mxnet tutorial

PrefaceThe sequence problem is also a interesting issue. Looking for a meeting LSTM of the material, found not a system of text, the early Sepp Hochreiter paper and disciple Felix Gers 's thesis did not look so relaxed. The first thing to start with was a review in 15, and it didn't look very smooth at the time, but looking at the first two (part) and then looking back at the formulation part of the article would be clearer.Originally intended to write a program of their own, found here a refere

), ' R ') as F: # Skips the header row (column name) of the file. lines = F.readlines () [1:] tokens = [L.rstrip (). Split (', ') for L in lines] # {index: label} Idx_label = Dict ((int (IDX), label) for IDX, label in tokens)) # Tag Collection labels = set (Idx_label.values ()) # Number of training data: '. /data/kaggle_cifar10/train ' Num_train = Len (Os.listdir (Os.path.join (Data_dir, Train_dir)) # Train number (corresponds to valid) num _train_tuning = Int (Num_train * (1-valid_ratio)) # Pr

): # Divide small batches of data samples and copy them onto each GPU. Gpu_xs = Split_and_load (X, ctx) Gpu_ys = Split_and_load (y, CTX) # Calculates the loss on each GPU. with Autograd.record (): ls = [loss (Lenet (gpu_x, Gpu_w), gpu_y) # Loss object on different devices for gpu_x, gpu_y, Gpu_w In Zip (Gpu_xs, Gpu_ys, Gpu_params)] # propagates backwards on each GPU. for L in LS: l.backward () # Adds the gradients on each GPU and then broadcast

Multi-index evaluation model ability for MXNET classification model Training (accuracy,cross-entropy,top_k_accuracy)The code is as follows Metric=[mx.metric.create (' acc '), mx.metric.create (' top_k_accuracy ', top_k=3), mx.metric.create (' CE ')] Mod.fit (Train, Val, Num_epoch=num_epoch, arg_params=arg_params, aux_params=aux_params, allow_ Missing=true, batch_end_callback = Mx.callback.Speedom

Linear regressionGiven a data point set X and the corresponding target value Y, the goal of the linear model is to find a use vector W and displacement BThe lines described, to be as close as possible to each sample x[i] and Y[i].The mathematical formula is represented as \ (\hat{y}=xw+b\)The objective function is to minimize a bit of squared error \ (\sum_{i=1}^{n} (\hat{y_i}-y_i) ^2\)A neural network is a collection of nodes (neurons) and forward edges. We? A few nodes to form a layer, each la

Operating system:64-bit WINDOWS10The construction and installation of Mxnet consists of two steps:The first step is to compile the shared library Libmxnet.dll.The second step is to install the language packs, such as Python.Minimum compilation requirements: The latest C + + compilers that support C + + 11, such as g++>=4.8 orclang A copy of the Blas library, for example: libblas , atlas , openblas orintelmkl 　　　Optional Libraries:

Mxnet/src/storage/gpu_device_storage.hSimilar to cpu_device_storage.h is defined by class Gpudevicestorage with level two namespaces mxnet and storage, with two static member functions insideAlloc and free implementations are implemented by invoking the Cuda API Cudamalloc and Cudafree, while the CPU portion is not logged when the memory request and the release error are written with log information.Persona

: Trainer.step (batch_size) Train_l_sum + = Nd.mean (l). Asscalar () Train_acc_sum + = accuracy (y_ Hat, y) Test_acc = evaluate_accuracy (test_iter, net) print ("Epoch%d, loss%.4f, train ACC%.3f, test acc%. 3f "% (Epoch, Train_l_sum/len (Train_iter), Train_acc_sum/len (Train_iter), TEST_ACC)) train_cpu (NET, Train_iter, Test_ ITER, loss, Num_epochs, batCh_size, params, LR) # outputepoch 1, Loss 0.7105, train ACC 0.842, test ACC 0.884epoch 2, Loss 0.4296, train acc 0.887, TE St ACC 0.899epoch 3,

Ndarray is similar to a multidimensional array of NumPy, but Ndarray provides more functionality: asynchronous computation of the GPU and CPU, and automatic derivation. This allows Ndarray to better support machine learning.Initializationfrom mxnet import ndarray as ndnd.zeros((3,4))nd.ones((3,4))nd.array([[1,2],[3,4]])out:[[1. 2.][3. 4.]] Operatoroperation by corresponding elementx+yx*ynd.exp(x)Multiplication of matricesnd.dot(x, y.T)Broadcast (beoad

