57 lines
2.6 KiB
Python
57 lines
2.6 KiB
Python
from pathlib import Path
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import tensorflow as tf
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from tensor.keras import datasets, layers, models
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class CNN(object):
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def __init__(self):
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model = models.Sequential()
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# 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
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model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
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model.add(layers.MaxPooling2D((2, 2)))
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# 第2层卷积，卷积核大小为3*3，64个
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model.add(layers.Conv2D(64, (3, 3), activation='relu'))
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model.add(layers.MaxPooling2D((2, 2)))
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# 第三层卷积，卷积核大小为3*3，64个
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model.add(layers.Conv2D(64, (3, 3), activation='relu'))
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model.add(layers.Flatten())
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model.add(layers.Dense(64, activation='relu'))
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model.add(layers.Dense(10, activation='softmax'))
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model.summary()
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self.model = model
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class DataSource(object):
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def __init__(self):
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# mnist数据集存储的位置，如何不存在将自动下载
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data_path = Path(__file__).resolve().parent.parent / 'datasets' / 'mnist.npz'
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(train_images, train_labels), (test_images,
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test_labels) = datasets.mnist.load_data(path=data_path)
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# 6万张训练图片，1万张测试图片
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train_images = train_images.reshape((60000, 28, 28, 1))
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test_images = test_images.reshape((10000, 28, 28, 1))
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# 像素值映射到 0 - 1 之间
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train_images, test_images = train_images / 255.0, test_images / 255.0
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self.train_images, self.train_labels = train_images, train_labels
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self.test_images, self.test_labels = test_images, test_labels
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class Train:
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def __init__(self):
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self.cnn = CNN()
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self.data = DataSource()
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def train(self):
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check_path = Path(__file__).resolve().parent.parent / 'models' / 'cnn.ckpt'
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# period 每隔5epoch保存一次
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save_model_cb = tf.keras.callbacks.ModelCheckpoint(
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str(check_path), save_weights_only=True, verbose=1, period=5)
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self.cnn.model.compile(optimizer='adam',
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loss='sparse_categorical_crossentropy',
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metrics=['accuracy'])
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self.cnn.model.fit(self.data.train_images, self.data.train_labels,
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epochs=5, batch_size=1000, callbacks=[save_model_cb])
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test_loss, test_acc = self.cnn.model.evaluate(
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self.data.test_images, self.data.test_labels)
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print("准确率: %.4f, 共测试了%d张图片 " % (test_acc, len(self.data.test_labels)))
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if __name__ == "__main__":
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app = Train()
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app.train()
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