Basic-Manual-RNN-SA-Udemy-Git.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Manual RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of inputs for each example\n",
    "num_inputs = 2\n",
    "\n",
    "# Number of neurons in first layer\n",
    "num_neurons = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Placeholders"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "# We now need two Xs! One for each timestamp (t=0 and t=1)\n",
    "x0 = tf.placeholder(tf.float32,[None,num_inputs])\n",
    "x1 = tf.placeholder(tf.float32,[None,num_inputs])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We'll also need a Weights variable for each x\n",
    "# Notice the shape dimensions on both!\n",
    "Wx = tf.Variable(tf.random_normal(shape=[num_inputs,num_neurons]))\n",
    "Wy = tf.Variable(tf.random_normal(shape=[num_neurons,num_neurons]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = tf.Variable(tf.zeros([1,num_neurons]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First Activation\n",
    "y0 = tf.tanh(tf.matmul(x0,Wx) + b)\n",
    "y1 = tf.tanh(tf.matmul(y0,Wy) + tf.matmul(x1,Wx) + b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Intialize Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# BATCH 0:       example1 , example2, example 3\n",
    "x0_batch = np.array([[0,1],  [2,3],    [4,5]]) # DATA AT TIMESTAMP = 0\n",
    "\n",
    "# BATCH 0:          example1 ,   example2,   example 3\n",
    "x1_batch = np.array([[100,101], [102,103],  [104,105]]) # DATA AT TIMESTAMP = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(x0_batch)\n",
    "print(x1_batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.Session() as sess:\n",
    "    \n",
    "    sess.run(init)\n",
    "    \n",
    "    y0_output_vals , y1_output_vals  = sess.run([y0,y1],feed_dict={x0:x0_batch,x1:x1_batch})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The output of values at t=0\n",
    "y0_output_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Output at t=1\n",
    "y1_output_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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