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

Project description

#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)

Project details


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