def main(args):
eddl.download_mnist()
in_ = eddl.Input([784])
layer = in_
layer = eddl.Activation(eddl.Dense(layer, 256), "relu")
layer = eddl.Activation(eddl.Dense(layer, 128), "relu")
layer = eddl.Activation(eddl.Dense(layer, 64), "relu")
layer = eddl.Activation(eddl.Dense(layer, 128), "relu")
layer = eddl.Activation(eddl.Dense(layer, 256), "relu")
out = eddl.Dense(layer, 784)
net = eddl.Model([in_], [out])
mse_loss = MSELoss()
mse_metric = MSEMetric()
net.build(
eddl.sgd(0.001, 0.9), [mse_loss], [mse_metric],
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")
x_train.div_(255.0)
eddl.fit(net, [x_train], [x_train], args.batch_size, args.epochs)
print("All done")
def main(args):
eddl.download_mnist()
in_ = eddl.Input([784])
layer = in_
layer = eddl.Activation(eddl.Dense(layer, 256), "relu")
layer = eddl.Activation(eddl.Dense(layer, 128), "relu")
layer = eddl.Activation(eddl.Dense(layer, 64), "relu")
layer = eddl.Activation(eddl.Dense(layer, 128), "relu")
layer = eddl.Activation(eddl.Dense(layer, 256), "relu")
out = eddl.Dense(layer, 784)
net = eddl.Model([in_], [out])
eddl.build(
net, eddl.sgd(0.001, 0.9), ["mean_squared_error"],
["mean_squared_error"],
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")
if args.small:
x_train = x_train.select([":6000"])
x_train.div_(255.0)
eddl.fit(net, [x_train], [x_train], args.batch_size, args.epochs)
tout = eddl.predict(net, [x_train])
tout[0].info()
print("All done")
def main(args):
eddl.download_mnist()
num_classes = 10
in_ = eddl.Input([784])
layer = in_
layer = eddl.Activation(eddl.Dense(layer, 1024), "relu")
layer = eddl.Activation(eddl.Dense(layer, 1024), "relu")
layer = eddl.Activation(eddl.Dense(layer, 1024), "relu")
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)
num_samples = x_train.shape[0]
num_batches = num_samples // args.batch_size
test_samples = x_test.shape[0]
test_batches = test_samples // args.batch_size
eddl.set_mode(net, TRMODE)
for i in range(args.epochs):
for j in range(num_batches):
print("Epoch %d/%d (batch %d/%d)" %
(i + 1, args.epochs, j + 1, num_batches))
indices = np.random.randint(0, num_samples, args.batch_size)
eddl.train_batch(net, [x_train], [y_train], indices)
for j in range(test_batches):
print("Epoch %d/%d (batch %d/%d)" %
(i + 1, args.epochs, j + 1, test_batches))
indices = np.random.randint(0, num_samples, args.batch_size)
eddl.eval_batch(net, [x_train], [y_train], indices)
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_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_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_cifar10()
num_classes = 10
in_ = eddl.Input([3, 32, 32])
layer = in_
layer = eddl.ReLu(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, 512, 2, True)
layer = ResBlock(layer, 512, 2, False)
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(0.01, 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("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.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")