<filename>python/mnist_tutorial.py<gh_stars>0
# https://www.tensorflow.org/tutorials/quickstart/beginner

import tensorflow as tf

"""Load and prepare the [MNIST dataset](http://yann.lecun.com/exdb/mnist/). Convert the samples from integers to floating-point numbers:"""

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:"""

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:"""

model.fit(x_train, y_train, epochs=5)

model.evaluate(x_test,  y_test, verbose=2)

"""The image classifier is now trained to ~98% accuracy on this dataset. To learn more, read the [TensorFlow tutorials](https://www.tensorflow.org/tutorials/)."""

<filename>python/mnist_tutorial.py<gh_stars>0
# https://www.tensorflow.org/tutorials/quickstart/beginner

import tensorflow as tf

"""Load and prepare the [MNIST dataset](http://yann.lecun.com/exdb/mnist/). Convert the samples from integers to floating-point numbers:"""

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:"""

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:"""

model.fit(x_train, y_train, epochs=5)

model.evaluate(x_test,  y_test, verbose=2)

"""The image classifier is now trained to ~98% accuracy on this dataset. To learn more, read the [TensorFlow tutorials](https://www.tensorflow.org/tutorials/)."""

# https://www.tensorflow.org/tutorials/quickstart/beginner Load and prepare the [MNIST dataset](http://yann.lecun.com/exdb/mnist/). Convert the samples from integers to floating-point numbers: Build the `tf.keras.Sequential` model by stacking layers. Choose an optimizer and loss function for training: Train and evaluate the model: The image classifier is now trained to ~98% accuracy on this dataset. To learn more, read the [TensorFlow tutorials](https://www.tensorflow.org/tutorials/).