def main():
ValidSampler = Sampler(utils.valid_file)
TestSampler = Sampler(utils.test_file)
networks = []
weights = []
for i in xrange(5):
if i == 0:
TrainSampler = Sampler(utils.train_file)
prev_ys = np.copy(TrainSampler.labels)
else:
TrainSampler = Sampler(utils.train_file, prev_ys)
network = Network()
network.train(TrainSampler)
cur_ys = network.predict(TrainSampler)
b1 = np.sum(np.multiply(cur_ys, prev_ys))
b2 = np.sum(np.multiply(cur_ys, cur_ys))
w = float(b1) / b2
prev_ys = np.subtract(prev_ys, w * cur_ys)
print i, 'done with weight', w
network.save('network_' + str(i) + '.ckpt')
weights.append(w)
networks.append(network)
validate_boost(ValidSampler, networks, weights)
validate_boost(TestSampler, networks, weights)
np.save('weights.npy', weights)
def main():
ValidSampler = Sampler(utils.valid_file)
TestSampler = Sampler(utils.test_file)
networks = []
weights = []
for i in xrange(5):
if i == 0:
TrainSampler = Sampler(utils.train_file)
prev_ys = np.copy(TrainSampler.labels)
else:
TrainSampler = Sampler(utils.train_file, prev_ys)
network = Network()
network.train(TrainSampler)
cur_ys = network.predict(TrainSampler)
b1 = np.sum(np.multiply(cur_ys, prev_ys))
b2 = np.sum(np.multiply(cur_ys, cur_ys))
w = float(b1) / b2
prev_ys = np.subtract(prev_ys, w * cur_ys)
print i, 'done with weight', w
network.save('network_' + str(i) + '.ckpt')
weights.append(w)
networks.append(network)
validate_boost(ValidSampler, networks, weights)
validate_boost(TestSampler, networks, weights)
np.save('weights.npy', weights)
import os
import numpy as np
from sklearn.tree import DecisionTreeClassifier as Model
from cnn import Network
DATA_DIR = 'data'
pipeline = [
(preprocessing.extract_rgb, True),
# (preprocessing.enrich_mirror, False),
]
data_file = os.path.join(DATA_DIR, 'data_train.dat')
targets_file = os.path.join(DATA_DIR, 'targets_train.dat')
data, targets = preprocessing.load_data(data_file), preprocessing.load_data(targets_file)
data_train, data_validation, targets_train, targets_validation = sklearn.model_selection.train_test_split(
data, targets, test_size=0.25, random_state=42, shuffle=True, stratify=targets
)
for func, apply_test in pipeline:
data_train, targets_train = func(data_train, targets_train)
if apply_test:
data_validation, targets_validation = func(data_validation, targets_validation)
print(data_train.shape[1:])
model = Network(input_shape=data_train.shape[1:])
model.fit(data_train, targets_train, data_validation, targets_validation)
predict = model.predict(data_validation)
print(sklearn.metrics.accuracy_score(targets_validation, predict))
args = arg_parse()
#Set up the neural network
print("Preparing network .....")
network = Network(args.cfgfile)
network.compile()
print("Loading input .....")
dataset = Dataset()
x_train, y_train, x_test, y_test = dataset.loadData(
network.net_info.input_shape)
# # Encode the data
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
print("Training network .....")
network.fit(x_train, y_train, x_test, y_test)
print("evaluation: ")
network.evaluate(x_test, y_test)
x_predict, y_predict = dataset.predictData(network.net_info.input_shape)
predict_images, predict_labels = dataset.predictImages()
print("predicting on remaining images ...")
prediction = network.predict(x_predict)
network.plotPrediction(predict_images, predict_labels, prediction)