add img and minor fix

Change-Id: I859208ab078d787bd6021a33fb4fc9a12f9aed4f
dltech-xyz · Jul 10, 2020 · 768c654 · 768c654
1 parent e2b0f8b
commit 768c654
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Showing 5 changed files with 95 additions and 42 deletions.
diff --git a/chapter_multilayer-perceptrons/dropout.ipynb b/chapter_multilayer-perceptrons/dropout.ipynb
@@ -686,7 +686,7 @@
     "DefaultTrainingConfig config = new DefaultTrainingConfig(loss)\n",
     "                .optOptimizer(sgd) // Optimizer (loss function)\n",
     "                .addEvaluator(new Accuracy()) // Model Accuracy\n",
-    "                .addTrainingListeners(TrainingListener.Defaults.basic()); // Logging\n",
+    "                .addTrainingListeners(TrainingListener.Defaults.logging()); // Logging\n",
     "\n",
     "    try (Model model = Model.newInstance(\"mlp\")) {\n",
     "        model.setBlock(net);\n",

diff --git a/chapter_multilayer-perceptrons/mlp-djl.ipynb b/chapter_multilayer-perceptrons/mlp-djl.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -144,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -183,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -201,7 +201,7 @@
     "DefaultTrainingConfig config = new DefaultTrainingConfig(loss)\n",
     "                .optOptimizer(sgd) // Optimizer (loss function)\n",
     "                .addEvaluator(new Accuracy()) // Model Accuracy\n",
-    "                .addTrainingListeners(TrainingListener.Defaults.basic()); // Logging\n",
+    "                .addTrainingListeners(TrainingListener.Defaults.logging()); // Logging\n",
     "\n",
     "    try (Model model = Model.newInstance(\"mlp\")) {\n",
     "        model.setBlock(net);\n",
@@ -228,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,6 +252,13 @@
     "render(LinePlot.create(\"\", data, \"epochCount\", \"loss\", \"lossLabel\"),\"text/html\");"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![lineplot](https://d2l-java-resources.s3.amazonaws.com/img/chapter_multilayer-perceptrons_mlp-djl_output1.png)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -284,7 +291,7 @@
    "mimetype": "text/x-java-source",
    "name": "Java",
    "pygments_lexer": "java",
-   "version": "11.0.5+10-LTS"
+   "version": "12.0.2+9-Ubuntu-116.04"
   }
  },
  "nbformat": 4,

diff --git a/chapter_multilayer-perceptrons/mlp-scratch.ipynb b/chapter_multilayer-perceptrons/mlp-scratch.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -207,7 +207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -237,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -276,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -306,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -326,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -390,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -410,6 +410,13 @@
     "render(LinePlot.create(\"\", data, \"epochCount\", \"loss\", \"lossLabel\"),\"text/html\");"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![lineplot](https://d2l-java-resources.s3.amazonaws.com/img/chapter_multilayer-perceptrons_mlp-scratch_output1.png)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -432,13 +439,6 @@
     "1. What is the smartest strategy you can think of for structuring a search over multiple hyperparameters?\n",
     "\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -453,7 +453,7 @@
    "mimetype": "text/x-java-source",
    "name": "Java",
    "pygments_lexer": "java",
-   "version": "11.0.5+10-LTS"
+   "version": "12.0.2+9-Ubuntu-116.04"
   }
  },
  "nbformat": 4,

diff --git a/chapter_multilayer-perceptrons/mlp.ipynb b/chapter_multilayer-perceptrons/mlp.ipynb
@@ -392,6 +392,13 @@
     "render(LinePlot.create(\"\", data, \"x\", \"relu(X)\"), \"text/html\");"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![relu](https://d2l-java-resources.s3.amazonaws.com/img/chapter_multilayer-perceptrons_mlp_relu.png)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -447,6 +454,13 @@
     "render(LinePlot.create(\"\", data, \"x\", \"grad of relu\"), \"text/html\");"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![grad_relu](https://d2l-java-resources.s3.amazonaws.com/img/chapter_multilayer-perceptrons_mlp_grad_relu.png)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -539,6 +553,13 @@
     "render(LinePlot.create(\"\", data, \"x\", \"sigmoid(X)\"));    "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![sigmoid](https://d2l-java-resources.s3.amazonaws.com/img/chapter_multilayer-perceptrons_mlp_sigmoid.png)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -591,6 +612,13 @@
     "render(LinePlot.create(\"\", data, \"x\", \"grad of sigmoid\"), \"text/html\");"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![grad_sigmoid](https://d2l-java-resources.s3.amazonaws.com/img/chapter_multilayer-perceptrons_mlp_grad_sigmoid.png)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -632,6 +660,13 @@
     "render(LinePlot.create(\"\", data, \"x\", \"tanh(X)\"));"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![tanh](https://d2l-java-resources.s3.amazonaws.com/img/chapter_multilayer-perceptrons_mlp_tanh.png)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -683,6 +718,13 @@
     "render(LinePlot.create(\"\", data, \"x\", \"grad of tanh\"), \"text/html\");"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![grad_tanh](https://d2l-java-resources.s3.amazonaws.com/img/chapter_multilayer-perceptrons_mlp_grad_tanh.png)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -713,10 +755,7 @@
     "1. Show that a multilayer perceptron using only ReLU (or pReLU) constructs a continuous piecewise linear function.\n",
     "1. Show that $\\mathrm{tanh}(x) + 1 = 2 \\mathrm{sigmoid}(2x)$.\n",
     "1. Assume we have a multilayer perceptron *without* nonlinearities between the layers. In particular, assume that we have $d$ input dimensions, $d$ output dimensions and that one of the layers has only $d/2$ dimensions. Show that this network is less expressive (powerful) than a single layer perceptron.\n",
-    "1. Assume that we have a nonlinearity that applies to one minibatch at a time. What kinds of problems do you expect this to cause?\n",
-    "\n",
-    "\n",
-    "![](https://raw.githubusercontent.com/d2l-ai/d2l-en/master/img/qr_mlp.svg)"
+    "1. Assume that we have a nonlinearity that applies to one minibatch at a time. What kinds of problems do you expect this to cause?\n"
    ]
   }
  ],
@@ -732,7 +771,7 @@
    "mimetype": "text/x-java-source",
    "name": "Java",
    "pygments_lexer": "java",
-   "version": "11.0.5+10-LTS"
+   "version": "12.0.2+9-Ubuntu-116.04"
   }
  },
  "nbformat": 4,