# construct the image generator for data augmentation
aug = ImageDataGenerator(rotation_range=0,
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 the model using a sigmoid activation as the final layer
# in the network so we can perform multi-label classification
print("[INFO] compiling model...")
model = SmallerVGGNet.build(width=IMAGE_DIMS[1],
height=IMAGE_DIMS[0],
depth=IMAGE_DIMS[2],
classes=len(mlb.classes_),
finalAct="sigmoid")
print(model.summary())
# initialize the optimizer (SGD is sufficient)
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
# compile the model using binary cross-entropy rather than
# categorical cross-entropy -- this may seem counterintuitive for
# multi-label classification, but keep in mind that the goal here
# is to treat each output label as an independent Bernoulli
# distribution
model.compile(loss="binary_crossentropy", optimizer=opt, metrics=["accuracy"])
test_size=0.2,
random_state=42)
# construct the image generator for data augmentation
aug = ImageDataGenerator(rotation_range=25,
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 the model
print("[INFO] compiling model...")
model = SmallerVGGNet.build(width=IMAGE_DIMS[1],
height=IMAGE_DIMS[0],
depth=IMAGE_DIMS[2],
classes=len(lb.classes_))
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss="categorical_crossentropy",
optimizer=opt,
metrics=["accuracy"])
# train the network
print("[INFO] training network...")
H = model.fit_generator(aug.flow(trainX, trainY, batch_size=BS),
validation_data=(testX, testY),
steps_per_epoch=len(trainX) // BS,
epochs=EPOCHS,
verbose=1)
# save the model to disk
random_state=42)
trainY = to_categorical(trainY, num_classes=2)
testY = to_categorical(testY, num_classes=2)
# augmenting dataset
aug = ImageDataGenerator(rotation_range=25,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode="nearest")
# build model
model = SmallerVGGNet.build(width=img_dims[0],
height=img_dims[1],
depth=img_dims[2],
classes=2)
# compile the model
opt = Adam(lr=lr, decay=lr / epochs)
model.compile(loss="binary_crossentropy", optimizer=opt, metrics=["accuracy"])
# train the model
H = model.fit_generator(aug.flow(trainX, trainY, batch_size=batch_size),
validation_data=(testX, testY),
steps_per_epoch=len(trainX) // batch_size,
epochs=epochs,
verbose=1)
# save the model to disk
model.save(args.model)
(x_train, x_test, y_train, y_test) = train_test_split(data,
labels,
test_size=0.2,
random_state=69)
augment = ImageDataGenerator(zoom_range=0.2,
horizontal_flip=True,
rotation_range=30,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.2,
fill_mode="nearest")
print("[INFO] Compiling model...")
model = SmallerVGGNet.build(w=IMAGE_SHAPE[1],
h=IMAGE_SHAPE[0],
d=IMAGE_SHAPE[2],
classes=len(lb.classes_))
optimizer = Adam(lr=LEARNING_RATE, decay=LEARNING_RATE / EPOCHS)
model.compile(loss="categorical_crossentropy",
optimizer=optimizer,
metrics=["accuracy"])
print("[INFO] Training network...")
history = model.fit_generator(augment.flow(x_train,
y_train,
batch_size=BATCH_SIZE),
validation_data=(x_test, y_test),
steps_per_epoch=len(x_train) // BATCH_SIZE,
epochs=EPOCHS,
verbose=1)