基于Theano的深度学习库:Keras

jopen 4年前

Keras是一个简约,高度模块化的神经网络库。采用Python / Theano开发。

使用Keras如果你需要一个深度学习库:

  • 可以很容易和快速实现原型(通过总模块化,极简主义,和可扩展性)
  • 同时支持卷积网络(vision)和复发性的网络(序列数据)。以及两者的组合。
  • 无缝地运行在CPU和GPU上。

Guiding principles

  • Modularity. A model is understood as a sequence of standalone, fully-configurable modules that can be plugged together with as little restrictions as possible. In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions and dropout are all standalone modules that you can combine to create new models.

  • Minimalism. Each module should be kept short and simple (<100 lines of code). Every piece of code should be transparent upon first reading. No black magic: it hurts iteration speed and ability to innovate.

  • Easy extensibility. A new feature (a new module, per the above definition, or a new way to combine modules together) are dead simple to add (as new classes/functions), and existing modules provide ample examples.

  • Work with Python. No separate models configuration files in a declarative format (like in Caffe or PyLearn2). Models are described in Python code, which is compact, easier to debug, benefits from syntax highlighting, and most of all, allows for ease of extensibility. See for yourself with the examples below.

示例

Multilayer Perceptron (MLP):

from keras.models import Sequential  from keras.layers.core import Dense, Dropout, Activation  from keras.optimizers import SGD    model = Sequential()  model.add(Dense(20, 64, init='uniform'))  model.add(Activation('tanh'))  model.add(Dropout(0.5))  model.add(Dense(64, 64, init='uniform'))  model.add(Activation('tanh'))  model.add(Dropout(0.5))  model.add(Dense(64, 1, init='uniform'))  model.add(Activation('softmax'))    sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)  model.compile(loss='mean_squared_error', optimizer=sgd)    model.fit(X_train, y_train, nb_epoch=20, batch_size=16)  score = model.evaluate(X_test, y_test, batch_size=16)

Alternative implementation of MLP:

model = Sequential()  model.add(Dense(20, 64, init='uniform', activation='tanh'))  model.add(Dropout(0.5))  model.add(Dense(64, 64, init='uniform', activation='tanh'))  model.add(Dropout(0.5))  model.add(Dense(64, 1, init='uniform', activation='softmax')    sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)  model.compile(loss='mean_squared_error', optimizer=sgd)

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