Introduction of version 2 ¶
In ReNom version 2, automatic differentiation feature have been added to version 1.0. Users are able to build neural network model with a lot of flexibility.
Concept of version 2 ¶
ReNom 2 is focusing on its usability first, as the same as previous version.
The syntax of ReNom version 2 is aligned to NumPy, so that users can compute differential value adding a tiny script change to the formula written in NumPy style.
By reducing user interfaces, ReNom became a NumPy user friendly library package while enables users to build a neural network model more flexibly.
Following is a comparison of NumPy and ReNom coding style.
 ● Numpy

>>> import numpy as np >>> a, b = np.arange(2), np.arange(2) >>> x = np.arange(2) >>> z = np.sum(a*x + b) >>> print(z) 2.0
 ● ReNom

>>> import numpy as np >>> import renom as rm >>> a, b = np.arange(2), np.arange(2) >>> x = rm.Variable(np.arange(2)) >>> z = rm.sum(a*x + b) >>> print(z) 2.0 >>> dx = z.grad().get(x) >>> print(dx) [0, 1]
Like this, ReNom users can compute gradient by changing only a few NumPy code.
Auto Differentiation ¶
In ReNom, users can create calculation graph with a simple step. First, defining differentiation target variable as Variable, then scripting formula as the same syntax as NumPy.
>>> import renom as rm >>> a, b = 2, 3 >>> x = rm.Variable(1) >>> z = a*x + b >>> gradient = z.grad().get(x) >>> print(gradient) 2.0
Variable class is inherited ndarray class of NumPy[ref], users can create/build/establish calculation graph similar way to NumPy.
Sequential Model ¶
As the same as previous ReNom versions, users can define the model, simply piling the layers up.
import renom as rm model = rm.Sequential([ rm.Dense(100), rm.Relu(), rm.Dense(10) ])
In ReNom, defined class names are capitalized. As mentioned, Sequential model can be instantiated by providing a layer object list.
Functional Model ¶
In ReNom 2, some layers previously regarded as objects such as Activation function layer, fully connected layer are able to be handled functionally.
import renom as rm class NN(rm.Model): def __init__(self): self._layer1 = rm.Dense(100) self._layer2 = rm.Dense(10) def forward(self, x): h = rm.relu(self._layer1(x)) z = rm._layer2(h) return z model = NN()
In ReNom, defined function names are small lettered. As above, defined functions are able to handle layer objects.
Computation with GPU ¶
In order to use GPU, users need to install CudaToolkit and cuDNN. To switch GPU on/off, simply call following function.
import renom as rm rm.set_cuda_active(True)