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 LeNet(in_layer, num_classes):
x = in_layer
x = eddl.MaxPool(eddl.ReLu(eddl.Conv(x, 20, [5, 5])), [2, 2], [2, 2])
x = eddl.MaxPool(eddl.ReLu(eddl.Conv(x, 50, [5, 5])), [2, 2], [2, 2])
x = eddl.Reshape(x, [-1])
x = eddl.ReLu(eddl.Dense(x, 500))
x = eddl.Softmax(eddl.Dense(x, num_classes))
return x
def Block3_2(layer, filters):
layer = eddl.ReLu(Normalization(eddl.Conv(
layer, filters, [3, 3], [1, 1], "same", False
)))
layer = eddl.ReLu(Normalization(eddl.Conv(
layer, filters, [3, 3], [1, 1], "same", False
)))
return layer
def Block3_2(layer, filters):
layer = eddl.ReLu(eddl.BatchNormalization(
eddl.Conv(layer, filters, [3, 3], [1, 1]), True
))
layer = eddl.ReLu(eddl.BatchNormalization(
eddl.Conv(layer, filters, [3, 3], [1, 1]), True
))
return layer
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")
def ResBlock(layer, filters, nconv, half):
in_ = layer
strides = [2, 2] if half else [1, 1]
layer = eddl.ReLu(BG(eddl.Conv(layer, filters, [3, 3], strides)))
for i in range(nconv - 1):
layer = eddl.ReLu(BG(eddl.Conv(layer, filters, [3, 3], [1, 1])))
if (half):
return eddl.Add(BG(eddl.Conv(in_, filters, [1, 1], [2, 2])), layer)
else:
return eddl.Add(layer, in_)
def main(args):
eddl.download_mnist()
in_ = eddl.Input([784])
target = eddl.Reshape(in_, [1, 28, 28])
layer = in_
layer = eddl.Reshape(layer, [1, 28, 28])
layer = eddl.ReLu(eddl.Conv(layer, 8, [3, 3]))
layer = eddl.ReLu(eddl.Conv(layer, 16, [3, 3]))
layer = eddl.ReLu(eddl.Conv(layer, 8, [3, 3]))
out = eddl.Sigmoid(eddl.Conv(layer, 1, [3, 3]))
net = eddl.Model([in_], [])
eddl.build(
net,
eddl.adam(0.001),
[],
[],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem)
)
eddl.summary(net)
x_train = Tensor.load("mnist_trX.bin")
if args.small:
x_train = x_train.select([":6000"])
x_train.div_(255.0)
mse = eddl.newloss(mse_loss, [out, target], "mse_loss")
dicei = eddl.newloss(dice_loss_img, [out, target], "dice_loss_img")
dicep = eddl.newloss(dice_loss_pixel, [out, target], "dice_loss_pixel")
batch = Tensor([args.batch_size, 784])
num_batches = x_train.shape[0] // args.batch_size
for i in range(args.epochs):
print("Epoch %d/%d (%d batches)" % (i + 1, args.epochs, num_batches))
diceploss = 0.0
diceiloss = 0.0
mseloss = 0
for j in range(num_batches):
print("Batch %d " % j, end="", flush=True)
eddl.next_batch([x_train], [batch])
eddl.zeroGrads(net)
eddl.forward(net, [batch])
diceploss += eddl.compute_loss(dicep) / args.batch_size
print("diceploss = %.6f " % (diceploss / (j + 1)), end="")
diceiloss += eddl.compute_loss(dicei) / args.batch_size
print("diceiloss = %.6f " % (diceiloss / (j + 1)), end="")
mseloss += eddl.compute_loss(mse) / args.batch_size
print("mseloss = %.6f\r" % (mseloss / (j + 1)), end="")
eddl.optimize(dicep)
eddl.update(net)
print()
print("All done")
def UNetWithPadding(layer):
x = layer
depth = 32
x = LBC(x, depth, [3, 3], [1, 1], "same")
x = LBC(x, depth, [3, 3], [1, 1], "same")
x2 = eddl.MaxPool(x, [2, 2], [2, 2])
x2 = LBC(x2, 2*depth, [3, 3], [1, 1], "same")
x2 = LBC(x2, 2*depth, [3, 3], [1, 1], "same")
x3 = eddl.MaxPool(x2, [2, 2], [2, 2])
x3 = LBC(x3, 4*depth, [3, 3], [1, 1], "same")
x3 = LBC(x3, 4*depth, [3, 3], [1, 1], "same")
x4 = eddl.MaxPool(x3, [2, 2], [2, 2])
x4 = LBC(x4, 8*depth, [3, 3], [1, 1], "same")
x4 = LBC(x4, 8*depth, [3, 3], [1, 1], "same")
x5 = eddl.MaxPool(x4, [2, 2], [2, 2])
x5 = LBC(x5, 8*depth, [3, 3], [1, 1], "same")
x5 = LBC(x5, 8*depth, [3, 3], [1, 1], "same")
x5 = eddl.BatchNormalization(eddl.Conv(
eddl.UpSampling(x5, [2, 2]), 8*depth, [3, 3], [1, 1], "same"
), True)
x4 = eddl.Concat([x4, x5]) if USE_CONCAT else eddl.Add([x4, x5])
x4 = LBC(x4, 8*depth, [3, 3], [1, 1], "same")
x4 = LBC(x4, 8*depth, [3, 3], [1, 1], "same")
x4 = eddl.BatchNormalization(eddl.Conv(
eddl.UpSampling(x4, [2, 2]), 4*depth, [3, 3], [1, 1], "same"
