如何使用keras加载下载好的数据集

https://blog.csdn.net/houchaoqun_xmu/article/details/78492718

【keras】解决 example 案例中 MNIST 数据集下载不了的问题

2017年11月10日 09:57:06 Houchaoqun_XMU 阅读数：15683

 

前言：

 

keras 源码中下载MNIST的方式是 path = get_file(path, origin='https://s3.amazonaws.com/img-datasets/mnist.npz')，数据源是通过 url = https://s3.amazonaws.com/img-datasets/mnist.npz 进行下载的。访问该 url 地址被墙了，导致 MNIST 相关的案例都卡在数据下载的环节。本文主要提供解决方案，让需要的读者可以跑案例的代码感受一下。

 

本文的贡献主要包括如下：

 

1）提供 mnist_npz 数据集；

2）分析了关于 mnist 几个相关的源代码；

3）提供了一种能够顺利运行 keras 源码中 example 下 mnist 的相关案例；

4）找到了另外几种解决方案，提供了相关的链接。

numpy.load(path)

numpy.load() 函数起到很重要的作用。它可以读取 .npy .npz 等文件类型，并返回对应的数据类型。

1）如果文件类型是 .pny 则返回一个1维数组。

2）如果文件类型是 .npz 则返回一个类似字典的数据类型，包含 {filename: array} 键值对。如，本例中的键值对如下所示：

1. f = np.load(path)
2. x_train, y_train = f['x_train'], f['y_train']
3. x_test, y_test = f['x_test'], f['y_test']
4. f.close()

详情请参考：https://docs.scipy.org/doc/numpy/reference/generated/numpy.load.html

原始 .kerasexamplesmnist_mlp.py

1. # -*- coding: utf-8 -*-
2. '''Trains a simple deep NN on the MNIST dataset.
3.  
4. Gets to 98.40% test accuracy after 20 epochs
5. (there is *a lot* of margin for parameter tuning).
6. 2 seconds per epoch on a K520 GPU.
7. '''
8.  
9. from __future__ import print_function
10.  
11. import keras
12. from keras.datasets import mnist
13. from keras.models import Sequential
14. from keras.layers import Dense, Dropout
15. from keras.optimizers import RMSprop
16.  
17.  
18. batch_size = 128
19. num_classes = 10
20. epochs = 20
21.  
22. # the data, shuffled and split between train and test sets
23. (x_train, y_train), (x_test, y_test) = mnist.load_data()
24.  
25. x_train = x_train.reshape(60000, 784)
26. x_test = x_test.reshape(10000, 784)
27. x_train = x_train.astype('float32')
28. x_test = x_test.astype('float32')
29. x_train /= 255
30. x_test /= 255
31. print(x_train.shape[0], 'train samples')
32. print(x_test.shape[0], 'test samples')
33.  
34. # convert class vectors to binary class matrices
35. y_train = keras.utils.to_categorical(y_train, num_classes)
36. y_test = keras.utils.to_categorical(y_test, num_classes)
37.  
38. model = Sequential()
39. model.add(Dense(512, activation='relu', input_shape=(784,)))
40. model.add(Dropout(0.2))
41. model.add(Dense(512, activation='relu'))
42. model.add(Dropout(0.2))
43. model.add(Dense(10, activation='softmax'))
44.  
45. model.summary()
46.  
47. ###
48. # 1）categorical_crossentropy(output, target, from_logits=False)：
49. # 计算输出张量和目标张量的Categorical crossentropy（类别交叉熵），目标张量与输出张量必须shape相同。
50. # 多分类的对数损失函数，与softmax分类器相对应的。
51. #
52. # 2）RMSprop()
53. # AdaGrad算法的改进。鉴于神经网络都是非凸条件下的，RMSProp在非凸条件下结果更好，改变梯度累积为指数衰减的移动平均以丢弃遥远的过去历史。
54. # reference：http://blog.csdn.net/bvl10101111/article/details/72616378
55. #
56. model.compile(loss='categorical_crossentropy',
57. optimizer=RMSprop(),
58. metrics=['accuracy'])
59.  
60. history = model.fit(x_train, y_train,
61. batch_size=batch_size,
62. epochs=epochs,
63. verbose=1,
64. validation_data=(x_test, y_test))
65. score = model.evaluate(x_test, y_test, verbose=0)
66. print('Test loss:', score[0])
67. print('Test accuracy:', score[1])

