lb = LabelBinarizer()
trainY = lb.fit_transform(trainY)
testY = lb.transform(testY)
# construct the image generator for data augmentation
aug = ImageDataGenerator(rotation_range=30,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode="nearest")
# initialize our VGG-like Convolutional Neural Network
model = SmallVGGNet.build(width=64,
height=64,
depth=3,
classes=len(lb.classes_))
# initialize our initial learning rate, # of epochs to train for,
# and batch size
INIT_LR = 0.01
EPOCHS = 20
BS = 8
# initialize the model and optimizer (you'll want to use
# binary_crossentropy for 2-class classification)
print("[INFO] training network...")
opt = SGD(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss="categorical_crossentropy",
optimizer=opt,
metrics=["accuracy"])
# vector)
lb = LabelBinarizer()
trainY = to_categorical(trainY)
testY = to_categorical(testY)
# construct the image generator for data augmentation
aug = ImageDataGenerator(rotation_range=30,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode="nearest")
# initialize our VGG-like Convolutional Neural Network
model = SmallVGGNet.build(width=128, height=128, depth=3, classes=2)
# initialize our initial learning rate, # of epochs to train for,
# and batch size
INIT_LR = 0.01
EPOCHS = 1
BS = 32
# initialize the model and optimizer (you'll want to use
# binary_crossentropy for 2-class classification)
print("[INFO] training network...")
opt = SGD(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss="categorical_crossentropy",
optimizer=opt,
metrics=["accuracy"])