TensorFlow|TensorFlow 2 quickstart for experts TensorFlow2quickstartforexperts

Import TensorFlow into your program:

import tensorflow as tffrom 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 epochtrain_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
【TensorFlow|TensorFlow 2 quickstart for experts】代码链接: https://codechina.csdn.net/cs...