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Tensorflow之MNIST CNN實(shí)現(xiàn)并保存、加載模型

更新時(shí)間：2020年06月17日 10:25:55 作者：uflswe

這篇文章主要為大家詳細(xì)介紹了Tensorflow之MNIST CNN實(shí)現(xiàn)并保存、加載模型，文中示例代碼介紹的非常詳細(xì)，具有一定的參考價(jià)值，感興趣的小伙伴們可以參考一下

本文實(shí)例為大家分享了Tensorflow之MNIST CNN實(shí)現(xiàn)并保存、加載模型的具體代碼，供大家參考，具體內(nèi)容如下

廢話不說(shuō)，直接上代碼

# 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.MaxPool2D(),
   keras.layers.Flatten(),
   keras.layers.Dense(576, activation=tf.nn.relu),
   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,[-1,28,28,1])
test_images = np.reshape(test_images,[-1,28,28,1])
 
 
#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)

模型保存后，自己手寫了幾張圖片，放在文件夾C:\pythonp\testdir2下，開(kāi)始測(cè)試

#Load the model
 
new_model = keras.models.load_model('my_model.h5')
new_model.compile(optimizer=tf.train.AdamOptimizer(), 
        loss='sparse_categorical_crossentropy',
        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],[-1,28,28,1])
 
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(cè)試結(jié)果