.keraskerasdatasetsmnist.py - load_data()

1. # -*- coding: utf-8 -*-
2. from ..utils.data_utils import get_file
3. import numpy as np
4.  
5. def load_data(path='mnist.npz'):
6. """Loads the MNIST dataset.
7.  
8. # Arguments
9. path: path where to cache the dataset locally
10. (relative to ~/.keras/datasets).
11.  
12. # Returns
13. Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
14.  
15. # numpy.load()
16. # numpy.load(file, mmap_mode=None, allow_pickle=True, fix_imports=True, encoding='ASCII')
17. # 1) Load arrays or pickled objects from .npy, .npz or pickled files
18. # 2)
19. # reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.load.html
20.  
21. """
22. path = get_file(path, origin='https://s3.amazonaws.com/img-datasets/mnist.npz')
23. f = np.load(path)
24. x_train, y_train = f['x_train'], f['y_train']
25. x_test, y_test = f['x_test'], f['y_test']
26. f.close()
27. return (x_train, y_train), (x_test, y_test)

下载 mnist.npz 数据集

本文使用的 mnist.npz 数据集是通过一个 japan 的服务器下载得到的，在此免费分享给大家。如果下载有问题的话，可以留言哈。

下载链接：https://pan.baidu.com/s/1jH6uFFC 密码: dw3d

改造 mnist_mlp.py

方法1：

mnist_mlp.py 源码是使用如下命令获取数据集：

1. # the data, shuffled and split between train and test sets
2. (x_train, y_train), (x_test, y_test) = mnist.load_data()

调用的是 .keraskerasdatasetsmnist.py 脚本中的 def load_data(path='mnist.npz') 函数，也就是因为网址被墙了导致不能正常运行的原因。本文事先下好了 mnist.npz 数据集，然后改动了一些代码使之正常运行。换句话说，本文使用的是“读取本地数据集”的方法，步骤如下：

1）下载好 mnist_npz 数据集，并将其放于 .kerasexamples 目录下。

2）改动后的 mnist_mlp.py 代码如下：

1. # -*- coding: utf-8 -*-
2. '''Trains a simple deep NN on the MNIST dataset.
3.  
4. Gets to 98.40% test accuracy after 20 epochs
5. (there is *a lot* of margin for parameter tuning).
6. 2 seconds per epoch on a K520 GPU.
7. '''
8.  
9. from __future__ import print_function
10.  
11. import keras
12. from keras.datasets import mnist
13. from keras.models import Sequential
14. from keras.layers import Dense, Dropout
15. from keras.optimizers import RMSprop
16.  
17. batch_size = 128
18. num_classes = 10
19. epochs = 20
20.  
21. # the data, shuffled and split between train and test sets
22. # (x_train, y_train), (x_test, y_test) = mnist.load_data()
23.  
24. import numpy as np
25. path='./mnist.npz'
26. f = np.load(path)
27. x_train, y_train = f['x_train'], f['y_train']
28. x_test, y_test = f['x_test'], f['y_test']
29. f.close()
30.  
31. x_train = x_train.reshape(60000, 784).astype('float32')
32. x_test = x_test.reshape(10000, 784).astype('float32')
33. x_train /= 255
34. x_test /= 255
35. print(x_train.shape[0], 'train samples')
36. print(x_test.shape[0], 'test samples')
37.  
38. # convert class vectors to binary class matrices
39. # label为0~9共10个类别，keras要求格式为binary class matrices
40.  
41. y_train = keras.utils.to_categorical(y_train, num_classes)
42. y_test = keras.utils.to_categorical(y_test, num_classes)
43.  
44. # add by hcq-20171106
45. # Dense of keras is full-connection.
46. model = Sequential()
47. model.add(Dense(512, activation='relu', input_shape=(784,)))
48. model.add(Dropout(0.2))
49. model.add(Dense(512, activation='relu'))
50. model.add(Dropout(0.2))
51. model.add(Dense(num_classes, activation='softmax'))
52.  
53. model.summary()
54.  
55. model.compile(loss='categorical_crossentropy',
56. optimizer=RMSprop(),
57. metrics=['accuracy'])
58.  
59. history = model.fit(x_train, y_train,
60. batch_size=batch_size,
61. epochs=epochs,
62. verbose=1,
63. validation_data=(x_test, y_test))
64. score = model.evaluate(x_test, y_test, verbose=0)
65. print('Test loss:', score[0])
66. print('Test accuracy:', score[1])

