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This commit is contained in:
Morten Hjorth-Jensen
2021-11-05 06:42:12 +01:00
parent 4565722caf
commit dae1ce2b13
9 changed files with 922 additions and 567 deletions
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doc/pub/week40/html/._week40-bs000.html
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@@ -334,7 +334,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Nov 3, 2021</h4>
<h4>Nov 5, 2021</h4>
</center> <!-- date -->
<br>
doc/pub/week40/html/._week40-bs032.html
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@@ -391,9 +391,9 @@ theta <span style="color: #666666">=</span> np<span style="color: #666666">.</sp
<span style="color: #008000; font-weight: bold">for</span> epoch <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(n_epochs):
<span style="color: #408080; font-style: italic"># Can you figure out a better way of setting up the contributions to each batch?</span>
<span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(m):
random_index <span style="color: #666666">=</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>randint(m)
xi <span style="color: #666666">=</span> X[random_index<span style="color: #666666">*</span>M:random_index<span style="color: #666666">*</span>M<span style="color: #666666">+</span>M]
yi <span style="color: #666666">=</span> y[random_index<span style="color: #666666">*</span>M:random_index<span style="color: #666666">*</span>M<span style="color: #666666">+</span>M]
random_index <span style="color: #666666">=</span> M<span style="color: #666666">*</span>np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>randint(m)
xi <span style="color: #666666">=</span> X[random_index:random_index<span style="color: #666666">+</span>M]
yi <span style="color: #666666">=</span> y[random_index:random_index<span style="color: #666666">+</span>M]
gradients <span style="color: #666666">=</span> (<span style="color: #666666">2.0/</span>M)<span style="color: #666666">*</span>training_gradient(yi, xi, theta)
eta <span style="color: #666666">=</span> learning_schedule(epoch<span style="color: #666666">*</span>m<span style="color: #666666">+</span>i)
theta <span style="color: #666666">=</span> theta <span style="color: #666666">-</span> eta<span style="color: #666666">*</span>gradients
doc/pub/week40/html/week40-bs.html
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@@ -334,7 +334,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Nov 3, 2021</h4>
<h4>Nov 5, 2021</h4>
</center> <!-- date -->
<br>
doc/pub/week40/html/week40-reveal.html
+4 -4
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@@ -184,7 +184,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Nov 3, 2021</h4>
<h4>Nov 5, 2021</h4>
</center> <!-- date -->
<br>
@@ -1633,9 +1633,9 @@ theta = np.random.randn(<span style="color: #B452CD">2</span>,<span style="color
<span style="color: #8B008B; font-weight: bold">for</span> epoch <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(n_epochs):
<span style="color: #228B22"># Can you figure out a better way of setting up the contributions to each batch?</span>
<span style="color: #8B008B; font-weight: bold">for</span> i <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(m):
random_index = np.random.randint(m)
xi = X[random_index*M:random_index*M+M]
yi = y[random_index*M:random_index*M+M]
random_index = M*np.random.randint(m)
xi = X[random_index:random_index+M]
yi = y[random_index:random_index+M]
gradients = (<span style="color: #B452CD">2.0</span>/M)*training_gradient(yi, xi, theta)
eta = learning_schedule(epoch*m+i)
theta = theta - eta*gradients
doc/pub/week40/html/week40-solarized.html
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@@ -267,7 +267,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Nov 3, 2021</h4>
<h4>Nov 5, 2021</h4>
</center> <!-- date -->
<br>
@@ -1651,9 +1651,9 @@ theta = np.random.randn(<span style="color: #B452CD">2</span>,<span style="color
<span style="color: #8B008B; font-weight: bold">for</span> epoch <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(n_epochs):
<span style="color: #228B22"># Can you figure out a better way of setting up the contributions to each batch?</span>
<span style="color: #8B008B; font-weight: bold">for</span> i <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(m):
random_index = np.random.randint(m)
xi = X[random_index*M:random_index*M+M]
yi = y[random_index*M:random_index*M+M]
random_index = M*np.random.randint(m)
xi = X[random_index:random_index+M]
yi = y[random_index:random_index+M]
gradients = (<span style="color: #B452CD">2.0</span>/M)*training_gradient(yi, xi, theta)
eta = learning_schedule(epoch*m+i)
theta = theta - eta*gradients
doc/pub/week40/html/week40.html
+4 -4
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@@ -344,7 +344,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Nov 3, 2021</h4>
<h4>Nov 5, 2021</h4>
</center> <!-- date -->
<br>
@@ -1728,9 +1728,9 @@ theta <span style="color: #666666">=</span> np<span style="color: #666666">.</sp
<span style="color: #008000; font-weight: bold">for</span> epoch <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(n_epochs):
<span style="color: #408080; font-style: italic"># Can you figure out a better way of setting up the contributions to each batch?</span>
<span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(m):
random_index <span style="color: #666666">=</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>randint(m)
xi <span style="color: #666666">=</span> X[random_index<span style="color: #666666">*</span>M:random_index<span style="color: #666666">*</span>M<span style="color: #666666">+</span>M]
yi <span style="color: #666666">=</span> y[random_index<span style="color: #666666">*</span>M:random_index<span style="color: #666666">*</span>M<span style="color: #666666">+</span>M]
random_index <span style="color: #666666">=</span> M<span style="color: #666666">*</span>np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>randint(m)
xi <span style="color: #666666">=</span> X[random_index:random_index<span style="color: #666666">+</span>M]
yi <span style="color: #666666">=</span> y[random_index:random_index<span style="color: #666666">+</span>M]
gradients <span style="color: #666666">=</span> (<span style="color: #666666">2.0/</span>M)<span style="color: #666666">*</span>training_gradient(yi, xi, theta)
eta <span style="color: #666666">=</span> learning_schedule(epoch<span style="color: #666666">*</span>m<span style="color: #666666">+</span>i)
theta <span style="color: #666666">=</span> theta <span style="color: #666666">-</span> eta<span style="color: #666666">*</span>gradients
doc/pub/week40/ipynb/ipynb-week40-src.tar.gz
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doc/pub/week40/ipynb/week40.ipynb
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doc/src/week40/week40.do.txt
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@@ -1039,9 +1039,9 @@ theta = np.random.randn(2,1)
for epoch in range(n_epochs):
# Can you figure out a better way of setting up the contributions to each batch?
for i in range(m):
random_index = np.random.randint(m)
xi = X[random_index*M:random_index*M+M]
yi = y[random_index*M:random_index*M+M]
random_index = M*np.random.randint(m)
xi = X[random_index:random_index+M]
yi = y[random_index:random_index+M]
gradients = (2.0/M)*training_gradient(yi, xi, theta)
eta = learning_schedule(epoch*m+i)
theta = theta - eta*gradients