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exp2/source/predict.py

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+import tensorflow as tf
+from PIL import Image
+import numpy as np
+import matplotlib.pyplot as plt
+
+from train import CNN
+
+'''
+python 3.9 
+tensorflow 2.0.0b0
+pillow(PIL) 4.3.0
+''' 
+
+
+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')
+        img = np.reshape(img, (28, 28, 1)) / 255.
+        x = np.array([1 - img])
+        y = self.cnn.model.predict(x)
+
+        # 因为x只传入了一张图片，取y[0]即可
+        # np.argmax()取得最大值的下标，即代表的数字
+        print(image_path)
+#        print(y[0])
+        print('        -> Predict digit', np.argmax(y[0]))
+        plt.figure("Image")  # 图像窗口名称
+        plt.imshow(img)
+        plt.axis('on')  # 关掉坐标轴为 off
+        plt.title(np.argmax(y[0]))  # 图像题目 # 必须有这个，要不然无法显示
+        plt.show()
+
+
+if __name__ == "__main__":
+    app = Predict()
+    app.predict('./test_images/0.png') 
+    app.predict('./test_images/1.png')
+    app.predict('./test_images/4.png')

exp2/source/train.py

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+import os
+import tensorflow as tf
+from tensorflow.keras import datasets, layers, models
+
+'''
+python 3.9
+tensorflow 2.0.0b0
+''' 
+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(32, (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(64, (3, 3), activation='relu'))
+        model.add(layers.MaxPooling2D((2, 2)))
+        # 第三层卷积，卷积核大小为3*3，64个
+        model.add(layers.Conv2D(64, (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
+
+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 = os.path.abspath(os.path.dirname(
+            __file__)) + '.'
+        (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
+
+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, period=5)
+        self.cnn.model.compile(optimizer='adam',
+                               loss='sparse_categorical_crossentropy',
+                               metrics=['accuracy'])
+        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)))
+
+if __name__ == "__main__":
+    app = Train()
+    app.train()