def resnet_block(l0, nf, bn, reps, downsample):
for i in range(reps):
stri = 2 if (downsample and i == 0) else 1
l1 = eddl.GlorotUniform(
eddl.Conv(l0, nf, [1, 1], [stri, stri], "same", False))
if (bn):
l1 = eddl.BatchNormalization(l1, 0.99, 0.001, True, "")
l1 = eddl.ReLu(l1)
l1 = eddl.GlorotUniform(
eddl.Conv(l1, nf, [3, 3], [1, 1], "same", False))
if (bn):
l1 = eddl.BatchNormalization(l1, 0.99, 0.001, True, "")
l1 = eddl.ReLu(l1)
l1 = eddl.GlorotUniform(
eddl.Conv(l1, nf * 4, [1, 1], [1, 1], "same", False))
if (bn):
l1 = eddl.BatchNormalization(l1, 0.99, 0.001, True, "")
if (i == 0):
l0 = eddl.GlorotUniform(
eddl.Conv(l0, nf * 4, [1, 1], [stri, stri], "same", False))
l0 = eddl.Add([l0, l1])
l0 = eddl.ReLu(l0)
return l0
def defblock(l, bn, nf, reps):
for i in range(reps):
l = eddl.GlorotUniform(eddl.Conv(l, nf, [3, 3]))
if bn:
l = eddl.BatchNormalization(l, 0.99, 0.001, True, "")
l = eddl.ReLu(l)
l = eddl.MaxPool(l, [2, 2], [2, 2], "valid")
return l
def main(args):
eddl.download_mnist()
num_classes = 10
in_ = eddl.Input([784])
layer = in_
layer = eddl.ReLu(eddl.GlorotNormal(eddl.Dense(layer, 1024)))
layer = eddl.ReLu(eddl.GlorotUniform(eddl.Dense(layer, 1024)))
layer = eddl.ReLu(eddl.RandomNormal(eddl.Dense(layer, 1024)))
out = eddl.Softmax(eddl.Dense(layer, num_classes))
net = eddl.Model([in_], [out])
eddl.build(
net, eddl.sgd(0.01, 0.9), ["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("mnist_trX.bin")
y_train = Tensor.load("mnist_trY.bin")
x_test = Tensor.load("mnist_tsX.bin")
y_test = Tensor.load("mnist_tsY.bin")
if args.small:
x_train = x_train.select([":6000"])
y_train = y_train.select([":6000"])
x_test = x_test.select([":1000"])
y_test = y_test.select([":1000"])
x_train.div_(255.0)
x_test.div_(255.0)
eddl.fit(net, [x_train], [y_train], args.batch_size, args.epochs)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
print("All done")
batch_size = 100
num_classes = 10
bn = int(sys.argv[1]) == 1
inp = eddl.Input([3, 32, 32])
l = inp
l = defblock(l, bn, 64, 2)
l = defblock(l, bn, 128, 2)
l = defblock(l, bn, 256, 3)
l = defblock(l, bn, 512, 3)
l = defblock(l, bn, 512, 3)
l = eddl.Flatten(l)
for i in range(2):
l = eddl.GlorotUniform(eddl.Dense(l, 4096))
if(bn):
l = eddl.BatchNormalization(l, 0.99, 0.001, True, "")
l = eddl.ReLu(l)
out = eddl.Softmax(eddl.GlorotUniform(eddl.Dense(l, num_classes)))
net = eddl.Model([inp], [out])
eddl.plot(net, "model.pdf")
eddl.build(net,
eddl.adam(0.0001),
["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU() if gpu else eddl.CS_CPU()
)
return l0
eddl.download_cifar10()
gpu = int(sys.argv[2]) == 1 if len(sys.argv) > 2 else True
epochs = 10 if gpu else 1
batch_size = 50
num_classes = 10
bn = int(sys.argv[1]) == 1
inp = eddl.Input([3, 32, 32])
l = inp
l = eddl.GlorotUniform(eddl.Conv(l, 64, [7, 7], [2, 2], "same", False))
l = eddl.MaxPool(l, [2, 2], [2, 2], "valid")
l = resnet_block(l, 64, bn, 2, False)
l = resnet_block(l, 128, bn, 2, True)
l = resnet_block(l, 256, bn, 2, True)
l = resnet_block(l, 512, bn, 2, True)
l = eddl.GlobalAveragePool(l)
l = eddl.Flatten(l)
out = eddl.Softmax(eddl.GlorotUniform(eddl.Dense(l, num_classes)))
net = eddl.Model([inp], [out])
eddl.plot(net, "model.pdf")
eddl.build(net, eddl.adam(0.0001), ["soft_cross_entropy"],
["categorical_accuracy"],
