refactor: merge multiple project into one and create new project

dl-exp/exp2/source/train.py

@@ -0,0 +1,56 @@
+from pathlib import Path
+import tensorflow as tf
+from tensor.keras import datasets, layers, models
+
+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 = Path(__file__).resolve().parent.parent / 'datasets' / '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
+
+class Train:
+    def __init__(self):
+        self.cnn = CNN()
+        self.data = DataSource()
+    def train(self):
+        check_path = Path(__file__).resolve().parent.parent / 'models' / 'cnn.ckpt'
+        # period 每隔5epoch保存一次
+        save_model_cb = tf.keras.callbacks.ModelCheckpoint(
+            str(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, batch_size=1000, 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()