30秒学习Keras

Keras 是开源的深度学习/神经网络框架， 其使用Python语言开发的， 底层引擎可以是Tensorfow, CNTK或者Theano. 其设计初衷是为了可以快速将想法转化为可以实验的代码， 因此其易用性在当前的深度学习框架里是屈指可数的. 正因为这一特质， Keras也非常适合深度学习的初学者作为入门的基础框架。 学习Keras并且编写一个简单的Keras网络仅仅需要30秒!
Keras中最重要的数据结构是 model. model是用来组织神经网络结构的核心对象. 最简单的model叫做 Sequential, 它是一种按顺序构成的网络层(linear stack of layers). 往Sequential中添加网络层只需要调用其add 函数. 网络层构建完成之后调用其compile函数来完成最终配置. 接下来调用fit函数进行训练. 结果用evaluate函数来获取. 下文就是一个最简单的例子, 70余行代码就可以构建一个卷积神经网络， 进行手写数字的识别.

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Dec  7 09:43:49 2017

@author: volvetzhang
"""

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import matplotlib.pyplot as plt

batch_size = 128
num_classes = 10
epochs = 12

(x_train, y_train), (x_test, y_test) = mnist.load_data()

img_rows = x_train.shape[1]
img_cols = x_train.shape[2]

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, x_train.shape[1], x_train.shape[2])
    x_test= x_test.reshape(x_test.shpae[0], 1, x_test.shape[1], x_test.shape[2])
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], x_train.shape[2], 1)
    x_test= x_test.reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2], 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

model = Sequential()
model.add(Conv2D(32, kernel_size=(3,3), activation='relu', input_shape=input_shape))
model.add(Conv2D(64, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.summary()

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))

plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('MNIST Training')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()

score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss: ', score[0])
print('Test accuracy: ', score[1])

Reference

https://keras.io/