MXNet的Model API
MXNet的API
训练一个模型
# configure a two layer neuralnetwork data = mx.symbol.Variable('data') fc1 = mx.symbol.FullyConnected(data, name='fc1', num_hidden=128) act1 = mx.symbol.Activation(fc1, name='relu1', act_type='relu') fc2 = mx.symbol.FullyConnected(act1, name='fc2', num_hidden=64) softmax = mx.symbol.SoftmaxOutput(fc2, name='sm') # create a model model = mx.model.FeedForward.create(softmax,X=data_set,num_epoch=num_epoch,learning_rate=0.01)
你还可以使用scikit-learn一样的风格来构造和拟合一个模型
# create a model using sklearn-style two step way model = mx.model.FeedForward(softmax,num_epoch=num_epoch,learning_rate=0.01)model.fit(X=data_set)
你如果想看更多的功能,请看Model API Reference
保存模型
# save a model to mymodel-symbol.json and mymodel-0100.params prefix = 'mymodel' iteration = 100 model.save(prefix, iteration)# load model back model_loaded = mx.model.FeedForward.load(prefix, iteration)
我们往往用一个脚本进行对数据的训练,往往以前缀加序号的形式如mymodel-0100.params这样的形式保存,然后用另一个脚本加载模型,并进行预测来完成相应的功能。
阶段性的点检测(Checkpoint)
prefix='models/chkpt' model = mx.model.FeedForward.create(softmax,X=data_set,iter_end_callback=mx.callback.do_checkpoint(prefix),...)
使用多个设备
devices = [mx.gpu(i) for i in range(num_device)] model = mx.model.FeedForward.create(softmax,X=dataset,ctx=devices,...)
这个训练过程将会通过一个并行的方式在你指定的GPUS进行。
模型API
MXNet模型模块
-
mxnet.model.
BatchEndParam
¶ -
alias of
BatchEndParams
-
mxnet.model.
save_checkpoint
(prefix, epoch, symbol, arg_params, aux_params)
-
Checkpoint the model data into file.
Parameters: - prefix (str) – Prefix of model name.
- epoch (int) – The epoch number of the model.
- symbol (Symbol) – The input symbol
- arg_params (dict of str to NDArray) – Model parameter, dict of name to NDArray of net’s weights.
- aux_params (dict of str to NDArray) – Model parameter, dict of name to NDArray of net’s auxiliary states.
Notes
prefix-symbol.json
will be saved for symbol.prefix-epoch.params
will be saved for parameters.
-
Notes
prefix-symbol.json
will be saved for symbol.prefix-epoch.params
will be saved for parameters.
注意:
mxnet.model.
load_checkpoint
(prefix, epoch)
-
Load model checkpoint from file.
Parameters: - prefix (str) – Prefix of model name.
- epoch (int) – Epoch number of model we would like to load.
Returns: - symbol (Symbol) – The symbol configuration of computation network.
- arg_params (dict of str to NDArray) – Model parameter, dict of name to NDArray of net’s weights.
- aux_params (dict of str to NDArray) – Model parameter, dict of name to NDArray of net’s auxiliary states.
类功能:加载检测点(感觉还是翻译成检测点比较好)
mxnet.model.
FeedForward
(symbol, ctx=None, num_epoch=None, epoch_size=None,optimizer='sgd', initializer=<mxnet.initializer.Uniform object>, numpy_batch_size=128,arg_params=None, aux_params=None, allow_extra_params=False, begin_epoch=0, **kwargs)¶
-
Model class of MXNet for training and predicting feedforward nets. This class is designed for a single-data single output supervised network.
Parameters: - symbol (Symbol) – The symbol configuration of computation network.
- ctx (Context or list of Context, optional) – The device context of training and prediction. To use multi GPU training, pass in a list of gpu contexts.
- num_epoch (int, optional) – Training parameter, number of training epochs(epochs).
- epoch_size (int, optional) – Number of batches in a epoch. In default, it is set to ceil(num_train_examples / batch_size)
- optimizer (str or Optimizer, optional) – Training parameter, name or optimizer object for training.
- initializer (initializer function, optional) – Training parameter, the initialization scheme used.
- numpy_batch_size (int, optional) – The batch size of training data. Only needed when input array is numpy.
