Skip to content

Update fine_tune_inceptionv3.py #3

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Update fine_tune_inceptionv3.py #3

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
70 changes: 41 additions & 29 deletions fine_tune_inceptionv3.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -49,15 +49,15 @@
base_model = InceptionV3(weights='imagenet', include_top=False)

# dimensions of our images.
#Inception input size
# Inception input size
img_width, img_height = 299, 299

top_layers_checkpoint_path = 'cp.top.best.hdf5'
fine_tuned_checkpoint_path = 'cp.fine_tuned.best.hdf5'
new_extended_inception_weights = 'final_weights.hdf5'

train_data_dir = '/tmp/data/train'
validation_data_dir = '/tmp/data/validation'
train_data_dir = 'D:/data/train'
validation_data_dir = 'D:/data/validation'

nb_train_samples = 2000
nb_validation_samples = 800
Expand All @@ -76,11 +76,11 @@
predictions = Dense(2, activation='softmax')(x)

# this is the model we will train
model = Model(input=base_model.input, output=predictions)
model = Model(inputs=base_model.input, outputs=predictions)

if os.path.exists(top_layers_checkpoint_path):
model.load_weights(top_layers_checkpoint_path)
print ("Checkpoint '" + top_layers_checkpoint_path + "' loaded.")
model.load_weights(top_layers_checkpoint_path)
print("Checkpoint '" + top_layers_checkpoint_path + "' loaded.")

# first: train only the top layers (which were randomly initialized)
# i.e. freeze all convolutional InceptionV3 layers
Expand Down Expand Up @@ -111,23 +111,33 @@
batch_size=batch_size,
class_mode='categorical')

# Save the model after every epoch.
mc_top = ModelCheckpoint(top_layers_checkpoint_path, monitor='val_acc', verbose=0, save_best_only=True,
save_weights_only=False, mode='auto', period=1)

#Save the model after every epoch.
mc_top = ModelCheckpoint(top_layers_checkpoint_path, monitor='val_acc', verbose=0, save_best_only=True, save_weights_only=False, mode='auto', period=1)

#Save the TensorBoard logs.
# Save the TensorBoard logs.
tb = TensorBoard(log_dir='./logs', histogram_freq=1, write_graph=True, write_images=True)

# train the model on the new data for a few epochs
#model.fit_generator(...)
# model.fit_generator(...)

model.fit_generator(
train_generator,
samples_per_epoch=nb_train_samples // batch_size,
nb_epoch=top_epochs,
steps_per_epoch=nb_train_samples // batch_size,
epochs=top_epochs,
validation_data=validation_generator,
nb_val_samples=nb_validation_samples // batch_size,
validation_steps=nb_validation_samples // batch_size,
callbacks=[mc_top, tb])
# model.fit_generator(train_generator, steps_per_epoch=nb_train_samples, epochs=5,
# validation_data=validation_generator, validation_steps=nb_validation_samples)

# model.fit_generator(generator=train_generator,
# steps_per_epoch=steps_per_epoch,
# epochs=epochs,
# callbacks=[model_checkpoint, tensorboard_callback],
# validation_data=validation_generator,
# validation_steps=validation_steps,
# validation_freq=[1, 2, 10])

# at this point, the top layers are well trained and we can start fine-tuning
# convolutional layers from inception V3. We will freeze the bottom N layers
Expand All @@ -136,39 +146,41 @@
# let's visualize layer names and layer indices to see how many layers
# we should freeze:
for i, layer in enumerate(base_model.layers):
print(i, layer.name)


#Save the model after every epoch.
mc_fit = ModelCheckpoint(fine_tuned_checkpoint_path, monitor='val_acc', verbose=0, save_best_only=True, save_weights_only=False, mode='auto', period=1)
print(i, layer.name)

# Save the model after every epoch.
mc_fit = ModelCheckpoint(fine_tuned_checkpoint_path, monitor='val_acc', verbose=0, save_best_only=True,
save_weights_only=False, mode='auto', period=1)

if os.path.exists(fine_tuned_checkpoint_path):
model.load_weights(fine_tuned_checkpoint_path)
print ("Checkpoint '" + fine_tuned_checkpoint_path + "' loaded.")
model.load_weights(fine_tuned_checkpoint_path)
print("Checkpoint '" + fine_tuned_checkpoint_path + "' loaded.")

# we chose to train the top 2 inception blocks, i.e. we will freeze
# the first 172 layers and unfreeze the rest:
for layer in model.layers[:172]:
layer.trainable = False
layer.trainable = False
for layer in model.layers[172:]:
layer.trainable = True
layer.trainable = True

# we need to recompile the model for these modifications to take effect
# we use SGD with a low learning rate
from keras.optimizers import SGD

from tensorflow.keras.optimizers import SGD

model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])

# we train our model again (this time fine-tuning the top 2 inception blocks
# alongside the top Dense layers
#model.fit_generator(...)
# model.fit_generator(...)


model.fit_generator(
train_generator,
samples_per_epoch=nb_train_samples // batch_size,
nb_epoch=fit_epochs,
steps_per_epoch=nb_train_samples // batch_size,
epochs=top_epochs,
validation_data=validation_generator,
nb_val_samples=nb_validation_samples // batch_size,
callbacks=[mc_fit, tb])
validation_steps=nb_validation_samples // batch_size,
callbacks=[mc_top, tb])

model.save_weights(new_extended_inception_weights)