import sys
sys.path.append("../") # noqa
import keras_helper
import keras_callbacks
from tensorflow.python.keras.applications.vgg16 import VGG16, preprocess_input
from tensorflow.python.keras.optimizers import Adam
import tensorflow as tf
if __name__ == "__main__":
log_dir, checkpoints = keras_helper.ask_what_to_do_with_logfiles(
log_dir='logs/vgg16/',
ckpt="checkpoints/vgg16/{epoch:02d}.ckpt")
dg = keras_helper.DataGenerators(train_path="../../Dataset/sample_sizes/64_images/",
test_path="../../Dataset/test/",
enable_augmentation=False,
preprocessing_fn=preprocess_input)
train_generator, test_generator, tensorboard_generator = dg.create_data_generators(224)
train_samples, test_samples = dg.get_samples()
batch_size = dg.get_batch_size()
model = keras_helper.create_model(VGG16(input_shape=(224, 224, 3), include_top=False), train_up_to=2)
model.compile(Adam(lr=0.0005), loss="categorical_crossentropy", metrics=["accuracy", keras_helper.top_3_acc])
print(model.summary())
tensorboard_callback = keras_callbacks.TensorBoardImage(log_dir=log_dir, model=model, DataGenerator=dg)
checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(checkpoints, period=20)
model.fit_generator(train_generator, steps_per_epoch=train_samples // batch_size, epochs=100,
import sys
sys.path.append("../") # noqa
import keras_helper
import keras_callbacks
from tensorflow.python.keras.applications.inception_v3 import InceptionV3, preprocess_input
from tensorflow.python.keras.optimizers import Adam
import tensorflow as tf
if __name__ == "__main__":
log_dir, checkpoints = keras_helper.ask_what_to_do_with_logfiles(
log_dir='./logs/inception_v3/',
ckpt="checkpoints/inception_v3/{epoch:02d}.ckpt")
dg = keras_helper.DataGenerators(
train_path="../../Dataset/sample_sizes/64_images/",
test_path="../../Dataset/test/",
enable_augmentation=True,
preprocessing_fn=preprocess_input)
train_generator, test_generator, tensorboard_generator = dg.create_data_generators(
299)
train_samples, test_samples = dg.get_samples()
batch_size = dg.get_batch_size()
model = keras_helper.create_model(InceptionV3(input_shape=(299, 299, 3),
include_top=False),
train_up_to=20)
model.compile(Adam(lr=0.0001),
loss="categorical_crossentropy",
metrics=["accuracy", keras_helper.top_3_acc])
import sys
sys.path.append("../") # noqa
import keras_helper
import keras_callbacks
from tensorflow.python.keras.models import load_model
from tensorflow.python.keras.applications.resnet50 import preprocess_input
from tensorflow.python.keras.optimizers import Adam
import tensorflow as tf
if __name__ == "__main__":
log_dir, checkpoints = keras_helper.ask_what_to_do_with_logfiles(
log_dir='./logs/resnet_final/',
ckpt="checkpoints/resnet_final/{epoch:02d}.ckpt")
dg = keras_helper.DataGenerators(
train_path="../../Dataset/sample_sizes/64_images/",
test_path="../../Dataset/test/",
enable_augmentation=True,
preprocessing_fn=preprocess_input)
train_generator, test_generator, tensorboard_generator = dg.create_data_generators(
224, 15, 0.15, 0.15, 0.25)
train_samples, test_samples = dg.get_samples()
batch_size = dg.get_batch_size()
# model = keras_helper.create_model(ResNet50(input_shape=(224, 224, 3), include_top=False), train_up_to=6)
model = load_model("checkpoints/resnet_final/200.ckpt", compile=False)
keras_helper.set_untrainable(model, 10)
model.compile(Adam(lr=0.000125),
loss="categorical_crossentropy",
metrics=["accuracy", keras_helper.top_3_acc])