运行效果如下所示：

1. 60000 train samples
2. 10000 test samples
3. _________________________________________________________________
4. Layer (type) Output Shape Param #
5. =================================================================
6. dense_1 (Dense) (None, 512) 401920
7. _________________________________________________________________
8. dropout_1 (Dropout) (None, 512) 0
9. _________________________________________________________________
10. dense_2 (Dense) (None, 512) 262656
11. _________________________________________________________________
12. dropout_2 (Dropout) (None, 512) 0
13. _________________________________________________________________
14. dense_3 (Dense) (None, 10) 5130
15. =================================================================
16. Total params: 669,706
17. Trainable params: 669,706
18. Non-trainable params: 0
19. _________________________________________________________________
20. Train on 60000 samples, validate on 10000 samples
21. Epoch 1/20
22. 2017-11-09 23:06:16.881800: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0)
23.  
24. ... ...
25.  
26. 60000/60000 [==============================] - 1s 23us/step - loss: 0.0387 - acc: 0.9888 - val_loss: 0.0706 - val_acc: 0.9814
27. Epoch 8/20
28. 60000/60000 [==============================] - 1s 23us/step - loss: 0.0341 - acc: 0.9899 - val_loss: 0.0789 - val_acc: 0.9827
29. Epoch 9/20
30. 60000/60000 [==============================] - 1s 23us/step - loss: 0.0304 - acc: 0.9911 - val_loss: 0.0851 - val_acc: 0.9833
31. Epoch 10/20
32. 60000/60000 [==============================] - 1s 23us/step - loss: 0.0290 - acc: 0.9918 - val_loss: 0.0867 - val_acc: 0.9818
33. Epoch 11/20
34. 60000/60000 [==============================] - 1s 23us/step - loss: 0.0264 - acc: 0.9924 - val_loss: 0.0881 - val_acc: 0.9833
35. Epoch 12/20
36. 60000/60000 [==============================] - 1s 23us/step - loss: 0.0261 - acc: 0.9928 - val_loss: 0.1095 - val_acc: 0.9801
37. Epoch 13/20
38. 60000/60000 [==============================] - 1s 23us/step - loss: 0.0246 - acc: 0.9931 - val_loss: 0.1012 - val_acc: 0.9830
39. Epoch 14/20
40. 60000/60000 [==============================] - 1s 23us/step - loss: 0.0233 - acc: 0.9935 - val_loss: 0.1116 - val_acc: 0.9812
41. Epoch 15/20
42. 60000/60000 [==============================] - 1s 23us/step - loss: 0.0223 - acc: 0.9942 - val_loss: 0.1016 - val_acc: 0.9832
43. Epoch 16/20
44. 60000/60000 [==============================] - 1s 23us/step - loss: 0.0214 - acc: 0.9943 - val_loss: 0.1053 - val_acc: 0.9832
45. Epoch 17/20
46. 60000/60000 [==============================] - 1s 23us/step - loss: 0.0178 - acc: 0.9950 - val_loss: 0.1095 - val_acc: 0.9838
47. Epoch 18/20
48. 60000/60000 [==============================] - 1s 23us/step - loss: 0.0212 - acc: 0.9949 - val_loss: 0.1158 - val_acc: 0.9822
49. Epoch 19/20
50. 60000/60000 [==============================] - 1s 23us/step - loss: 0.0197 - acc: 0.9951 - val_loss: 0.1112 - val_acc: 0.9831
51. Epoch 20/20
52. 60000/60000 [==============================] - 1s 23us/step - loss: 0.0203 - acc: 0.9951 - val_loss: 0.1097 - val_acc: 0.9833
53. Test loss: 0.109655842465
54. Test accuracy: 0.9833

方法2：参考该【博文】

 

 (x_train, y_train), (x_test, y_test) = mnist.load_data(path='/home/duchao/下载/mnist.npz')

Reference：

 

keras 中文文档：http://keras-cn.readthedocs.io/en/latest/

阅读源码遇到的一些TF、keras函数及问题：http://blog.csdn.net/jsliuqun/article/details/64444302

python读取mnist数据集：https://blog.mythsman.com/2016/01/25/1/