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updating keras

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mhjensen
2020-10-09 06:53:22 +02:00
parent c3d2f6d940
commit c97becab11
7 changed files with 124 additions and 120 deletions
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doc/pub/week41/html/._week41-bs061.html
+24 -24
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@@ -259,37 +259,37 @@ MathJax.Hub.Config({
<p>
<!-- code=text typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span># visual representation of grid search
# uses seaborn heatmap, could probably do this in matplotlib
import seaborn as sns
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #408080; font-style: italic"># visual representation of grid search</span>
<span style="color: #408080; font-style: italic"># uses seaborn heatmap, could probably do this in matplotlib</span>
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">seaborn</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">sns</span>
sns.set()
sns<span style="color: #666666">.</span>set()
train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
train_accuracy <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros((<span style="color: #008000">len</span>(eta_vals), <span style="color: #008000">len</span>(lmbd_vals)))
test_accuracy <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros((<span style="color: #008000">len</span>(eta_vals), <span style="color: #008000">len</span>(lmbd_vals)))
for i in range(len(eta_vals)):
for j in range(len(lmbd_vals)):
CNN = CNN_keras[i][j]
<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>(<span style="color: #008000">len</span>(eta_vals)):
<span style="color: #008000; font-weight: bold">for</span> j <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(<span style="color: #008000">len</span>(lmbd_vals)):
CNN <span style="color: #666666">=</span> CNN_keras[i][j]
train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[1]
test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]
train_accuracy[i][j] <span style="color: #666666">=</span> CNN<span style="color: #666666">.</span>evaluate(X_train, Y_train)[<span style="color: #666666">1</span>]
test_accuracy[i][j] <span style="color: #666666">=</span> CNN<span style="color: #666666">.</span>evaluate(X_test, Y_test)[<span style="color: #666666">1</span>]
fig, ax = plt.subplots(figsize = (10, 10))
sns.heatmap(train_accuracy, annot=True, ax=ax, cmap=&quot;viridis&quot;)
ax.set_title(&quot;Training Accuracy&quot;)
ax.set_ylabel(&quot;$\eta$&quot;)
ax.set_xlabel(&quot;$\lambda$&quot;)
plt.show()
fig, ax <span style="color: #666666">=</span> plt<span style="color: #666666">.</span>subplots(figsize <span style="color: #666666">=</span> (<span style="color: #666666">10</span>, <span style="color: #666666">10</span>))
sns<span style="color: #666666">.</span>heatmap(train_accuracy, annot<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>, ax<span style="color: #666666">=</span>ax, cmap<span style="color: #666666">=</span><span style="color: #BA2121">&quot;viridis&quot;</span>)
ax<span style="color: #666666">.</span>set_title(<span style="color: #BA2121">&quot;Training Accuracy&quot;</span>)
ax<span style="color: #666666">.</span>set_ylabel(<span style="color: #BA2121">&quot;$\eta$&quot;</span>)
ax<span style="color: #666666">.</span>set_xlabel(<span style="color: #BA2121">&quot;$\lambda$&quot;</span>)
plt<span style="color: #666666">.</span>show()
fig, ax = plt.subplots(figsize = (10, 10))
sns.heatmap(test_accuracy, annot=True, ax=ax, cmap=&quot;viridis&quot;)
ax.set_title(&quot;Test Accuracy&quot;)
ax.set_ylabel(&quot;$\eta$&quot;)
ax.set_xlabel(&quot;$\lambda$&quot;)
plt.show()
fig, ax <span style="color: #666666">=</span> plt<span style="color: #666666">.</span>subplots(figsize <span style="color: #666666">=</span> (<span style="color: #666666">10</span>, <span style="color: #666666">10</span>))
sns<span style="color: #666666">.</span>heatmap(test_accuracy, annot<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>, ax<span style="color: #666666">=</span>ax, cmap<span style="color: #666666">=</span><span style="color: #BA2121">&quot;viridis&quot;</span>)
ax<span style="color: #666666">.</span>set_title(<span style="color: #BA2121">&quot;Test Accuracy&quot;</span>)
ax<span style="color: #666666">.</span>set_ylabel(<span style="color: #BA2121">&quot;$\eta$&quot;</span>)
ax<span style="color: #666666">.</span>set_xlabel(<span style="color: #BA2121">&quot;$\lambda$&quot;</span>)
plt<span style="color: #666666">.</span>show()
</pre></div>
<p>
<p>
