关于keras分类模型中输入数据与标签维度的案例-创新互联
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在《python深度学习》这本书中。
一、21页mnist十分类
导入数据集 from keras.datasets import mnist (train_images, train_labels), (test_images, test_labels) = mnist.load_data() 初始数据维度: >>> train_images.shape (60000, 28, 28) >>> len(train_labels) 60000 >>> train_labels array([5, 0, 4, ..., 5, 6, 8], dtype=uint8) 数据预处理: train_images = train_images.reshape((60000, 28 * 28)) train_images = train_images.astype('float32') / 255 train_labels = to_categorical(train_labels) 之后: print(train_images, type(train_images), train_images.shape, train_images.dtype) print(train_labels, type(train_labels), train_labels.shape, train_labels.dtype) 结果: [[0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]](60000, 784) float32 [[0. 0. 0. ... 0. 0. 0.] [1. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 1. 0.]] (60000, 10) float32
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