autoencoder_for_MIMO_with_gaussian.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# MIT License\n",
    "\n",
    "# Copyright (c) [2019] [Jayden Booth]\n",
    "\n",
    "# Permission is hereby granted, free of charge, to any person obtaining a copy\n",
    "# of this software and associated documentation files (the \"Software\"), to deal\n",
    "# in the Software without restriction, including without limitation the rights\n",
    "# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n",
    "# copies of the Software, and to permit persons to whom the Software is\n",
    "# furnished to do so, subject to the following conditions:\n",
    "\n",
    "# The above copyright notice and this permission notice shall be included in all\n",
    "# copies or substantial portions of the Software.\n",
    "\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n",
    "# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n",
    "# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
    "# SOFTWARE.\n",
    "\n",
    "\n",
    "# Import Libraries\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras.layers import Input, Dense, GaussianNoise,Lambda,Dropout, concatenate, Layer\n",
    "from keras.models import Model\n",
    "from keras import regularizers\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.optimizers import Adam,SGD\n",
    "from keras import backend as K\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set random seeds\n",
    "from numpy.random import seed\n",
    "seed(1)\n",
    "from tensorflow import set_random_seed\n",
    "set_random_seed(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "M: 16 \t n: 1\n"
     ]
    }
   ],
   "source": [
    "# Set the defining parameters\n",
    "# n = n_channel complex numbers (so 2n real numbers)\n",
    "# k = log2(M), where M is the number of messages to encode\n",
    "# EbNo is the energy per bit to noise power density\n",
    "\n",
    "# Encoder Parameters\n",
    "M = 16\n",
    "k = np.log2(M)\n",
    "n_channel = 1\n",
    "R = k/n_channel\n",
    "print('M:',M,'\\t','n:',n_channel)\n",
    "\n",
    "# Channel Parameters\n",
    "EbNo=10.0**(7/10.0)\n",
    "noise_std = np.sqrt(1/(2*R*EbNo))\n",
    "num_taps = 1\n",
    "reyleigh_std = num_taps/np.sqrt(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#generating data of size N\n",
    "batches = 128\n",
    "N = batches * 1000\n",
    "label = np.random.randint(M,size=[N,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating one hot encoded vectors\n",
    "data = []\n",
    "for i in label:\n",
    "    temp = np.zeros(2*M)\n",
    "    temp[i[0]] = 1\n",
    "    temp[i[1]+M] = 1\n",
    "    data.append(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100000, 32)\n"
     ]
    }
   ],
   "source": [
    "# checking data shape\n",
    "data = np.array(data)\n",
    "print (data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9 1] [0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[6 9] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      "[8 6] [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[6 2] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1 2] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[7 7] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[12 11] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 1. 0. 0. 0. 0.]\n",
      "[4 9] [0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      "[8 8] [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 1. 0. 0. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "# checking generated data with it's label\n",
    "temp_check = [17,23,45,67,89,96,72,250,350]\n",
    "for i in temp_check:\n",
    "    print(label[i],data[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2x2 MIMO gaussian Channel\n",
    "def gaussian_mimo_channel(x):\n",
    "    EbNo_train = 5.01187 #  coverted 7 db of EbNo\n",
    "    noise_std = np.sqrt(1/(2*R*EbNo_train))\n",
    "    \n",
    "    x1 = x[:,0:2*n_channel]\n",
    "    x2 = x[:,2*n_channel:4*n_channel]\n",
    "    y1 = x1 + x2 + K.random_normal((2*n_channel,),mean=0,stddev=noise_std)\n",
    "    y2 = x2 + x1 + K.random_normal((2*n_channel,),mean=0,stddev=noise_std)\n",
    "    return K.concatenate([y1,y2],axis=-1)\n",
    "\n",
    "# Custom Layer to implement custom Loss Function\n",
    "class MyLayer(Layer):\n",
    "\n",
    "    def __init__(self, output_dim, **kwargs):\n",
    "        self.output_dim = output_dim\n",
    "        self.alpha = 0.5\n",
    "        super(MyLayer, self).__init__(**kwargs)\n",