), True)
x3 = eddl.Concat([x3, x4]) if USE_CONCAT else eddl.Add([x3, x4])
x3 = LBC(x3, 4*depth, [3, 3], [1, 1], "same")
x3 = LBC(x3, 4*depth, [3, 3], [1, 1], "same")
x3 = eddl.Conv(
eddl.UpSampling(x3, [2, 2]), 2*depth, [3, 3], [1, 1], "same"
)
x2 = eddl.Concat([x2, x3]) if USE_CONCAT else eddl.Add([x2, x3])
x2 = LBC(x2, 2*depth, [3, 3], [1, 1], "same")
x2 = LBC(x2, 2*depth, [3, 3], [1, 1], "same")
x2 = eddl.BatchNormalization(eddl.Conv(
eddl.UpSampling(x2, [2, 2]), depth, [3, 3], [1, 1], "same"
), True)
x = eddl.Concat([x, x2]) if USE_CONCAT else eddl.Add([x, x2])
x = LBC(x, depth, [3, 3], [1, 1], "same")
x = LBC(x, depth, [3, 3], [1, 1], "same")
x = eddl.BatchNormalization(eddl.Conv(x, 1, [1, 1]), True)
return x
def defblock(l, bn, nf, reps, initializer):
for i in range(reps):
l = initializer(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_cifar10()
num_classes = 10
in_ = eddl.Input([3, 32, 32])
layer = in_
layer = eddl.RandomCropScale(layer, [0.8, 1.0])
layer = eddl.RandomHorizontalFlip(layer)
layer = eddl.ReLu(BG(eddl.Conv(layer, 64, [3, 3], [1, 1], "same", False)))
layer = eddl.Pad(layer, [0, 1, 1, 0])
for i in range(3):
layer = ResBlock(layer, 64, 0, i == 0)
for i in range(4):
layer = ResBlock(layer, 128, i == 0)
for i in range(6):
layer = ResBlock(layer, 256, i == 0)
for i in range(3):
layer = ResBlock(layer, 512, i == 0)
layer = eddl.MaxPool(layer, [4, 4])
layer = eddl.Reshape(layer, [-1])
out = eddl.Softmax(eddl.Dense(layer, num_classes))
net = eddl.Model([in_], [out])
eddl.build(
net, eddl.sgd(0.001, 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", "TB")
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:
# this is slow, make it really small
x_train = x_train.select([":500"])
y_train = y_train.select([":500"])
x_test = x_test.select([":100"])
y_test = y_test.select([":100"])
lr = 0.01
for j in range(3):
lr /= 10.0
eddl.setlr(net, [lr, 0.9])
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")
def main(args):
eddl.download_cifar10()
num_classes = 10
in_ = eddl.Input([3, 32, 32])
layer = in_
layer = eddl.RandomCropScale(layer, [0.8, 1.0])
layer = eddl.RandomFlip(layer, 1)
layer = eddl.ReLu(BG(eddl.Conv(layer, 64, [3, 3], [1, 1])))
layer = eddl.Pad(layer, [0, 1, 1, 0])
layer = ResBlock(layer, 64, 2, True)
layer = ResBlock(layer, 64, 2, False)
layer = ResBlock(layer, 128, 2, True)
layer = ResBlock(layer, 128, 2, False)
layer = ResBlock(layer, 256, 2, True)
layer = ResBlock(layer, 256, 2, False)
layer = ResBlock(layer, 256, 2, True)
layer = ResBlock(layer, 256, 2, False)
layer = eddl.Reshape(layer, [-1])
layer = eddl.ReLu(BG(eddl.Dense(layer, 512)))
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", "TB")
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")
def main(args):
eddl.download_mnist()
num_classes = 10
in_ = eddl.Input([784])
layer = in_
layer = eddl.Reshape(layer, [1, 28, 28])
layer = eddl.MaxPool(eddl.ReLu(eddl.Conv(layer, 32, [3, 3], [1, 1])),
[3, 3], [1, 1], "same")
layer = eddl.MaxPool(eddl.ReLu(eddl.Conv(layer, 64, [3, 3], [1, 1])),
[2, 2], [2, 2], "same")
layer = eddl.MaxPool(eddl.ReLu(eddl.Conv(layer, 128, [3, 3], [1, 1])),
[3, 3], [2, 2], "none")
layer = eddl.MaxPool(eddl.ReLu(eddl.Conv(layer, 256, [3, 3], [1, 1])),
[2, 2], [2, 2], "none")
layer = eddl.Reshape(layer, [-1])
out = eddl.Softmax(eddl.Dense(layer, num_classes))
net = eddl.Model([in_], [out])
eddl.build(
net, eddl.rmsprop(0.01), ["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem))
eddl.summary(net)
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")
def ResBlock(layer, filters, half, expand=0):
in_ = layer
layer = eddl.ReLu(
BG(eddl.Conv(layer, filters, [1, 1], [1, 1], "same", False)))
strides = [2, 2] if half else [1, 1]
layer = eddl.ReLu(
BG(eddl.Conv(layer, filters, [3, 3], strides, "same", False)))
