updated book
This commit is contained in:
Morten Hjorth-Jensen
@@ -2706,20 +2706,19 @@ Using TensorFlow results in a much better execution time. Try it!</p>
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<span class="g g-Whitespace"> </span><span class="mi">60</span> <span class="n">x</span><span class="p">,</span><span class="n">t</span> <span class="o">=</span> <span class="n">point</span>
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<span class="ne">---> </span><span class="mi">61</span> <span class="k">return</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">t</span><span class="p">)</span><span class="o">*</span><span class="n">u</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="n">x</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">x</span><span class="p">)</span><span class="o">*</span><span class="n">t</span><span class="o">*</span><span class="n">deep_neural_network</span><span class="p">(</span><span class="n">P</span><span class="p">,</span><span class="n">point</span><span class="p">)</span>
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<span class="nn">Cell In[9], line 48,</span> in <span class="ni">deep_neural_network</span><span class="nt">(deep_params, x)</span>
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<span class="g g-Whitespace"> </span><span class="mi">45</span> <span class="n">w_output</span> <span class="o">=</span> <span class="n">deep_params</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
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<span class="g g-Whitespace"> </span><span class="mi">47</span> <span class="c1"># Include bias:</span>
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<span class="ne">---> </span><span class="mi">48</span> <span class="n">x_prev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="n">num_points</span><span class="p">)),</span> <span class="n">x_prev</span><span class="p">),</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
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<span class="g g-Whitespace"> </span><span class="mi">50</span> <span class="n">z_output</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">w_output</span><span class="p">,</span> <span class="n">x_prev</span><span class="p">)</span>
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<span class="g g-Whitespace"> </span><span class="mi">51</span> <span class="n">x_output</span> <span class="o">=</span> <span class="n">z_output</span>
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<span class="nn">Cell In[9], line 37,</span> in <span class="ni">deep_neural_network</span><span class="nt">(deep_params, x)</span>
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<span class="g g-Whitespace"> </span><span class="mi">34</span> <span class="n">x_prev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="n">num_points</span><span class="p">)),</span> <span class="n">x_prev</span> <span class="p">),</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
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<span class="g g-Whitespace"> </span><span class="mi">36</span> <span class="n">z_hidden</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">w_hidden</span><span class="p">,</span> <span class="n">x_prev</span><span class="p">)</span>
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<span class="ne">---> </span><span class="mi">37</span> <span class="n">x_hidden</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">z_hidden</span><span class="p">)</span>
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<span class="g g-Whitespace"> </span><span class="mi">39</span> <span class="c1"># Update x_prev such that next layer can use the output from this layer</span>
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<span class="g g-Whitespace"> </span><span class="mi">40</span> <span class="n">x_prev</span> <span class="o">=</span> <span class="n">x_hidden</span>
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<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:38,</span> in <span class="ni"><lambda></span><span class="nt">(arr_list, axis)</span>
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<span class="g g-Whitespace"> </span><span class="mi">35</span> <span class="nd">@primitive</span>
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<span class="g g-Whitespace"> </span><span class="mi">36</span> <span class="k">def</span> <span class="nf">concatenate_args</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
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<span class="g g-Whitespace"> </span><span class="mi">37</span> <span class="k">return</span> <span class="n">_np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">axis</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">ndarray</span><span class="p">)</span>
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<span class="ne">---> </span><span class="mi">38</span> <span class="n">concatenate</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">arr_list</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span> <span class="p">:</span> <span class="n">concatenate_args</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="o">*</span><span class="n">arr_list</span><span class="p">)</span>
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<span class="g g-Whitespace"> </span><span class="mi">39</span> <span class="n">vstack</span> <span class="o">=</span> <span class="n">row_stack</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">tup</span><span class="p">:</span> <span class="n">concatenate</span><span class="p">([</span><span class="n">atleast_2d</span><span class="p">(</span><span class="n">_m</span><span class="p">)</span> <span class="k">for</span> <span class="n">_m</span> <span class="ow">in</span> <span class="n">tup</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<span class="g g-Whitespace"> </span><span class="mi">40</span> <span class="k">def</span> <span class="nf">hstack</span><span class="p">(</span><span class="n">tup</span><span class="p">):</span>
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<span class="nn">Cell In[9], line 11,</span> in <span class="ni">sigmoid</span><span class="nt">(z)</span>
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<span class="g g-Whitespace"> </span><span class="mi">10</span> <span class="k">def</span> <span class="nf">sigmoid</span><span class="p">(</span><span class="n">z</span><span class="p">):</span>
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<span class="ne">---> </span><span class="mi">11</span> <span class="k">return</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">z</span><span class="p">))</span>
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<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_boxes.py:39,</span> in <span class="ni">ArrayBox.__rtruediv__</span><span class="nt">(self, other)</span>
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<span class="ne">---> </span><span class="mi">39</span> <span class="k">def</span> <span class="fm">__rtruediv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> <span class="k">return</span> <span class="n">anp</span><span class="o">.</span><span class="n">true_divide</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span>
