keras導(dǎo)入weights的方法是什么？

發(fā)布時(shí)間：2020-06-23 11:05:03 來(lái)源：億速云閱讀：320 作者：清晨欄目：開(kāi)發(fā)技術(shù)

這篇文章將為大家詳細(xì)講解有關(guān)keras導(dǎo)入weights的方法是什么？，小編覺(jué)得挺實(shí)用的，因此分享給大家做個(gè)參考，希望大家閱讀完這篇文章后可以有所收獲。

keras源碼engine中toplogy.py定義了加載權(quán)重的函數(shù)：

load_weights(self, filepath, by_name=False)

其中默認(rèn)by_name為False,這時(shí)候加載權(quán)重按照網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)加載,適合直接使用keras中自帶的網(wǎng)絡(luò)模型，如VGG16

VGG19/resnet50等，源碼描述如下:

If `by_name` is False (default) weights are loaded
based on the network's topology, meaning the architecture
should be the same as when the weights were saved.
Note that layers that don't have weights are not taken
into account in the topological ordering, so adding or
removing layers is fine as long as they don't have weights.

若將by_name改為T(mén)rue則加載權(quán)重按照l(shuí)ayer的name進(jìn)行,layer的name相同時(shí)加載權(quán)重,適合用于改變了

模型的相關(guān)結(jié)構(gòu)或增加了節(jié)點(diǎn)但利用了原網(wǎng)絡(luò)的主體結(jié)構(gòu)情況下使用,源碼描述如下：

If `by_name` is True, weights are loaded into layers
only if they share the same name. This is useful
for fine-tuning or transfer-learning models where
some of the layers have changed.

在進(jìn)行邊緣檢測(cè)時(shí),利用VGG網(wǎng)絡(luò)的主體結(jié)構(gòu),網(wǎng)絡(luò)中增加反卷積層,這時(shí)加載權(quán)重應(yīng)該使用

model.load_weights(filepath,by_name=True)

補(bǔ)充知識(shí)：Keras下實(shí)現(xiàn)mnist手寫(xiě)數(shù)字

之前一直在用tensorflow，被同學(xué)推薦來(lái)用keras了，把之前文檔中的mnist手寫(xiě)數(shù)字?jǐn)?shù)據(jù)集拿來(lái)練手，

代碼如下。

import struct
import numpy as np
import os
 
import keras
from keras.models import Sequential 
from keras.layers import Dense
from keras.optimizers import SGD
 
def load_mnist(path, kind):
  labels_path = os.path.join(path, '%s-labels.idx1-ubyte' % kind)
  images_path = os.path.join(path, '%s-images.idx3-ubyte' % kind)
  with open(labels_path, 'rb') as lbpath:
    magic, n = struct.unpack('>II', lbpath.read(8))
    labels = np.fromfile(lbpath, dtype=np.uint8)
  with open(images_path, 'rb') as imgpath:
    magic, num, rows, cols = struct.unpack(">IIII", imgpath.read(16))
    images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 784) #28*28=784
  return images, labels
 
#loading train and test data
X_train, Y_train = load_mnist('.\\data', kind='train')
X_test, Y_test = load_mnist('.\\data', kind='t10k')
 
#turn labels to one_hot code
Y_train_ohe = keras.utils.to_categorical(Y_train, num_classes=10)
 
#define models
model = Sequential()
 
model.add(Dense(input_dim=X_train.shape[1],output_dim=50,init='uniform',activation='tanh'))
model.add(Dense(input_dim=50,output_dim=50,init='uniform',activation='tanh'))
model.add(Dense(input_dim=50,output_dim=Y_train_ohe.shape[1],init='uniform',activation='softmax')) 
 
sgd = SGD(lr=0.001, decay=1e-7, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=["accuracy"])
 
#start training
model.fit(X_train,Y_train_ohe,epochs=50,batch_size=300,shuffle=True,verbose=1,validation_split=0.3)
 
#count accuracy
y_train_pred = model.predict_classes(X_train, verbose=0)
 
train_acc = np.sum(Y_train == y_train_pred, axis=0) / X_train.shape[0] 
print('Training accuracy: %.2f%%' % (train_acc * 100))
 
y_test_pred = model.predict_classes(X_test, verbose=0)
test_acc = np.sum(Y_test == y_test_pred, axis=0) / X_test.shape[0] 
print('Test accuracy: %.2f%%' % (test_acc * 100))

訓(xùn)練結(jié)果如下：

Epoch 45/50
42000/42000 [==============================] - 1s 17us/step - loss: 0.2174 - acc: 0.9380 - val_loss: 0.2341 - val_acc: 0.9323
Epoch 46/50
42000/42000 [==============================] - 1s 17us/step - loss: 0.2061 - acc: 0.9404 - val_loss: 0.2244 - val_acc: 0.9358
Epoch 47/50
42000/42000 [==============================] - 1s 17us/step - loss: 0.1994 - acc: 0.9413 - val_loss: 0.2295 - val_acc: 0.9347
Epoch 48/50
42000/42000 [==============================] - 1s 17us/step - loss: 0.2003 - acc: 0.9413 - val_loss: 0.2224 - val_acc: 0.9350
Epoch 49/50
42000/42000 [==============================] - 1s 18us/step - loss: 0.2013 - acc: 0.9417 - val_loss: 0.2248 - val_acc: 0.9359
Epoch 50/50
42000/42000 [==============================] - 1s 17us/step - loss: 0.1960 - acc: 0.9433 - val_loss: 0.2300 - val_acc: 0.9346
Training accuracy: 94.11%
Test accuracy: 93.61%

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keras導(dǎo)入weights的方法是什么？

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