如何实现并保存加载Tensorflow的MNIST CNN模型？

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本文以MNIST数据集为例，介绍了使用TensorFlow实现CNN模型并进行模型保存与加载的具体步骤。以下为代码示例：

pythonimport tensorflow as tffrom tensorflow.keras.datasets import mnistfrom tensorflow.keras.models import Sequentialfrom tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2Dfrom tensorflow.keras.utils import to_categorical

加载数据集(x_train, y_train), (x_test, y_test)=mnist.load_data()

数据预处理x_train=x_train.reshape(-1, 28, 28, 1).astype('float32') / 255.0x_test=x_test.reshape(-1, 28, 28, 1).astype('float32') / 255.0y_train=to_categorical(y_train, 10)y_test=to_categorical(y_test, 10)

构建模型model=Sequential([ Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)), MaxPooling2D((2, 2)), Flatten(), Dense(64, activation='relu'), Dense(10, activation='softmax')])

编译模型model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

训练模型model.fit(x_train, y_train, epochs=5, batch_size=32, validation_data=(x_test, y_test))

保存模型model.save('mnist_cnn_model.h5')

加载模型loaded_model=tf.keras.models.load_model('mnist_cnn_model.h5')

评估模型loaded_model.evaluate(x_test, y_test)

本文实例为大家分享了Tensorflow之MNIST CNN实现并保存、加载模型的具体代码，供大家参考，具体内容如下

废话不说，直接上代码

# 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，自己手写了几张图片，放在文件夹C:\pythonp\testdir2下，开始测试

#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()

测试结果

自己手写的图片截的时候要注意，空白部分尽量不要太大，否则测试结果就呵呵了

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持易盾网络。