-
Notifications
You must be signed in to change notification settings - Fork 1
/
smallvggnet_cifar10.py
55 lines (41 loc) · 1.63 KB
/
smallvggnet_cifar10.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
import smallvggnet
from sklearn.preprocessing import LabelBinarizer
from sklearn.metrics import classification_report
from keras.optimizers import SGD
from keras.datasets import cifar10
import matplotlib.pyplot as plt
import numpy as np
import argparse
# grab dataset
print("loading cifar-10 data...")
((trainX, trainY), (testX, testY)) = cifar10.load_data()
trainX = trainX.astype("float") / 255.0
testX = testX.astype("float") / 255.0
# convert the labels from integers to vectors
lb = LabelBinarizer()
trainY = lb.fit_transform(trainY)
testY = lb.transform(testY)
labelNames = ["airplane", "automobile", "bird", "cat", "deer",
"dog", "frog", "horse", "ship", "truck"]
# initialize the optimizer and model
print("compiling model...")
opt = SGD(lr=0.01)
model = smallvggnet.SmallVGGNet.build(width=32, height=32, depth=3, classes=10)
model.compile(loss="categorical_crossentropy",optimizer=opt,metrics=["accuracy"])
# train
print("trainig model...")
H = model.fit(trainX, trainY, validation_data = (testX,testY),batch_size=64,epochs=40,verbose=1)
print("evaluating network...")
predictions = model.predict(testX,batch_size=64)
print(classification_report(testY.argmax(axis=1),predictions.argmax(axis=1),target_names=labelNames))
plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, 40), H.history["acc"], label="train_acc")
plt.plot(np.arange(0, 40), H.history["val_acc"], label="val_acc")
plt.plot(np.arange(0, 40), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, 40), H.history["val_loss"], label="val_loss")
plt.title("Training Loss and Accuracy on CIFAR-10")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend()
plt.show()