-
Notifications
You must be signed in to change notification settings - Fork 0
/
digits.py
144 lines (109 loc) · 3.76 KB
/
digits.py
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
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
from __future__ import absolute_import, division, print_function, unicode_literals
# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
import os
import imageio
import sys
images = []
labels = []
for filename in os.listdir("coil-20-proc"):
filepath = "coil-20-proc/" + filename
im = imageio.imread(filepath)
images.append(im)
if filename[4] == '_':
labels.append(int(filename[3])-1)
else:
labels.append(int(filename[3:5])-1)
class_names = []
for i in range(20):
class_names.append("obj{0}".format(i))
print(class_names)
test_images = []
test_labels = []
train_images = []
train_labels = []
# Splice into training sets and test sets
for i in range(len(images)):
if i % 10 == 5:
test_images.append(images[i])
test_labels.append(labels[i])
else:
train_images.append(images[i])
train_labels.append(labels[i])
'''
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images,
test_labels) = fashion_mnist.load_data()
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
'''
def divideImageArray(imageArr):
for i in range(len(imageArr)):
for rowIdx in range(len(imageArr[i])):
for colIdx in range(len(imageArr[i][rowIdx])):
imageArr[i][rowIdx][colIdx] = imageArr[i][rowIdx][colIdx] / 255.0
test_images = np.asarray(test_images)
test_labels = np.asarray(test_labels)
train_images = np.asarray(train_images)
train_labels = np.asarray(train_labels)
test_images = test_images / 255.0
train_images = train_images / 255.0
model = keras.Sequential([
keras.layers.Flatten(input_shape=(128, 128)), # Size of image is 128 x 128
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(20, activation='softmax') # Number of classifiers
])
'''
plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()
'''
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=10)
test_loss, test_acc = model.evaluate(test_images, test_labels)
print('\nTest accuracy:', test_acc)
predictions = model.predict(test_images)
def plot_image(i, predictions_array, true_label, img):
predictions_array, true_label, img = predictions_array, true_label[i], img[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
plt.imshow(img, cmap=plt.cm.binary)
predicted_label = np.argmax(predictions_array)
if predicted_label == true_label:
color = 'blue'
else:
color = 'red'
plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
100*np.max(predictions_array),
class_names[true_label]),
color=color)
def plot_value_array(i, predictions_array, true_label):
predictions_array, true_label = predictions_array, true_label[i]
plt.grid(False)
plt.xticks(range(20))
plt.yticks([])
thisplot = plt.bar(range(20), predictions_array, color="#777777")
plt.ylim([0, 1])
predicted_label = np.argmax(predictions_array)
thisplot[predicted_label].set_color('red')
thisplot[true_label].set_color('blue')
num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
plt.subplot(num_rows, 2*num_cols, 2*i+1)
plot_image(i, predictions[i], test_labels, test_images)
plt.subplot(num_rows, 2*num_cols, 2*i+2)
plot_value_array(i, predictions[i], test_labels)
plt.tight_layout()
plt.show()