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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 follow this tutorial, run the notebook in Google Colab by clicking the button at the top of this page.
Download and install the TensorFlow 2.0 Beta package. Import TensorFlow into your program:
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from __future__ import absolute_import, division, print_function, unicode_literals
# Install TensorFlow
!pip install tensorflow==2.0.0-beta1
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.Sequential model by stacking layers. Choose an optimizer and loss function for training:
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model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
Train and evaluate the model:
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model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)
The image classifier is now trained to ~98% accuracy on this dataset. To learn more, read the TensorFlow tutorials.