TensorFlow 2 quickstart for experts

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Import TensorFlow into your program:

import tensorflow as tf​from tensorflow.keras.layers import Dense, Flatten, Conv2Dfrom tensorflow.keras import Model

Load and prepare the MNIST dataset.

@H_441_1@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")

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@H_441_1@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. @R_944_8270@ 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.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)

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)

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

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