<filename>classify_images_number_cnn.py
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
from PIL import Image
import numpy as np


# build cnn
class CNN(object):
    def __init__(self):
        model = models.Sequential()
        # 第1层卷积，卷积核大小为3*3，32个，28*28为待训练图片的大小
        model.add(layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))
        model.add(layers.MaxPooling2D((2, 2)))
        # 第2层卷积，卷积核大小为3*3，64个
        model.add(layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))
        model.add(layers.MaxPooling2D((2, 2)))
        # 第3层卷积，卷积核大小为3*3，64个
        model.add(layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))

        model.add(layers.Flatten())
        model.add(layers.Dense(64, activation='relu'))
        model.add(layers.Dense(10, activation='softmax'))

        model.summary()

        self.model = model


# load data set
class DataSource(object):
    def __init__(self):
        # mnist数据集存储的位置，如何不存在将自动下载
        data_path = '/Users/terry-jri/Desktop/Program/PythonDemo/tensorflow_test/data/mnist/mnist.npz'
        (train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data(path=data_path)

        # 6万张训练图片，1万张测试图片
        train_images = train_images.reshape((60000, 28, 28, 1))
        test_images = test_images.reshape((10000, 28, 28, 1))
        # 像素值映射到 0 - 1 之间
        train_images, test_images = train_images / 255.0, test_images / 255.0

        self.train_images, self.train_labels = train_images, train_labels
        self.test_images, self.test_labels = test_images, test_labels


# model train
class Train:
    def __init__(self):
        self.cnn = CNN()
        self.data = DataSource()

    def train(self):
        check_path = './ckpt/cp-{epoch:04d}.ckpt'
        # period 每隔5epoch保存一次
        save_model_cb = tf.keras.callbacks.ModelCheckpoint(check_path, save_weights_only=True, verbose=1, save_freq=5)

        self.cnn.model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

        # begin train
        self.cnn.model.fit(self.data.train_images, self.data.train_labels, epochs=5, callbacks=[save_model_cb])

        test_loss, test_acc = self.cnn.model.evaluate(self.data.test_images, self.data.test_labels)
        print("准确率: %.4f，共测试了%d张图片 " % (test_acc, len(self.data.test_labels)))


class Predict(object):
    def __init__(self):
        latest = tf.train.latest_checkpoint('./ckpt')
        self.cnn = CNN()
        # 恢复网络权重
        self.cnn.model.load_weights(latest)

    def predict(self, image_path):
        # 以黑白方式读取图片
        img = Image.open(image_path).convert('L')

        flatten_img = np.reshape(img, (28, 28, 1)) / 255
        x = np.array([1 - flatten_img])

        y = self.cnn.model.predict(x)

        print(image_path)
        print(y[0])
        print('-> Predict digit', np.argmax(y[0]))


if __name__ == "__main__":
    app = Train()
    app.train()

    app = Predict()
    app.predict('/Users/Desktop/4.png')

<filename>classify_images_number_cnn.py
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
from PIL import Image
import numpy as np


# build cnn
class CNN(object):
    def __init__(self):
        model = models.Sequential()
        # 第1层卷积，卷积核大小为3*3，32个，28*28为待训练图片的大小
        model.add(layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))
        model.add(layers.MaxPooling2D((2, 2)))
        # 第2层卷积，卷积核大小为3*3，64个
        model.add(layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))
        model.add(layers.MaxPooling2D((2, 2)))
        # 第3层卷积，卷积核大小为3*3，64个
        model.add(layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))

        model.add(layers.Flatten())
        model.add(layers.Dense(64, activation='relu'))
        model.add(layers.Dense(10, activation='softmax'))

        model.summary()

        self.model = model


# load data set
class DataSource(object):
    def __init__(self):
        # mnist数据集存储的位置，如何不存在将自动下载
        data_path = '/Users/terry-jri/Desktop/Program/PythonDemo/tensorflow_test/data/mnist/mnist.npz'
        (train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data(path=data_path)

        # 6万张训练图片，1万张测试图片
        train_images = train_images.reshape((60000, 28, 28, 1))
        test_images = test_images.reshape((10000, 28, 28, 1))
        # 像素值映射到 0 - 1 之间
        train_images, test_images = train_images / 255.0, test_images / 255.0

        self.train_images, self.train_labels = train_images, train_labels
        self.test_images, self.test_labels = test_images, test_labels


# model train
class Train:
    def __init__(self):
        self.cnn = CNN()
        self.data = DataSource()

    def train(self):
        check_path = './ckpt/cp-{epoch:04d}.ckpt'
        # period 每隔5epoch保存一次
        save_model_cb = tf.keras.callbacks.ModelCheckpoint(check_path, save_weights_only=True, verbose=1, save_freq=5)

        self.cnn.model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

        # begin train
        self.cnn.model.fit(self.data.train_images, self.data.train_labels, epochs=5, callbacks=[save_model_cb])

        test_loss, test_acc = self.cnn.model.evaluate(self.data.test_images, self.data.test_labels)
        print("准确率: %.4f，共测试了%d张图片 " % (test_acc, len(self.data.test_labels)))


class Predict(object):
    def __init__(self):
        latest = tf.train.latest_checkpoint('./ckpt')
        self.cnn = CNN()
        # 恢复网络权重
        self.cnn.model.load_weights(latest)

    def predict(self, image_path):
        # 以黑白方式读取图片
        img = Image.open(image_path).convert('L')

        flatten_img = np.reshape(img, (28, 28, 1)) / 255
        x = np.array([1 - flatten_img])

        y = self.cnn.model.predict(x)

        print(image_path)
        print(y[0])
        print('-> Predict digit', np.argmax(y[0]))


if __name__ == "__main__":
    app = Train()
    app.train()

    app = Predict()
    app.predict('/Users/Desktop/4.png')

# build cnn # 第1层卷积，卷积核大小为3*3，32个，28*28为待训练图片的大小 # 第2层卷积，卷积核大小为3*3，64个 # 第3层卷积，卷积核大小为3*3，64个 # load data set # mnist数据集存储的位置，如何不存在将自动下载 # 6万张训练图片，1万张测试图片 # 像素值映射到 0 - 1 之间 # model train # period 每隔5epoch保存一次 # begin train # 恢复网络权重 # 以黑白方式读取图片