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<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:45,</span> in <span class="ni">primitive.<locals>.f_wrapped</span><span class="nt">(*args, **kwargs)</span>
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<span class="g g-Whitespace"> </span><span class="mi">43</span> <span class="n">argnums</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">argnum</span> <span class="k">for</span> <span class="n">argnum</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">boxed_args</span><span class="p">)</span>
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@@ -2734,24 +2733,32 @@ Using TensorFlow results in a much better execution time. Try it!</p>
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<span class="g g-Whitespace"> </span><span class="mi">35</span> <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fun_name</span><span class="p">,</span> <span class="n">parent_argnums</span><span class="p">))</span>
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<span class="ne">---> </span><span class="mi">36</span> <span class="bp">self</span><span class="o">.</span><span class="n">vjp</span> <span class="o">=</span> <span class="n">vjpmaker</span><span class="p">(</span><span class="n">parent_argnums</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
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<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:48,</span> in <span class="ni">defvjp_argnum.<locals>.vjp_argnums</span><span class="nt">(argnums, *args)</span>
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<span class="g g-Whitespace"> </span><span class="mi">47</span> <span class="k">def</span> <span class="nf">vjp_argnums</span><span class="p">(</span><span class="n">argnums</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
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<span class="ne">---> </span><span class="mi">48</span> <span class="n">vjps</span> <span class="o">=</span> <span class="p">[</span><span class="n">vjpmaker</span><span class="p">(</span><span class="n">argnum</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="k">for</span> <span class="n">argnum</span> <span class="ow">in</span> <span class="n">argnums</span><span class="p">]</span>
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<span class="g g-Whitespace"> </span><span class="mi">49</span> <span class="k">return</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="p">(</span><span class="n">vjp</span><span class="p">(</span><span class="n">g</span><span class="p">)</span> <span class="k">for</span> <span class="n">vjp</span> <span class="ow">in</span> <span class="n">vjps</span><span class="p">)</span>
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<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:66,</span> in <span class="ni">defvjp.<locals>.vjp_argnums</span><span class="nt">(argnums, ans, args, kwargs)</span>
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<span class="g g-Whitespace"> </span><span class="mi">63</span> <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
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<span class="g g-Whitespace"> </span><span class="mi">64</span> <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span>
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<span class="g g-Whitespace"> </span><span class="mi">65</span> <span class="s2">"VJP of </span><span class="si">{}</span><span class="s2"> wrt argnum 0 not defined"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fun</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span>
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<span class="ne">---> </span><span class="mi">66</span> <span class="n">vjp</span> <span class="o">=</span> <span class="n">vjpfun</span><span class="p">(</span><span class="n">ans</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
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<span class="g g-Whitespace"> </span><span class="mi">67</span> <span class="k">return</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="p">(</span><span class="n">vjp</span><span class="p">(</span><span class="n">g</span><span class="p">),)</span>
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<span class="g g-Whitespace"> </span><span class="mi">68</span> <span class="k">elif</span> <span class="n">L</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
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<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:48,</span> in <span class="ni"><listcomp></span><span class="nt">(.0)</span>
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<span class="g g-Whitespace"> </span><span class="mi">47</span> <span class="k">def</span> <span class="nf">vjp_argnums</span><span class="p">(</span><span class="n">argnums</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
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<span class="ne">---> </span><span class="mi">48</span> <span class="n">vjps</span> <span class="o">=</span> <span class="p">[</span><span class="n">vjpmaker</span><span class="p">(</span><span class="n">argnum</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="k">for</span> <span class="n">argnum</span> <span class="ow">in</span> <span class="n">argnums</span><span class="p">]</span>
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<span class="g g-Whitespace"> </span><span class="mi">49</span> <span class="k">return</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="p">(</span><span class="n">vjp</span><span class="p">(</span><span class="n">g</span><span class="p">)</span> <span class="k">for</span> <span class="n">vjp</span> <span class="ow">in</span> <span class="n">vjps</span><span class="p">)</span>
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<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:53,</span> in <span class="ni"><lambda></span><span class="nt">(ans, x, y)</span>
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<span class="g g-Whitespace"> </span><span class="mi">48</span> <span class="n">defvjp</span><span class="p">(</span><span class="n">anp</span><span class="o">.</span><span class="n">logaddexp</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">g</span> <span class="o">*</span> <span class="n">anp</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">x</span><span class="o">-</span><span class="n">ans</span><span class="p">)),</span>
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<span class="g g-Whitespace"> </span><span class="mi">49</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">g</span> <span class="o">*</span> <span class="n">anp</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">y</span><span class="o">-</span><span class="n">ans</span><span class="p">)))</span>
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<span class="g g-Whitespace"> </span><span class="mi">50</span> <span class="n">defvjp</span><span class="p">(</span><span class="n">anp</span><span class="o">.</span><span class="n">logaddexp2</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">g</span> <span class="o">*</span> <span class="mi">2</span><span class="o">**</span><span class="p">(</span><span class="n">x</span><span class="o">-</span><span class="n">ans</span><span class="p">)),</span>
