{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras import activations\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def format_decimal(arr):\n",
    "    return [round(x * 1e6) / 1e6 for x in arr]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### softmax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.softmax.0] 1D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
      "in shape: (6,)\n",
      "out shape: (6,)\n",
      "out: [0.067194, 0.082071, 0.110784, 0.0608, 0.182652, 0.4965]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2]\n",
    "data_in_shape = (6,)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.softmax(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.softmax.1] 2D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
      "in shape: (2, 6)\n",
      "out shape: (2, 6)\n",
      "out: [0.067194, 0.082071, 0.110784, 0.0608, 0.182652, 0.4965, 0.107768, 0.149902, 0.11105, 0.247147, 0.082268, 0.301865]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
    "data_in_shape = (2, 6)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.softmax(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### softplus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.softplus.0] 1D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
      "in shape: (6,)\n",
      "out shape: (6,)\n",
      "out: [0.693147, 0.798139, 0.974077, 0.644397, 1.313262, 2.126928]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2]\n",
    "data_in_shape = (6,)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.softplus(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.softplus.1] 2D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
      "in shape: (2, 6)\n",
      "out shape: (2, 6)\n",
      "out: [0.693147, 0.798139, 0.974077, 0.644397, 1.313262, 2.126928, 0.67826, 0.854355, 0.693147, 1.171101, 0.554355, 1.313262]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
    "data_in_shape = (2, 6)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.softplus(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.softplus.2] 3D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
      "in shape: (2, 2, 3)\n",
      "out shape: (2, 2, 3)\n",
      "out: [0.693147, 0.798139, 0.474077, 0.644397, 1.313262, 2.126928, 0.67826, 2.395545, 0.693147, 1.171101, 0.554355, 1.313262]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
    "data_in_shape = (2, 2, 3)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.softplus(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### softsign"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.softsign.0] 1D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
      "in shape: (6,)\n",
      "out shape: (6,)\n",
      "out: [0.0, 0.166667, 0.333333, -0.090909, 0.5, 0.666667]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2]\n",
    "data_in_shape = (6,)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.softsign(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.softsign.1] 2D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
      "in shape: (2, 6)\n",
      "out shape: (2, 6)\n",
      "out: [0.0, 0.166667, 0.333333, -0.090909, 0.5, 0.666667, -0.029126, 0.230769, 0.0, 0.444444, -0.230769, 0.5]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
    "data_in_shape = (2, 6)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.softsign(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.softsign.2] 3D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
      "in shape: (2, 2, 3)\n",
      "out shape: (2, 2, 3)\n",
      "out: [0.0, 0.166667, -0.333333, -0.090909, 0.5, 0.666667, -0.029126, 0.69697, 0.0, 0.444444, -0.230769, 0.5]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
    "data_in_shape = (2, 2, 3)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.softsign(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ReLU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.relu.0] 1D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
      "in shape: (6,)\n",
      "out shape: (6,)\n",
      "out: [0.0, 0.2, 0.5, 0.0, 1.0, 2.0]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2]\n",
    "data_in_shape = (6,)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.relu(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.relu.1] 2D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
      "in shape: (2, 6)\n",
      "out shape: (2, 6)\n",
      "out: [0.0, 0.2, 0.5, 0.0, 1.0, 2.0, 0.0, 0.3, 0.0, 0.8, 0.0, 1.0]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
    "data_in_shape = (2, 6)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.relu(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.relu.2] 3D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
      "in shape: (2, 2, 3)\n",
      "out shape: (2, 2, 3)\n",
      "out: [0.0, 0.2, 0.0, 0.0, 1.0, 2.0, 0.0, 2.3, 0.0, 0.8, 0.0, 1.0]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
    "data_in_shape = (2, 2, 3)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.relu(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.relu.3] 3D, max_value=0.5**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
      "in shape: (2, 2, 3)\n",
      "out shape: (2, 2, 3)\n",
      "out: [0.0, 0.2, 0.0, 0.0, 0.5, 0.5, 0.0, 0.5, 0.0, 0.5, 0.0, 0.5]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
    "data_in_shape = (2, 2, 3)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.relu(K.variable(np.array([arr_in])), max_value=0.5)\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.relu.4] 3D, alpha=0.3**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
      "in shape: (2, 2, 3)\n",
      "out shape: (2, 2, 3)\n",
