94 lines
2.9 KiB
Python
94 lines
2.9 KiB
Python
#调用 TensorFlow
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from __future__ import absolute_import, division, print_function, unicode_literals
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import tensorflow as tf
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from tensorflow.keras.layers import Dense, Flatten, Conv2D
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from tensorflow.keras import Model
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#载入并准备好 MNIST 数据集：
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mnist = tf.keras.datasets.mnist
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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x_train, x_test = x_train / 255.0, x_test / 255.0
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# Add a channels dimension
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x_train = x_train[..., tf.newaxis]
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x_test = x_test[..., tf.newaxis]
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#使用 tf.data 来将数据集切分为 batch 以及混淆数据集：
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train_ds = tf.data.Dataset.from_tensor_slices(
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(x_train, y_train)).shuffle(10000).batch(32)
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test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)
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#使用 Keras 模型子类化（model subclassing） API 构建 tf.keras 模型：
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class MyModel(Model):
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def __init__(self):
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super(MyModel, self).__init__()
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self.conv1 = Conv2D(32, 3, activation='relu')
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self.flatten = Flatten()
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self.d1 = Dense(128, activation='relu')
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self.d2 = Dense(10, activation='softmax')
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def call(self, x):
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x = self.conv1(x)
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x = self.flatten(x)
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x = self.d1(x)
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return self.d2(x)
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model = MyModel()
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#为训练选择优化器与损失函数：
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loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
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optimizer = tf.keras.optimizers.Adam()
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#选择衡量指标来度量模型的损失值（loss）和准确率（accuracy）。这些指标在 epoch 上累积值，然后打印出整体结果
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train_loss = tf.keras.metrics.Mean(name='train_loss')
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train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(
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name='train_accuracy')
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test_loss = tf.keras.metrics.Mean(name='test_loss')
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test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(
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name='test_accuracy')
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#使用 tf.GradientTape 来训练模型
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@tf.function
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def train_step(images, labels):
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with tf.GradientTape() as tape:
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predictions = model(images)
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loss = loss_object(labels, predictions)
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gradients = tape.gradient(loss, model.trainable_variables)
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optimizer.apply_gradients(zip(gradients, model.trainable_variables))
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train_loss(loss)
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train_accuracy(labels, predictions)
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#测试模型：
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@tf.function
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def test_step(images, labels):
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predictions = model(images)
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t_loss = loss_object(labels, predictions)
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test_loss(t_loss)
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test_accuracy(labels, predictions)
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EPOCHS = 5
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for epoch in range(EPOCHS):
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for images, labels in train_ds:
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train_step(images, labels)
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for test_images, test_labels in test_ds:
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test_step(test_images, test_labels)
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template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
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print(template.format(epoch + 1,
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train_loss.result(),
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train_accuracy.result() * 100,
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test_loss.result(),
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test_accuracy.result() * 100))
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