def train(self):
if self.find_lr == True:
# Navigate to output folder in parent directory
go_up_three_dirs()
# Plot learning rate finder plot
self.lr_finder.plot_loss(
"output/lr_finder_plot_A1.png")
else:
# Plot training loss accuracy and learning rate change
# Navigate to output folder in parent directory
go_up_three_dirs()
plot_train_loss_acc_lr(
self.history,
int(self.epochs/2),
self.schedule,
self.schedule_type,
"A1",
"output/train_loss_acc_A1_xception.png",
"output/lr_A1_xception.png")
# Get the training accuracy
training_accuracy = self.history.history['val_acc'][-1]
return training_accuracy
def train(self):
if self.find_lr == True:
# Navigate to output folder in parent directory
go_up_three_dirs()
print("[INFO] Creating learning rate finder plot...")
# Plot learning rate finder plot
self.lr_finder.plot_loss(
"output/lr_finder_plot_B2.png")
else:
# Plot training loss accuracy and learning rate change
# Navigate to output folder in parent directory
go_up_three_dirs()
plot_train_loss_acc_lr(
self.history,
self.epochs,
self.schedule,
self.schedule_type,
"B2",
"output/train_loss_acc_B2_cnn.png",
"output/lr_B2_cnn.png")
# Get the training accuracy
training_accuracy = self.history.history['val_acc'][-1]
return training_accuracy
def train_frozen_xception(height, width, num_classes, batch_size, epochs,
learning_rate, schedule_type, train_gen, val_gen,
frozen_model_path, frozen_training_plot_path,
frozen_training_plot_name):
# Store the number of epochs to train for in a convenience variable,
# then initialize the list of callbacks and learning rate scheduler
# to be used
callbacks = []
schedule = None
# check to see if step-based learning rate decay should be used
if schedule_type == "step":
print("[INFO] using 'step-based' learning rate decay...")
schedule = StepDecay(initAlpha=1e-1,
factor=0.25,
dropEvery=int(epochs / 5))
# check to see if linear learning rate decay should should be used
elif schedule_type == "linear":
print("[INFO] using 'linear' learning rate decay...")
schedule = PolynomialDecay(maxEpochs=epochs, initAlpha=1e-1, power=1)
# check to see if a polynomial learning rate decay should be used
elif schedule_type == "poly":
print("[INFO] using 'polynomial' learning rate decay...")
schedule = PolynomialDecay(maxEpochs=epochs, initAlpha=1e-1, power=5)
elif schedule_type == "one_cycle":
print("[INFO] using 'one cycle' learning...")
schedule = OneCycleScheduler(learning_rate)
callbacks = [schedule]
# if the learning rate schedule is not empty, add it to the list of
# callbacks
if schedule_type != "none" and schedule_type != "one_cycle":
callbacks = [LearningRateScheduler(schedule)]
# initialize the decay for the optimizer
decay = 0.0
# if we are using Keras' "standard" decay, then we need to set the
# decay parameter
if schedule_type == "standard":
print("[INFO] using 'keras standard' learning rate decay...")
decay = 1e-1 / epochs
# otherwise, no learning rate schedule is being used
elif schedule_type == "none":
print("[INFO] no learning rate schedule being used")
# Xception is used as the base architecture for the model.
# The top layers are not included in order to perform transfer learning.
# Modified to allow for a custom input size
base_model = Xception(weights="imagenet",
include_top=False,
input_shape=(height, width, 3))
# Implement own pooling layer
avg = GlobalAveragePooling2D()(base_model.output)
# Output layer
output = Dense(num_classes, activation="softmax")(avg)
# Build model
frozen_model = Model(inputs=base_model.input, outputs=output)
# First, we freeze the layers for the first part of the training
for layer in base_model.layers:
layer.trainable = False
if schedule_type != "one_cycle":
# initialize optimizer and model, then compile it
opt = SGD(lr=learning_rate, momentum=0.9, decay=decay)
else:
opt = SGD()
# We now compile the Xception model for the first stage
frozen_model.compile(loss="sparse_categorical_crossentropy",
optimizer=opt,
metrics=["accuracy"])
# Training and evaluating the Xception model for the first stage
history = frozen_model.fit(train_gen,
steps_per_epoch=train_gen.samples // batch_size,
validation_data=val_gen,
validation_steps=val_gen.samples // batch_size,
callbacks=callbacks,
epochs=int(epochs / 2))
# Plot training plot for the frozen model
plot_train_loss_acc_lr(history, int(epochs / 2), schedule, "none",
frozen_training_plot_name,
frozen_training_plot_path, None)
# Save model
frozen_model.save(frozen_model_path)
