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This is a notebook file. Python programs are run directly in the browser—a great way to learn and use TensorFlow. To run the Colab notebook:
For more examples and guides (including details for this program), see Get Started with TensorFlow.
Let's get started, import the TensorFlow library into your program:
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from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
Load and prepare the MNIST dataset. Convert the samples from integers to floating-point numbers:
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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
Build the tf.keras model by stacking layers. Select an optimizer and loss function used for training:
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model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
Train and evaluate model:
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model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)
You’ve now trained an image classifier with ~98% accuracy on this dataset. See Get Started with TensorFlow to learn more.