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TensorFlow 2 quickstart for experts

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This is a Google Colaboratory notebook file. Python programs are run directly in the browser—a great way to learn and use TensorFlow. To follow this tutorial, run the notebook in Google Colab by clicking the button at the top of this page.

  1. In Colab, connect to a Python runtime: At the top-right of the menu bar, select CONNECT.
  2. Run all the notebook code cells: Select Runtime > Run all.

Download and install the TensorFlow 2 package:

Import TensorFlow into your program:

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf

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

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, activation='softmax')

  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()

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:
    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)

Test the model:

@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):
  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))

  # Reset the metrics for the next epoch
  train_loss.reset_states()
  train_accuracy.reset_states()
  test_loss.reset_states()
  test_accuracy.reset_states()
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: 0.1358345001935959, Accuracy: 95.85333251953125, Test Loss: 0.061788953840732574, Test Accuracy: 98.11000061035156
Epoch 2, Loss: 0.04279247298836708, Accuracy: 98.63333129882812, Test Loss: 0.05333545058965683, Test Accuracy: 98.12999725341797
Epoch 3, Loss: 0.02246130257844925, Accuracy: 99.24832916259766, Test Loss: 0.05552060902118683, Test Accuracy: 98.3499984741211
Epoch 4, Loss: 0.012189255096018314, Accuracy: 99.62333679199219, Test Loss: 0.06037171557545662, Test Accuracy: 98.29000091552734
Epoch 5, Loss: 0.010506633669137955, Accuracy: 99.65666198730469, Test Loss: 0.05619392916560173, Test Accuracy: 98.47999572753906

The image classifier is now trained to ~98% accuracy on this dataset. To learn more, read the TensorFlow tutorials.