In [2]:
# coding: utf-8
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
WARNING:tensorflow:From <ipython-input-2-01cbae6e9ee7>:4: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use alternatives such as official/mnist/dataset.py from tensorflow/models.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.
Instructions for updating:
Please write your own downloading logic.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.data to implement this functionality.
Extracting MNIST_data/train-images-idx3-ubyte.gz
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.data to implement this functionality.
Extracting MNIST_data/train-labels-idx1-ubyte.gz
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.one_hot on tensors.
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: __init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use alternatives such as official/mnist/dataset.py from tensorflow/models.
In [3]:
print mnist.train.images.shape
(55000, 784)
In [5]:
print mnist.train.labels.shape
(55000, 10)
In [6]:
print mnist.train.images[0,:]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0.3803922 0.37647063 0.3019608
0.46274513 0.2392157 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.3529412
0.5411765 0.9215687 0.9215687 0.9215687 0.9215687 0.9215687
0.9215687 0.9843138 0.9843138 0.9725491 0.9960785 0.9607844
0.9215687 0.74509805 0.08235294 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0.54901963 0.9843138 0.9960785 0.9960785
0.9960785 0.9960785 0.9960785 0.9960785 0.9960785 0.9960785
0.9960785 0.9960785 0.9960785 0.9960785 0.9960785 0.9960785
0.7411765 0.09019608 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.8862746 0.9960785 0.81568635 0.7803922 0.7803922 0.7803922
0.7803922 0.54509807 0.2392157 0.2392157 0.2392157 0.2392157
0.2392157 0.5019608 0.8705883 0.9960785 0.9960785 0.7411765
0.08235294 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.14901961 0.32156864
0.0509804 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.13333334 0.8352942 0.9960785 0.9960785 0.45098042 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.32941177
0.9960785 0.9960785 0.9176471 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0.32941177 0.9960785 0.9960785
0.9176471 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.4156863 0.6156863 0.9960785 0.9960785 0.95294124 0.20000002
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.09803922
0.45882356 0.8941177 0.8941177 0.8941177 0.9921569 0.9960785
0.9960785 0.9960785 0.9960785 0.94117653 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0.26666668 0.4666667 0.86274517 0.9960785 0.9960785
0.9960785 0.9960785 0.9960785 0.9960785 0.9960785 0.9960785
0.9960785 0.5568628 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0.14509805 0.73333335 0.9921569
0.9960785 0.9960785 0.9960785 0.8745099 0.8078432 0.8078432
0.29411766 0.26666668 0.8431373 0.9960785 0.9960785 0.45882356
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.4431373 0.8588236 0.9960785 0.9490197 0.89019614 0.45098042
0.34901962 0.12156864 0. 0. 0. 0.
0.7843138 0.9960785 0.9450981 0.16078432 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.6627451 0.9960785
0.6901961 0.24313727 0. 0. 0. 0.
0. 0. 0. 0.18823531 0.9058824 0.9960785
0.9176471 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0.07058824 0.48627454 0. 0.
0. 0. 0. 0. 0. 0.
0. 0.32941177 0.9960785 0.9960785 0.6509804 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.54509807
0.9960785 0.9333334 0.22352943 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0.8235295 0.9803922 0.9960785 0.65882355
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.9490197 0.9960785 0.93725497 0.22352943 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0.34901962 0.9843138 0.9450981
0.3372549 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.01960784 0.8078432 0.96470594 0.6156863 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.01568628 0.45882356
0.27058825 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. ]
In [38]:
image0 = mnist.train.images[0,:].reshape(28,28)
In [8]:
print image0
[[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.3803922
0.37647063 0.3019608 0.46274513 0.2392157 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0.3529412 0.5411765 0.9215687
0.9215687 0.9215687 0.9215687 0.9215687 0.9215687 0.9843138
0.9843138 0.9725491 0.9960785 0.9607844 0.9215687 0.74509805
0.08235294 0. 0. 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0.54901963 0.9843138 0.9960785 0.9960785
0.9960785 0.9960785 0.9960785 0.9960785 0.9960785 0.9960785
0.9960785 0.9960785 0.9960785 0.9960785 0.9960785 0.9960785
0.7411765 0.09019608 0. 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0.8862746 0.9960785 0.81568635 0.7803922
0.7803922 0.7803922 0.7803922 0.54509807 0.2392157 0.2392157
0.2392157 0.2392157 0.2392157 0.5019608 0.8705883 0.9960785
0.9960785 0.7411765 0.08235294 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0.14901961 0.32156864 0.0509804 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.13333334 0.8352942
0.9960785 0.9960785 0.45098042 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.32941177
0.9960785 0.9960785 0.9176471 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.32941177
0.9960785 0.9960785 0.9176471 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.4156863 0.6156863
0.9960785 0.9960785 0.95294124 0.20000002 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.09803922
0.45882356 0.8941177 0.8941177 0.8941177 0.9921569 0.9960785
0.9960785 0.9960785 0.9960785 0.94117653 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0.26666668 0.4666667 0.86274517
0.9960785 0.9960785 0.9960785 0.9960785 0.9960785 0.9960785
0.9960785 0.9960785 0.9960785 0.5568628 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0.14509805 0.73333335 0.9921569 0.9960785 0.9960785
0.9960785 0.8745099 0.8078432 0.8078432 0.29411766 0.26666668
0.8431373 0.9960785 0.9960785 0.45882356 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0.4431373 0.8588236 0.9960785 0.9490197 0.89019614 0.45098042
0.34901962 0.12156864 0. 0. 0. 0.
0.7843138 0.9960785 0.9450981 0.16078432 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0.6627451 0.9960785 0.6901961 0.24313727 0. 0.
0. 0. 0. 0. 0. 0.18823531
0.9058824 0.9960785 0.9176471 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0.07058824 0.48627454 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.32941177
0.9960785 0.9960785 0.6509804 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.54509807
0.9960785 0.9333334 0.22352943 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.8235295 0.9803922
0.9960785 0.65882355 0. 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.9490197 0.9960785
0.93725497 0.22352943 0. 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0.34901962 0.9843138 0.9450981
0.3372549 0. 0. 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0.01960784 0.8078432 0.96470594 0.6156863
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0.01568628 0.45882356 0.27058825 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. ]
[0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. ]]
In [39]:
%pylab
%matplotlib inline
from matplotlib.pyplot import imshow
from PIL import Image
import numpy as np
for i in xrange(28):
for j in xrange(28):
image0[i,j] = image0[i,j] * 255
a = Image.fromarray(image0)
imshow(a)
Using matplotlib backend: TkAgg
Populating the interactive namespace from numpy and matplotlib
Out[39]:
<matplotlib.image.AxesImage at 0x7f8e41c85c10>
In [37]:
print image0
0.0
In [ ]:
Content source: iYefeng/traits
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