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Note: This is an archived TF1 notebook. These are configured to run in TF2's compatbility mode but will run in TF1 as well. To use TF1 in Colab, use the magic.
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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import tensorflow.compat.v1 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, verbose=2)
You’ve now trained an image classifier with ~98% accuracy on this dataset. See Get Started with TensorFlow to learn more.