    "\n",
    "    def build(self, input_shape):\n",
    "        super(MyLayer, self).build(input_shape)  # Be sure to call this at the end\n",
    "\n",
    "    def call(self, x):\n",
    "        y1true = x[:,0:M]\n",
    "        y2true = x[:,M:2*M]\n",
    "        y1pred = x[:,2*M:3*M]\n",
    "        y2pred = x[:,3*M:4*M]\n",
    "        \n",
    "        \n",
    "        return K.dot(x, self.kernel)\n",
    "\n",
    "    def compute_output_shape(self, input_shape):\n",
    "        return (input_shape[0], self.output_dim)\n",
    "\n",
    "# A test class to implement adaptive loss function\n",
    "class CustomLoss:\n",
    "    def __init__(self):\n",
    "        self.alpha = 0.5\n",
    "        \n",
    "    def adaptive_mimo_loss(self,ytrue,ypred):\n",
    "        y1true = ytrue[:,0:M]\n",
    "        y2true = ytrue[:,M:2*M]\n",
    "        y1pred = ypred[:,0:M]\n",
    "        y2pred = ypred[:,M:2*M]\n",
    "        \n",
    "        L1 = K.categorical_crossentropy(y1true,y1pred)\n",
    "        L2 = K.categorical_crossentropy(y2true,y2pred)\n",
    "        \n",
    "        size = K.shape(y1true)[0]\n",
    "        size = size/4\n",
    "        \n",
    "        loss = self.alpha*L1 + (1-self.alpha)*L2\n",
    "        temp = L1 / (L1 + L2)\n",
    "        self.alpha = K.eval(temp)\n",
    "        K.print_tensor(self.alpha)\n",
    "        \n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0718 08:06:50.954123 140111347230528 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "W0718 08:06:50.955015 140111347230528 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W0718 08:06:50.963168 140111347230528 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "W0718 08:06:51.038891 140111347230528 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "W0718 08:06:51.159911 140111347230528 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4115: The name tf.random_normal is deprecated. Please use tf.random.normal instead.\n",
      "\n",
      "W0718 08:06:51.290082 140111347230528 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    },
    {
     "ename": "InvalidArgumentError",
     "evalue": "You must feed a value for placeholder tensor 'input_1' with dtype float and shape [?,32]\n\t [[node input_1 (defined at /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517) ]]\n\nOriginal stack trace for 'input_1':\n  File \"/usr/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/usr/lib/python3.6/runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"/usr/local/lib/python3.6/dist-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel/kernelapp.py\", line 505, in start\n    self.io_loop.start()\n  File \"/usr/local/lib/python3.6/dist-packages/tornado/platform/asyncio.py\", line 148, in start\n    self.asyncio_loop.run_forever()\n  File \"/usr/lib/python3.6/asyncio/base_events.py\", line 438, in run_forever\n    self._run_once()\n  File \"/usr/lib/python3.6/asyncio/base_events.py\", line 1451, in _run_once\n    handle._run()\n  File \"/usr/lib/python3.6/asyncio/events.py\", line 145, in _run\n    self._callback(*self._args)\n  File \"/usr/local/lib/python3.6/dist-packages/tornado/ioloop.py\", line 690, in <lambda>\n    lambda f: self._run_callback(functools.partial(callback, future))\n  File \"/usr/local/lib/python3.6/dist-packages/tornado/ioloop.py\", line 743, in _run_callback\n    ret = callback()\n  File \"/usr/local/lib/python3.6/dist-packages/tornado/gen.py\", line 787, in inner\n    self.run()\n  File \"/usr/local/lib/python3.6/dist-packages/tornado/gen.py\", line 748, in run\n    yielded = self.gen.send(value)\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\", line 365, in process_one\n    yield gen.maybe_future(dispatch(*args))\n  File \"/usr/local/lib/python3.6/dist-packages/tornado/gen.py\", line 209, in wrapper\n    yielded = next(result)\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\", line 272, in dispatch_shell\n    yield gen.maybe_future(handler(stream, idents, msg))\n  File \"/usr/local/lib/python3.6/dist-packages/tornado/gen.py\", line 209, in wrapper\n    yielded = next(result)\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\", line 542, in execute_request\n    user_expressions, allow_stdin,\n  File \"/usr/local/lib/python3.6/dist-packages/tornado/gen.py\", line 209, in wrapper\n    yielded = next(result)\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel/ipkernel.py\", line 294, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel/zmqshell.py\", line 536, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\", line 