The 2--python API for Mxnet Research

0. Python API http://mxnet.io/api/python/ 1. Ndarray

A ndarray is a multidimensional container of items of the same type and size.

>>> x = Mx.nd.array ([[1, 2, 3], [4, 5, 6]])
>>> type (x)
<class ' Mxnet.ndarray.NDArray ' >< C4/>>>> X.shape
(2, 3)
>>> y = x + mx.nd.ones (x.shape) *3
>>> print (Y.asnumpy ())
[[4.  5.  6.]
 [7.  8.  9.]]
>>> z = y.as_in_context (mx.gpu (0))
>>> print (z)
<ndarray 2x3 @gpu (0) >

2. Symbol

A symbol declares computation. It is composited by operators, such as simple matrix operations (e.g. "+"), or a neural network (layer e.g. convolution LA Yer). We can bind data to a symbol to execute the computation

>>> a = mx.sym.Variable (' a ')
>>> b = mx.sym.Variable (' b ')
>>> c = 2 * A + b
>> ;> type (c)
<class ' Mxnet.symbol.Symbol ' >
>>> e = C.bind (Mx.cpu (), {' A ': Mx.nd.array ([1,2]), ' B ': Mx.nd.array ([2,3])})
>>> y = E.forward ()
>>> y
[<ndarray 2 @cpu (0)]
>>> y[0].asnumpy ()
Array ([4.,  7.], Dtype=float32)

bind (Data_shapes, Label_shapes=none, For_training=true, Inputs_need_grad=false, Force_rebind=false, Shared_ Module=none, grad_req= ' write ')
Bind the symbols to construct executors. This is necessary before one can perform computation with the module.

Parameters: Data_shapes(List of (str, tuple)) –typically is data_iter.provide_data. "Label_shapes (List of (str, tuple)) –typically is Data_iter.provide_label.for_training(bool) –default is True. Whether The executors should is bind for training.Input_need_grad(bool) –default is False. Whether the gradients to the input data need to is computed. Typically this isn't needed. But this might is needed when implementing composition of modules.
Force_rebind ' (bool) –default is False. This function does the executors are already binded. But with this True, the executors is forced to rebind.Shared_module(Module) –default is None. This is used in bucketing. When isn't None, the shared module essentially corresponds to a different bucket–a module with different symbol but with T He same sets of parameters, e.g. unrolled Rnns with different).Grad_req(str, List of STR, dict of STR to str) –requirement for gradient accumulation. Can is ' write ', ' add ', or ' null ' (Default to ' write '). Can is specified globally (str) or for each argument (list, dict).3. Module

The module API, defined in the module (or simply MoD) package, provides a intermediate and high-level for interface Rming computation with a Symbol. One can roughly a module is the machine which can execute a program defined by a Symbol.

The class module. Module is a commonly used module, which accepts a Symbol as the input:

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=10)
Out  = Mx.symbol.SoftmaxOutput (FC2, name = ' Softmax ')
mod = Mx.mod.Module (out)  # Create A module by given a Symbol

Assume there is a valid mxnet data iterator data. We can initialize the module:

Mod.bind (Data_shapes=data.provide_data,
         Label_shapes=data.provide_label)  # Create memory by given input  Shapes
mod.init_params ()  # Initial parameters with the ' default random initializer Now ' the
module is able to Compute. We can call high-level APIs to train and predict:

mod.fit (data, num_epoch=10, ...)  # train
mod.predict (new_data)  # predict on new data

Or use intermediate APIs to perform step-by-step computations

Mod.forward (Data_batch)  # Forward on the provided data batch
Mod.backward ()  # Backward to calculate the Gradi Ents
mod.update ()  # update parameters using the default optimizer

A detailed tutorial is available at http://mxnet.io/tutorials/python/module.html. 4. Kvstore

Provides basic push&pull operations over multiple devices (GPUs) on a single device.

# initialation >>> kv = mx.kv.create (' local ') # Create a local kv store. >>> shape = (2,3) # 2 rows 3 cols >>> kv.init (3, Mx.nd.ones (SHAPE) *2) >>> a = Mx.nd.zeros (shape  ) >>> Kv.pull (3, out = a) >>> print a.asnumpy () [2.
 2.2.]  [2.2.  2.]] # Push, Aggregation and Updater >>> Kv.push (3, Mx.nd.ones (SHAPE) *8) >>> Kv.pull (3, out = a) # pull  Out of the value >>> print a.asnumpy () [8.
 8.8.]  [8.8. 8.]] # You can push multiple values into the same key, # where Kvstore ' a sums all of these values, # and then Pushe s the aggregated value, as follows: >>> cpus = [Mx.cpu (i) for I in range (4)] >>> B = [Mx.nd.ones (Shape,  CPU) for CPUs in CPUs] >>> Kv.push (3, B) >>> Kv.pull (3, out = a) >>> print a.asnumpy () [4.
 4.4.]  [4.4. 4.]] # You can replace the ' default to control ' how-to merged >>> def update (key, input, stored): >>&gt    ; Print "Update on key:%d"% key >>> stored + = input * 2 >>> kv._set_updater (update) >>> KV  . Pull (3, out=a) >>> print A.asnumpy () [4.
 4.4.]  [4.4. 4.]] >>> Kv.push (3, Mx.nd.ones (SHAPE)) Update on Key:3 >>> kv.pull (3, out=a) >>> print A.asnum  PY () [6.
 6.6.]  [6.6.

 6.]]

5. Data Loading

Class Mxnet.io.NDArrayIter (data, Label=none, batch_size=1, shuffle=false, last_batch_handle= ' pad ', data_name= ') Data ', label_name= ' Softmax_label ')

Iterating on either Mx.nd.NDArray or Numpy.ndarray.

Parameters: Data (array or List of array or dict of. String to array) –input Data label (array or list of array or dict of string To array, optional) –input label batch_size (int) –batch size
Shuffle (bool, optional) –whether to shuffle the data Last_batch_handl (str, optional) –how to handle the last Batch, can be ' pad ', ' discard ' or ' roll_over '. ' Roll_over ' is intended to training and can cause problems if used for prediction. Data_name (str, optional) –the data name label_name (str, optional) –the label name

A data iterator reads data batch by batch:

>>> data = Mx.nd.ones ((100,10))
>>> nd_iter = mx.io.NDArrayIter (data, batch_size=25)
> >> for batch in Nd_iter:
...     Print (Batch.data)
[<ndarray 25x10 @cpu (0)]
[<ndarray 25x10 @cpu (0)]
[<ndarray 25x10 @ CPU (0)
[<ndarray 25x10 @cpu (0)]

In addition, a iterator provides information about the batch, including the shapes and name:

>>> nd_iter = mx.io.NDArrayIter (data={' data ': Mx.nd.ones ((100,10))},
...                             label={' Softmax_label ': Mx.nd.ones ((M.))},
...                             batch_size=25)
>>> print (nd_iter.provide_data)
[Datadesc[data, (10L), <type ' Numpy.float32 ' >,NCHW]]
>>> print (Nd_iter.provide_label)
[Datadesc[softmax_label, (25,), <type ' Numpy.float32 ' >,NCHW]

6 optimization:initialize and update weights