doc/pub/week41/html/week41-reveal.html
+20 -20
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@@ -2637,36 +2637,36 @@ lmbd_vals = np.logspace(-<span style="color: #B452CD">5</span>, <span style="col
<p>
<!-- code=text typeset with pygments style "perldoc" -->
<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span># visual representation of grid search
# uses seaborn heatmap, could probably do this in matplotlib
import seaborn as sns
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #228B22"># visual representation of grid search</span>
<span style="color: #228B22"># uses seaborn heatmap, could probably do this in matplotlib</span>
<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">seaborn</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">sns</span>
sns.set()
train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
train_accuracy = np.zeros((<span style="color: #658b00">len</span>(eta_vals), <span style="color: #658b00">len</span>(lmbd_vals)))
test_accuracy = np.zeros((<span style="color: #658b00">len</span>(eta_vals), <span style="color: #658b00">len</span>(lmbd_vals)))
for i in range(len(eta_vals)):
for j in range(len(lmbd_vals)):
<span style="color: #8B008B; font-weight: bold">for</span> i <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(<span style="color: #658b00">len</span>(eta_vals)):
<span style="color: #8B008B; font-weight: bold">for</span> j <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(<span style="color: #658b00">len</span>(lmbd_vals)):
CNN = CNN_keras[i][j]
train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[1]
test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]
train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[<span style="color: #B452CD">1</span>]
test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[<span style="color: #B452CD">1</span>]
fig, ax = plt.subplots(figsize = (10, 10))
sns.heatmap(train_accuracy, annot=True, ax=ax, cmap=&quot;viridis&quot;)
ax.set_title(&quot;Training Accuracy&quot;)
ax.set_ylabel(&quot;$\eta$&quot;)
ax.set_xlabel(&quot;$\lambda$&quot;)
fig, ax = plt.subplots(figsize = (<span style="color: #B452CD">10</span>, <span style="color: #B452CD">10</span>))
sns.heatmap(train_accuracy, annot=<span style="color: #8B008B; font-weight: bold">True</span>, ax=ax, cmap=<span style="color: #CD5555">&quot;viridis&quot;</span>)
ax.set_title(<span style="color: #CD5555">&quot;Training Accuracy&quot;</span>)
ax.set_ylabel(<span style="color: #CD5555">&quot;$\eta$&quot;</span>)
ax.set_xlabel(<span style="color: #CD5555">&quot;$\lambda$&quot;</span>)
plt.show()
fig, ax = plt.subplots(figsize = (10, 10))
sns.heatmap(test_accuracy, annot=True, ax=ax, cmap=&quot;viridis&quot;)
ax.set_title(&quot;Test Accuracy&quot;)
ax.set_ylabel(&quot;$\eta$&quot;)
ax.set_xlabel(&quot;$\lambda$&quot;)
fig, ax = plt.subplots(figsize = (<span style="color: #B452CD">10</span>, <span style="color: #B452CD">10</span>))
sns.heatmap(test_accuracy, annot=<span style="color: #8B008B; font-weight: bold">True</span>, ax=ax, cmap=<span style="color: #CD5555">&quot;viridis&quot;</span>)
ax.set_title(<span style="color: #CD5555">&quot;Test Accuracy&quot;</span>)
ax.set_ylabel(<span style="color: #CD5555">&quot;$\eta$&quot;</span>)
ax.set_xlabel(<span style="color: #CD5555">&quot;$\lambda$&quot;</span>)
plt.show()
</pre></div>
</section>
doc/pub/week41/html/week41-solarized.html
+20 -20
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@@ -2549,36 +2549,36 @@ lmbd_vals = np.logspace(-<span style="color: #B452CD">5</span>, <span style="col
<p>
<!-- code=text typeset with pygments style "perldoc" -->
<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span># visual representation of grid search
# uses seaborn heatmap, could probably do this in matplotlib
import seaborn as sns
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #228B22"># visual representation of grid search</span>
<span style="color: #228B22"># uses seaborn heatmap, could probably do this in matplotlib</span>
<span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">seaborn</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">sns</span>
sns.set()
train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
train_accuracy = np.zeros((<span style="color: #658b00">len</span>(eta_vals), <span style="color: #658b00">len</span>(lmbd_vals)))
test_accuracy = np.zeros((<span style="color: #658b00">len</span>(eta_vals), <span style="color: #658b00">len</span>(lmbd_vals)))
for i in range(len(eta_vals)):
for j in range(len(lmbd_vals)):
<span style="color: #8B008B; font-weight: bold">for</span> i <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(<span style="color: #658b00">len</span>(eta_vals)):
