TensorFlow 2 quickstart for experts

from tensorflow.keras.layers import Dense, Flatten, Conv2D from 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 dimension
x_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 model
model = 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.function
def 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.function
def 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/csdn_codechina/enterprise_technology/-/blob/master/CV_Classification/TensorFlow%202%20quickstart%20for%20experts.ipynb

本文转自网络，原文链接：https://developer.aliyun.com/article/785899
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