- arg_params (dict of str to NDArray, optional) – Model parameter, dict of name to NDArray of net’s weights.
- aux_params (dict of str to NDArray, optional) – Model parameter, dict of name to NDArray of net’s auxiliary states.
- allow_extra_params (boolean, optional) – Whether allow extra parameters that are not needed by symbol to be passed by aux_params and arg_params. If this is True, no error will be thrown when aux_params and arg_params contain extra parameters than needed.
- begin_epoch (int, optional) – The begining training epoch.
- kwargs (dict) – The additional keyword arguments passed to optimizer.
类功能:
allow_extra_params(boolean,optional)-是否需要一些额外的参数,aux_params和arg_params不需要的。如果这是真的,那么就不会抛出错误当参数的个数超出所需要的参数的时候。
predict
(X, num_batch=None, return_data=False, reset=True)¶
-
Run the prediction, always only use one device. :param X: :type X: mxnet.DataIter :param num_batch: the number of batch to run. Go though all batches if None :type num_batch: int or None
Returns: y – The predicted value of the output. Return type: numpy.ndarray or a list of numpy.ndarray if the network has multiple outputs. 类方法功能:进行预测,只能使用一个device.参数X是X类型的,batch的运行数量,如果被设置为None的话,会对里面的所有的批进行处理。
score
(X, eval_metric='acc', num_batch=None, batch_end_callback=None, reset=True)
-
Run the model on X and calculate the score with eval_metric :param X: :type X: mxnet.DataIter :param eval_metric: The metric for calculating score :type eval_metric: metric.metric :param num_batch: the number of batch to run. Go though all batches if None :type num_batch: int or None
Returns: s – the final score Return type: float 类方法功能:在X上运行模型并且用评估矩阵计算分数。
fit
(X, y=None, eval_data=None, eval_metric='acc', epoch_end_callback=None,batch_end_callback=None, kvstore='local', logger=None, work_load_list=None, monitor=None,eval_batch_end_callback=None)
-
Fit the model.
Parameters: - X (DataIter, or numpy.ndarray/NDArray) – Training data. If X is an DataIter, the name or, if not available, position, of its outputs should match the corresponding variable names defined in the symbolic graph.
- y (numpy.ndarray/NDArray, optional) – Training set label. If X is numpy.ndarray/NDArray, y is required to be set. While y can be 1D or 2D (with 2nd dimension as 1), its 1st dimension must be the same as X, i.e. the number of data points and labels should be equal.
- eval_data (DataIter or numpy.ndarray/list/NDArray pair) – If eval_data is numpy.ndarray/list/NDArray pair, it should be (valid_data, valid_label).
- eval_metric (metric.EvalMetric or str or callable) – The evaluation metric, name of evaluation metric. Or a customize evaluation function that returns the statistics based on minibatch.
- epoch_end_callback (callable(epoch, symbol, arg_params, aux_states)) – A callback that is invoked at end of each epoch. This can be used to checkpoint model each epoch.
- batch_end_callback (callable(epoch)) – A callback that is invoked at end of each batch For print purpose
- kvstore (KVStore or str, optional) – The KVStore or a string kvstore type: ‘local’, ‘dist_sync’, ‘dist_async’ In default uses ‘local’, often no need to change for single machiine.
- logger (logging logger, optional) – When not specified, default logger will be used.
- work_load_list (float or int, optional) – The list of work load for different devices, in the same order as ctx
类方法功能:模型拟合
-
save
(prefix, epoch=None) -
Checkpoint the model checkpoint into file. You can also use pickle to do the job if you only work on python. The advantage of load/save is the file is language agnostic. This means the file saved using save can be loaded by other language binding of mxnet. You also get the benefit being able to directly load/save from cloud storage(S3, HDFS)
Parameters: prefix (str) – Prefix of model name. Notes
prefix-symbol.json
will be saved for symbol.prefix-epoch.params
will be saved for parameters.
-
static
load
(prefix, epoch, ctx=None, **kwargs) -
Load model checkpoint from file.
Parameters: - prefix (str) – Prefix of model name.
- epoch (int) – epoch number of model we would like to load.
- ctx (Context or list of Context, optional) – The device context of training and prediction.
- kwargs (dict) – other parameters for model, including num_epoch, optimizer and numpy_batch_size
Returns: model – The loaded model that can be used for prediction.