<span style="color: #8B008B; font-weight: bold">for</span> j <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(<span style="color: #658b00">len</span>(lmbd_vals)):
CNN = CNN_keras[i][j]
train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[1]
test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]
train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[<span style="color: #B452CD">1</span>]
test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[<span style="color: #B452CD">1</span>]
fig, ax = plt.subplots(figsize = (10, 10))
sns.heatmap(train_accuracy, annot=True, ax=ax, cmap=&quot;viridis&quot;)
ax.set_title(&quot;Training Accuracy&quot;)
ax.set_ylabel(&quot;$\eta$&quot;)
ax.set_xlabel(&quot;$\lambda$&quot;)
fig, ax = plt.subplots(figsize = (<span style="color: #B452CD">10</span>, <span style="color: #B452CD">10</span>))
sns.heatmap(train_accuracy, annot=<span style="color: #8B008B; font-weight: bold">True</span>, ax=ax, cmap=<span style="color: #CD5555">&quot;viridis&quot;</span>)
ax.set_title(<span style="color: #CD5555">&quot;Training Accuracy&quot;</span>)
ax.set_ylabel(<span style="color: #CD5555">&quot;$\eta$&quot;</span>)
ax.set_xlabel(<span style="color: #CD5555">&quot;$\lambda$&quot;</span>)
plt.show()
fig, ax = plt.subplots(figsize = (10, 10))
sns.heatmap(test_accuracy, annot=True, ax=ax, cmap=&quot;viridis&quot;)
ax.set_title(&quot;Test Accuracy&quot;)
ax.set_ylabel(&quot;$\eta$&quot;)
ax.set_xlabel(&quot;$\lambda$&quot;)
fig, ax = plt.subplots(figsize = (<span style="color: #B452CD">10</span>, <span style="color: #B452CD">10</span>))
sns.heatmap(test_accuracy, annot=<span style="color: #8B008B; font-weight: bold">True</span>, ax=ax, cmap=<span style="color: #CD5555">&quot;viridis&quot;</span>)
ax.set_title(<span style="color: #CD5555">&quot;Test Accuracy&quot;</span>)
ax.set_ylabel(<span style="color: #CD5555">&quot;$\eta$&quot;</span>)
ax.set_xlabel(<span style="color: #CD5555">&quot;$\lambda$&quot;</span>)
plt.show()
</pre></div>
<p>
doc/pub/week41/html/week41.html
+24 -24
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@@ -2554,37 +2554,37 @@ lmbd_vals <span style="color: #666666">=</span> np<span style="color: #666666">.
<p>
<!-- code=text typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span># visual representation of grid search
# uses seaborn heatmap, could probably do this in matplotlib
import seaborn as sns
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #408080; font-style: italic"># visual representation of grid search</span>
<span style="color: #408080; font-style: italic"># uses seaborn heatmap, could probably do this in matplotlib</span>
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">seaborn</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">sns</span>
sns.set()
sns<span style="color: #666666">.</span>set()
train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
train_accuracy <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros((<span style="color: #008000">len</span>(eta_vals), <span style="color: #008000">len</span>(lmbd_vals)))
test_accuracy <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros((<span style="color: #008000">len</span>(eta_vals), <span style="color: #008000">len</span>(lmbd_vals)))
for i in range(len(eta_vals)):
for j in range(len(lmbd_vals)):
CNN = CNN_keras[i][j]
<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>(<span style="color: #008000">len</span>(eta_vals)):
<span style="color: #008000; font-weight: bold">for</span> j <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(<span style="color: #008000">len</span>(lmbd_vals)):
CNN <span style="color: #666666">=</span> CNN_keras[i][j]
train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[1]
test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]
train_accuracy[i][j] <span style="color: #666666">=</span> CNN<span style="color: #666666">.</span>evaluate(X_train, Y_train)[<span style="color: #666666">1</span>]
test_accuracy[i][j] <span style="color: #666666">=</span> CNN<span style="color: #666666">.</span>evaluate(X_test, Y_test)[<span style="color: #666666">1</span>]
fig, ax = plt.subplots(figsize = (10, 10))
sns.heatmap(train_accuracy, annot=True, ax=ax, cmap=&quot;viridis&quot;)
ax.set_title(&quot;Training Accuracy&quot;)
ax.set_ylabel(&quot;$\eta$&quot;)
ax.set_xlabel(&quot;$\lambda$&quot;)
plt.show()
fig, ax <span style="color: #666666">=</span> plt<span style="color: #666666">.</span>subplots(figsize <span style="color: #666666">=</span> (<span style="color: #666666">10</span>, <span style="color: #666666">10</span>))
