In [33]:
import tensorflow as tf
In [34]:
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/tensorflow/alex/mnist/input_data", one_hot=True)
# Load data
x_train = mnist.train.images
y_train = mnist.train.labels
x_test = mnist.test.images
y_test = mnist.test.labels
Extracting /tmp/tensorflow/alex/mnist/input_data/train-images-idx3-ubyte.gz
Extracting /tmp/tensorflow/alex/mnist/input_data/train-labels-idx1-ubyte.gz
Extracting /tmp/tensorflow/alex/mnist/input_data/t10k-images-idx3-ubyte.gz
Extracting /tmp/tensorflow/alex/mnist/input_data/t10k-labels-idx1-ubyte.gz
In [37]:
import numpy as np
In [40]:
np.sum(y_train, axis = 0)
Out[40]:
array([ 5444., 6179., 5470., 5638., 5307., 4987., 5417., 5715.,
5389., 5454.])
In [4]:
print("x_train: ", x_train.shape)
print("y_train: ", y_train.shape)
print("x_test: ", x_test.shape)
print("y_test: ", y_test.shape)
x_train: (55000, 784)
y_train: (55000, 10)
x_test: (10000, 784)
y_test: (10000, 10)
In [7]:
mnist.train.images[0]
Out[7]:
array([ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.38039219, 0.37647063, 0.3019608 ,
0.46274513, 0.2392157 , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.35294119, 0.5411765 , 0.92156869,
0.92156869, 0.92156869, 0.92156869, 0.92156869, 0.92156869,
0.98431379, 0.98431379, 0.97254908, 0.99607849, 0.96078438,
0.92156869, 0.74509805, 0.08235294, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0.54901963,
0.98431379, 0.99607849, 0.99607849, 0.99607849, 0.99607849,
0.99607849, 0.99607849, 0.99607849, 0.99607849, 0.99607849,
0.99607849, 0.99607849, 0.99607849, 0.99607849, 0.99607849,
0.74117649, 0.09019608, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.88627458, 0.99607849, 0.81568635,
0.78039223, 0.78039223, 0.78039223, 0.78039223, 0.54509807,
0.2392157 , 0.2392157 , 0.2392157 , 0.2392157 , 0.2392157 ,
0.50196081, 0.8705883 , 0.99607849, 0.99607849, 0.74117649,
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,
0.83529419, 0.99607849, 0.99607849, 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, 0.99607849,
0.99607849, 0.91764712, 0. , 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, 0.99607849, 0.99607849, 0.91764712,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0.41568631, 0.6156863 ,
0.99607849, 0.99607849, 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, 0.89411771, 0.89411771,
0.89411771, 0.99215692, 0.99607849, 0.99607849, 0.99607849,
0.99607849, 0.94117653, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.26666668, 0.4666667 , 0.86274517,
0.99607849, 0.99607849, 0.99607849, 0.99607849, 0.99607849,
0.99607849, 0.99607849, 0.99607849, 0.99607849, 0.55686277,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0.14509805, 0.73333335,
0.99215692, 0.99607849, 0.99607849, 0.99607849, 0.87450987,
0.80784321, 0.80784321, 0.29411766, 0.26666668, 0.84313732,
0.99607849, 0.99607849, 0.45882356, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.44313729, 0.8588236 , 0.99607849, 0.94901967, 0.89019614,
0.45098042, 0.34901962, 0.12156864, 0. , 0. ,
0. , 0. , 0.7843138 , 0.99607849, 0.9450981 ,
0.16078432, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0.66274512, 0.99607849,
0.6901961 , 0.24313727, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0.18823531,
0.90588242, 0.99607849, 0.91764712, 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, 0.99607849, 0.99607849,
0.65098041, 0. , 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, 0.99607849, 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. , 0.82352948, 0.98039222, 0.99607849,
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. ,
0.94901967, 0.99607849, 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, 0.98431379, 0.9450981 ,
0.33725491, 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,
0.80784321, 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. ], dtype=float32)
In [23]:
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
def plot_mnist(data, classes):
for i in range(10):
idxs = (classes == i)
# get 10 images for class i
images = data[idxs][0:10]
for j in range(5):
plt.subplot(5, 10, i + j*10 + 1)
plt.imshow(images[j].reshape(28, 28), cmap='gray')
if j == 0:
plt.title(i)
plt.axis('off')
plt.show()
classes = np.argmax(y_train, 1)
plot_mnist(x_train, classes)
In [25]:
plt.imshow(mnist.train.images[1].reshape(28, 28), cmap='gray')
plt.show()
In [15]:
number_to_show = 1
for i in range(10):
image = x_train[np.argmax(y_train, 1) == number_to_show][i]
plt.imshow(image.reshape(28, 28), cmap='gray')
plt.show()
Content source: alexandrnikitin/workshops
Similar notebooks: