def main(args):
if not os.path.isfile(args.input):
raise RuntimeError("input file '%s' not found" % args.input)
eddl.download_mnist()
print("importing net from", args.input)
net = eddl.import_net_from_onnx_file(args.input)
print("input.shape:", net.layers[0].input.shape)
print("output size =", len(net.lout))
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),
False # do not initialize weights to random values
)
net.resize(args.batch_size) # resize manually since we don't use "fit"
eddl.summary(net)
x_test = Tensor.load("mnist_tsX.bin")
y_test = Tensor.load("mnist_tsY.bin")
x_test.div_(255.0)
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.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.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 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_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.RandomCutout(layer, [0.1, 0.3], [0.1, 0.3])
layer = eddl.MaxPool(Block3_2(layer, 64))
layer = eddl.MaxPool(Block3_2(layer, 128))
layer = eddl.MaxPool(Block1(Block3_2(layer, 256), 256))
layer = eddl.MaxPool(Block1(Block3_2(layer, 512), 512))
layer = eddl.MaxPool(Block1(Block3_2(layer, 512), 512))
layer = eddl.Reshape(layer, [-1])
layer = eddl.Activation(eddl.Dense(layer, 512), "relu")
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.setlogfile(net, "vgg16")
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 main(args):
eddl.download_mnist()
num_classes = 10
in_ = eddl.Input([784])
layer = in_
layer = eddl.Reshape(layer, [1, 784]) # image as a 1D signal with depth 1
layer = eddl.MaxPool1D(eddl.ReLu(eddl.Conv1D(layer, 16, [3], [1])), [4],
[4])
layer = eddl.MaxPool1D(
eddl.ReLu(eddl.Conv1D(layer, 32, [3], [1])),
[4],
[4],
)
layer = eddl.MaxPool1D(
eddl.ReLu(eddl.Conv1D(layer, 64, [3], [1])),
[4],
[4],
)
layer = eddl.MaxPool1D(
eddl.ReLu(eddl.Conv1D(layer, 64, [3], [1])),
[4],
[4],
)
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 main(args):
eddl.download_mnist()
num_classes = 10
in_ = eddl.Input([784])
layer = in_
layer = eddl.Reshape(layer, [1, 28, 28])
layer = eddl.RandomCropScale(layer, [0.9, 1.0])
layer = eddl.Reshape(layer, [-1])
layer = eddl.ReLu(
eddl.GaussianNoise(
eddl.BatchNormalization(eddl.Dense(layer, 1024), True), 0.3))
layer = eddl.ReLu(
eddl.GaussianNoise(
eddl.BatchNormalization(eddl.Dense(layer, 1024), True), 0.3))
layer = eddl.ReLu(
eddl.GaussianNoise(
eddl.BatchNormalization(eddl.Dense(layer, 1024), True), 0.3))
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)
# LR annealing
if args.epochs < 4:
return
eddl.setlr(net, [0.005, 0.9])
eddl.fit(net, [x_train], [y_train], args.batch_size, args.epochs // 2)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
eddl.setlr(net, [0.001, 0.9])
eddl.fit(net, [x_train], [y_train], args.batch_size, args.epochs // 2)
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
eddl.setlr(net, [0.0001, 0.9])
eddl.fit(net, [x_train], [y_train], args.batch_size, args.epochs // 4)
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])
layer = eddl.ReLu(eddl.Dense(layer, 1024))
layer = eddl.BatchNormalization(layer, True)
layer = eddl.ReLu(eddl.Dense(layer, 1024))
layer = eddl.BatchNormalization(layer, True)
layer = eddl.ReLu(eddl.Dense(layer, 1024))
layer = eddl.BatchNormalization(layer, True)
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),
True # initialize weights to random values
)
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)
eddl.save_net_to_onnx_file(net, args.output)
print("saved net to", args.output)
print("All done")
def main(args):
eddl.download_mnist()
num_classes = 10
in_ = eddl.Input([28])
layer = in_
layer = eddl.LeakyReLu(eddl.Dense(layer, 32))
layer = eddl.L2(eddl.LSTM(layer, 128), 0.001)
ls = layer
out = eddl.Softmax(eddl.Dense(layer, num_classes))
net = eddl.Model([in_], [out])
eddl.build(
net, eddl.rmsprop(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("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.reshape_([x_train.shape[0], 28, 28])
x_test.reshape_([x_test.shape[0], 28, 28])
