11_RELU.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ef0da0f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "import tensorflow.compat.v1 as tf\n",
    "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)\n",
    "tf.disable_v2_behavior()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "689aa2c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "output = None\n",
    "\n",
    "hidden_layer_weights = [\n",
    "    [0.1, 0.2, 0.4],\n",
    "    [0.4, 0.6, 0.6],\n",
    "    [0.5, 0.9, 0.1],\n",
    "    [0.8, 0.2, 0.8]]\n",
    "\n",
    "out_weights = [\n",
    "    [0.1, 0.6],\n",
    "    [0.2, 0.1],\n",
    "    [0.7, 0.9]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bfe8dd83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Weights and biases\n",
    "weights = [\n",
    "    tf.Variable(hidden_layer_weights),\n",
    "    tf.Variable(out_weights)]\n",
    "\n",
    "biases = [\n",
    "    tf.Variable(tf.zeros(3)),\n",
    "    tf.Variable(tf.zeros(2))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "001c4c37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input\n",
    "features = tf.Variable([\n",
    "    [1.0, 2.0, 3.0, 4.0], \n",
    "    [-1.0, -2.0, -3.0, -4.0], \n",
    "    [11.0, 12.0, 13.0, 14.0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d763ecb8",
   "metadata": {},
   "source": [
    "## Quiz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb4ad24b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Create Model\n",
    "hidden_layer = None\n",
    "hidden_layer = None\n",
    "logits = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f059164",
   "metadata": {},
   "source": [
    "## Run this cell to test your model implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd53cb65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5.1099997  8.44     ]\n",
      " [ 0.         0.       ]\n",
      " [24.01      38.24     ]]\n"
     ]
    }
   ],
   "source": [
    "with tf.compat.v1.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    print(sess.run(logits))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "303f3d97",
   "metadata": {},
   "source": [
    "## Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b5f539c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Create Model\n",
    "hidden_layer = tf.add(tf.matmul(features, weights[0]), biases[0])\n",
    "hidden_layer = tf.nn.relu(hidden_layer)\n",
    "logits = tf.add(tf.matmul(hidden_layer, weights[1]), biases[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab89532a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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