Import TensorFlow into your program:

import tensorflow as tffrom tensorflow.keras.layers import Dense, Flatten, Conv2Dfrom tensorflow.keras import Model

Load and prepare the MNIST dataset.

mnist = tf.keras.datasets.mnist(x_train, y_train), (x_test, y_test) = mnist.load_data()x_train, x_test = x_train / 255.0, x_test / 255.0# Add a channels dimensionx_train = x_train[..., tf.newaxis].astype("float32")x_test = x_test[..., tf.newaxis].astype("float32")

Use tf.data to batch and shuffle the dataset:

train_ds = tf.data.Dataset.from_tensor_slices(    (x_train, y_train)).shuffle(10000).batch(32)test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)

Build the tf.keras model using the Keras model subclassing API:

class MyModel(Model):  def __init__(self):    super(MyModel, self).__init__()    self.conv1 = Conv2D(32, 3, activation='relu')    self.flatten = Flatten()    self.d1 = Dense(128, activation='relu')    self.d2 = Dense(10)  def call(self, x):    x = self.conv1(x)    x = self.flatten(x)    x = self.d1(x)    return self.d2(x)# Create an instance of the modelmodel = MyModel()

Choose an optimizer and loss function for training:

loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)optimizer = tf.keras.optimizers.Adam()

Select metrics to measure the loss and the accuracy of the model. These metrics accumulate the values over epochs and then print the overall result.

train_loss = tf.keras.metrics.Mean(name='train_loss')train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')test_loss = tf.keras.metrics.Mean(name='test_loss')test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')

Use tf.GradientTape to train the model:

@tf.functiondef train_step(images, labels):  with tf.GradientTape() as tape:    # training=True is only needed if there are layers with different    # behavior during training versus inference (e.g. Dropout).    predictions = model(images, training=True)    loss = loss_object(labels, predictions)  gradients = tape.gradient(loss, model.trainable_variables)  optimizer.apply_gradients(zip(gradients, model.trainable_variables))  train_loss(loss)  train_accuracy(labels, predictions)

Test the model:

@tf.functiondef test_step(images, labels):  # training=False is only needed if there are layers with different  # behavior during training versus inference (e.g. Dropout).  predictions = model(images, training=False)  t_loss = loss_object(labels, predictions)  test_loss(t_loss)  test_accuracy(labels, predictions)
EPOCHS = 5for epoch in range(EPOCHS):  # Reset the metrics at the start of the next epoch  train_loss.reset_states()  train_accuracy.reset_states()  test_loss.reset_states()  test_accuracy.reset_states()  for images, labels in train_ds:    train_step(images, labels)  for test_images, test_labels in test_ds:    test_step(test_images, test_labels)  print(    f'Epoch {epoch + 1}, '    f'Loss: {train_loss.result()}, '    f'Accuracy: {train_accuracy.result() * 100}, '    f'Test Loss: {test_loss.result()}, '    f'Test Accuracy: {test_accuracy.result() * 100}'  )

The image classifier is now trained to ~98% accuracy on this dataset

代码链接: https://codechina.csdn.net/cs...