y_train.reshape_([y_train.shape[0], 1, 10])
y_test.reshape_([y_test.shape[0], 1, 10])
x_train.div_(255.0)
x_test.div_(255.0)
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)
ls_in = eddl.getInput(ls)
ls_in.info()
ls_out = eddl.getOutput(ls)
ls_out.info()
print("All done")
def main(args):
eddl.download_imdb_2000()
epochs = 2 if args.small else 10
length = 250
embdim = 33
vocsize = 2000
in_ = eddl.Input([1]) # 1 word
layer = in_
layer = eddl.RandomUniform(eddl.Embedding(layer, vocsize, 1, embdim),
-0.05, 0.05)
layer = eddl.GRU(layer, 37)
layer = eddl.ReLu(eddl.Dense(layer, 256))
out = eddl.Sigmoid(eddl.Dense(layer, 1))
net = eddl.Model([in_], [out])
eddl.build(
net, eddl.adam(0.001), ["cross_entropy"], ["binary_accuracy"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem))
eddl.summary(net)
x_train = Tensor.load("imdb_2000_trX.bin")
y_train = Tensor.load("imdb_2000_trY.bin")
x_test = Tensor.load("imdb_2000_tsX.bin")
y_test = Tensor.load("imdb_2000_tsY.bin")
# batch x timesteps x input_dim
x_train.reshape_([x_train.shape[0], length, 1])
x_test.reshape_([x_test.shape[0], length, 1])
y_train.reshape_([y_train.shape[0], 1, 1])
y_test.reshape_([y_test.shape[0], 1, 1])
if args.small:
x_train = x_train.select([":64", ":", ":"])
y_train = y_train.select([":64", ":", ":"])
x_test = x_test.select([":64", ":", ":"])
y_test = y_test.select([":64", ":", ":"])
for i in range(epochs):
eddl.fit(net, [x_train], [y_train], args.batch_size, 1)
eddl.evaluate(net, [x_test], [y_test])
print("All done")
def main(args):
eddl.download_mnist()
num_classes = 10
in_ = eddl.Input([784])
layer = in_
layer = eddl.BatchNormalization(
eddl.Activation(eddl.L2(eddl.Dense(layer, 1024), 0.0001), "relu"), True
)
layer = eddl.BatchNormalization(
eddl.Activation(eddl.L2(eddl.Dense(layer, 1024), 0.0001), "relu"), True
)
layer = eddl.BatchNormalization(
eddl.Activation(eddl.L2(eddl.Dense(layer, 1024), 0.0001), "relu"), True
)
out = eddl.Softmax(eddl.Dense(layer, num_classes))
net = eddl.Model([in_], [out])
acc = CategoricalAccuracy()
net.build(
eddl.sgd(0.01, 0.9),
[eddl.getLoss("soft_cross_entropy")],
[acc],
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")
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 main(args):
eddl.download_mnist()
num_classes = 10
in_ = eddl.Input([784])
layer = in_
layer = eddl.ReLu(eddl.L2(eddl.Dense(layer, 1024), 0.0001))
layer = eddl.ReLu(eddl.L1(eddl.Dense(layer, 1024), 0.0001))
layer = eddl.ReLu(eddl.L1L2(eddl.Dense(layer, 1024), 0.00001, 0.0001))
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")
def main(args):
## Read input dataset
x_train, y_train, x_test, y_test = read_input(args.in_ds)
## Net architecture
net = models.tissue_detector_DNN()
## Net compilation
eddl.build(
net,
eddl.rmsprop(0.00001),
["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU() if args.gpu else eddl.CS_CPU()
)
eddl.summary(net)
## Fit and evaluation
eddl.fit(net, [x_train], [y_train], args.batch_size, args.epochs)
eddl.evaluate(net, [x_test], [y_test])
eddl.save(net, "tissue_detector_model.bin")
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 main(args):
eddl.download_mnist()
num_classes = 10
in_ = eddl.Input([784])
layer = in_
layer = eddl.Reshape(layer, [-1])
layer = eddl.ReLu(eddl.Dense(layer, 1024))
layer = eddl.ReLu(eddl.Dense(layer, 1024))
layer = eddl.ReLu(eddl.Dense(layer, 1024))
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),
True # initialize weights to random values
)
serialized_net = eddl.serialize_net_to_onnx_string(net, False)
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)
print("evaluating before import")
eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
imported_net = eddl.import_net_from_onnx_string(serialized_net)
eddl.build(
imported_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),
False # do not initialize weights to random values
)
eddl.summary(imported_net)
print("net layers:", len(net.layers))
print("imported_net layers:", len(imported_net.layers))
print("evaluating imported net")
