Python實現Keras搭建神經網路訓練分類模型教程
阿新 • • 發佈：2020-06-15
我就廢話不多說了，大家還是直接看程式碼吧~
註釋講解版：
# Classifier example
import numpy as np
# for reproducibility
np.random.seed(1337)
# from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense,Activation
from keras.optimizers import RMSprop
# 程式中用到的資料是經典的手寫體識別mnist資料集
# download the mnist to the path if it is the first time to be called
# X shape (60,000 28x28),y
# (X_train,y_train),(X_test,y_test) = mnist.load_data()
# 下載minst.npz：
# 連結: https://pan.baidu.com/s/1b2ppKDOdzDJxivgmyOoQsA
# 提取碼: y5ir
# 將下載好的minst.npz放到當前目錄下
path='./mnist.npz'
f = np.load(path)
X_train,y_train = f['x_train'],f['y_train']
X_test,y_test = f['x_test'],f['y_test']
f.close()
# data pre-processing
# 資料預處理
# normalize
# X shape (60,000 28x28)，表示輸入資料 X 是個三維的資料
# 可以理解為 60000行資料，每一行是一張28 x 28 的灰度圖片
# X_train.reshape(X_train.shape[0],-1)表示：只保留第一維，其餘的緯度，不管多少緯度，重新排列為一維
# 引數-1就是不知道行數或者列數多少的情況下使用的引數
# 所以先確定除了引數-1之外的其他引數，然後通過(總引數的計算) / (確定除了引數-1之外的其他引數) = 該位置應該是多少的引數
# 這裡用-1是偷懶的做法,等同於 28*28
# reshape後的資料是：共60000行，每一行是784個數據點（feature）
# 輸入的 x 變成 60,000*784 的資料，然後除以 255 進行標準化
# 因為每個畫素都是在 0 到 255 之間的，標準化之後就變成了 0 到 1 之間
X_train = X_train.reshape(X_train.shape[0],-1) / 255
X_test = X_test.reshape(X_test.shape[0],-1) / 255
# 分類標籤編碼
# 將y轉化為one-hot vector
y_train = np_utils.to_categorical(y_train,num_classes = 10)
y_test = np_utils.to_categorical(y_test,num_classes = 10)
# Another way to build your neural net
# 建立神經網路
# 應用了2層的神經網路，前一層的啟用函式用的是relu，後一層的啟用函式用的是softmax
#32是輸出的維數
model = Sequential([
Dense(32,input_dim=784),Activation('relu'),Dense(10),Activation('softmax')
])
# Another way to define your optimizer
# 優化函式
# 優化演算法用的是RMSprop
rmsprop = RMSprop(lr=0.001,rho=0.9,epsilon=1e-08,decay=0.0)
# We add metrics to get more results you want to see
# 不自己定義，直接用內建的優化器也行，optimizer='rmsprop'
#啟用模型：接下來用 model.compile 激勵神經網路
model.compile(
optimizer=rmsprop,loss='categorical_crossentropy',metrics=['accuracy']
)
print('Training------------')
# Another way to train the model
# 訓練模型
# 上一個程式是用train_on_batch 一批一批的訓練 X_train,Y_train
# 預設的返回值是 cost，每100步輸出一下結果
# 輸出的樣式與上一個程式的有所不同，感覺用model.fit()更清晰明瞭
# 上一個程式是Python實現Keras搭建神經網路訓練迴歸模型：
# https://blog.csdn.net/weixin_45798684/article/details/106503685
model.fit(X_train,y_train,nb_epoch=2,batch_size=32)
print('\nTesting------------')
# Evaluate the model with the metrics we defined earlier
# 測試
loss,accuracy = model.evaluate(X_test,y_test)
print('test loss:',loss)
print('test accuracy:',accuracy)執行結果：
Using TensorFlow backend. Training------------ Epoch 1/2 32/60000 [..............................] - ETA: 5:03 - loss: 2.4464 - accuracy: 0.0625 864/60000 [..............................] - ETA: 14s - loss: 1.8023 - accuracy: 0.4850 1696/60000 [..............................] - ETA: 9s - loss: 1.5119 - accuracy: 0.6002 2432/60000 [>.............................] - ETA: 7s - loss: 1.3151 - accuracy: 0.6637 3200/60000 [>.............................] - ETA: 6s - loss: 1.1663 - accuracy: 0.7056 3968/60000 [>.............................] - ETA: 5s - loss: 1.0533 - accuracy: 