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)

Answer 1

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})