      "out: [0.0, 0.2, -0.15, -0.03, 1.0, 2.0, -0.009, 2.3, 0.0, 0.8, -0.09, 1.0]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
    "data_in_shape = (2, 2, 3)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.relu(K.variable(np.array([arr_in])), alpha=0.3)\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tanh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.tanh.0] 1D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
      "in shape: (6,)\n",
      "out shape: (6,)\n",
      "out: [0.0, 0.197375, 0.462117, -0.099668, 0.761594, 0.964028]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2]\n",
    "data_in_shape = (6,)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.tanh(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.tanh.1] 2D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
      "in shape: (2, 6)\n",
      "out shape: (2, 6)\n",
      "out: [0.0, 0.197375, 0.462117, -0.099668, 0.761594, 0.964028, -0.029991, 0.291313, 0.0, 0.664037, -0.291313, 0.761594]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
    "data_in_shape = (2, 6)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.tanh(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.tanh.2] 3D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
      "in shape: (2, 2, 3)\n",
      "out shape: (2, 2, 3)\n",
      "out: [0.0, 0.197375, -0.462117, -0.099668, 0.761594, 0.964028, -0.029991, 0.980096, 0.0, 0.664037, -0.291313, 0.761594]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
    "data_in_shape = (2, 2, 3)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.tanh(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sigmoid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.sigmoid.0] 1D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
      "in shape: (6,)\n",
      "out shape: (6,)\n",
      "out: [0.5, 0.549834, 0.622459, 0.475021, 0.731059, 0.880797]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2]\n",
    "data_in_shape = (6,)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.sigmoid(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.sigmoid.1] 2D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
      "in shape: (2, 6)\n",
      "out shape: (2, 6)\n",
      "out: [0.5, 0.549834, 0.622459, 0.475021, 0.731059, 0.880797, 0.492501, 0.574443, 0.5, 0.689974, 0.425557, 0.731059]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
    "data_in_shape = (2, 6)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.sigmoid(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.sigmoid.2] 3D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
      "in shape: (2, 2, 3)\n",
      "out shape: (2, 2, 3)\n",
      "out: [0.5, 0.549834, 0.377541, 0.475021, 0.731059, 0.880797, 0.492501, 0.908877, 0.5, 0.689974, 0.425557, 0.731059]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
    "data_in_shape = (2, 2, 3)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.sigmoid(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### hardSigmoid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.hardSigmoid.0] 1D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
      "in shape: (6,)\n",
      "out shape: (6,)\n",
      "out: [0.5, 0.54, 0.6, 0.48, 0.7, 0.9]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2]\n",
    "data_in_shape = (6,)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.hard_sigmoid(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.hardSigmoid.1] 2D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
      "in shape: (2, 6)\n",
      "out shape: (2, 6)\n",
      "out: [0.5, 0.54, 0.6, 0.48, 0.7, 0.9, 0.494, 0.56, 0.5, 0.66, 0.44, 0.7]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
    "data_in_shape = (2, 6)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.hard_sigmoid(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.hardSigmoid.2] 3D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
      "in shape: (2, 2, 3)\n",
      "out shape: (2, 2, 3)\n",
      "out: [0.5, 0.54, 0.4, 0.48, 0.7, 0.9, 0.494, 0.96, 0.5, 0.66, 0.44, 0.7]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
    "data_in_shape = (2, 2, 3)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.hard_sigmoid(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### linear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.linear.0] 1D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
      "in shape: (6,)\n",
      "out shape: (6,)\n",
      "out: [0.0, 0.2, 0.5, -0.1, 1.0, 2.0]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2]\n",
    "data_in_shape = (6,)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.linear(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.linear.1] 2D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
      "in shape: (2, 6)\n",
      "out shape: (2, 6)\n",
      "out: [0.0, 0.2, 0.5, -0.1, 1.0, 2.0, -0.03, 0.3, 0.0, 0.8, -0.3, 1.0]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
    "data_in_shape = (2, 6)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.linear(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[activations.linear.2] 3D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
      "in shape: (2, 2, 3)\n",
      "out shape: (2, 2, 3)\n",
      "out: [0.0, 0.2, -0.5, -0.1, 1.0, 2.0, -0.03, 2.3, 0.0, 0.8, -0.3, 1.0]\n"
     ]
    }
   ],
   "source": [
    "data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
    "data_in_shape = (2, 2, 3)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "\n",
    "result = activations.linear(K.variable(np.array([arr_in])))\n",
    "\n",
    "arr_out = K.eval(result)[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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