From c97becab11402c48e0748e88da41cd9b8dbb79fd Mon Sep 17 00:00:00 2001 From: mhjensen Date: Fri, 9 Oct 2020 06:53:22 +0200 Subject: [PATCH] updating keras --- doc/pub/week41/html/._week41-bs061.html | 48 +++++++------- doc/pub/week41/html/week41-reveal.html | 40 +++++------ doc/pub/week41/html/week41-solarized.html | 40 +++++------ doc/pub/week41/html/week41.html | 48 +++++++------- doc/pub/week41/ipynb/ipynb-week41-src.tar.gz | Bin 87344 -> 87344 bytes doc/pub/week41/ipynb/week41.ipynb | 66 ++++++++++--------- doc/src/week41/week41.do.txt | 2 +- 7 files changed, 124 insertions(+), 120 deletions(-) diff --git a/doc/pub/week41/html/._week41-bs061.html b/doc/pub/week41/html/._week41-bs061.html index a6ec6ebfa..368b51ec0 100644 --- a/doc/pub/week41/html/._week41-bs061.html +++ b/doc/pub/week41/html/._week41-bs061.html @@ -259,37 +259,37 @@ MathJax.Hub.Config({

- -

# visual representation of grid search
-# uses seaborn heatmap, could probably do this in matplotlib
-import seaborn as sns
+
+
# visual representation of grid search
+# uses seaborn heatmap, could probably do this in matplotlib
+import seaborn as sns
 
-sns.set()
+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((len(eta_vals), len(lmbd_vals)))
+test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
 
-for i in range(len(eta_vals)):
-    for j in range(len(lmbd_vals)):
-        CNN = CNN_keras[i][j]
+for i in range(len(eta_vals)):
+    for j in range(len(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)[1]
+        test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]
 
         
-fig, ax = plt.subplots(figsize = (10, 10))
-sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
-ax.set_title("Training Accuracy")
-ax.set_ylabel("$\eta$")
-ax.set_xlabel("$\lambda$")
-plt.show()
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Training Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
+plt.show()
 
-fig, ax = plt.subplots(figsize = (10, 10))
-sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
-ax.set_title("Test Accuracy")
-ax.set_ylabel("$\eta$")
-ax.set_xlabel("$\lambda$")
-plt.show()
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Test Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
+plt.show()
 

 

 

diff --git a/doc/pub/week41/html/week41-reveal.html b/doc/pub/week41/html/week41-reveal.html
index 77b82bde6..bf44b4652 100644
--- a/doc/pub/week41/html/week41-reveal.html
+++ b/doc/pub/week41/html/week41-reveal.html
@@ -2637,36 +2637,36 @@ lmbd_vals = np.logspace(-5, 
-
# visual representation of grid search
-# uses seaborn heatmap, could probably do this in matplotlib
-import seaborn as sns
+
+
# visual representation of grid search
+# uses seaborn heatmap, could probably do this in matplotlib
+import seaborn as sns
 
 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((len(eta_vals), len(lmbd_vals)))
+test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
 
-for i in range(len(eta_vals)):
-    for j in range(len(lmbd_vals)):
+for i in range(len(eta_vals)):
+    for j in range(len(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)[1]
+        test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]
 
         
-fig, ax = plt.subplots(figsize = (10, 10))
-sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
-ax.set_title("Training Accuracy")
-ax.set_ylabel("$\eta$")
-ax.set_xlabel("$\lambda$")
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Training Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
 plt.show()
 
-fig, ax = plt.subplots(figsize = (10, 10))
-sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
-ax.set_title("Test Accuracy")
-ax.set_ylabel("$\eta$")
-ax.set_xlabel("$\lambda$")
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Test Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
 plt.show()
 

 
diff --git a/doc/pub/week41/html/week41-solarized.html b/doc/pub/week41/html/week41-solarized.html
index e6d546293..0673a9412 100644
--- a/doc/pub/week41/html/week41-solarized.html
+++ b/doc/pub/week41/html/week41-solarized.html
@@ -2549,36 +2549,36 @@ lmbd_vals = np.logspace(-5, 
-
# visual representation of grid search
-# uses seaborn heatmap, could probably do this in matplotlib
-import seaborn as sns
+
+
# visual representation of grid search
+# uses seaborn heatmap, could probably do this in matplotlib
+import seaborn as sns
 
 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((len(eta_vals), len(lmbd_vals)))
+test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
 
-for i in range(len(eta_vals)):
-    for j in range(len(lmbd_vals)):
+for i in range(len(eta_vals)):
+    for j in range(len(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)[1]
+        test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]
 
         
-fig, ax = plt.subplots(figsize = (10, 10))
-sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
-ax.set_title("Training Accuracy")
-ax.set_ylabel("$\eta$")
-ax.set_xlabel("$\lambda$")
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Training Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
 plt.show()
 
-fig, ax = plt.subplots(figsize = (10, 10))
-sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
-ax.set_title("Test Accuracy")
-ax.set_ylabel("$\eta$")
-ax.set_xlabel("$\lambda$")
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Test Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
 plt.show()
 

 

diff --git a/doc/pub/week41/html/week41.html b/doc/pub/week41/html/week41.html
index eaefcfb47..6f9dc016d 100644
--- a/doc/pub/week41/html/week41.html
+++ b/doc/pub/week41/html/week41.html
@@ -2554,37 +2554,37 @@ lmbd_vals = np.
 
 

 
-
-
# visual representation of grid search
-# uses seaborn heatmap, could probably do this in matplotlib
-import seaborn as sns
+
+
# visual representation of grid search
+# uses seaborn heatmap, could probably do this in matplotlib
+import seaborn as sns
 
-sns.set()
+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((len(eta_vals), len(lmbd_vals)))
+test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
 
-for i in range(len(eta_vals)):
-    for j in range(len(lmbd_vals)):
-        CNN = CNN_keras[i][j]
+for i in range(len(eta_vals)):
+    for j in range(len(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)[1]
+        test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]
 
         
-fig, ax = plt.subplots(figsize = (10, 10))
-sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
-ax.set_title("Training Accuracy")
-ax.set_ylabel("$\eta$")
-ax.set_xlabel("$\lambda$")
-plt.show()
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Training Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
+plt.show()
 
-fig, ax = plt.subplots(figsize = (10, 10))
-sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
-ax.set_title("Test Accuracy")
-ax.set_ylabel("$\eta$")
-ax.set_xlabel("$\lambda$")
-plt.show()
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Test Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
+plt.show()
 

 

 










diff --git a/doc/pub/week41/ipynb/ipynb-week41-src.tar.gz b/doc/pub/week41/ipynb/ipynb-week41-src.tar.gz
index 13b8b61b8c870302b7f57ec06a49c449c4b436d6..a6271cad596a885cf647ba71aae85467082416e4 100644
GIT binary patch
delta 20
ccmdn6igm*(RyO%=4u;)t8rin8F=~YZ08kYM-~a#s

delta 20
ccmdn6igm*(RyO%=4u-9-8`-w9F=~YZ08iHj*#H0l

diff --git a/doc/pub/week41/ipynb/week41.ipynb b/doc/pub/week41/ipynb/week41.ipynb
index b9f20116c..446285be2 100644
--- a/doc/pub/week41/ipynb/week41.ipynb
+++ b/doc/pub/week41/ipynb/week41.ipynb
@@ -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()"
    ]
   },
   {
diff --git a/doc/src/week41/week41.do.txt b/doc/src/week41/week41.do.txt
index f14a8b8c7..e055736d5 100644
--- a/doc/src/week41/week41.do.txt
+++ b/doc/src/week41/week41.do.txt
@@ -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