keras_quickstart .py mnist datasets

keras_quickstart.py

import tensorflow as tf

# Load and prepare the MNIST dataset.  
# 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

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(),
  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'])