class OverfitGenerator(object):
@staticmethod
def generate_batch(batch_size):
return overfit_inputs, overfit_targets
overfit_inputs, overfit_targets = DataGenerator(
pattern='data/train/*',
image_size=64,
max_flow=5,
max_scale=5,
noise_level=5,
interp='bicubic').generate_batch(batch_size=100)
validation_data = DataGenerator(
pattern='data/test/*',
image_size=64,
max_flow=5,
max_scale=5,
noise_level=5,
interp='bicubic').generate_batch(batch_size=100)
cnn = CNN(split=False,
normalize=True,
fully_connected=None,
learning_rate=1e-4)
cnn.train(OverfitGenerator, validation_data, 1000)
import datetime
from models.generator import DataGenerator
from models.cnn import CNN
train_generator = DataGenerator(pattern='data/train/*',
image_size=64,
max_flow=5,
max_scale=5,
noise_level=5,
interp='bicubic')
validation_data = DataGenerator(
pattern='data/test/*',
image_size=64,
max_flow=5,
max_scale=5,
noise_level=5,
interp='bicubic').generate_batch(batch_size=100)
log_path = datetime.datetime.now().strftime('.logs/%Y%m%d-%H%M%S/')
cnn = CNN(split=True, fully_connected=500, normalize=True, learning_rate=2e-4)
cnn.train(train_generator, validation_data, 5000, log_path=log_path)
except IndexError:
print(f"Supply a pretrained model with -p. Exiting.")
exit(0)
# check the supplied model
if selected_model not in models.keys():
print(f"Model unknown: {selected_model}. Exiting.")
exit(0)
else:
print(f"Running model: {selected_model}")
model = None
if models.get(selected_model) == "CNN":
model = CNN(selected_model, save_name=save_name, pretrained_model=pretrained_model)
elif models.get(selected_model) == "RNN":
model = RNN(selected_model, save_name=save_name, pretrained_model=pretrained_model)
# proceed if we've got a valid model
if model is not None:
# train and evaluate
if pretrained_model is None:
model.train()
model.evaluate()
# export the model for importing later (save time training from scratch)
if save_name is not None:
model.export_model()
from models.generator import DataGenerator
from models.cnn import CNN
train_generator = DataGenerator(pattern='data/train/garden.png',
image_size=64,
max_flow=5,
max_scale=1,
noise_level=0)
validation_data = DataGenerator(pattern='data/train/carpet.png',
image_size=64,
max_flow=5,
max_scale=1,
noise_level=0).generate_batch(batch_size=1000)
convolution_layers = [
[32, 3, 2], # 32 filters, kernel size 3, stride 2
[64, 3, 2],
[128, 3, 1],
[256, 3, 2]
]
cnn = CNN(config=convolution_layers,
split=False,
normalize=False,
fully_connected=None,
learning_rate=1e-4)
cnn.train(train_generator, validation_data, 25000)
import os
from processing import get_train_val_split, get_jpeg_files
from models.cnn import CNN
labels_file = os.path.join(os.getcwd(), "data", "train.csv")
train_X, val_X, train_Y, val_Y = get_train_val_split(labels_file,
split_ratio=0.1)
model = CNN()
model.init()
model.train(train_X, train_Y, val_X, val_Y, epochs=50)
print "model training complete!!"
# Predict top k labels for test data images, and store in submission format
k = 5
test_images_dir = os.path.join(os.getcwd(), "data", "test")
test_images = get_jpeg_files(test_images_dir)
model.write_test_output(test_images, k)
