Tensorflow之MNIST CNN實現並儲存、載入模型
阿新 • • 發佈:2020-06-17
本文例項為大家分享了Tensorflow之MNIST CNN實現並儲存、載入模型的具體程式碼,供大家參考,具體內容如下
廢話不說,直接上程式碼
# TensorFlow and tf.keras import tensorflow as tf from tensorflow import keras # Helper libraries import numpy as np import matplotlib.pyplot as plt import os #download the data mnist = keras.datasets.mnist (train_images,train_labels),(test_images,test_labels) = mnist.load_data() class_names = ['0','1','2','3','4','5','6','7','8','9'] train_images = train_images / 255.0 test_images = test_images / 255.0 def create_model(): # It's necessary to give the input_shape,or it will fail when you load the model # The error will be like : You are trying to load the 4 layer models to the 0 layer model = keras.Sequential([ keras.layers.Conv2D(32,[5,5],activation=tf.nn.relu,input_shape = (28,28,1)),keras.layers.MaxPool2D(),keras.layers.Conv2D(64,[7,7],activation=tf.nn.relu),keras.layers.Flatten(),keras.layers.Dense(576,keras.layers.Dense(10,activation=tf.nn.softmax) ]) model.compile(optimizer=tf.train.AdamOptimizer(),loss='sparse_categorical_crossentropy',metrics=['accuracy']) return model #reshape the shape before using it,for that the input of cnn is 4 dimensions train_images = np.reshape(train_images,[-1,1]) test_images = np.reshape(test_images,1]) #train model = create_model() model.fit(train_images,train_labels,epochs=4) #save the model model.save('my_model.h5') #Evaluate test_loss,test_acc = model.evaluate(test_images,test_labels,verbose = 0) print('Test accuracy:',test_acc)
模型儲存後,自己手寫了幾張圖片,放在資料夾C:\pythonp\testdir2下,開始測試
#Load the model new_model = keras.models.load_model('my_model.h5') new_model.compile(optimizer=tf.train.AdamOptimizer(),metrics=['accuracy']) new_model.summary() #Evaluate # test_loss,test_acc = new_model.evaluate(test_images,test_labels) # print('Test accuracy:',test_acc) #Predicte mypath = 'C:\\pythonp\\testdir2' def getimg(mypath): listdir = os.listdir(mypath) imgs = [] for p in listdir: img = plt.imread(mypath+'\\'+p) # I save the picture that I draw myself under Windows,but the saved picture's # encode style is just opposite with the experiment data,so I transfer it with # this line. img = np.abs(img/255-1) imgs.append(img[:,:,0]) return np.array(imgs),len(imgs) imgs = getimg(mypath) test_images = np.reshape(imgs[0],1]) predictions = new_model.predict(test_images) plt.figure() for i in range(imgs[1]): c = np.argmax(predictions[i]) plt.subplot(3,3,i+1) plt.xticks([]) plt.yticks([]) plt.imshow(test_images[i,0]) plt.title(class_names[c]) plt.show()
測試結果
自己手寫的圖片截的時候要注意,空白部分儘量不要太大,否則測試結果就呵呵了
以上就是本文的全部內容,希望對大家的學習有所幫助,也希望大家多多支援我們。