0.7344 4704/60000 [=>............................] - ETA: 5s - loss: 0.9696 - accuracy: 0.7564 5408/60000 [=>............................] - ETA: 5s - loss: 0.9162 - accuracy: 0.7681 6112/60000 [==>...........................] - ETA: 5s - loss: 0.8692 - accuracy: 0.7804 6784/60000 [==>...........................] - ETA: 4s - loss: 0.8225 - accuracy: 0.7933 7424/60000 [==>...........................] - ETA: 4s - loss: 0.7871 - accuracy: 0.8021 8128/60000 [===>..........................] - ETA: 4s - loss: 0.7546 - accuracy: 0.8099 8960/60000 [===>..........................] - ETA: 4s - loss: 0.7196 - accuracy: 0.8183 9568/60000 [===>..........................] - ETA: 4s - loss: 0.6987 - accuracy: 0.8230 10144/60000 [====>.........................] - ETA: 4s - loss: 0.6812 - accuracy: 0.8262 10784/60000 [====>.........................] - ETA: 4s - loss: 0.6640 - accuracy: 0.8297 11456/60000 [====>.........................] - ETA: 4s - loss: 0.6462 - accuracy: 0.8329 12128/60000 [=====>........................] - ETA: 4s - loss: 0.6297 - accuracy: 0.8366 12704/60000 [=====>........................] - ETA: 4s - loss: 0.6156 - accuracy: 0.8405 13408/60000 [=====>........................] - ETA: 3s - loss: 0.6009 - accuracy: 0.8430 14112/60000 [======>.......................] - ETA: 3s - loss: 0.5888 - accuracy: 0.8457 14816/60000 [======>.......................] - ETA: 3s - loss: 0.5772 - accuracy: 0.8487 15488/60000 [======>.......................] - ETA: 3s - loss: 0.5685 - accuracy: 0.8503 16192/60000 [=======>......................] - ETA: 3s - loss: 0.5576 - accuracy: 0.8534 16896/60000 [=======>......................] - ETA: 3s - loss: 0.5477 - accuracy: 0.8555 17600/60000 [=======>......................] - ETA: 3s - loss: 0.5380 - accuracy: 0.8576 18240/60000 [========>.....................] - ETA: 3s - loss: 0.5279 - accuracy: 0.8600 18976/60000 [========>.....................] - ETA: 3s - loss: 0.5208 - accuracy: 0.8617 19712/60000 [========>.....................] - ETA: 3s - loss: 0.5125 - accuracy: 0.8634 20416/60000 [=========>....................] - ETA: 3s - loss: 0.5046 - accuracy: 0.8654 21088/60000 [=========>....................] - ETA: 3s - loss: 0.4992 - accuracy: 0.8669 21792/60000 [=========>....................] - ETA: 3s - loss: 0.4932 - accuracy: 0.8684 22432/60000 [==========>...................] - ETA: 3s - loss: 0.4893 - accuracy: 0.8693 23072/60000 [==========>...................] - ETA: 2s - loss: 0.4845 - accuracy: 0.8703 23648/60000 [==========>...................] - 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accuracy: 0.9016 56064/60000 [===========================>..] - ETA: 0s - loss: 0.3526 - accuracy: 0.9021 56736/60000 [===========================>..] - ETA: 0s - loss: 0.3514 - accuracy: 0.9026 57376/60000 [===========================>..] - ETA: 0s - loss: 0.3499 - accuracy: 0.9029 58112/60000 [============================>.] - ETA: 0s - loss: 0.3482 - accuracy: 0.9033 58880/60000 [============================>.] - ETA: 0s - loss: 0.3459 - accuracy: 0.9039 59584/60000 [============================>.] - ETA: 0s - loss: 0.3444 - accuracy: 0.9043 60000/60000 [==============================] - 5s 87us/step - loss: 0.3435 - accuracy: 0.9046 Epoch 2/2 32/60000 [..............................] - ETA: 11s - loss: 0.0655 - accuracy: 1.0000 736/60000 [..............................] - ETA: 4s - loss: 0.2135 - accuracy: 0.9389 1408/60000 [..............................] - ETA: 4s - loss: 0.2217 - accuracy: 0.9361 1984/60000 [..............................] - ETA: 4s - loss: 0.2316 - accuracy: 0.9390 2432/60000 [>.............................] - 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ETA: 0s - loss: 0.1956 - accuracy: 0.9438 58560/60000 [============================>.] - ETA: 0s - loss: 0.1951 - accuracy: 0.9440 59360/60000 [============================>.] - ETA: 0s - loss: 0.1947 - accuracy: 0.9440 60000/60000 [==============================] - 5s 76us/step - loss: 0.1946 - accuracy: 0.9440 Testing------------ 32/10000 [..............................] - ETA: 15s 1248/10000 [==>...........................] - ETA: 0s 2656/10000 [======>.......................] - ETA: 0s 4064/10000 [===========>..................] - ETA: 0s 5216/10000 [==============>...............] - ETA: 0s 6464/10000 [==================>...........] - ETA: 0s 7744/10000 [======================>.......] - ETA: 0s 9056/10000 [==========================>...] - ETA: 0s 9984/10000 [============================>.] - ETA: 0s 10000/10000 [==============================] - 0s 47us/step test loss: 0.17407772153392434 test accuracy: 0.9513000249862671
補充知識：Keras 搭建簡單神經網路：順序模型+迴歸問題
多層全連線神經網路
每層神經元個數、神經網路層數、啟用函式等可自由修改
使用不同的損失函式可適用於其他任務，比如：分類問題
這是Keras搭建神經網路模型最基礎的方法之一，Keras還有其他進階的方法，官網給出了一些基本使用方法：Keras官網
# 這裡搭建了一個4層全連線神經網路（不算輸入層），傳入函式以及函式內部的引數均可自由修改
def ann(X,y):
'''
X: 輸入的訓練集資料
y: 訓練集對應的標籤
'''
'''初始化模型'''
# 首先定義了一個順序模型作為框架，然後往這個框架裡面新增網路層
# 這是最基礎搭建神經網路的方法之一
model = Sequential()
'''開始新增網路層'''
# Dense表示全連線層，第一層需要我們提供輸入的維度 input_shape
# Activation表示每層的啟用函式，可以傳入預定義的啟用函式，也可以傳入符合介面規則的其他高階啟用函式
model.add(Dense(64,input_shape=(X.shape[1],)))
model.add(Activation('sigmoid'))
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dense(256))
model.add(Activation('tanh'))
model.add(Dense(32))
model.add(Activation('tanh'))
# 輸出層，輸出的維度大小由具體任務而定
# 這裡是一維輸出的迴歸問題
model.add(Dense(1))
model.add(Activation('linear'))
'''模型編譯'''
# optimizer表示優化器（可自由選擇），loss表示使用哪一種
model.compile(optimizer='rmsprop',loss='mean_squared_error')
# 自定義學習率，也可以使用原始的基礎學習率
reduce_lr = ReduceLROnPlateau(monitor='loss',factor=0.1,patience=10,verbose=0,mode='auto',min_delta=0.001,cooldown=0,min_lr=0)
'''模型訓練'''
# 這裡的模型也可以先從函式返回後，再進行訓練
# epochs表示訓練的輪數，batch_size表示每次訓練的樣本數量（小批量學習），validation_split表示用作驗證集的訓練資料的比例
# callbacks表示回撥函式的集合，用於模型訓練時檢視模型的內在狀態和統計資料，相應的回撥函式方法會在各自的階段被呼叫
# verbose表示輸出的詳細程度，值越大輸出越詳細
model.fit(X,y,epochs=100,batch_size=50,validation_split=0.0,callbacks=[reduce_lr],verbose=0)
# 列印模型結構
print(model.summary())
return model
下圖是此模型的結構圖，其中下劃線後面的數字是根據呼叫次數而定
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