How to convert a tensor into a numpy array when using Tensorflow with Python bindings?
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python
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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!
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
If you see there is a method _numpy(), e.g for an EagerTensor simply call the above method and you will get an ndarray.
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.
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
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'>
You need to:
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
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)
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!
To convert back from tensor to numpy array you can simply run .eval()
on the transformed tensor.
Source: Stackoverflow.com