05_專家的快速入門、加載MNIST數據集、數據集切分和混淆、定義類的方式構建模型、選擇優化器和損失函數、訓練模型和測試模型準確率

https://tensorflow.google.cn/tutorials/quickstart/advanced

導入TensorFlow到你的程序中：

import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model

加載和準備MNIST數據集

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 dimension
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]

使用 tf.data 來將數據集切分爲 batch 以及混淆數據集：

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)

使用 Keras 模型子類化（model subclassing） API構建tf.keras模型：

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, activation='softmax')

  def call(self, x):
    x = self.conv1(x)
    x = self.flatten(x)
    x = self.d1(x)
    return self.d2(x)

model = MyModel()

爲訓練選擇優化器與損失函數：

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

選擇衡量指標來度量模型的損失值（loss）和準確率（accuracy）。這些指標在epoch上累積值，然後打印出整理結果。

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')

使用tf.GradientTape來訓練模型：

@tf.function
def train_step(images, labels):
    with tf.GradientTape() as tape:
        predictions = model(images)
        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)

測試模型：

@tf.function
def test_step(images, labels):
  predictions = model(images)
  t_loss = loss_object(labels, predictions)

  test_loss(t_loss)
  test_accuracy(labels, predictions)

EPOCHS = 5
for epoch in range(EPOCHS):
    # 在下一個epoch開始是，重置評估指標
    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)

    template = "Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}"
    print(template.format(
            epoch + 1,
            train_loss.result(),
            train_accuracy.result() * 100,
            test_loss.result(),
            test_accuracy.result() * 100))

輸出結果：

WARNING:tensorflow:Layer my_model is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.
If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.
To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.
Epoch 1, Loss: 0.13633669912815094, Accuracy: 95.92000579833984, Test Loss: 0.054682306945323944, Test Accuracy: 98.19999694824219
Epoch 2, Loss: 0.041911669075489044, Accuracy: 98.70333099365234, Test Loss: 0.04665009677410126, Test Accuracy: 98.4000015258789
Epoch 3, Loss: 0.021748166531324387, Accuracy: 99.31666564941406, Test Loss: 0.05017175152897835, Test Accuracy: 98.36000061035156
Epoch 4, Loss: 0.01320651639252901, Accuracy: 99.55166625976562, Test Loss: 0.058168746531009674, Test Accuracy: 98.30999755859375
Epoch 5, Loss: 0.008145572617650032, Accuracy: 99.7316665649414, Test Loss: 0.06632857024669647, Test Accuracy: 98.30999755859375