A tool for converting a Caffe model into a mxnet model Https://github.com/dmlc/mxnet/tree/master/tools/caffe_converter Model conversion ./run.sh Vgg16 (Note: The process is lengthy and sometimes it resolves a bash error) Enter the pre-training model and network definition for the Caffe format, outputting the two items corresponding to the mxnet. Finally,

Mxnet's Ps-lite and parameter server principlesThe Ps-lite framework is the parameter server communication framework that is implemented by the DMLC group, and is the core of DMLC other projects, for example, its deep learning framework mxnet distributed training relies on the implementation of Ps-lite.Parameter Server principleIn the field of machine learning and deep learning, distributed optimization has become a prerequisite, because single-machin

the system should be exploration-exploitation that part, after viewing reinforcement Learning:an introduction 13th chapter, found that should be carried out sampling (in fact, if you follow the inertial thinking of statistical learning, you should also use sampled samples instead of expectations).ResultAfter the results out, found convergence is also OK, compared to the results of the r=-0.04 , textbook on the results are as follows: Figure 1. Ground Truth Predicted resul

:', bSetting the Lambd=0, the training error is much smaller than the test (generalization) error, which is a typical overfitting phenomenon.fit_and_plot(lambd=0)# output('w[:10]:', [[ 0.30343655 -0.08110731 0.64756584 -1.51627898 0.16536537 0.42101485 0.41159022 0.8322348 -0.66477555 3.56285167]]Using regularization, the overfitting phenomenon is alleviated to some extent. However, more accurate model parameters are still not being learned. This is mainly because the number of samples i

))(' Stage4_unit2_bn1_gamma ', (512L,))(' Stage3_unit3_bn1_gamma ', (256L,))(' Stage1_unit3_bn2_gamma ', (64L,))(' Stage1_unit3_bn3_gamma ', (64L,))(' Stage4_unit2_relu1_gamma ', (512L,))(' Stage3_unit10_conv2_weight ', (256L, 256L, 3L, 3L))(' Stage3_unit12_conv1_weight ', (256L, 256L, 3L, 3L))(' Stage3_unit2_relu1_gamma ', (256L,))(' Stage3_unit10_bn2_beta ', (256L,))(' Stage2_unit3_bn3_gamma ', (128L,))(' Stage2_unit3_bn2_beta ', (128L,))(' Stage3_unit8_bn3_beta ', (256L,))(' Fc1_gamma ', (512

binaries will be generated based on the entries in the. lst Root: Picture file directory, the default is the category folder, the category name to do label, each folder to store images If. LST is specified, each picture path changes to the path of each picture in the root path +.lst --pack-label: Useful when a. LST is specified, allowing label to be a high dimension (mainly label can be set to an array) As we have actually described before,. lst files are not required, Only. Re

Pre-and API introduction Mxnet.metricFrom mxnet Import metriccls_metric = metric. Accuracy () Box_metric = metric. MAE () cls_metric.update ([Cls_target], [Class_preds.transpose ((0,2,1))]) box_metric.update ([Box_target], [box_preds * Box_mask]) cls_metric.get () Box_metric.get ()Gluon.loss.LossClass Focalloss (Gluon.loss.Loss): def __init__ (self, axis=-1, alpha=0.25, gamma=2, Batch_axis=0, **kwargs): Super (Focalloss, self). __init__ (Non

with model parametersIn the custom layer we can also use the Block's own parameterdict type member variable params. As the name implies, this is a dictionary of model parameters that are mapped to the Parameter type by the parameter name of the string type. We can create Parameter from parameterdict through the Get function.params = gluon.ParameterDict()params.get('param2', shape=(2, 3))params# ouput( Parameter param2 (shape=(2, 3), dtype=Now let's look at how to implement a full-join layer wi

)gb.train_cpu(net, train_iter, test_iter, loss, num_epochs, batch_size, params, lr)Result output:epoch 1, loss 0.9913, train acc 0.663, test acc 0.931epoch 2, loss 0.2302, train acc 0.933, test acc 0.954epoch 3, loss 0.1601, train acc 0.953, test acc 0.958epoch 4, loss 0.1250, train acc 0.964, test acc 0.973epoch 5, loss 0.1045, train acc 0.969, test acc 0.974Gluon implementationWhen the model is trained, the dropout layer randomly discards the previous layer's output elements at the

When the model is more accurate on the training data set, the accuracy on the test data set can be both up and down. What is this for?Training error and generalization errorBefore explaining the above mentioned phenomena, we need to distinguish

Ver2.0: struct Node:Std::unique_ptr op; Node action function class, variable node pointer nullstd::string name; Node nameStd::vector inputs; The input port of the node, including the output of the upper node, the variable node, etc.Std::shared_ptr