2848, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\", line 2874, in _run_cell\n    return runner(coro)\n  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/async_helpers.py\", line 67, in _pseudo_sync_runner\n    coro.send(None)\n  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\", line 3049, in run_cell_async\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\", line 3214, in run_ast_nodes\n    if (yield from self.run_code(code, result)):\n  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\", line 3296, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-9-04f70afb57aa>\", line 3, in <module>\n    input_signal = Input(shape=(2*M,))\n  File \"/usr/local/lib/python3.6/dist-packages/keras/engine/input_layer.py\", line 178, in Input\n    input_tensor=tensor)\n  File \"/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py\", line 91, in wrapper\n    return func(*args, **kwargs)\n  File \"/usr/local/lib/python3.6/dist-packages/keras/engine/input_layer.py\", line 87, in __init__\n    name=self.name)\n  File \"/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\", line 517, in placeholder\n    x = tf.placeholder(dtype, shape=shape, name=name)\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/array_ops.py\", line 2143, in placeholder\n    return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_array_ops.py\", line 6262, in placeholder\n    \"Placeholder\", dtype=dtype, shape=shape, name=name)\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py\", line 788, in _apply_op_helper\n    op_def=op_def)\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/deprecation.py\", line 507, in new_func\n    return func(*args, **kwargs)\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py\", line 3616, in create_op\n    op_def=op_def)\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py\", line 2005, in __init__\n    self._traceback = tf_stack.extract_stack()\n",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mInvalidArgumentError\u001B[0m                      Traceback (most recent call last)",
      "\u001B[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001B[0m in \u001B[0;36m_do_call\u001B[0;34m(self, fn, *args)\u001B[0m\n\u001B[1;32m   1355\u001B[0m     \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1356\u001B[0;31m       \u001B[0;32mreturn\u001B[0m \u001B[0mfn\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1357\u001B[0m     \u001B[0;32mexcept\u001B[0m \u001B[0merrors\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mOpError\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0me\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001B[0m in \u001B[0;36m_run_fn\u001B[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001B[0m\n\u001B[1;32m   1340\u001B[0m       return self._call_tf_sessionrun(\n\u001B[0;32m-> 1341\u001B[0;31m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001B[0m\u001B[1;32m   1342\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001B[0m in \u001B[0;36m_call_tf_sessionrun\u001B[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001B[0m\n\u001B[1;32m   1428\u001B[0m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_session\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0moptions\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mfeed_dict\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mfetch_list\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mtarget_list\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1429\u001B[0;31m         run_metadata)\n\u001B[0m\u001B[1;32m   1430\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mInvalidArgumentError\u001B[0m: You must feed a value for placeholder tensor 'input_1' with dtype float and shape [?,32]\n\t [[{{node input_1}}]]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001B[0;31mInvalidArgumentError\u001B[0m                      Traceback (most recent call last)",
