方便呀,关键的我都放了,那我再放一点。
下面是我的代码:# TensorFlow and tf.keras
import tensorflow as tf
import sys
from tensorflow import keras
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
#print(sys.path)
print(tf.__version__)
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
下面是"""Fashion-MNIST dataset."""
import gzip
import os
import numpy as np
from keras.utils.data_utils import get_file
from tensorflow.python.util.tf_export import keras_export
@keras_export('keras.datasets.fashion_mnist.load_data')
def load_data():
print('aaaaaaaaaaaaaaaaaaaaaaa')
dirname = os.path.join('datasets', 'fashion-mnist')
base = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'
files = [
'train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz',
't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz'
]
paths = []
for fname in files:
paths.append(get_file(fname, origin=base + fname, cache_subdir=dirname))
with gzip.open(paths[0], 'rb') as lbpath:
y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8)
with gzip.open(paths[1], 'rb') as imgpath:
x_train = np.frombuffer(
imgpath.read(), np.uint8, offset=16).reshape(len(y_train), 28, 28)
with gzip.open(paths[2], 'rb') as lbpath:
y_test = np.frombuffer(lbpath.read(), np.uint8, offset=8)
with gzip.open(paths[3], 'rb') as imgpath:
x_test = np.frombuffer(
imgpath.read(), np.uint8, offset=16).reshape(len(y_test), 28, 28)
return (x_train, y_train), (x_test, y_test)
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