TensorFlow is an open-source Python library designed by Google to develop Machine Learning models and deep-learning, neural networks.
Create a Numpy array from a torch.tensor
A Pytorch Tensor is basically the same as a NumPy array. This means it does not know anything about deep learning or computational graphs or gradients and is just a generic n-dimensional array to be used for arbitrary numeric computation.
Example 1:
To create a Numpy array from Tensor, Tensor is converted to a tensor.numpy() first.
Python3
# pip install torchimport torchtensor = torch.tensor([1, 2, 3, 4, 5])np_a = tensor.numpy() |
Output:
array([1, 2, 3, 4, 5])
Example 2:
To create a Numpy array from Tensor, Tensor is converted to a tensor.detach.numpy() first.
Python3
# pip install torchimport torchtensor = torch.tensor([1, 2, 3, 4, 5])np_b = tensor.detach().numpy() |
Output:
array([1, 2, 3, 4, 5])
Example 3:
To create a Numpy array from Tensor, Tensor is converted to a tensor.detach().cpu().numpy() first.
Python3
# pip install torchimport torchtensor = torch.tensor([1, 2, 3, 4, 5])np_c = tensor.detach().cpu().numpy() |
Output:
array([1, 2, 3, 4, 5])
Create a numpy ndarray from a Tensorflow.tensor
A torch in TensorFlow, as the name indicates, is a framework to define and run computations involving tensors. A tensor is a generalization of vectors and matrices to potentially higher dimensions.
Example 1:
To create a Numpy array from Tensor, Tensor is converted to a proto tensor first.
Python3
# importing the libraryimport tensorflow as tf# Initializing Inputvalue = tf.constant([1, 15, 10], dtype = tf.float64)# Printing the Inputprint("Value: ", value)# Converting Tensor to TensorProtoproto = tf.make_tensor_proto(value)# Generating numpy arrayres = tf.make_ndarray(proto)# Printing the resulting numpy arrayprint("Result: ", res) |
Output:
Value: tf.Tensor([ 1. 15. 10.], shape=(3, ), dtype=float64) Result: [ 1. 15. 10.]
Example 2:
This method uses a Tensor with shape (2, 2) so the shape of the resulting array will be (2, 2).
Python3
# importing the libraryimport tensorflow as tf# Initializing Inputvalue = tf.constant([[1, 2], [3, 4]], dtype = tf.float64)# Printing the Inputprint("Value: ", value)# Converting Tensor to TensorProtoproto = tf.make_tensor_proto(value)# Generating numpy arrayres = tf.make_ndarray(proto)# Printing the resulting numpy arrayprint("Result: ", res) |
Output:
Value: tf.Tensor( [[1. 2.] [3. 4.]], shape=(2, 2), dtype=float64) Result: [[1. 2.] [3. 4.]]
