Convert a tensor to numpy array in Tensorflow?

Question

How to convert a tensor into a numpy array when using Tensorflow with Python bindings?

Answer 1

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)

Answer 2

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).

Answer 3

TensorFlow 2.x

Eager Execution is enabled by default, so just call .numpy() on the Tensor object.

import tensorflow as tf

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

a.numpy()
# array([[1, 2],
#        [3, 4]], dtype=int32)

b.numpy()
# array([[2, 3],
#        [4, 5]], dtype=int32)

tf.multiply(a, b).numpy()
# array([[ 2,  6],
#        [12, 20]], dtype=int32)

See NumPy Compatibility for more. It is worth noting (from the docs),

Numpy array may share a memory with the Tensor object. Any changes to one may be reflected in the other.

Bold emphasis mine. A copy may or may not be returned, and this is an implementation detail based on whether the data is in CPU or GPU (in the latter case, a copy has to be made from GPU to host memory).

But why am I getting the AttributeError: 'Tensor' object has no attribute 'numpy'?.
A lot of folks have commented about this issue, there are a couple of possible reasons:

• TF 2.0 is not correctly installed (in which case, try re-installing), or
• TF 2.0 is installed, but eager execution is disabled for some reason. In such cases, call tf.compat.v1.enable_eager_execution() to enable it, or see below.

---

If Eager Execution is disabled, you can build a graph and then run it through tf.compat.v1.Session:

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

out.eval(session=tf.compat.v1.Session())    
# array([[ 2,  6],
#        [12, 20]], dtype=int32)

See also TF 2.0 Symbols Map for a mapping of the old API to the new one.

Answer 4

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.

Answer 5

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

Answer 6

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!

Answer 7

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

Answer 8

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!

Answer 9

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.

Answer 10

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'>

Answer 11

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)

Answer 12

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

Answer 13

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