To convert back from tensor to numpy array you can simply run .eval() on the transformed tensor.

You need to:

1. encode the image tensor in some format (jpeg, png) to binary tensor
2. evaluate (run) the binary tensor in a session
3. turn the binary to stream
4. feed to PIL image
5. (optional) displaythe image with matplotlib

Code:

import tensorflow as tf
import matplotlib.pyplot as plt
import PIL

...

image_tensor = <your decoded image tensor>
jpeg_bin_tensor = tf.image.encode_jpeg(image_tensor)

with tf.Session() as sess:
    # display encoded back to image data
    jpeg_bin = sess.run(jpeg_bin_tensor)
    jpeg_str = StringIO.StringIO(jpeg_bin)
    jpeg_image = PIL.Image.open(jpeg_str)
    plt.imshow(jpeg_image)

This worked for me. You can try it in a ipython notebook. Just don’t forget to add the following line:

%matplotlib inline

Any tensor returned by Session.run or eval is a NumPy array.

>>> print(type(tf.Session().run(tf.constant([1,2,3]))))
<class 'numpy.ndarray'>

Or:

>>> sess = tf.InteractiveSession()
>>> print(type(tf.constant([1,2,3]).eval()))
<class 'numpy.ndarray'>

Or, equivalently:

>>> sess = tf.Session()
>>> with sess.as_default():
>>>    print(type(tf.constant([1,2,3]).eval()))
<class 'numpy.ndarray'>

EDIT: Not any tensor returned by Session.run or eval() is a NumPy array. Sparse Tensors for example are returned as SparseTensorValue:

>>> print(type(tf.Session().run(tf.SparseTensor([[0, 0]],[1],[1,2]))))
<class 'tensorflow.python.framework.sparse_tensor.SparseTensorValue'>

Maybe you can try，this method:

import tensorflow as tf
W1 = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
array = W1.eval(sess)
print (array)

I have faced and solved the tensor->ndarray conversion in the specific case of tensors representing (adversarial) images, obtained with cleverhans library/tutorials.

I think that my question/answer (here) may be an helpful example also for other cases.

I’m new with TensorFlow, mine is an empirical conclusion:

It seems that tensor.eval() method may need, in order to succeed, also the value for input placeholders.
Tensor may work like a function that needs its input values (provided into feed_dict) in order to return an output value, e.g.

array_out = tensor.eval(session=sess, feed_dict={x: x_input})

Please note that the placeholder name is x in my case, but I suppose you should find out the right name for the input placeholder.
x_input is a scalar value or array containing input data.

In my case also providing sess was mandatory.

My example also covers the matplotlib image visualization part, but this is OT.

A simple example could be,

    import tensorflow as tf
    import numpy as np
    a=tf.random_normal([2,3],0.0,1.0,dtype=tf.float32)  #sampling from a std normal
    print(type(a))
    #<class 'tensorflow.python.framework.ops.Tensor'>
    tf.InteractiveSession()  # run an interactive session in Tf.

n
now if we want this tensor a to be converted into a numpy array

    a_np=a.eval()
    print(type(a_np))
    #<class 'numpy.ndarray'>

As simple as that!

I was searching for days for this command.

This worked for me outside any session or somthing like this.

# you get an array = your tensor.eval(session=tf.compat.v1.Session())
an_array = a_tensor.eval(session=tf.compat.v1.Session())

https://kite.com/python/answers/how-to-convert-a-tensorflow-tensor-to-a-numpy-array-in-python

You can use keras backend function.

import tensorflow as tf
from tensorflow.python.keras import backend 

sess = backend.get_session()
array = sess.run(< Tensor >)

print(type(array))

<class 'numpy.ndarray'>

I hope it helps!

If you see there is a method _numpy(),
e.g for an EagerTensor simply call the above method and you will get an ndarray.

You can convert a tensor in tensorflow to numpy array in the following ways.

First:
Use np.array(your_tensor)

Second:
Use your_tensor.numpy

Regarding Tensorflow 2.x

The following generally works, since eager execution is activated by default:

import tensorflow as tf

a = tf.constant([[1, 2], [3, 4]])                 
b = tf.add(a, 1)

print(a.numpy())
# [[1 2]
#  [3 4]]

However, since a lot of people seem to be posting the error:

AttributeError: 'Tensor' object has no attribute 'numpy'

I think it is fair to mention that calling tensor.numpy() in graph mode will not work. That is why you are seeing this error. Here is a simple example:

import tensorflow as tf

@tf.function
def add():
  a = tf.constant([[1, 2], [3, 4]])                 
  b = tf.add(a, 1)
  tf.print(a.numpy()) # throws an error!
  return a
add()

A simple explanation can be found here:

Fundamentally, one cannot convert a graph tensor to numpy array because the graph does not execute in Python – so there is no NumPy at graph execution. […]

It is also worth taking a look at the TF docs.

Regarding Keras models with Tensorflow 2.x

This also applies to Keras models, which are wrapped in a tf.function by default. If you really need to run tensor.numpy(), you can set the parameter run_eagerly=True in model.compile(*), but this will influence the performance of your model.

I managed to transform a TensorGPU into an np.array using the following :

np.array(tensor_gpu.as_cpu())

(using the TensorGPU directly would only lead to a single-element array containing the TensorGPU).

TensorFlow 1.x

Folder tf.1, just use the following commands:

a = tf.constant([[1, 2], [3, 4]])                 
b = tf.add(a, 1)
out = tf.multiply(a, b)
out.eval(session=tf.Session())

And the output would be:

# array([[ 2,  6],
#       [12, 20]], dtype=int32)