【Keras】30 秒上手 Keras+執行個體對mnist手寫數字進行識别準确率達99%以上

本文我們将學習使用Keras一步一步搭建一個卷積神經網絡。具體來說，我們将使用卷積神經網絡對手寫數字(MNIST資料集)進行識别，并達到99%以上的正确率。

@為什麼選擇Keras呢？

主要是因為簡單友善。更多細節請看：https://keras.io/

@什麼卷積神經網絡？

簡單地說，卷積神經網絡(CNNs)是一種多層神經網絡，它可以有效地減少全連接配接神經網絡參數量太大的問題。

下面就直接進入主題吧！

import keras 
keras.__version__      

‘2.1.5’

from keras.models import Sequential
# 序貫模型
model = Sequential() 
from keras.layers import Dense 
import numpy as np 
import tensorflow as tf      

配置keras模型

# units 矩陣運算輸出的特征次元，input_dim 輸入資料特征次元
model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dense(units=10, activation='softmax')) 
model.add(Dense(units = 1024,activation='tanh')) 
model.output_shape      

(None, 10)

model.output_shape      

(None, 1024)

在完成了模型的建構後, 可以使用 .compile() 來配置學習過程

model.compile(loss='categorical_crossentropy',
              optimizer='sgd',
              metrics=['accuracy'])      

訓練

不需要寫for循環

model.fit(x_train, y_train, epochs=5, batch_size=32)      

一批批交給模型，需要自己寫for循環

model.train_on_batch(x_batch, y_batch)      

模型評估

loss_and_metrics = model.evaluate(x_test, y_test, batch_size=128)      

模型預測

classes = model.predict(x_test, batch_size=128)      

執行個體 mnist 手寫數字進行識别

（外網下載下傳資料可能很慢或者timeouts）

導包、定義變量

import keras
# 資料集
from keras.datasets import mnist
# 序貫模型
from keras.models import Sequential
# Dense：矩陣運算
# Dropout：防止過拟合
# Flatten：reshape(None,-1)
from keras.layers import Dense, Dropout, Flatten
# Conv2D：卷積運算
# MaxPooling2D：池化
from keras.layers import Conv2D, MaxPooling2D

# 後端，背景
# 預設Tensorflow
from keras import backend as K

batch_size = 128
num_classes = 10
epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28      

資料操作，轉換

import tensorflow as tf 
# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 四維的NHWC---->卷積運算需要
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)

# 類型轉換
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')

# 歸一化 0 ~1
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

print(y_train.shape)
# convert class vectors to binary class matrices
# one-hot
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)      

模型建構

# 聲明序貫模型
model = Sequential()
# 第一層卷積
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
# 第二層卷積
model.add(Conv2D(64, (3, 3), activation='relu'))
# 池化層
model.add(MaxPooling2D(pool_size=(2, 2)))
# dropout層
model.add(Dropout(0.25))
# reshape
model.add(Flatten())
# 全連接配接層，矩陣運算
model.add(Dense(1024, activation='relu'))
# dropout層
model.add(Dropout(0.5))
# 輸出層
model.add(Dense(num_classes, activation='softmax'))      

編譯，最優化

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])      

訓練

x_train.shape      

(60000, 28, 28, 1)

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))      

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