layer = eddl.ReLu(
BG(eddl.Conv(layer, 4 * filters, [1, 1], [1, 1], "same", False)))
if (half):
return eddl.ReLu(
eddl.Add(
BG(eddl.Conv(in_, 4 * filters, [1, 1], [2, 2], "same", False)),
layer))
else:
if expand:
return eddl.ReLu(
eddl.Add(
BG(
eddl.Conv(in_, 4 * filters, [1, 1], [1, 1], "same",
False)), layer))
else:
return eddl.ReLu(eddl.Add(in_, layer))
def SegNet(x, num_classes):
x = eddl.ReLu(eddl.Conv(x, 64, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 64, [3, 3], [1, 1], "same"))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(eddl.Conv(x, 128, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 128, [3, 3], [1, 1], "same"))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(eddl.Conv(x, 256, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 256, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 256, [3, 3], [1, 1], "same"))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 256, [3, 3], [1, 1], "same"))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(eddl.Conv(x, 256, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 256, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 128, [3, 3], [1, 1], "same"))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(eddl.Conv(x, 128, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 64, [3, 3], [1, 1], "same"))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(eddl.Conv(x, 64, [3, 3], [1, 1], "same"))
x = eddl.Conv(x, num_classes, [3, 3], [1, 1], "same")
return x
def main(args):
eddl.download_cifar10()
num_classes = 10
in_ = eddl.Input([3, 32, 32])
layer = in_
layer = eddl.RandomHorizontalFlip(layer)
layer = eddl.RandomCropScale(layer, [0.8, 1.0])
layer = eddl.RandomCutout(layer, [0.1, 0.5], [0.1, 0.5])
layer = eddl.MaxPool(eddl.ReLu(eddl.BatchNormalization(
eddl.HeUniform(eddl.Conv(layer, 32, [3, 3], [1, 1], "same", False)),
True)), [2, 2])
layer = eddl.MaxPool(eddl.ReLu(eddl.BatchNormalization(
eddl.HeUniform(eddl.Conv(layer, 64, [3, 3], [1, 1], "same", False)),
True)), [2, 2])
layer = eddl.MaxPool(eddl.ReLu(eddl.BatchNormalization(
eddl.HeUniform(eddl.Conv(layer, 128, [3, 3], [1, 1], "same", False)),
True)), [2, 2])
layer = eddl.MaxPool(eddl.ReLu(eddl.BatchNormalization(
eddl.HeUniform(eddl.Conv(layer, 256, [3, 3], [1, 1], "same", False)),
True)), [2, 2])
layer = eddl.Reshape(layer, [-1])
layer = eddl.Activation(eddl.BatchNormalization(
eddl.Dense(layer, 128), True
), "relu")
out = eddl.Softmax(eddl.BatchNormalization(
eddl.Dense(layer, num_classes), True
))
net = eddl.Model([in_], [out])
eddl.build(
net,
eddl.adam(0.001),
["softmax_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)
eddl.setlr(net, [0.0001])
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")
def LBC(layer, *args, **kwargs):
return eddl.LeakyReLu(eddl.BatchNormalization(eddl.Conv(
layer, *args, **kwargs
), True))
def VGG16(in_layer, num_classes, seed=1234, init=eddl.HeNormal, l2_reg=None, dropout=None):
x = in_layer
x = eddl.ReLu(init(eddl.Conv(x, 64, [3, 3]), seed))
x = eddl.MaxPool(eddl.ReLu(init(eddl.Conv(x, 64, [3, 3]), seed)), [2, 2], [2, 2])
x = eddl.ReLu(init(eddl.Conv(x, 128, [3, 3]), seed))
x = eddl.MaxPool(eddl.ReLu(init(eddl.Conv(x, 128, [3, 3]), seed)), [2, 2], [2, 2])
x = eddl.ReLu(init(eddl.Conv(x, 256, [3, 3]), seed))
x = eddl.ReLu(init(eddl.Conv(x, 256, [3, 3]), seed))
x = eddl.MaxPool(eddl.ReLu(init(eddl.Conv(x, 256, [3, 3]), seed)), [2, 2], [2, 2])
x = eddl.ReLu(init(eddl.Conv(x, 512, [3, 3]), seed))
x = eddl.ReLu(init(eddl.Conv(x, 512, [3, 3]), seed))
x = eddl.MaxPool(eddl.ReLu(init(eddl.Conv(x, 512, [3, 3]), seed)), [2, 2], [2, 2])
x = eddl.ReLu(init(eddl.Conv(x, 512, [3, 3]), seed))
x = eddl.ReLu(init(eddl.Conv(x, 512, [3, 3]), seed))
x = eddl.MaxPool(eddl.ReLu(init(eddl.Conv(x, 512, [3, 3]), seed)), [2, 2], [2, 2])
x = eddl.Reshape(x, [-1])
