shinnosuke-gpu 0.7.1

A keras-like API deep learning framework,realized by cupy

#Shinnosuke-GPU : Deep learning framework ##Descriptions

1. Based on Cupy(GPU version)

2. Completely realized by Python only

3. Keras-like API

4. For deep learning studying

##Features

1. Native to Python

2. Keras-like API

3. Easy to get start

4. Commonly used models are provided: Dense, Conv2D, MaxPooling2D, LSTM, SimpleRNN, etc

5. Several basic networks Examples

6. Sequential model and Functional model are implemented

7. Autograd is supported

##Installation Using pip:

$ pip install shinnosuke-gpu

##Supports

Two model types:

1.Sequential

from shinnosuke.models import Sequential
from shinnosuke.layers.FC import Dense

m=Sequential()

m.add(Dense(500,activation='relu',n_in=784))

m.add(Dense(10,activation='softmax'))

m.compile(optimizer='sgd',loss='sparse_categorical_crossentropy',learning_rate=0.1)

m.fit(trainX,trainy,batch_size=512,epochs=1,validation_ratio=0.)

2.Model

from shinnosuke.models import Model
from shinnosuke.layers.FC import Dense
from shinnosuke.layers.Base import Input

X_input=Input(shape=(None,784))

X=Dense(500,activation='relu')(X_input)

X=Dense(10,activation='softmax')(X)

model=Model(inputs=X_input,outputs=X)

model.compile(optimizer='sgd',loss='sparse_categorical_crossentropy',learning_rate=0.1)

model.fit(trainX,trainy,batch_size=512,epochs=1,validation_ratio=0.)

Two basic class:

- Layer:

• Dense

• Conv2D

• MaxPooling2D

• MeanPooling2D

• Activation

• Input

• Dropout

• BatchNormalization

• TimeDistributed

• SimpleRNN

• LSTM

• GRU (waiting for implemented)

• ZeroPadding2D

• Operations( includes Add, Minus, Multiply, Matmul, and so on basic operations for Layer and Node)

####- Node:

• Variable
• Constant

###Optimizers

• StochasticGradientDescent

• Momentum

• RMSprop

• AdaGrad

• AdaDelta

• Adam

Waiting for implemented more

###Objectives

• MeanSquaredError

• MeanAbsoluteError

• BinaryCrossEntropy

• SparseCategoricalCrossEntropy

• CategoricalCrossEntropy

###Activations

• Relu

• Linear

• Sigmoid

• Tanh

• Softmax

###Initializations

• Zeros

• Ones

• Uniform

• LecunUniform

• GlorotUniform

• HeUniform

• Normal

• LecunNormal

• GlorotNormal

• HeNormal

• Orthogonal

###Regularizes waiting for implement.

###Utils

• get_batches (generate mini-batch)

• to_categorical (convert inputs to one-hot vector/matrix)

• concatenate (concatenate Nodes that have the same shape in specify axis)

• pad_sequences (pad sequences to the same length)