def initialize_mnist(self):
# This method is provided as an example of how to use the provided
# initialize_dataset method for a given set of data with known shapes.
import sys
sys.path.append("..")
from datasets.Load import mnist
trX, trY, teX, teY = mnist(onehot = True)
shape_dict = {
"w1": (32, 1, 3, 3),
"w2": (64, 32, 3, 3),
"w3": (128, 64, 3, 3),
"w4": (128 * 3 * 3, 625),
"wo": (625, 10)
}
self.initialize_dataset(trX, trY, teX, teY, shape_dict)
return self
for o in parser.option_list
])
length_help = max([len(o.help) for o in parser.option_list])
print("\n" + parser.description + "\n")
for option in parser.option_list:
print(" {0: <{1}} {2: <{3}} (default: {4})".format(
", ".join(option._short_opts + option._long_opts), length_name,
option.help, length_help, option.default))
print("")
sys.exit()
if options.clock:
start = time.time()
# load data
trX09, trY09, teX09, teY09 = mnist(onehot=False)
# prep training data
trX08, trY08, trX_9, trY_9 = remove_class(trX09, trY09, 9)
trX07, trY07, trX_8, trY_8 = remove_class(trX08, trY08, 8)
# prep testing data
teX08, teY08, teX_9, teY_9 = remove_class(teX09, teY09, 9)
teX07, teY07, teX_8, teY_8 = remove_class(teX08, teY08, 8)
# initialize, train, and evaluate multi-net model on classes 0-7
print("Batch training model on starting tasks 0-7...")
mnm = MultiNetModel(options.mode).train(trX07,
trY07,
epochs=options.epochs,
verbose=options.verbose)
import numpy as np
import sys
sys.path.append("..")
from convnet import ConvolutionalNeuralNetwork
from datasets.Load import mnist, cifar10
if __name__ == "__main__":
if len(sys.argv) < 2:
print("Usage: {filename} <dataset>".format(
filename=sys.argv[0].split("/")[-1]))
sys.exit(1)
dataset = sys.argv[1].lower()
if dataset == "mnist":
trX, trY, teX, teY = mnist(onehot=True)
shape_dict = {
"w1": (32, 1, 3, 3),
"w2": (64, 32, 3, 3),
"w3": (128, 64, 3, 3),
"w4": (128 * 3 * 3, 625),
"wo": (625, 10)
}
elif dataset == "cifar":
trX, trY, teX, teY = cifar10(onehot=True)
shape_dict = {
"w1": (32, 1, 3, 3),
"w2": (64, 32, 3, 3),
"w3": (128, 64, 3, 3),
"w4": (128 * 3 * 3, 841),
# custom help message
if options.help:
length_name = max([len(", ".join(o._short_opts + o._long_opts)) for o in parser.option_list])
length_help = max([len(o.help) for o in parser.option_list])
print("\n" + parser.description + "\n")
for option in parser.option_list:
print(" {0: <{1}} {2: <{3}} (default: {4})".format(", ".join(option._short_opts + option._long_opts), length_name, option.help, length_help, option.default))
print("")
sys.exit()
if options.clock:
start = time.time()
# load data
trX09, trY09, teX09, teY09 = mnist(onehot = False)
# prep training data
trX08, trY08, trX_9, trY_9 = remove_class(trX09, trY09, 9)
trX07, trY07, trX_8, trY_8 = remove_class(trX08, trY08, 8)
# prep testing data
teX08, teY08, teX_9, teY_9 = remove_class(teX09, teY09, 9)
teX07, teY07, teX_8, teY_8 = remove_class(teX08, teY08, 8)
# initialize, train, and evaluate multi-net model on classes 0-7
print("Batch training model on starting tasks 0-7...")
mnm = MultiNetModel(options.mode).train(trX07, trY07, epochs = options.epochs, verbose = options.verbose)
if options.test:
for t in range(8):
print("Accuracy on task {0}: {1:0.04f}".format(t, mnm.test(teX07, teY07, t)))
import numpy as np
import sys
sys.path.append("..")
from convnet import ConvolutionalNeuralNetwork
from datasets.Load import mnist, cifar10
if __name__ == "__main__":
if len(sys.argv) < 2:
print("Usage: {filename} <dataset>".format(filename = sys.argv[0].split("/")[-1]))
sys.exit(1)
dataset = sys.argv[1].lower()
if dataset == "mnist":
trX, trY, teX, teY = mnist(onehot = True)
shape_dict = {
"w1": (32, 1, 3, 3),
"w2": (64, 32, 3, 3),
"w3": (128, 64, 3, 3),
"w4": (128 * 3 * 3, 625),
"wo": (625, 10)
}
elif dataset == "cifar":
trX, trY, teX, teY = cifar10(onehot = True)
shape_dict = {
"w1": (32, 1, 3, 3),
"w2": (64, 32, 3, 3),
"w3": (128, 64, 3, 3),
"w4": (128 * 3 * 3, 841),
