update

1-Introduction/.ipynb_checkpoints/activation-checkpoint.ipynb

@@ -0,0 +1,80 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# fake data\n",
+    "x = np.linspace(-5, 5, 100)\n",
+    "\n",
+    "# following are popular activation functions\n",
+    "y_relu = tf.nn.relu(x)\n",
+    "y_sigmoid = tf.nn.sigmoid(x)\n",
+    "y_tanh = tf.nn.tanh(x)\n",
+    "y_softplus = tf.nn.softplus(x)\n",
+    "# y_softmax = tf.nn.softmax(x)  softmax is a special kind of activation function, it is about probability\n",
+    "\n",
+    "# plt to visualize these activation function\n",
+    "plt.figure(1, figsize=(8, 6))\n",
+    "plt.subplot(221)\n",
+    "plt.plot(x, y_relu, c='red', label='relu')\n",
+    "plt.ylim((-1, 5))\n",
+    "plt.legend(loc='best')\n",
+    "\n",
+    "plt.subplot(222)\n",
+    "plt.plot(x, y_sigmoid, c='red', label='sigmoid')\n",
+    "plt.ylim((-0.2, 1.2))\n",
+    "plt.legend(loc='best')\n",
+    "\n",
+    "plt.subplot(223)\n",
+    "plt.plot(x, y_tanh, c='red', label='tanh')\n",
+    "plt.ylim((-1.2, 1.2))\n",
+    "plt.legend(loc='best')\n",
+    "\n",
+    "plt.subplot(224)\n",
+    "plt.plot(x, y_softplus, c='red', label='softplus')\n",
+    "plt.ylim((-0.2, 6))\n",
+    "plt.legend(loc='best')\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

1-Introduction/activation.ipynb

@@ -0,0 +1,80 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# fake data\n",
+    "x = np.linspace(-5, 5, 100)\n",
+    "\n",
+    "# following are popular activation functions\n",
+    "y_relu = tf.nn.relu(x)\n",
+    "y_sigmoid = tf.nn.sigmoid(x)\n",
+    "y_tanh = tf.nn.tanh(x)\n",
+    "y_softplus = tf.nn.softplus(x)\n",
+    "# y_softmax = tf.nn.softmax(x)  softmax is a special kind of activation function, it is about probability\n",
+    "\n",
+    "# plt to visualize these activation function\n",
+    "plt.figure(1, figsize=(8, 6))\n",
+    "plt.subplot(221)\n",
+    "plt.plot(x, y_relu, c='red', label='relu')\n",
+    "plt.ylim((-1, 5))\n",
+    "plt.legend(loc='best')\n",
+    "\n",
+    "plt.subplot(222)\n",
+    "plt.plot(x, y_sigmoid, c='red', label='sigmoid')\n",
+    "plt.ylim((-0.2, 1.2))\n",
+    "plt.legend(loc='best')\n",
+    "\n",
+    "plt.subplot(223)\n",
+    "plt.plot(x, y_tanh, c='red', label='tanh')\n",
+    "plt.ylim((-1.2, 1.2))\n",
+    "plt.legend(loc='best')\n",
+    "\n",
+    "plt.subplot(224)\n",
+    "plt.plot(x, y_softplus, c='red', label='softplus')\n",
+    "plt.ylim((-0.2, 6))\n",
+    "plt.legend(loc='best')\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

1-Introduction/activation.py

@@ -0,0 +1,54 @@
+#! /usr/bin/env python
+# coding=utf-8
+#================================================================
+#   Copyright (C) 2019 * Ltd. All rights reserved.
+#
+#   Editor      : VIM
+#   File name   : activation.py
+#   Author      : YunYang1994
+#   Created date: 2019-03-08 22:05:51
+#   Description :
+#
+#================================================================
+
+import tensorflow as tf
+import numpy as np
+import matplotlib.pyplot as plt
+
+# fake data
+x = np.linspace(-5, 5, 100)
+
+# following are popular activation functions
+y_relu = tf.nn.relu(x)
+y_sigmoid = tf.nn.sigmoid(x)
+y_tanh = tf.nn.tanh(x)
+y_softplus = tf.nn.softplus(x)
+# y_softmax = tf.nn.softmax(x)  softmax is a special kind of activation function, it is about probability
+
+# plt to visualize these activation function
+plt.figure(1, figsize=(8, 6))
+plt.subplot(221)
+plt.plot(x, y_relu, c='red', label='relu')
+plt.ylim((-1, 5))
+plt.legend(loc='best')
+
+plt.subplot(222)
+plt.plot(x, y_sigmoid, c='red', label='sigmoid')
+plt.ylim((-0.2, 1.2))
+plt.legend(loc='best')
+
+plt.subplot(223)
+plt.plot(x, y_tanh, c='red', label='tanh')
+plt.ylim((-1.2, 1.2))
+plt.legend(loc='best')
+
+plt.subplot(224)
+plt.plot(x, y_softplus, c='red', label='softplus')
+plt.ylim((-0.2, 6))
+plt.legend(loc='best')
+
+plt.show()
+
+
+
+