XShinnosuke is a high-level neural network framework which supports for both Dynamic Graph and Static Graph, and has almost the same API to Keras and Pytorch with slightly differences. It was written by Python only, and dedicated to realize experimentations quickly.
Here are some features of Shinnosuke:
- Based on Cupy(GPU version)/Numpy and native to Python.
- Without any other 3rd-party deep learning library.
- Keras and Pytorch style API, easy to start.
- Supports commonly used layers such as: Dense, Conv2D, MaxPooling2D, LSTM, SimpleRNN, etc, and commonly used function: conv2d, max_pool2d, relu, etc.
- Sequential model (for most sequence network combinations ) and Functional model (for resnet, etc) are implemented. Meanwhile, XShinnosuke also supports for design your own network by Module.
- Training and inference supports for both dynamic graph and static graph.
- Autograd is supported .
XShinnosuke is compatible with: Python 3.x (3.7 is recommended)
Here are some demos~
The core networks of XShinnosuke is a model, which provide a way to combine layers. There are two model types: Sequential (a linear stack of layers) and Functional (build a graph for layers).
For Sequential model:
from xshinnosuke.models import Sequential
model = Sequential()Using .add() to connect layers:
from xshinnosuke.layers import Dense
model.add(Dense(out_features=500, activation='relu', input_shape=(784, ))) # must be specify input_shape if current layer is the first layer of model
model.add(Dense(out_features=10))Once you have constructed your model, you should configure it with .compile() before training or inference:
model.compile(loss='sparse_crossentropy', optimizer='sgd')If your labels are one-hot encoded vectors/matrix, you shall specify loss as sparse_crossentropy, otherwise use crossentropy instead.
Use print(model) to see details of model:
***************************************************************************
Layer(type) Output Shape Param Connected to
###########################################################################
dense0 (Dense) (None, 500) 392500
---------------------------------------------------------------------------
dense1 (Dense) (None, 10) 5010 dense0
---------------------------------------------------------------------------
***************************************************************************
Total params: 397510
Trainable params: 397510
Non-trainable params: 0Start training your network by fit():
# trainX and trainy are Cupy ndarray
history = model.fit(trainX, trainy, batch_size=128, epochs=5)Once completing training your model, you can save or load your model by save() / load(), respectively.
model.save(save_path)
model.load(model_path)Evaluate your model performance by evaluate():
# testX and testy are Cupy/Numpy ndarray
accuracy, loss = model.evaluate(testX, testy, batch_size=128)Inference through predict():
predict = model.predict(testX)For Functional model:
Combine your layers by functional API:
from xshinnosuke.models import Model
from xshinnosuke.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense
X_input = Input(input_shape = (1, 28, 28)) # (channels, height, width)
X = Conv2D(8, (2, 2), activation='relu')(X_input)
X = MaxPooling2D((2, 2))(X)
X = Flatten()(X)
X = Dense(10)(X)
model = Model(inputs=X_input, outputs=X)
model.compile(optimizer='sgd', loss='sparse_cross_entropy')
model.fit(trainX, trainy, batch_size=256, epochs=80)Pass inputs and outputs layer to Model(), then compile and fit model as Sequentialmodel.
First design your own network, make sure your network is inherited from Module and override the __init__() and forward() function:
from xshinnosuke.models import Module
from xshinnosuke.layers import Conv2D, ReLU, Flatten, Dense
import xshinnosuke.nn.functional as F
class MyNet(Module):
def __init__(self):
super().__init__()
self.conv1 = Conv2D(out_channels=8, kernel_size=3)
self.relu = ReLU(inplace=True)
self.flat = Flatten()
self.fc = Dense(10)
def forward(self, x, *args):
x = self.conv1(x)
x = self.relu(x)
x = F.max_pool2d(x, kernel_size=2)
x = self.flat(x)
x = self.fc(x)
return xThen manually set the training/ testing flow:
from xshinnosuke.nn.optimizers import SGD
from xshinnosuke.utils import DataSet, DataLoader
from xshinnosuke.nn import CrossEntropy
import cupy as np
# random generate data
X = np.random.randn(100, 3, 12, 12)
Y = np.random.randint(0, 10, (100, ))
# generate training dataloader
train_dataset = DataSet(X, Y)
train_loader = DataLoader(dataset=train_dataset, batch_size=10, shuffle=True)
# initialize net
net = MyNet()
# specify optimizer and critetion
optimizer = SGD(net.parameters())
critetion = CrossEntropy()
# start training
EPOCH = 5
for epoch in range(EPOCH):
for x, y in train_loader:
optimizer.zero_grad()
out = net(x)
loss = critetion(out, y)
loss.backward()
optimizer.step()
train_acc, train_loss = critetion.metric(out, y)
print(f'epoch -> {epoch}, train_acc: {train_acc}, train_loss: {train_loss}')Building an image classification model, a question answering system or any other model is just as convenient and fast~
Xshinnosuke's autograd supports for basic operators such as: +, -, *, \, **, etc and some common functions: matmul(), mean(), sum(), log(), view(), etc .
from xshinnosuke.nn import Variable
a = Variable(5)
b = Variable(10)
c = Variable(3)
x = (a + b) * c
y = x ** 2
print('x: ', x) # x: Variable(45.0, requires_grad=True, grad_fn=<MultiplyBackward>)
print('y: ', y) # y: Variable(2025.0, requires_grad=True, grad_fn=<PowBackward>)
x.retain_grad()
y.backward()
print('x grad:', x.grad) # x grad: 90.0
print('c grad:', c.grad) # c grad: 1350.0
print('b grad:', b.grad) # b grad: 270.0
print('a grad:', a.grad) # a grad: 270.0Here are examples of autograd.
Before installing XShinnosuke, please install the following dependencies:
- Numpy
- Cupy = 6.0.0 (Optional, if installed, Xshinnosuke will use this to speed up)
notice that cupy requires **Microsoft Visual C++ 14.0**Then you can install XShinnosuke by using pip:
$ pip install xshinnosuke
- Dense
- Flatten
- Conv2D
- MaxPooling2D
- AvgPooling2D
- ChannelMaxPooling
- ChannelAvgPooling
- Activation
- Input
- Dropout
- Batch Normalization
- Layer Normalization
- Group Normalization
- TimeDistributed
- SimpleRNN
- LSTM
- Embedding
- ZeroPadding2D
- Add
- Multiply
- Matmul
- Log
- Negative
- Exp
- Sum
- Abs
- Mean
- Pow
- Variable
- Constant
- SGD
- Momentum
- RMSprop
- AdaGrad
- AdaDelta
- Adam
Waiting for implemented more
- MeanSquaredError
- MeanAbsoluteError
- BinaryCrossEntropy
- SparseCrossEntropy
- CrossEntropy
- Relu
- Linear
- Sigmoid
- Tanh
- Softmax
- Zeros
- Ones
- Uniform
- LecunUniform
- GlorotUniform
- HeUniform
- Normal
- LecunNormal
- GlorotNormal
- HeNormal
- Orthogonal
waiting for implement.
- to_categorical (convert inputs to one-hot vector/matrix)
- pad_sequences (pad sequences to the same length)
- Email: [email protected]