Skip to main content
Join the official 2019 Python Developers SurveyStart the survey!

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


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for shinnosuke-gpu, version 0.6.9
Filename, size File type Python version Upload date Hashes
Filename, size shinnosuke_gpu-0.6.9-py3-none-any.whl (33.1 kB) File type Wheel Python version py3 Upload date Hashes View hashes
Filename, size shinnosuke-gpu-0.6.9.tar.gz (26.9 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page