sdl = SimpleDatasetLoader(preprocessors=[aap, iap])
(data, labels) = sdl.load(imagePaths, verbose=500)
data = data.astype("float") / 255.0
(trainX, testX, trainY, testY) = train_test_split(data,
labels,
test_size=0.25,
random_state=42)
trainY = LabelBinarizer().fit_transform(trainY)
testY = LabelBinarizer().fit_transform(testY)
print("[INFO] compiling model ...")
opt = SGD(lr=0.05)
model = MiniVGGNet.build(width=64, height=64, depth=3, classes=len(classNames))
model.compile(loss="categorical_crossentropy",
optimizer=opt,
metrics=["accuracy"])
print("[INFO] training network ...")
H = model.fit(trainX,
trainY,
validation_data=(testX, testY),
batch_size=32,
epochs=100,
verbose=1)
print("[INFO] evaluating network ...")
predictions = model.predict(testX, batch_size=32)
print(
"ship", "truck"
]
# construct the image generator for data augmentation
aug = ImageDataGenerator(rotation_range=10,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True,
fill_mode="nearest")
# loop over the number of models to train
for i in np.arange(0, args["num_models"]):
# initialize the optimizer and model
print("[INFO] training model {}/{}".format(i + 1, args["num_models"]))
opt = SGD(lr=0.01, decay=0.01 / 40, momentum=0.9, nesterov=True)
model = MiniVGGNet.build(width=32, height=32, depth=3, classes=10)
model.compile(loss="categorical_crossentropy",
optimizer=opt,
metrics=["accuracy"])
# train the network
H = model.fit(aug.flow(trainX, trainY, batch_size=64),
validation_data=(testX, testY),
epochs=40,
steps_per_epoch=len(trainX) // 64,
verbose=1)
# save the model to disk
p = [args["models"], "model_{}.model".format(i)]
model.save(os.path.sep.join(p))
((trainX, trainY), (testX, testY)) = cifar10.load_data()
trainX = trainX.astype('float') / 255.0
testX = testX.astype('float') / 255.0
lb = LabelBinarizer()
trainY = lb.fit_transform(trainY)
testY = lb.transform(testY)
labelNames = [
'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck'
]
print('[INFO] compiling model...')
opt = SGD(lr=0.01, decay=0.01 / 40, momentum=0.9, nesterov=True)
model = MiniVGGNet.build(width=32,
height=32,
depth=3,
classes=10,
useBatchNormalization=useBatchNormalization)
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
print('[INFO] training network...')
H = model.fit(trainX,
trainY,
validation_data=(testX, testY),
batch_size=64,
epochs=40,
verbose=2)
print('[INFO] serializing network...')