69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
# 导入必要的库
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import tensorflow as tf
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from keras import datasets, layers, models
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import matplotlib.pyplot as plt
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import matplotlib
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matplotlib.use('TkAgg') # 使用 TkAgg 后端
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import matplotlib.pyplot as plt
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# 主函数入口
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if __name__ == '__main__':
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# 加载 MNIST 数据集。分为训练集和测试集
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(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()
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# 对图像数据进行标准化处理，将像素值缩放到 0-1 之间
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train_images, test_images = train_images / 255.0, test_images / 255.0
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# 打印数据维度信息
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print("data_shpe:",train_images.shape,test_images.shape,train_labels.shape,test_labels.shape)
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"""
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输出：((60000, 28, 28), (10000, 28, 28), (60000,), (10000,))
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"""
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# 绘制训练图像的示例
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plt.figure(figsize=(20, 10))
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for i in range(20):
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plt.subplot(2, 10, i + 1)
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plt.xticks([])
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plt.yticks([])
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plt.grid(False)
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plt.imshow(train_images[i], cmap=plt.cm.binary)
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plt.xlabel(train_labels[i])
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plt.show()
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# 调整图像数据的维度，以符合模型的输入要求
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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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# 再次打印调整后的数据维度信息
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print("data_shpe:",train_images.shape, test_images.shape, train_labels.shape, test_labels.shape)
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"""
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输出：((60000, 28, 28, 1), (10000, 28, 28, 1), (60000,), (10000,))
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"""
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# 构建卷积神经网络模型
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model = models.Sequential([
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layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
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layers.MaxPooling2D((2, 2)),
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layers.Conv2D(64, (3, 3), activation='relu'),
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layers.MaxPooling2D((2, 2)),
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layers.Flatten(),
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layers.Dense(64, activation='relu'),
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layers.Dense(10)
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])
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model.summary() # 打印模型结构
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# 编译模型，设置优化器、损失函数和评价指标
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model.compile(
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optimizer='adam',
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loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
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metrics=['accuracy'])
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# 训练模型
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history = model.fit(
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train_images,
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train_labels,
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epochs=10,
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) |