf mul X  w      Y              Finally  when you run the model cost  feed the numpy arrays using feed dict   with tf Session   as sess            sess run model  feed dict  X  trY  Y  trY

[python] How to convert numpy arrays to standard TensorFlow format?

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