Import TensorFlow into your program:
import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2Dfrom tensorflow.keras import Model
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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")
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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)
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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()
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Choose an optimizer and loss function for training:
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam()
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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')
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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)
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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)
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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}' )
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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
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