digit-recognizer/train.py at master · recursive-beast/digit-recognizer · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
from cost_funcs import CrossEntropyCost
from network import Network

from matplotlib import gridspec, pyplot
import pickle

epochs = 60
mini_batch_size = 10
learning_rate = 0.1
lmbda = 5.0

cost = CrossEntropyCost()

net = Network([784, 100, 10], cost)

ea, ec, ta, tc = net.SGD(
    "mnist/data/training.gz",
    epochs,
    mini_batch_size,
    learning_rate,
    lmbda,
    "mnist/data/testing.gz",
    True,
    True,
    True,
    True,
)

with open("progress_data.pkl", "wb") as f:
    pickle.dump((ea, ec, ta, tc), f)

x = list(range(epochs))

gs = gridspec.GridSpec(3, 2)

pyplot.figure()

# ----------------------------
acc_plot = pyplot.subplot(gs[0, :]).set_xticklabels([])

pyplot.plot(x, ea, label="evaluation accuracy")
pyplot.plot(x, ta, label="training accuracy")

pyplot.ylabel("accuracy")

pyplot.legend()

# ----------------------------

pyplot.subplot(gs[1, :]).set_xticklabels([])

pyplot.plot(x, ec, label="evaluation cost")

pyplot.ylabel("cost")

pyplot.legend()
# ----------------------------

pyplot.subplot(gs[2, :])

pyplot.plot(x, tc, label="training cost")

pyplot.xlabel("epochs")
pyplot.ylabel("cost")

pyplot.legend()
# ----------------------------

pyplot.show()
pyplot.close()