message: switch mnist dataset mirror to a more reliable one
diff: @@ -24,9 +24,11 @@ import tensorflow as tf
  import tensorflow_io as tfio
  # Read the MNIST data into the IODataset.
  +dataset_url = "http://storage.googleapis.com/cvdf-datasets/mnist/"
  d_train = tfio.IODataset.from_mnist(
  - 'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
  - 'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz')
  + dataset_url + "train-images-idx3-ubyte.gz",
  + dataset_url + "train-labels-idx1-ubyte.gz",
  +)
  # Shuffle the elements of the dataset.
  d_train = d_train.shuffle(buffer_size=1024)
  @@ -38,17 +40,19 @@ d_train = d_train.map(lambda x, y: (tf.image.convert_image_dtype(x, tf.float32),
  d_train = d_train.batch(32)
  # Build the model.
  -model = tf.keras.models.Sequential([
  +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)
  -])
  + tf.keras.layers.Dense(10, activation=tf.nn.softmax),
  + ]
  +)
  # Compile the model.
  -model.compile(optimizer='adam',
  - loss='sparse_categorical_crossentropy',
  - metrics=['accuracy'])
  +model.compile(
  + optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]
  +)
  # Fit the model.
  model.fit(d_train, epochs=5, steps_per_epoch=200)
  