      "\u001B[0;32m<ipython-input-9-04f70afb57aa>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m     41\u001B[0m \u001B[0msgd\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mSGD\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mlr\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m0.02\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     42\u001B[0m \u001B[0mtest\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mCustomLoss\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 43\u001B[0;31m \u001B[0mautoencoder\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcompile\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0moptimizer\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0msgd\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mloss\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mtest\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0madaptive_mimo_loss\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001B[0m in \u001B[0;36mcompile\u001B[0;34m(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, **kwargs)\u001B[0m\n\u001B[1;32m    340\u001B[0m                 \u001B[0;32mwith\u001B[0m \u001B[0mK\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mname_scope\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0moutput_names\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mi\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m+\u001B[0m \u001B[0;34m'_loss'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    341\u001B[0m                     output_loss = weighted_loss(y_true, y_pred,\n\u001B[0;32m--> 342\u001B[0;31m                                                 sample_weight, mask)\n\u001B[0m\u001B[1;32m    343\u001B[0m                 \u001B[0;32mif\u001B[0m \u001B[0mlen\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0moutputs\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m>\u001B[0m \u001B[0;36m1\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    344\u001B[0m                     \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mmetrics_tensors\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0moutput_loss\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py\u001B[0m in \u001B[0;36mweighted\u001B[0;34m(y_true, y_pred, weights, mask)\u001B[0m\n\u001B[1;32m    402\u001B[0m         \"\"\"\n\u001B[1;32m    403\u001B[0m         \u001B[0;31m# score_array has ndim >= 2\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 404\u001B[0;31m         \u001B[0mscore_array\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mfn\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0my_true\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0my_pred\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    405\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mmask\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    406\u001B[0m             \u001B[0;31m# Cast the mask to floatX to avoid float64 upcasting in Theano\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m<ipython-input-8-e4e7820e47cc>\u001B[0m in \u001B[0;36madaptive_mimo_loss\u001B[0;34m(self, ytrue, ypred)\u001B[0m\n\u001B[1;32m     52\u001B[0m         \u001B[0mloss\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0malpha\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0mL1\u001B[0m \u001B[0;34m+\u001B[0m \u001B[0;34m(\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m-\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0malpha\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0mL2\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     53\u001B[0m         \u001B[0mtemp\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mL1\u001B[0m \u001B[0;34m/\u001B[0m \u001B[0;34m(\u001B[0m\u001B[0mL1\u001B[0m \u001B[0;34m+\u001B[0m \u001B[0mL2\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 54\u001B[0;31m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0malpha\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mK\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0meval\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtemp\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     55\u001B[0m         \u001B[0mK\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mprint_tensor\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0malpha\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     56\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001B[0m in \u001B[0;36meval\u001B[0;34m(x)\u001B[0m\n\u001B[1;32m    671\u001B[0m     \u001B[0;31m`\u001B[0m\u001B[0;31m`\u001B[0m\u001B[0;31m`\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    672\u001B[0m     \"\"\"\n\u001B[0;32m--> 673\u001B[0;31m     \u001B[0;32mreturn\u001B[0m \u001B[0mto_dense\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0meval\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0msession\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mget_session\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    674\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    675\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py\u001B[0m in \u001B[0;36meval\u001B[0;34m(self, feed_dict, session)\u001B[0m\n\u001B[1;32m    729\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    