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<span class="g g-Whitespace"> </span><span class="mi">51</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">g</span> <span class="o">*</span> <span class="mi">2</span><span class="o">**</span><span class="p">(</span><span class="n">y</span><span class="o">-</span><span class="n">ans</span><span class="p">)))</span>
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<span class="g g-Whitespace"> </span><span class="mi">52</span> <span class="n">defvjp</span><span class="p">(</span><span class="n">anp</span><span class="o">.</span><span class="n">true_divide</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">g</span> <span class="o">/</span> <span class="n">y</span><span class="p">),</span>
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<span class="ne">---> </span><span class="mi">53</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="o">-</span> <span class="n">g</span> <span class="o">*</span> <span class="n">x</span> <span class="o">/</span> <span class="n">y</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
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<span class="g g-Whitespace"> </span><span class="mi">54</span> <span class="n">defvjp</span><span class="p">(</span><span class="n">anp</span><span class="o">.</span><span class="n">mod</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">g</span><span class="p">),</span>
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<span class="g g-Whitespace"> </span><span class="mi">55</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="o">-</span><span class="n">g</span> <span class="o">*</span> <span class="n">anp</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">x</span><span class="o">/</span><span class="n">y</span><span class="p">)))</span>
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<span class="g g-Whitespace"> </span><span class="mi">56</span> <span class="n">defvjp</span><span class="p">(</span><span class="n">anp</span><span class="o">.</span><span class="n">remainder</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">g</span><span class="p">),</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">57</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="o">-</span><span class="n">g</span> <span class="o">*</span> <span class="n">anp</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">x</span><span class="o">/</span><span class="n">y</span><span class="p">)))</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:537,</span> in <span class="ni">grad_concatenate_args</span><span class="nt">(argnum, ans, axis_args, kwargs)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">532</span> <span class="n">defvjp</span><span class="p">(</span><span class="n">tensordot_adjoint_1</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">G</span><span class="p">,</span> <span class="n">axes</span><span class="p">,</span> <span class="n">An</span><span class="p">,</span> <span class="n">Bn</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">B</span><span class="p">:</span> <span class="n">match_complex</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">tensordot_adjoint_0</span><span class="p">(</span><span class="n">B</span><span class="p">,</span> <span class="n">G</span><span class="p">,</span> <span class="n">axes</span><span class="p">,</span> <span class="n">An</span><span class="p">,</span> <span class="n">Bn</span><span class="p">)),</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">533</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">G</span><span class="p">,</span> <span class="n">axes</span><span class="p">,</span> <span class="n">An</span><span class="p">,</span> <span class="n">Bn</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">B</span><span class="p">:</span> <span class="n">match_complex</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">anp</span><span class="o">.</span><span class="n">tensordot</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">axes</span><span class="p">)))</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">534</span> <span class="n">defvjp</span><span class="p">(</span><span class="n">anp</span><span class="o">.</span><span class="n">outer</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="p">:</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">match_complex</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">anp</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">g</span><span class="p">,</span> <span class="n">b</span><span class="o">.</span><span class="n">T</span><span class="p">)),</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">535</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="p">:</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">match_complex</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">anp</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">g</span><span class="p">)))</span>
|
||||
<span class="ne">--> </span><span class="mi">537</span> <span class="k">def</span> <span class="nf">grad_concatenate_args</span><span class="p">(</span><span class="n">argnum</span><span class="p">,</span> <span class="n">ans</span><span class="p">,</span> <span class="n">axis_args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">538</span> <span class="n">axis</span><span class="p">,</span> <span class="n">args</span> <span class="o">=</span> <span class="n">axis_args</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">axis_args</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">539</span> <span class="n">sizes</span> <span class="o">=</span> <span class="p">[</span><span class="n">anp</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">a</span><span class="p">)[</span><span class="n">axis</span><span class="p">]</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">args</span><span class="p">[:</span><span class="n">argnum</span><span class="p">]]</span>
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:658,</span> in <span class="ni">unbroadcast_f</span><span class="nt">(target, f)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">655</span> <span class="n">x</span> <span class="o">=</span> <span class="n">anp</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">656</span> <span class="k">return</span> <span class="n">x</span>
|
||||
<span class="ne">--> </span><span class="mi">658</span> <span class="k">def</span> <span class="nf">unbroadcast_f</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">f</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">659</span> <span class="n">target_meta</span> <span class="o">=</span> <span class="n">anp</span><span class="o">.</span><span class="n">metadata</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">660</span> <span class="k">return</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">unbroadcast</span><span class="p">(</span><span class="n">f</span><span class="p">(</span><span class="n">g</span><span class="p">),</span> <span class="n">target_meta</span><span class="p">)</span>
|
||||
|
||||
<span class="ne">KeyboardInterrupt</span>:
|
||||
</pre></div>
|
||||
|
||||
Reference in New Issue
Block a user