def main(args):
eddl.download_cifar10()
num_classes = 10
in_ = eddl.Input([3, 32, 32])
layer = in_
layer = eddl.MaxPool(eddl.ReLu(Normalization(
eddl.Conv(layer, 32, [3, 3], [1, 1])
)), [2, 2])
layer = eddl.MaxPool(eddl.ReLu(Normalization(
eddl.Conv(layer, 64, [3, 3], [1, 1])
)), [2, 2])
layer = eddl.MaxPool(eddl.ReLu(Normalization(
eddl.Conv(layer, 128, [3, 3], [1, 1])
)), [2, 2])
layer = eddl.MaxPool(eddl.ReLu(Normalization(
eddl.Conv(layer, 256, [3, 3], [1, 1])
)), [2, 2])
layer = eddl.GlobalMaxPool(layer)
layer = eddl.Flatten(layer)
layer = eddl.Activation(eddl.Dense(layer, 128), "relu")
out = eddl.Softmax(eddl.Dense(layer, num_classes))
net = eddl.Model([in_], [out])
eddl.build(
net,
eddl.adam(0.001),
["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem)
)
eddl.summary(net)
eddl.plot(net, "model.pdf")
x_train = Tensor.load("cifar_trX.bin")
y_train = Tensor.load("cifar_trY.bin")
x_train.div_(255.0)
x_test = Tensor.load("cifar_tsX.bin")
y_test = Tensor.load("cifar_tsY.bin")
x_test.div_(255.0)
if args.small:
x_train = x_train.select([":5000"])
y_train = y_train.select([":5000"])
x_test = x_test.select([":1000"])
y_test = y_test.select([":1000"])
for i in range(args.epochs):
eddl.fit(net, [x_train], [y_train], args.batch_size, 1)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
print("All done")
epochs = 50 if gpu else 1
batch_size = 50
num_classes = 10
bn = int(sys.argv[1]) == 1
initializer = eddl.GlorotUniform if bn else eddl.HeUniform
inp = eddl.Input([3, 32, 32])
l = inp
l = defblock(l, bn, 64, 2, initializer)
l = defblock(l, bn, 128, 2, initializer)
l = defblock(l, bn, 256, 4, initializer)
l = defblock(l, bn, 512, 4, initializer)
l = defblock(l, bn, 512, 4, initializer)
l = eddl.Flatten(l)
for i in range(2):
l = initializer(eddl.Dense(l, 4096))
if (bn):
l = eddl.BatchNormalization(l, 0.99, 0.001, True, "")
l = eddl.ReLu(l)
out = eddl.Softmax(initializer(eddl.Dense(l, num_classes)))
net = eddl.Model([inp], [out])
eddl.plot(net, "model.pdf")
eddl.build(net, eddl.adam(0.00001), ["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU() if gpu else eddl.CS_CPU())