eddl.evaluate(imported_net, [x_test], [y_test], bs=args.batch_size)
print("All done")
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())
eddl.summary(net)
x_train = Tensor.load("cifar_trX.bin")
y_train = Tensor.load("cifar_trY.bin")
x_train.div_(255)
x_test = Tensor.load("cifar_tsX.bin")
y_test = Tensor.load("cifar_tsY.bin")
x_test.div_(255)
from time import time
import numpy as np
tiempos = []
for i in range(epochs):
s = time()
print(i)
res = eddl.fit(net, [x_train], [y_train], batch_size, 1)
tiempos.append(time() - s)
if gpu:
print("Evaluate test")
res1 = eddl.evaluate(net, [x_test], [y_test])
print(np.mean(tiempos))
def main(args):
freeze_epochs = 2
unfreeze_epochs = 5
num_classes = 10 # 10 labels in cifar10
eddl.download_cifar10()
eddl.download_model("resnet18.onnx", "re7jodd12srksd7")
net = eddl.import_net_from_onnx_file("resnet18.onnx", [3, 32, 32], DEV_CPU)
names = [_.name for _ in net.layers]
# Remove dense output layer
eddl.removeLayer(net, "resnetv15_dense0_fwd")
# Get last layer to connect the new dense
layer = eddl.getLayer(net, "flatten_170")
out = eddl.Softmax(eddl.Dense(layer, num_classes, True, "new_dense"))
# Get input layer
in_ = eddl.getLayer(net, "data")
# Create a new model
net = eddl.Model([in_], [out])
eddl.build(
net,
eddl.adam(0.0001),
["softmax_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem),
False # do not initialize weights to random values
)
eddl.summary(net)
# Force initialization of new layers
eddl.initializeLayer(net, "new_dense")
x_train = Tensor.load("cifar_trX.bin")
y_train = Tensor.load("cifar_trY.bin")
x_test = Tensor.load("cifar_tsX.bin")
y_test = Tensor.load("cifar_tsY.bin")
if args.small:
sel = [f":{2 * args.batch_size}"]
x_train = x_train.select(sel)
y_train = y_train.select(sel)
x_test = x_test.select(sel)
y_test = y_test.select(sel)
x_train.div_(255.0)
x_test.div_(255.0)
# Freeze pretrained weights
for n in names:
eddl.setTrainable(net, n, False)
# Train new layers
eddl.fit(net, [x_train], [y_train], args.batch_size, freeze_epochs)
# Unfreeze weights
for n in names:
eddl.setTrainable(net, n, True)
# Train all layers
eddl.fit(net, [x_train], [y_train], args.batch_size, unfreeze_epochs)
# Evaluate
eddl.evaluate(net, [x_test], [y_test], args.batch_size)
print("All done")
def main(args):
num_classes = 10
size = [28, 28] # size of images
ctype = ecvl.ColorType.GRAY
in_ = eddl.Input([1, size[0], size[1]])
out = LeNet(in_, num_classes)
net = eddl.Model([in_], [out])
eddl.build(net, eddl.sgd(0.001, 0.9), ["soft_cross_entropy"],
["categorical_accuracy"],
eddl.CS_GPU([1]) if args.gpu else eddl.CS_CPU())
eddl.summary(net)
eddl.setlogfile(net, "mnist")
training_augs = ecvl.SequentialAugmentationContainer([
ecvl.AugRotate([-5, 5]),
ecvl.AugAdditivePoissonNoise([0, 10]),
ecvl.AugGaussianBlur([0, 0.8]),
ecvl.AugCoarseDropout([0, 0.3], [0.02, 0.05], 0),
])
dataset_augs = ecvl.DatasetAugmentations([training_augs, None, None])
print("Reading dataset")
d = ecvl.DLDataset(args.in_ds, args.batch_size, dataset_augs, ctype)
x_train = Tensor([args.batch_size, d.n_channels_, size[0], size[1]])
y_train = Tensor([args.batch_size, len(d.classes_)])
num_samples = len(d.GetSplit())
num_batches = num_samples // args.batch_size
indices = list(range(args.batch_size))
print("Training")
for i in range(args.epochs):
eddl.reset_loss(net)
s = d.GetSplit()
random.shuffle(s)
d.split_.training_ = s
d.ResetCurrentBatch()
for j in range(num_batches):
print("Epoch %d/%d (batch %d/%d) - " %
(i + 1, args.epochs, j + 1, num_batches),
end="",
flush=True)
d.LoadBatch(x_train, y_train)
x_train.div_(255.0)
tx, ty = [x_train], [y_train]
eddl.train_batch(net, tx, ty, indices)
eddl.print_loss(net, j)
print()
print("Saving weights")
eddl.save(net, "mnist_checkpoint.bin", "bin")
print("Evaluation")
d.SetSplit(ecvl.SplitType.test)
num_samples = len(d.GetSplit())
num_batches = num_samples // args.batch_size
for i in range(num_batches):
print("batch %d / %d - " % (i, num_batches), end="", flush=True)
d.LoadBatch(x_train, y_train)
x_train.div_(255.0)
eddl.evaluate(net, [x_train], [y_train])
