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

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

‎1-Introduction/activation.ipynb

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

‎1-Introduction/activation.py

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