68 lines
2.5 KiB
Python
68 lines
2.5 KiB
Python
# Copyright 2015 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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"""MNIST handwritten digits dataset.
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"""
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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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from tensorflow.python.keras.utils.data_utils import get_file
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from tensorflow.python.util.tf_export import keras_export
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@keras_export('keras.datasets.mnist.load_data')
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def load_data(path='mnist.npz'):
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"""Loads the [MNIST dataset](http://yann.lecun.com/exdb/mnist/).
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This is a dataset of 60,000 28x28 grayscale images of the 10 digits,
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along with a test set of 10,000 images.
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More info can be found at the
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[MNIST homepage](http://yann.lecun.com/exdb/mnist/).
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Arguments:
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path: path where to cache the dataset locally
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(relative to `~/.keras/datasets`).
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Returns:
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Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
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**x_train, x_test**: uint8 arrays of grayscale image data with shapes
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(num_samples, 28, 28).
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**y_train, y_test**: uint8 arrays of digit labels (integers in range 0-9)
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with shapes (num_samples,).
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License:
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Yann LeCun and Corinna Cortes hold the copyright of MNIST dataset,
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which is a derivative work from original NIST datasets.
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MNIST dataset is made available under the terms of the
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[Creative Commons Attribution-Share Alike 3.0 license.](
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https://creativecommons.org/licenses/by-sa/3.0/)
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"""
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origin_folder = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'
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path = get_file(
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path,
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origin=origin_folder + 'mnist.npz',
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file_hash=
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'731c5ac602752760c8e48fbffcf8c3b850d9dc2a2aedcf2cc48468fc17b673d1')
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with np.load(path, allow_pickle=True) as f:
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x_train, y_train = f['x_train'], f['y_train']
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x_test, y_test = f['x_test'], f['y_test']
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return (x_train, y_train), (x_test, y_test)
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