I tried searching for the documentation online but I can't find anything that gives me an answer. What does .numpy() function do? The example code given is:
y_true = []
for X_batch, y_batch in mnist_test:
y_true.append(y_batch.numpy()[0].tolist())
Both in Pytorch and Tensorflow, the .numpy() method is pretty much straightforward. It converts a tensor object into an numpy.ndarray object. This implicitly means that the converted tensor will be now processed on the CPU.
Ever getting a problem understanding some PyTorch function you may ask help().
import torch
t = torch.tensor([1,2,3])
help(t.numpy)
Out:
Help on built-in function numpy:
numpy(...) method of torch.Tensor instance
numpy() -> numpy.ndarray
Returns :attr:`self` tensor as a NumPy :class:`ndarray`. This tensor and the
returned :class:`ndarray` share the same underlying storage. Changes to
:attr:`self` tensor will be reflected in the :class:`ndarray` and vice versa.
This numpy() function is the converter form torch.Tensor to numpy array.
If we look at this code below, we see a simple example where the .numpy() convert Tensors to numpy arrays automatically.
import numpy as np
ndarray = np.ones([3, 3])
print("TensorFlow operations convert numpy arrays to Tensors automatically")
tensor = tf.multiply(ndarray, 42)
print(tensor)
print("And NumPy operations convert Tensors to numpy arrays automatically")
print(np.add(tensor, 1))
print("The .numpy() method explicitly converts a Tensor to a numpy array")
print(tensor.numpy())
In the 2nd last line of code, we see that the tensorflow officials declared it as the converter of Tensor to a numpy array.
You may check it out here
Related
Can I use numpy arrays when using pytorch?
I am converting a code from tensorflow to pytorch and the code uses numpy arrays during the computation. Can I keep my inputs as numpy arrays during the computation or do I have to convert them to torch tensors?
If that array is being passed to a Pytorch model with pytorch nn layers, then it MUST be a <torch.tensor> and NOT a numpy array.
Depending on the Pytorch layer, the tensor has to be in a specific shape like for nn.Conv2d layers you must have a 4d torch tensor and for nn.Linear you must have a 2d torch tensor.
This is among many reasons, it cannot be a numpy array.
Sarthak
I want to get the scalar value of a function parameter like following code does:
import tensorflow as tf
#tf.function
def test(key_value):
tf.print(key_value.numpy())
a = tf.constant(0)
test(a)
But there is no numpy function when running in autograph.
numpy is only available outside of tf.function, where Tensors have actual values. Within tf.function, you have access to a restricted API. As long as you pass the tensor to a TensorFlow op, you don't need to call numpy:
import tensorflow as tf
#tf.function
def test(key_value):
tf.print(key_value)
a = tf.constant(0)
test(a)
Have a look at this guide for more info.
I have a numpy array list something like the follows:
a=np.array([tf.convert_to_tensor(1),tf.convert_to_tensor(2)])
I want to convert this list into a tensor.
My real list is not like the constant example but some complex tensor, so does anyone know how to do this?
I assume all of the tensors have the same shape. Then you can just call tf.stack:
>>> print(tf.stack([tf.convert_to_tensor(1), tf.convert_to_tensor(2)]))
Tensor("stack:0", shape=(2,), dtype=int32)
Note that it accepts the list, not numpy array.
It's possible to read dense data by this way:
# tf - tensorflow, np - numpy, sess - session
m = np.ones((2, 3))
placeholder = tf.placeholder(tf.int32, shape=m.shape)
sess.run(placeholder, feed_dict={placeholder: m})
How to read scipy sparse matrix (for example scipy.sparse.csr_matrix) into tf.placeholder or maybe tf.sparse_placeholder ?
I think that currently TF does not have a good way to read from sparse data. If you do not want to convert a your sparse matrix into a dense one, you can try to construct a sparse tensor..
Here is what official tutorial tells you:
SparseTensors don't play well with queues. If you use SparseTensors
you have to decode the string records using tf.parse_example after
batching (instead of using tf.parse_single_example before batching).
To feed SciPy sparse matrix to TF placeholder
Option 1: you need to use tf.sparse_placeholder. In Use coo_matrix in TensorFlow shows the way to feed data to a sparse_placeholder
Option 2: you need to convert sparse matrix to NumPy dense matrix and feed to tf.place_holder (of course, this way is impossible when the converted dense matrix is out of memory)
I have a placeholder variable that expects a batch of input images:
input_placeholder = tf.placeholder(tf.float32, [None] + image_shape, name='input_images')
Now I have 2 sources for the input data:
1) a tensor and
2) some numpy data.
For the numpy input data, I know how to feed data to the placeholder variable:
sess = tf.Session()
mLoss, = sess.run([loss], feed_dict = {input_placeholder: myNumpyData})
How can I feed a tensor to that placeholder variable?
mLoss, = sess.run([loss], feed_dict = {input_placeholder: myInputTensor})
gives me an error:
TypeError: The value of a feed cannot be a tf.Tensor object. Acceptable feed values include Python scalars, strings, lists, or numpy ndarrays.
I don't want to convert the tensor into a numpy array using .eval(), since that would slow my program down, is there any other way?
This has been discussed on GitHub in 2016, and please check here. Here is the key point by concretevitamin:
One key thing to note is that Tensor is simply a symbolic object. The values of your feed_dict are the actual values, e.g. a Numpy ndarry.
The tensor as a symbolic object is flowing in the graph while the actual values are outside of it, then we can only pass the actual values into the graph and the symbolic object can not exist outside the graph.
You can use feed_dict to feed data into non-placeholders. So, first, wire up your dataflow graph directly to your myInputTensor tensor data source (i.e. don't use a placeholder). Then when you want to run with your numpy data you can effectively mask myImportTensor with myNumpyData, like this:
mLoss, = sess.run([loss], feed_dict={myImportTensor: myNumpyData})
[I'm still trying to figure out how to do this with multiple tensor data sources however.]
One way of solving the problem is to actually remove the Placeholder tensor and replace it by your "myInputTensor".
You will use the myInputTensor as the source for the other operations in the graph and when you want to infer the graph with your np array as input data, you will feed a value to this tensor directly.
Here is a quick example:
import tensorflow as tf
import numpy as np
# Input Tensor
myInputTensor = tf.ones(dtype=tf.float32, shape=1) # In your case, this would be the results of some ops
output = myInputTensor * 5.0
with tf.Session() as sess:
print(sess.run(output)) # == 5.0, using the Tensor value
myNumpyData = np.zeros(1)
print(sess.run(output, {myInputTensor: myNumpyData}) # == 0.0 * 5.0 = 0.0, using the np value
This works for me in latest version...maybe you have older version of TF?
a = tf.Variable(1)
sess.run(2*a, feed_dict={a:5}) # prints 10