API Reference¶

Visualization module

mxnet.visualization.print_summary(symbol, shape=None, line_length=120, positions=[0.44, 0.64, 0.74, 1.0])[source]¶

Convert symbol for detail information.

Parameters:	symbol (Symbol) – Symbol to be visualized. shape (dict) – A dict of shapes, str->shape (tuple), given input shapes. line_length (int) – Rotal length of printed lines positions (list) – Relative or absolute positions of log elements in each line.
Returns:
Return type:	None

Notes

If mxnet is imported, the visualization module can be used in its short-form. For example, if we import mxnet as follows:

import mxnet

this method in visualization module can be used in its short-form as:

mxnet.viz.print_summary(...)

mxnet.visualization.plot_network(symbol, title='plot', save_format='pdf', shape=None, dtype=None, node_attrs={}, hide_weights=True)[source]¶

Creates a visualization (Graphviz digraph object) of the given computation graph. Graphviz must be installed for this function to work.

Parameters:	title (str, optional) – Title of the generated visualization. symbol (Symbol) – A symbol from the computation graph. The generated digraph will visualize the part of the computation graph required to compute symbol. shape (dict, optional) – Specifies the shape of the input tensors. If specified, the visualization will include the shape of the tensors between the nodes. shape is a dictionary mapping input symbol names (str) to the corresponding tensor shape (tuple). dtype (dict, optional) – Specifies the type of the input tensors. If specified, the visualization will include the type of the tensors between the nodes. dtype is a dictionary mapping input symbol names (str) to the corresponding tensor type (e.g. numpy.float32). node_attrs (dict, optional) – Specifies the attributes for nodes in the generated visualization. node_attrs is a dictionary of Graphviz attribute names and values. For example: node_attrs={"shape":"oval","fixedsize":"false"} will use oval shape for nodes and allow variable sized nodes in the visualization. hide_weights (bool, optional) – If True (default), then inputs with names of form _weight (corresponding to weight tensors) or _bias (corresponding to bias vectors) will be hidden for a cleaner visualization.
Returns:	dot – A Graphviz digraph object visualizing the computation graph to compute symbol.
Return type:	Digraph

Example

>>> net = mx.sym.Variable('data')
>>> net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=128)
>>> net = mx.sym.Activation(data=net, name='relu1', act_type="relu")
>>> net = mx.sym.FullyConnected(data=net, name='fc2', num_hidden=10)
>>> net = mx.sym.SoftmaxOutput(data=net, name='out')
>>> digraph = mx.viz.plot_network(net, shape={'data':(100,200)},
... node_attrs={"fixedsize":"false"})
>>> digraph.view()

Notes

If mxnet is imported, the visualization module can be used in its short-form. For example, if we import mxnet as follows:

import mxnet

this method in visualization module can be used in its short-form as:

mxnet.viz.plot_network(...)