sns<span style="color: #666666">.</span>heatmap(train_accuracy, annot<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>, ax<span style="color: #666666">=</span>ax, cmap<span style="color: #666666">=</span><span style="color: #BA2121">&quot;viridis&quot;</span>)
ax<span style="color: #666666">.</span>set_title(<span style="color: #BA2121">&quot;Training Accuracy&quot;</span>)
ax<span style="color: #666666">.</span>set_ylabel(<span style="color: #BA2121">&quot;$\eta$&quot;</span>)
ax<span style="color: #666666">.</span>set_xlabel(<span style="color: #BA2121">&quot;$\lambda$&quot;</span>)
plt<span style="color: #666666">.</span>show()
fig, ax = plt.subplots(figsize = (10, 10))
sns.heatmap(test_accuracy, annot=True, ax=ax, cmap=&quot;viridis&quot;)
ax.set_title(&quot;Test Accuracy&quot;)
ax.set_ylabel(&quot;$\eta$&quot;)
ax.set_xlabel(&quot;$\lambda$&quot;)
plt.show()
fig, ax <span style="color: #666666">=</span> plt<span style="color: #666666">.</span>subplots(figsize <span style="color: #666666">=</span> (<span style="color: #666666">10</span>, <span style="color: #666666">10</span>))
sns<span style="color: #666666">.</span>heatmap(test_accuracy, annot<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>, ax<span style="color: #666666">=</span>ax, cmap<span style="color: #666666">=</span><span style="color: #BA2121">&quot;viridis&quot;</span>)
ax<span style="color: #666666">.</span>set_title(<span style="color: #BA2121">&quot;Test Accuracy&quot;</span>)
ax<span style="color: #666666">.</span>set_ylabel(<span style="color: #BA2121">&quot;$\eta$&quot;</span>)
ax<span style="color: #666666">.</span>set_xlabel(<span style="color: #BA2121">&quot;$\lambda$&quot;</span>)
plt<span style="color: #666666">.</span>show()
</pre></div>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
doc/pub/week41/ipynb/ipynb-week41-src.tar.gz
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doc/pub/week41/ipynb/week41.ipynb
+35 -31
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@@ -2570,39 +2570,43 @@
]
},
{
"cell_type": "markdown",
"metadata": {},
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
" # visual representation of grid search\n",
" # uses seaborn heatmap, could probably do this in matplotlib\n",
" import seaborn as sns\n",
"# visual representation of grid search\n",
"# uses seaborn heatmap, could probably do this in matplotlib\n",
"import seaborn as sns\n",
"\n",
"sns.set()\n",
"\n",
"train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))\n",
"test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))\n",
"\n",
"for i in range(len(eta_vals)):\n",
" for j in range(len(lmbd_vals)):\n",
" CNN = CNN_keras[i][j]\n",
"\n",
" train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[1]\n",
" test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]\n",
"\n",
" \n",
" sns.set()\n",
" \n",
" train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))\n",
" test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))\n",
" \n",
" for i in range(len(eta_vals)):\n",
" for j in range(len(lmbd_vals)):\n",
" CNN = CNN_keras[i][j]\n",
" \n",
" train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[1]\n",
" test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]\n",
" \n",
" \n",
" fig, ax = plt.subplots(figsize = (10, 10))\n",
" sns.heatmap(train_accuracy, annot=True, ax=ax, cmap=\"viridis\")\n",
" ax.set_title(\"Training Accuracy\")\n",
" ax.set_ylabel(\"$\\eta$\")\n",
" ax.set_xlabel(\"$\\lambda$\")\n",
" plt.show()\n",
" \n",
" fig, ax = plt.subplots(figsize = (10, 10))\n",
" sns.heatmap(test_accuracy, annot=True, ax=ax, cmap=\"viridis\")\n",
" ax.set_title(\"Test Accuracy\")\n",
" ax.set_ylabel(\"$\\eta$\")\n",
" ax.set_xlabel(\"$\\lambda$\")\n",
" plt.show()\n"
"fig, ax = plt.subplots(figsize = (10, 10))\n",
"sns.heatmap(train_accuracy, annot=True, ax=ax, cmap=\"viridis\")\n",
"ax.set_title(\"Training Accuracy\")\n",
"ax.set_ylabel(\"$\\eta$\")\n",
"ax.set_xlabel(\"$\\lambda$\")\n",
"plt.show()\n",
"\n",
"fig, ax = plt.subplots(figsize = (10, 10))\n",
"sns.heatmap(test_accuracy, annot=True, ax=ax, cmap=\"viridis\")\n",
"ax.set_title(\"Test Accuracy\")\n",
"ax.set_ylabel(\"$\\eta$\")\n",
"ax.set_xlabel(\"$\\lambda$\")\n",
"plt.show()"
]
},
{
doc/src/week41/week41.do.txt
+1 -1
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@@ -2100,7 +2100,7 @@ for i, eta in enumerate(eta_vals):
!split
===== Final visualization =====
!bc
!bc pycod
# visual representation of grid search
# uses seaborn heatmap, could probably do this in matplotlib
import seaborn as sns