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.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_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.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_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_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): 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")
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()) 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)
def main(args): eddl.download_mnist() num_classes = 10 in_ = eddl.Input([784]) layer = in_ 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.sgd(0.001, 0.9), ["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") eddl.setlogfile(net, "mnist") 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) s = x_train.shape num_batches = s[0] // args.batch_size for i in range(args.epochs): eddl.reset_loss(net) print("Epoch %d/%d (%d batches)" % (i + 1, args.epochs, num_batches)) for j in range(num_batches): indices = np.random.randint(0, s[0], args.batch_size) eddl.train_batch(net, [x_train], [y_train], indices) losses1 = eddl.get_losses(net) metrics1 = eddl.get_metrics(net) for l, m in zip(losses1, metrics1): print("Loss: %.6f\tMetric: %.6f" % (l, m)) s = x_test.shape num_batches = s[0] // args.batch_size for j in range(num_batches): indices = np.arange(j * args.batch_size, j * args.batch_size + args.batch_size) eddl.eval_batch(net, [x_test], [y_test], indices) losses2 = eddl.get_losses(net) metrics2 = eddl.get_metrics(net) for l, m in zip(losses2, metrics2): print("Loss: %.6f\tMetric: %.6f" % (l, m)) last_batch_size = s[0] % args.batch_size if last_batch_size: indices = np.arange(j * args.batch_size, j * args.batch_size + args.batch_size) eddl.eval_batch(net, [x_test], [y_test], indices) losses3 = eddl.get_losses(net) metrics3 = eddl.get_metrics(net) for l, m in zip(losses3, metrics3): print("Loss: %.6f\tMetric: %.6f" % (l, m)) print("All done")