Return type: FeedForward
保存和加载的比较简单,我就不说了。
create
(symbol, X, y=None, ctx=None, num_epoch=None, epoch_size=None,optimizer='sgd', initializer=<mxnet.initializer.Uniform object>, eval_data=None, eval_metric='acc',epoch_end_callback=None, batch_end_callback=None, kvstore='local', logger=None,work_load_list=None, eval_batch_end_callback=None, **kwargs)¶
-
Functional style to create a model. This function will be more consistent with functional languages such as R, where mutation is not allowed.
Parameters: - symbol (Symbol) – The symbol configuration of computation network.
- X (DataIter) – Training data
- y (numpy.ndarray, optional) – If X is numpy.ndarray y is required to set
- ctx (Context or list of Context, optional) – The device context of training and prediction. To use multi GPU training, pass in a list of gpu contexts.
- num_epoch (int, optional) – Training parameter, number of training epochs(epochs).
- epoch_size (int, optional) – Number of batches in a epoch. In default, it is set to ceil(num_train_examples / batch_size)
- optimizer (str or Optimizer, optional) – Training parameter, name or optimizer object for training.
- initializier (initializer function, optional) – Training parameter, the initialization scheme used.
- eval_data (DataIter or numpy.ndarray pair) – If eval_set is numpy.ndarray pair, it should be (valid_data, valid_label)
- eval_metric (metric.EvalMetric or str or callable) – The evaluation metric, name of evaluation metric. Or a customize evaluation function that returns the statistics based on minibatch.
- epoch_end_callback (callable(epoch, symbol, arg_params, aux_states)) – A callback that is invoked at end of each epoch. This can be used to checkpoint model each epoch.
- batch_end_callback (callable(epoch)) – A callback that is invoked at end of each batch For print purpose
- kvstore (KVStore or str, optional) – The KVStore or a string kvstore type: ‘local’, ‘dist_sync’, ‘dis_async’ In default uses ‘local’, often no need to change for single machiine.
- logger (logging logger, optional) – When not specified, default logger will be used.
- work_load_list (list of float or int, optional) – The list of work load for different devices, in the same order as ctx
创建模型这个API和前面也是大同小异。
接下去的这些API不常用到
初使化的API参考
-
-
class
mxnet.initializer.
Initializer
¶ -
Base class for Initializer.
-
__call__
(name, arr) -
Override () function to do Initialization
Parameters: - name (str) – name of corrosponding ndarray
- arr (NDArray) – ndarray to be Initialized
-
-
class
mxnet.initializer.
Load
(param, default_init=None, verbose=False) -
Initialize by loading pretrained param from file or dict
Parameters: - param (str or dict of str->NDArray) – param file or dict mapping name to NDArray.
- default_init (Initializer) – default initializer when name is not found in param.
- verbose (bool) – log source when initializing.
-
class
mxnet.initializer.
Mixed
(patterns, initializers) -
Initialize with mixed Initializer
Parameters: - patterns (list of str) – list of regular expression patterns to match parameter names.
- initializers (list of Initializer) – list of Initializer corrosponding to patterns
-
class
mxnet.initializer.
Uniform
(scale=0.07) -
Initialize the weight with uniform [-scale, scale]
Parameters: scale (float, optional) – The scale of uniform distribution
-
class
mxnet.initializer.
Normal
(sigma=0.01) -
Initialize the weight with normal(0, sigma)
Parameters: sigma (float, optional) – Standard deviation for gaussian distribution.
-
class
mxnet.initializer.
Orthogonal
(scale=1.414, rand_type='uniform') -
Intialize weight as Orthogonal matrix
Parameters: - scale (float optional) – scaling factor of weight
- rand_type (string optional) – use “uniform” or “normal” random number to initialize weight
- Reference –
- --------- –
- solutions to the nonlinear dynamics of learning in deep linear neural networks(Exact) –
- preprint arXiv (arXiv) –
-
class
mxnet.initializer.
Xavier
(rnd_type='uniform', factor_type='avg', magnitude=3) -
Initialize the weight with Xavier or similar initialization scheme.
