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<h1>Source code for mxnet.random</h1><div class="highlight"><pre>
<span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span>
<span class="c1"># or more contributor license agreements. See the NOTICE file</span>
<span class="c1"># distributed with this work for additional information</span>
<span class="c1"># regarding copyright ownership. The ASF licenses this file</span>
<span class="c1"># to you under the Apache License, Version 2.0 (the</span>
<span class="c1"># "License"); you may not use this file except in compliance</span>
<span class="c1"># with the License. You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing,</span>
<span class="c1"># software distributed under the License is distributed on an</span>
<span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span>
<span class="c1"># KIND, either express or implied. See the License for the</span>
<span class="c1"># specific language governing permissions and limitations</span>
<span class="c1"># under the License.</span>
<span class="c1"># coding: utf-8</span>
<span class="c1"># pylint: disable=no-member, protected-access, unused-import, no-name-in-module</span>
<span class="c1"># pylint: disable=wildcard-import, unused-wildcard-import</span>
<span class="sd">"""Random number interface of MXNet."""</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">absolute_import</span>
<span class="kn">import</span> <span class="nn">ctypes</span>
<span class="kn">from</span> <span class="nn">.base</span> <span class="k">import</span> <span class="n">_LIB</span><span class="p">,</span> <span class="n">check_call</span>
<span class="kn">from</span> <span class="nn">.ndarray.random</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.context</span> <span class="k">import</span> <span class="n">Context</span>
<div class="viewcode-block" id="seed"><a class="viewcode-back" href="../../api/python/symbol/random.html#mxnet.random.seed">[docs]</a><span class="k">def</span> <span class="nf">seed</span><span class="p">(</span><span class="n">seed_state</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="s2">"all"</span><span class="p">):</span>
<span class="sd">"""Seeds the random number generators in MXNet.</span>
<span class="sd"> This affects the behavior of modules in MXNet that uses random number generators,</span>
<span class="sd"> like the dropout operator and `NDArray`'s random sampling operators.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> seed_state : int</span>
<span class="sd"> The random number seed.</span>
<span class="sd"> ctx : Context</span>
<span class="sd"> The device context of the generator. The default is "all" which means seeding random</span>
<span class="sd"> number generators of all devices.</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> Random number generators in MXNet are device specific.</span>
<span class="sd"> `mx.random.seed(seed_state)` sets the state of each generator using `seed_state` and the</span>
<span class="sd"> device id. Therefore, random numbers generated from different devices can be different</span>
<span class="sd"> even if they are seeded using the same seed.</span>
<span class="sd"> To produce identical random number sequences independent of the device id,</span>
<span class="sd"> set optional `ctx` argument. This produces the same sequence of random numbers independent</span>
<span class="sd"> of the device id, but the sequence can be different on different kind of devices as MXNet's</span>
<span class="sd"> random number generators for CPU and GPU use different algorithms.</span>
<span class="sd"> Example</span>
<span class="sd"> -------</span>
<span class="sd"> >>> print(mx.nd.random.normal(shape=(2,2)).asnumpy())</span>
<span class="sd"> [[ 1.36481571 -0.62203991]</span>
<span class="sd"> [-1.4962182 -0.08511394]]</span>
<span class="sd"> >>> print(mx.nd.random.normal(shape=(2,2)).asnumpy())</span>
<span class="sd"> [[ 1.09544981 -0.20014545]</span>
<span class="sd"> [-0.20808885 0.2527658 ]]</span>
<span class="sd"> # Same results on the same device with the same seed</span>
<span class="sd"> >>> mx.random.seed(128)</span>
<span class="sd"> >>> print(mx.nd.random.normal(shape=(2,2)).asnumpy())</span>
<span class="sd"> [[ 0.47400656 -0.75213492]</span>
<span class="sd"> [ 0.20251541 0.95352972]]</span>
<span class="sd"> >>> mx.random.seed(128)</span>
<span class="sd"> >>> print(mx.nd.random.normal(shape=(2,2)).asnumpy())</span>
<span class="sd"> [[ 0.47400656 -0.75213492]</span>
<span class="sd"> [ 0.20251541 0.95352972]]</span>
<span class="sd"> # Different results on gpu(0) and gpu(1) with the same seed</span>
<span class="sd"> >>> mx.random.seed(128)</span>
<span class="sd"> >>> print(mx.nd.random.normal(shape=(2,2), ctx=mx.gpu(0)).asnumpy())</span>
<span class="sd"> [[ 2.5020072 -1.6884501]</span>
<span class="sd"> [-0.7931333 -1.4218881]]</span>
<span class="sd"> >>> mx.random.seed(128)</span>
<span class="sd"> >>> print(mx.nd.random.normal(shape=(2,2), ctx=mx.gpu(1)).asnumpy())</span>
<span class="sd"> [[ 0.24336822 -1.664805 ]</span>
<span class="sd"> [-1.0223296 1.253198 ]]</span>
<span class="sd"> # Seeding with `ctx` argument produces identical results on gpu(0) and gpu(1)</span>
<span class="sd"> >>> mx.random.seed(128, ctx=mx.gpu(0))</span>
<span class="sd"> >>> print(mx.nd.random.normal(shape=(2,2), ctx=mx.gpu(0)).asnumpy())</span>
<span class="sd"> [[ 2.5020072 -1.6884501]</span>
<span class="sd"> [-0.7931333 -1.4218881]]</span>
<span class="sd"> >>> mx.random.seed(128, ctx=mx.gpu(1))</span>
<span class="sd"> >>> print(mx.nd.random.normal(shape=(2,2), ctx=mx.gpu(1)).asnumpy())</span>
<span class="sd"> [[ 2.5020072 -1.6884501]</span>
<span class="sd"> [-0.7931333 -1.4218881]]</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">seed_state</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'seed_state must be int'</span><span class="p">)</span>
<span class="n">seed_state</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">seed_state</span><span class="p">)</span>
<span class="k">if</span> <span class="n">ctx</span> <span class="o">==</span> <span class="s2">"all"</span><span class="p">:</span>
<span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXRandomSeed</span><span class="p">(</span><span class="n">seed_state</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">ctx</span> <span class="o">=</span> <span class="n">Context</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span>
<span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXRandomSeedContext</span><span class="p">(</span><span class="n">seed_state</span><span class="p">,</span> <span class="n">ctx</span><span class="o">.</span><span class="n">device_typeid</span><span class="p">,</span> <span class="n">ctx</span><span class="o">.</span><span class="n">device_id</span><span class="p">))</span></div>
</pre></div>
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