TensorFlow:手写数字识别

加载库

import tensorflow as tf

加载数据集

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
#归一化处理
x_train, x_test = x_train / 255.0, x_test / 255.0

def load_data(path="mnist.npz"):
    """Loads the MNIST dataset.

    This is a dataset of 60,000 28x28 grayscale images of the 10 digits,
    along with a test set of 10,000 images.
    More info can be found at the
    [MNIST homepage](http://yann.lecun.com/exdb/mnist/).

    Args:
      path: path where to cache the dataset locally
        (relative to `~/.keras/datasets`).

    Returns:
      Tuple of NumPy arrays: `(x_train, y_train), (x_test, y_test)`.

    **x_train**: uint8 NumPy array of grayscale image data with shapes
      `(60000, 28, 28)`, containing the training data. Pixel values range
      from 0 to 255.

    **y_train**: uint8 NumPy array of digit labels (integers in range 0-9)
      with shape `(60000,)` for the training data.

    **x_test**: uint8 NumPy array of grayscale image data with shapes
      (10000, 28, 28), containing the test data. Pixel values range
      from 0 to 255.

    **y_test**: uint8 NumPy array of digit labels (integers in range 0-9)
      with shape `(10000,)` for the test data.

根据文档的介绍，发现数据的返回格式，以及数据集的形状

搭建网络模型

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10)
])
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer='adam',
              loss=loss_fn,
              metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
print(model.evaluate(x_test,  y_test, verbose=2))