Parameters: - rnd_type (str, optional) – Use
`gaussian`
or`uniform`
to init - factor_type (str, optional) – Use
`avg`
,`in`
, or`out`
to init - magnitude (float, optional) – scale of random number range
- rnd_type (str, optional) – Use
-
class
-
Online evaluation metric module.
-
mxnet.metric.
check_label_shapes
(labels, preds, shape=0) -
Check to see if the two arrays are the same size.
-
class
mxnet.metric.
EvalMetric
(name, num=None) -
Base class of all evaluation metrics.
-
update
(label, pred) -
Update the internal evaluation.
Parameters: - labels (list of NDArray) – The labels of the data.
- preds (list of NDArray) – Predicted values.
-
reset
() -
Clear the internal statistics to initial state.
-
get
() -
Get the current evaluation result.
Returns: - name (str) – Name of the metric.
- value (float) – Value of the evaluation.
-
get_name_value
() -
Get zipped name and value pairs
-
-
class
mxnet.metric.
CompositeEvalMetric
(**kwargs) -
Manage multiple evaluation metrics.
-
add
(metric) -
Add a child metric.
-
get_metric
(index) -
Get a child metric.
-
-
class
mxnet.metric.
Accuracy
-
Calculate accuracy
-
class
mxnet.metric.
TopKAccuracy
(**kwargs) -
Calculate top k predictions accuracy
-
class
mxnet.metric.
F1
-
Calculate the F1 score of a binary classification problem.
-
class
mxnet.metric.
MAE
-
Calculate Mean Absolute Error loss
-
class
mxnet.metric.
MSE
-
Calculate Mean Squared Error loss
-
class
mxnet.metric.
RMSE
-
Calculate Root Mean Squred Error loss
-
class
mxnet.metric.
CrossEntropy
-
Calculate Cross Entropy loss
-
class
mxnet.metric.
Torch
-
Dummy metric for torch criterions
-
class
mxnet.metric.
CustomMetric
(feval, name=None, allow_extra_outputs=False) -
Custom evaluation metric that takes a NDArray function.
Parameters: - feval (callable(label, pred)) – Customized evaluation function.
- name (str, optional) – The name of the metric
- allow_extra_outputs (bool) – If true, the prediction outputs can have extra outputs. This is useful in RNN, where the states are also produced in outputs for forwarding.
-
mxnet.metric.
np
(numpy_feval, name=None, allow_extra_outputs=False) -
Create a customized metric from numpy function.
Parameters: - numpy_feval (callable(label, pred)) – Customized evaluation function.
- name (str, optional) – The name of the metric.
- allow_extra_outputs (bool) – If true, the prediction outputs can have extra outputs. This is useful in RNN, where the states are also produced in outputs for forwarding.
-
mxnet.metric.
create
(metric, **kwargs) -
Create an evaluation metric.
Parameters: metric (str or callable) – The name of the metric, or a function providing statistics given pred, label NDArray
优化API
Common Optimization algorithms with regularizations.
-
class
mxnet.optimizer.
Optimizer
(rescale_grad=1.0, param_idx2name=None, wd=0.0,clip_gradient=None, learning_rate=0.01, lr_scheduler=None, sym=None) -
Base class of all optimizers.
-
static
register
(klass) -
Register optimizers to the optimizer factory
-
static
create_optimizer
(name, rescale_grad=1, **kwargs) -
Create an optimizer with specified name.
Parameters: - name (str) – Name of required optimizer. Should be the name of a subclass of Optimizer. Case insensitive.
- rescale_grad (float) – Rescaling factor on gradient.
- kwargs (dict) – Parameters for optimizer
Returns: opt – The result optimizer.
Return type: Optimizer
-
create_state
(index, weight) -
Create additional optimizer state such as momentum. override in implementations.
-
update
(index, weight, grad, state) -
Update the parameters. override in implementations
-
set_lr_scale
(args_lrscale) -
set lr scale is deprecated. Use set_lr_mult instead.
-
set_lr_mult
(args_lr_mult) -
Set individual learning rate multipler for parameters
Parameters: args_lr_mult (dict of string/int to float) – set the lr multipler for name/index to float. setting multipler by index is supported for backward compatibility, but we recommend using name and symbol.
-
set_wd_mult
(args_wd_mult) -
Set individual weight decay multipler for parameters. By default wd multipler is 0 for all params whose name doesn’t end with _weight, if param_idx2name is provided.
