Ktorch: PyTorch Backend for Keras

Note : As of now, there is no integration with PyTorch. This is simply a template for accomodating both imperative and symbolic programming.

---

Examples

Imperative

from ktorch import *
import numpy as np

a = Variable(np.zeros((2, 3, 4)))
b = Variable(np.ones((3, 4)))
c = a + 0.2 + b * 0.3
print c
'''
<ktorch.graph.tensor.Tensor object at 0x0000000003E82DA0>
'''
print c.value
'''
[[[ 0.5  0.5  0.5  0.5]
  [ 0.5  0.5  0.5  0.5]
  [ 0.5  0.5  0.5  0.5]]

 [[ 0.5  0.5  0.5  0.5]
  [ 0.5  0.5  0.5  0.5]
  [ 0.5  0.5  0.5  0.5]]]
'''

Symbolic

from ktorch import *
import numpy as np

a = Tensor()
b = Tensor()
c = a + 0.2 + b * 0.3
f = Function([a, b], c)

x = np.zeros((2, 3, 4))
y = np.ones((3, 4))

print f([x, y])[0]  # Function returns a list

'''
[[[ 0.5  0.5  0.5  0.5]
  [ 0.5  0.5  0.5  0.5]
  [ 0.5  0.5  0.5  0.5]]

 [[ 0.5  0.5  0.5  0.5]
  [ 0.5  0.5  0.5  0.5]
  [ 0.5  0.5  0.5  0.5]]]
'''

Note that evaluation is greedy. The value of a tensor is computed the instant all the information required to compute it is available. The value will be cached in the .value attribute of the tensor. You can explicitly set the value for an input tensor using the .set_value() method, and all the tensors in the graph depending on that input will be updated in real time.

from ktorch import *
import numpy as np

a = Tensor()
b = Tensor()
c = Tensor()
d = a + b * c
print d.value
'''
AttributeError: 'Tensor' object has no attribute 'value'
'''
#Obviously, because we haven't set values for a, b and c
a.set_value(5)
b.set_value(3)
c.set_value(2)
print d.value
'''
11
'''
'''
Change the value for any of the inputs, and value of d will be automatically updated:
'''
c.set_value(4)
print d.value
'''
17
'''