x = eddl.Dense(x, 4096)
if dropout:
x = eddl.Dropout(x, dropout, iw=False)
if l2_reg:
x = eddl.L2(x, l2_reg)
x = eddl.ReLu(init(x,seed))
x = eddl.Dense(x, 4096)
if dropout:
x = eddl.Dropout(x, dropout, iw=False)
if l2_reg:
x = eddl.L2(x, l2_reg)
x = eddl.ReLu(init(x,seed))
x = eddl.Softmax(eddl.Dense(x, num_classes))
return x
def Block1(layer, filters):
return eddl.ReLu(
eddl.GroupNormalization(eddl.Conv(layer, filters, [1, 1], [1, 1]), 4))
def Block3_2(layer, filters):
layer = eddl.ReLu(
eddl.GroupNormalization(eddl.Conv(layer, filters, [3, 3], [1, 1]), 4))
layer = eddl.ReLu(
eddl.GroupNormalization(eddl.Conv(layer, filters, [3, 3], [1, 1]), 4))
return layer
def Block1(layer, filters):
return eddl.ReLu(eddl.BatchNormalization(
eddl.Conv(layer, filters, [1, 1], [1, 1]), True
))
def Block1(layer, filters):
return eddl.ReLu(Normalization(eddl.Conv(
layer, filters, [1, 1], [1, 1], "same", False
)))
def SegNetBN(x, num_classes):
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 64, [3, 3], [1, 1], "same"),
True))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 64, [3, 3], [1, 1], "same"),
True))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 128, [3, 3], [1, 1], "same"),
True))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 128, [3, 3], [1, 1], "same"),
True))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same"),
True))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same"),
True))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same"),
True))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"),
True))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"),
True))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"),
True))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"),
True))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"),
True))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"),
True))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"),
True))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"),
True))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"),
True))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"),
True))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same"),
True))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same"),
True))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same"),
True))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same"),
True))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 128, [3, 3], [1, 1], "same"),
True))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 128, [3, 3], [1, 1], "same"),
True))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 64, [3, 3], [1, 1], "same"),
True))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 64, [3, 3], [1, 1], "same"),
True))
x = eddl.Conv(x, num_classes, [3, 3], [1, 1], "same")
return x
def VGG16(in_layer, num_classes):
x = in_layer
x = eddl.ReLu(eddl.Conv(x, 64, [3, 3]))
x = eddl.MaxPool(eddl.ReLu(eddl.Conv(x, 64, [3, 3])), [2, 2], [2, 2])
x = eddl.ReLu(eddl.Conv(x, 128, [3, 3]))
x = eddl.MaxPool(eddl.ReLu(eddl.Conv(x, 128, [3, 3])), [2, 2], [2, 2])
x = eddl.ReLu(eddl.Conv(x, 256, [3, 3]))
x = eddl.ReLu(eddl.Conv(x, 256, [3, 3]))
x = eddl.MaxPool(eddl.ReLu(eddl.Conv(x, 256, [3, 3])), [2, 2], [2, 2])
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3]))
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3]))
x = eddl.MaxPool(eddl.ReLu(eddl.Conv(x, 512, [3, 3])), [2, 2], [2, 2])
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3]))
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3]))
x = eddl.MaxPool(eddl.ReLu(eddl.Conv(x, 512, [3, 3])), [2, 2], [2, 2])
x = eddl.Reshape(x, [-1])
x = eddl.ReLu(eddl.Dense(x, 4096))
x = eddl.ReLu(eddl.Dense(x, 4096))
x = eddl.Softmax(eddl.Dense(x, num_classes))
return x
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"],