Parameters: args_wd_mult (dict of string/int to float) – set the wd multipler for name/index to float. setting multipler by index is supported for backward compatibility, but we recommend using name and symbol.
-
static
-
mxnet.optimizer.
register
(klass) -
Register optimizers to the optimizer factory
-
class
mxnet.optimizer.
SGD
(momentum=0.0, **kwargs) -
A very simple SGD optimizer with momentum and weight regularization.
Parameters: - learning_rate (float, optional) – learning_rate of SGD
- momentum (float, optional) – momentum value
- wd (float, optional) – L2 regularization coefficient add to all the weights
- rescale_grad (float, optional) – rescaling factor of gradient.
- clip_gradient (float, optional) – clip gradient in range [-clip_gradient, clip_gradient]
- param_idx2name (dict of string/int to float, optional) – special treat weight decay in parameter ends with bias, gamma, and beta
-
create_state
(index, weight) -
Create additional optimizer state such as momentum.
Parameters: weight (NDArray) – The weight data
-
update
(index, weight, grad, state) -
Update the parameters.
Parameters: - index (int) – An unique integer key used to index the parameters
- weight (NDArray) – weight ndarray
- grad (NDArray) – grad ndarray
- state (NDArray or other objects returned by init_state) – The auxiliary state used in optimization.
-
class
mxnet.optimizer.
NAG
(**kwargs) -
SGD with nesterov It is implemented according to https://github.com/torch/optim/blob/master/sgd.lua
-
update
(index, weight, grad, state) -
Update the parameters.
Parameters: - index (int) – An unique integer key used to index the parameters
- weight (NDArray) – weight ndarray
- grad (NDArray) – grad ndarray
- state (NDArray or other objects returned by init_state) – The auxiliary state used in optimization.
-
-
class
mxnet.optimizer.
SGLD
(**kwargs) -
Stochastic Langevin Dynamics Updater to sample from a distribution.
Parameters: - learning_rate (float, optional) – learning_rate of SGD
- wd (float, optional) – L2 regularization coefficient add to all the weights
- rescale_grad (float, optional) – rescaling factor of gradient.
- clip_gradient (float, optional) – clip gradient in range [-clip_gradient, clip_gradient]
- param_idx2name (dict of string/int to float, optional) – special treat weight decay in parameter ends with bias, gamma, and beta
-
create_state
(index, weight) -
Create additional optimizer state such as momentum.
Parameters: weight (NDArray) – The weight data
-
update
(index, weight, grad, state) -
Update the parameters.
Parameters: - index (int) – An unique integer key used to index the parameters
- weight (NDArray) – weight ndarray
- grad (NDArray) – grad ndarray
- state (NDArray or other objects returned by init_state) – The auxiliary state used in optimization.
-
class
mxnet.optimizer.
ccSGD
(momentum=0.0, **kwargs) -
A very simple SGD optimizer with momentum and weight regularization. Implemented in C++.
Parameters: - learning_rate (float, optional) – learning_rate of SGD
- momentum (float, optional) – momentum value
- wd (float, optional) – L2 regularization coefficient add to all the weights
- rescale_grad (float, optional) – rescaling factor of gradient.
- clip_gradient (float, optional) – clip gradient in range [-clip_gradient, clip_gradient]
-
update
(index, weight, grad, state) -
Update the parameters.
Parameters: - index (int) – An unique integer key used to index the parameters
- weight (NDArray) – weight ndarray
- grad (NDArray) – grad ndarray
- state (NDArray or other objects returned by init_state) – The auxiliary state used in optimization.
-
class
mxnet.optimizer.
Adam
(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08,decay_factor=0.99999999, **kwargs) -
Adam optimizer as described in [King2014].
[King2014] Diederik Kingma, Jimmy Ba, Adam: A Method for Stochastic Optimization,http://arxiv.org/abs/1412.6980 the code in this class was adapted from https://github.com/mila-udem/blocks/blob/master/blocks/algorithms/__init__.py#L765
Parameters: - learning_rate (float, optional) – Step size. Default value is set to 0.002.
- beta1 (float, optional) – Exponential decay rate for the first moment estimates. Default value is set to 0.9.
- beta2 (float, optional) – Exponential decay rate for the second moment estimates. Default value is set to 0.999.
