# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Convenience wrapper around Keras' MNIST and Fashion MNIST data."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import tensorflow.compat.v2 as tf
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INPUT_SHAPE = (28, 28, 1)
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NUM_CLASSES = 10
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def load_reshaped_data(use_fashion_mnist=False, fake_tiny_data=False):
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"""Returns MNIST or Fashion MNIST train and test data."""
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if fake_tiny_data:
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num_fakes = 10
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x_train = x_test = np.zeros((num_fakes, 28, 28), dtype=np.uint8)
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y_train = y_test = np.zeros((num_fakes,), dtype=np.int64)
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else:
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mnist = (tf.keras.datasets.fashion_mnist if use_fashion_mnist else
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tf.keras.datasets.mnist)
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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return ((_prepare_image(x_train), _prepare_label(y_train)),
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(_prepare_image(x_test), _prepare_label(y_test)))
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def _prepare_image(x):
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"""Converts images to [n,h,w,c] format in range [0,1]."""
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return x[..., None].astype('float32') / 255.
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def _prepare_label(y):
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"""Conerts labels to one-hot encoding."""
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return tf.keras.utils.to_categorical(y, NUM_CLASSES)
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