730\u001B[0m     \"\"\"\n\u001B[0;32m--> 731\u001B[0;31m     \u001B[0;32mreturn\u001B[0m \u001B[0m_eval_using_default_session\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mfeed_dict\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mgraph\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0msession\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    732\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    733\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py\u001B[0m in \u001B[0;36m_eval_using_default_session\u001B[0;34m(tensors, feed_dict, graph, session)\u001B[0m\n\u001B[1;32m   5577\u001B[0m                        \u001B[0;34m\"the tensor's graph is different from the session's \"\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   5578\u001B[0m                        \"graph.\")\n\u001B[0;32m-> 5579\u001B[0;31m   \u001B[0;32mreturn\u001B[0m \u001B[0msession\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mrun\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtensors\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mfeed_dict\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   5580\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   5581\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001B[0m in \u001B[0;36mrun\u001B[0;34m(self, fetches, feed_dict, options, run_metadata)\u001B[0m\n\u001B[1;32m    948\u001B[0m     \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    949\u001B[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001B[0;32m--> 950\u001B[0;31m                          run_metadata_ptr)\n\u001B[0m\u001B[1;32m    951\u001B[0m       \u001B[0;32mif\u001B[0m \u001B[0mrun_metadata\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    952\u001B[0m         \u001B[0mproto_data\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mtf_session\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mTF_GetBuffer\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mrun_metadata_ptr\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001B[0m in \u001B[0;36m_run\u001B[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001B[0m\n\u001B[1;32m   1171\u001B[0m     \u001B[0;32mif\u001B[0m \u001B[0mfinal_fetches\u001B[0m \u001B[0;32mor\u001B[0m \u001B[0mfinal_targets\u001B[0m \u001B[0;32mor\u001B[0m \u001B[0;34m(\u001B[0m\u001B[0mhandle\u001B[0m \u001B[0;32mand\u001B[0m \u001B[0mfeed_dict_tensor\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1172\u001B[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001B[0;32m-> 1173\u001B[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001B[0m\u001B[1;32m   1174\u001B[0m     \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1175\u001B[0m       \u001B[0mresults\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001B[0m in \u001B[0;36m_do_run\u001B[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001B[0m\n\u001B[1;32m   1348\u001B[0m     \u001B[0;32mif\u001B[0m \u001B[0mhandle\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1349\u001B[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001B[0;32m-> 1350\u001B[0;31m                            run_metadata)\n\u001B[0m\u001B[1;32m   1351\u001B[0m     \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1352\u001B[0m       \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_do_call\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0m_prun_fn\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mhandle\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mfeeds\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mfetches\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001B[0m in \u001B[0;36m_do_call\u001B[0;34m(self, fn, *args)\u001B[0m\n\u001B[1;32m   1368\u001B[0m           \u001B[0;32mpass\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1369\u001B[0m       \u001B[0mmessage\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0merror_interpolation\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0minterpolate\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmessage\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_graph\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1370\u001B[0;31m       \u001B[0;32mraise\u001B[0m \u001B[0mtype\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0me\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mnode_def\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mop\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmessage\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1371\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1372\u001B[0m   \u001B[0;32mdef\u001B[0m \u001B[0m_extend_graph\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mInvalidArgumentError\u001B[0m: You must feed a value for placeholder tensor 'input_1' with dtype float and shape [?,32]\n\t [[node input_1 (defined at /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517) ]]\n\nOriginal stack trace for 'input_1':\n  File \"/usr/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/usr/lib/python3.6/runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"/usr/local/lib/python3.6/dist-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel/kernelapp.py\", line 505, in start\n    self.io_loop.start()\n  File \"/usr/local/lib/python3.6/dist-packages/tornado/platform/asyncio.py\", line 148, in start\n    self.asyncio_loop.run_forever()\n  File \"/usr/lib/python3.6/asyncio/base_events.py\", line 438, in run_forever\n    self._run_once()\n  File \"/usr/lib/python3.6/asyncio/base_events.py\", line 1451, in _run_once\n    handle._run()\n  File \"/usr/lib/python3.6/asyncio/events.py\", line 145, in _run\n    self._callback(*self._args)\n  File \"/usr/local/lib/python3.6/dist-packages/tornado/ioloop.py\", line 690, in <lambda>\n    lambda f: self._run_callback(functools.partial(callback, future))\n  File \"/usr/local/lib/python3.6/dist-packages/tornado/ioloop.py\", line 743, in _run_callback\n    ret = callback()\n  File \"/usr/local/lib/python3.6/dist-packages/tornado/gen.py\", line 787, in inner\n    self.run()\n  File \"/usr/local/lib/python3.6/dist-packages/tornado/gen.py\", line 748, in run\n    yielded = self.gen.send(value)\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\", line 365, in process_one\n    yield gen.maybe_future(dispatch(*args))\n  File \"/usr/local/lib/python3.6/dist-packages/tornado/gen.py\", line 209, in wrapper\n    yielded = next(result)\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\", line 272, in dispatch_shell\n    yield gen.maybe_future(handler(stream, idents, msg))\n  File \"/usr/local/lib/python3.6/dist-packages/tornado/gen.py\", line 209, in wrapper\n    yielded = next(result)\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\", line 542, in execute_request\n    user_expressions, allow_stdin,\n  File \"/usr/local/lib/python3.6/dist-packages/tornado/gen.py\", line 209, in wrapper\n    yielded = next(result)\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel/ipkernel.py\", line 294, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/usr/local/lib/python3.6/dist-packages/ipykernel/zmqshell.py\", line 536, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\", line 2848, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\", line 2874, in _run_cell\n    return runner(coro)\n  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/async_helpers.py\", line 67, in _pseudo_sync_runner\n    coro.send(None)\n  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\", line 3049, in run_cell_async\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\", line 3214, in run_ast_nodes\n    if (yield from self.run_code(code, result)):\n  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\", line 3296, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-9-04f70afb57aa>\", line 3, in <module>\n    input_signal = Input(shape=(2*M,))\n  File \"/usr/local/lib/python3.6/dist-packages/keras/engine/input_layer.py\", line 178, in Input\n    input_tensor=tensor)\n  File \"/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py\", line 91, in wrapper\n    return func(*args, **kwargs)\n  File \"/usr/local/lib/python3.6/dist-packages/keras/engine/input_layer.py\", line 87, in __init__\n    name=self.name)\n  File \"/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\", line 517, in placeholder\n    x = tf.placeholder(dtype, shape=shape, name=name)\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/array_ops.py\", line 2143, in placeholder\n    return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_array_ops.py\", line 6262, in placeholder\n    \"Placeholder\", dtype=dtype, shape=shape, name=name)\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py\", line 788, in _apply_op_helper\n    op_def=op_def)\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/deprecation.py\", line 507, in new_func\n    return func(*args, **kwargs)\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py\", line 3616, in create_op\n    op_def=op_def)\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py\", line 2005, in __init__\n    self._traceback = tf_stack.extract_stack()\n"
     ]
    }
   ],
   "source": [
    "# Defined Autoencoder\n",
