How to convert numpy arrays to standard TensorFlow format?

You can use tf.convert_to_tensor():

import tensorflow as tfimport numpy as npdata = [[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

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 npimport tensorflow as tfrandom_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.

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})