- epsilon (float, optional) – Default value is set to 1e-8.
- decay_factor (float, optional) – Default value is set to 1 - 1e-8.
- wd (float, optional) – L2 regularization coefficient add to all the weights
- rescale_grad (float, optional) – rescaling factor of gradient.
- clip_gradient (float, optional) – clip gradient in range [-clip_gradient, clip_gradient]
-
create_state
(index, weight) -
Create additional optimizer state: mean, variance
Parameters: weight (NDArray) – The weight data
-
update
(index, weight, grad, state) -
Update the parameters.
Parameters: - index (int) – An unique integer key used to index the parameters
- weight (NDArray) – weight ndarray
- grad (NDArray) – grad ndarray
- state (NDArray or other objects returned by init_state) – The auxiliary state used in optimization.
-
class
mxnet.optimizer.
AdaGrad
(eps=1e-07, **kwargs) -
AdaGrad optimizer of Duchi et al., 2011,
This code follows the version in http://arxiv.org/pdf/1212.5701v1.pdf Eq(5) by Matthew D. Zeiler, 2012. AdaGrad will help the network to converge faster in some cases.
Parameters: - learning_rate (float, optional) – Step size. Default value is set to 0.05.
- wd (float, optional) – L2 regularization coefficient add to all the weights
- rescale_grad (float, optional) – rescaling factor of gradient.
- eps (float, optional) – A small float number to make the updating processing stable Default value is set to 1e-7.
- clip_gradient (float, optional) – clip gradient in range [-clip_gradient, clip_gradient]
-
class
mxnet.optimizer.
RMSProp
(gamma1=0.95, gamma2=0.9, **kwargs) -
RMSProp optimizer of Tieleman & Hinton, 2012,
This code follows the version in http://arxiv.org/pdf/1308.0850v5.pdf Eq(38) - Eq(45) by Alex Graves, 2013.
Parameters: - learning_rate (float, optional) – Step size. Default value is set to 0.002.
- gamma1 (float, optional) – decay factor of moving average for gradient, gradient^2. Default value is set to 0.95.
- gamma2 (float, optional) – “momentum” factor. Default value if set to 0.9.
- wd (float, optional) – L2 regularization coefficient add to all the weights
- rescale_grad (float, optional) – rescaling factor of gradient.
- clip_gradient (float, optional) – clip gradient in range [-clip_gradient, clip_gradient]
-
create_state
(index, weight) -
Create additional optimizer state: mean, variance :param weight: The weight data :type weight: NDArray
-
update
(index, weight, grad, state) -
Update the parameters. :param index: An unique integer key used to index the parameters
Parameters: - weight (NDArray) – weight ndarray
- grad (NDArray) – grad ndarray
- state (NDArray or other objects returned by init_state) – The auxiliary state used in optimization.
-
class
mxnet.optimizer.
AdaDelta
(rho=0.9, epsilon=1e-05, **kwargs) -
AdaDelta optimizer as described in Zeiler, M. D. (2012). ADADELTA: An adaptive learning rate method.
http://arxiv.org/abs/1212.5701
Parameters: - rho (float) – Decay rate for both squared gradients and delta x
- epsilon (float) – The constant as described in the thesis
- wd (float) – L2 regularization coefficient add to all the weights
- rescale_grad (float, optional) – rescaling factor of gradient.
- clip_gradient (float, optional) – clip gradient in range [-clip_gradient, clip_gradient]
-
class
mxnet.optimizer.
Test
(**kwargs) -
For test use
-
create_state
(index, weight) -
Create a state to duplicate weight
-
update
(index, weight, grad, state) -
performs w += rescale_grad * grad
-
-
mxnet.optimizer.
create
(name, rescale_grad=1, **kwargs) -
Create an optimizer with specified name.
Parameters: - name (str) – Name of required optimizer. Should be the name of a subclass of Optimizer. Case insensitive.
- rescale_grad (float) – Rescaling factor on gradient.
- kwargs (dict) – Parameters for optimizer
Returns: opt – The result optimizer.
Return type: Optimizer
-
mxnet.optimizer.
get_updater
(optimizer) -
Return a clossure of the updater needed for kvstore
Parameters: optimizer (Optimizer) – The optimizer Returns: updater – The clossure of the updater Return type: function
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