    "\n",
    "input_signal = Input(shape=(2*M,))\n",
    "\n",
    "# Transmitter 1 Layers\n",
    "split1_1 = Lambda(lambda x:x[:,0:M])(input_signal)\n",
    "encoded1 = Dense(M, activation='relu')(split1_1)\n",
    "encoded1_1 = Dense(2*n_channel, activation='linear')(encoded1)\n",
    "encoded1_2 = BatchNormalization()(encoded1_1)\n",
    "encoded1_3 = Lambda(lambda x: 2*K.l2_normalize(x,axis=-1))(encoded1_2)\n",
    "\n",
    "# Transmitter 2 Layers\n",
    "split2_1 = Lambda(lambda x: x[:,M:2*M])(input_signal)\n",
    "encoded2 = Dense(M, activation='relu')(split2_1)\n",
    "encoded2_1 = Dense(2*n_channel, activation='linear')(encoded2)\n",
    "encoded2_2 = BatchNormalization()(encoded2_1)\n",
    "encoded2_3 = Lambda(lambda x: 2*K.l2_normalize(x,axis=-1))(encoded2_2)\n",
    "\n",
    "# Gaussian Channel Layer\n",
    "EbNo_train = 5.01187 #  coverted 7 db of EbNo\n",
    "merge = concatenate([encoded1_3,encoded2_3])\n",
    "channel1 = Lambda(gaussian_mimo_channel)(merge)\n",
    "#channel1 = GaussianNoise(np.sqrt(1/(2*R*EbNo_train)))(merge)\n",
    "\n",
    "# Reciever 1 Layers\n",
    "split1_2 = Lambda(lambda x:x[:,0:2*n_channel])(channel1)\n",
    "decoded1 = Dense(M, activation='relu')(split1_2)\n",
    "decoded1_1 = Dense(M, activation='softmax')(decoded1)\n",
    "\n",
    "# Reciever 2 Layers\n",
    "split2_2 = Lambda(lambda x:x[:,2*n_channel:4*n_channel])(channel1)\n",
    "decoded2 = Dense(M, activation='relu')(split2_2)\n",
    "decoded2_1 = Dense(M, activation='softmax')(decoded2)\n",
    "\n",
    "# Output Layers\n",
    "output_signal = concatenate([decoded1_1,decoded2_1])\n",
    "\n",
    "# Model\n",
    "autoencoder = Model(input_signal, output_signal)\n",
    "adam = Adam(lr=0.01)\n",
    "sgd = SGD(lr=0.02)\n",
    "test = CustomLoss()\n",
    "autoencoder.compile(optimizer=sgd, loss=test.adaptive_mimo_loss, run_eagerly=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# printing summary of layers and it's trainable parameters \n",
    "print (autoencoder.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "100000/100000 [==============================] - 2s 21us/step - loss: 2.3439\n",
      "Epoch 2/10\n",
      "100000/100000 [==============================] - 1s 12us/step - loss: 2.1022\n",
      "Epoch 3/10\n",
      "100000/100000 [==============================] - 1s 12us/step - loss: 2.0280\n",
      "Epoch 4/10\n",
      "100000/100000 [==============================] - 1s 12us/step - loss: 1.9851\n",
      "Epoch 5/10\n",
      "100000/100000 [==============================] - 1s 13us/step - loss: 1.9513\n",
      "Epoch 6/10\n",
      "100000/100000 [==============================] - 1s 12us/step - loss: 1.9163\n",
      "Epoch 7/10\n",
      "100000/100000 [==============================] - 1s 12us/step - loss: 1.8770\n",
      "Epoch 8/10\n",
      "100000/100000 [==============================] - 1s 12us/step - loss: 1.8381\n",
      "Epoch 9/10\n",
      "100000/100000 [==============================] - 1s 13us/step - loss: 1.7944\n",
      "Epoch 10/10\n",
      "100000/100000 [==============================] - 1s 12us/step - loss: 1.7547\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f7974445da0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# traning auto encoder\n",
    "autoencoder.fit(data, data,\n",
    "                epochs=10,\n",
    "                batch_size=batches,\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making encoder1 from full autoencoder\n",
    "input1 = Input(shape=(M,))\n",
    "enco1 = autoencoder.layers[3](input1)\n",
    "enco1 = autoencoder.layers[5](enco1)\n",
    "enco1 = autoencoder.layers[7](enco1)\n",
    "enco1 = autoencoder.layers[9](enco1)\n",
    "encoder1 = Model(input1,enco1)\n",
    "\n",
    "# making encoder2 from full autoencoder\n",
    "input2 = Input(shape=(M,))\n",
    "enco2 = autoencoder.layers[4](input2)\n",
    "enco2 = autoencoder.layers[6](enco2)\n",
    "enco2 = autoencoder.layers[8](enco2)\n",
    "enco2 = autoencoder.layers[10](enco2)\n",
    "encoder2 = Model(input2,enco2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making decoder1 from full autoencoder\n",
    "encoded_input1 = Input(shape=(2*n_channel,))\n",
    "deco1 = autoencoder.layers[-5](encoded_input1)\n",
    "deco1 = autoencoder.layers[-3](deco1)\n",
    "decoder1 = Model(encoded_input1, deco1)\n",
    "\n",
    "# making decoder1 from full autoencoder\n",
    "encoded_input2 = Input(shape=(2*n_channel,))\n",
    "deco2 = autoencoder.layers[-4](encoded_input2)\n",
    "deco2 = autoencoder.layers[-2](deco2)\n",
