In [1]:
from tensorflow.examples.tutorials.mnist import input_data
mnist=input_data.read_data_sets('MNIST_data',one_hot=True)
Extracting MNIST_data\train-images-idx3-ubyte.gz
Extracting MNIST_data\train-labels-idx1-ubyte.gz
Extracting MNIST_data\t10k-images-idx3-ubyte.gz
Extracting MNIST_data\t10k-labels-idx1-ubyte.gz
In [2]:
batch=mnist.train.next_batch(3)
In [3]:
batch
Out[3]:
(array([[ 0., 0., 0., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32),
array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],
[ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.]]))
In [4]:
x=batch[0]
y=batch[1]
In [5]:
y[0]
Out[5]:
array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.])
In [6]:
x[0].shape
Out[6]:
(784,)
In [7]:
import numpy as np
img=np.reshape(x[0], newshape=(28, -1))
In [8]:
%matplotlib inline
from matplotlib import pyplot as plt
plt.imshow(img,cmap='gray')
Out[8]:
<matplotlib.image.AxesImage at 0x2e800ca0e48>
In [9]:
x[0]
Out[9]:
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In [ ]:
Content source: wasit7/cs634
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