    "decoder2 = Model(encoded_input2, deco2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generating data for checking BER\n",
    "N = 100000\n",
    "test_label = np.random.randint(M,size=[N,2])\n",
    "test_data1 = []\n",
    "\n",
    "for i in test_label[:,0]:\n",
    "    temp = np.zeros(M)\n",
    "    temp[i] = 1\n",
    "    test_data1.append(temp)\n",
    "\n",
    "test_data2 = []\n",
    "\n",
    "for i in test_label[:,1]:\n",
    "    temp = np.zeros(M)\n",
    "    temp[i] = 1\n",
    "    test_data2.append(temp)\n",
    "    \n",
    "test_data1 = np.array(test_data1)\n",
    "test_data2 = np.array(test_data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 1, 2)\n"
     ]
    }
   ],
   "source": [
    "# for plotting learned consteallation diagram\n",
    "\n",
    "scatter_plot1 = []\n",
    "for i in range(0,M):\n",
    "    temp = np.zeros(M)\n",
    "    temp[i] = 1\n",
    "    scatter_plot1.append(encoder1.predict(np.expand_dims(temp,axis=0)))\n",
    "scatter_plot1 = np.array(scatter_plot1)\n",
    "\n",
    "scatter_plot2 = []\n",
    "for i in range(0,M):\n",
    "    temp = np.zeros(M)\n",
    "    temp[i] = 1\n",
    "    scatter_plot2.append(encoder2.predict(np.expand_dims(temp,axis=0)))\n",
    "scatter_plot2 = np.array(scatter_plot2)\n",
    "print (scatter_plot2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ploting constellation diagram\n",
    "import matplotlib.pyplot as plt\n",
    "scatter_plot1 = scatter_plot1.reshape(M,2,1)\n",
    "scatter_plot2 = scatter_plot2.reshape(M,2,1)\n",
    "plt.scatter(scatter_plot1[:,0],scatter_plot1[:,1])\n",
    "plt.scatter(scatter_plot2[:,0],scatter_plot2[:,1])\n",
    "#plt.axis((-2.5,2.5,-2.5,2.5))\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SNR: 0 BER1: 0.39794 BER2: 0.86545\n",
      "SNR: 2 BER1: 0.29107 BER2: 0.86171\n",
      "SNR: 4 BER1: 0.19758 BER2: 0.8545\n",
      "SNR: 6 BER1: 0.11696 BER2: 0.84822\n",
      "SNR: 8 BER1: 0.06328 BER2: 0.84272\n",
      "SNR: 10 BER1: 0.02983 BER2: 0.83575\n",
      "SNR: 12 BER1: 0.01355 BER2: 0.83276\n",
      "SNR: 14 BER1: 0.00514 BER2: 0.82807\n",
      "SNR: 16 BER1: 0.00212 BER2: 0.82508\n",
      "SNR: 18 BER1: 0.0006 BER2: 0.82255\n",
      "SNR: 20 BER1: 0.00016 BER2: 0.82091\n"
     ]
    }
   ],
   "source": [
    "# calculating BER\n",
    "# this is optimized BER function so it can handle large number of N\n",
    "# previous code has another for loop which was making it slow\n",
    "EbNodB_range = list(np.arange(0,20+1,2))\n",
    "ber1 = [None]*len(EbNodB_range)\n",
    "ber2 = [None]*len(EbNodB_range)\n",
    "for n in range(0,len(EbNodB_range)):\n",
    "    EbNo=10.0**(EbNodB_range[n]/10.0)\n",
    "    noise_std = np.sqrt(1/(2*R*EbNo))\n",
    "    noise_mean = 0\n",
    "    no_errors = 0\n",
    "    nn = N\n",
    "    noise1 = noise_std*np.random.randn(nn,2*n_channel)\n",
    "    noise2 = noise_std*np.random.randn(nn,2*n_channel)\n",
    "    encoded_signal1 = encoder1.predict(test_data1)\n",
    "    encoded_signal2 = encoder2.predict(test_data2)\n",
    "    final_signal1 = encoded_signal1+encoded_signal2+noise1\n",
    "    final_signal2 = encoded_signal2+encoded_signal1+noise2\n",
    "    pred_final_signal1 =  decoder1.predict(final_signal1)\n",
    "    pred_final_signal2 =  decoder2.predict(final_signal2)\n",
    "    pred_output1 = np.argmax(pred_final_signal1,axis=1)\n",
    "    pred_output2 = np.argmax(pred_final_signal2,axis=1)\n",
    "    no_errors1 = (pred_output1 != test_label[:,0])\n",
    "    no_errors2 = (pred_output2 != test_label[:,1])\n",
    "    no_errors1 =  no_errors1.astype(int).sum()\n",
    "    no_errors2 =  no_errors2.astype(int).sum()\n",
    "    ber1[n] = no_errors1 / nn \n",
    "    ber2[n] = no_errors2 / nn\n",
    "    print ('SNR:',EbNodB_range[n],'BER1:',ber1[n],'BER2:',ber2[n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ploting ber curve\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import interpolate\n",
    "fig = plt.figure()\n",
    "plt.plot(EbNodB_range, ber1, 'o',label='Autoencoder 1')\n",
    "plt.plot(EbNodB_range, ber2, 'o',label='Autoencoder 2')\n",
    "plt.yscale('log')\n",
    "plt.xlabel('SNR Range')\n",
    "plt.ylabel('Block Error Rate')\n",
    "plt.grid()\n",
    "plt.legend(loc='lower left',ncol = 1)\n",
    "plt.show()\n",
    "#fig.savefig('stantard_loss_mimo.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'loss_5/concatenate_12_loss/truediv_2:0' shape=(?,) dtype=float32>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}