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‎01_tf_basics.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 01 TensorFlow Basics"
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]
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},
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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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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# import\n",
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"import tensorflow as tf"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Tensors\n",
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"```\n",
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"Tensor is an n-dimensional matrix\n",
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"0-d tensor: scalar (number)\n",
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"1-d tensor: vector\n",
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"2-d tensor: matrix\n",
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"...\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Tensor's Rank\n",
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"```\n",
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"The number of dimensions in a tensor.\n",
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"[]: a rank 0 tensor\n",
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"[1,2,3]: a rank 1 tensor - a vector with shape [3]\n",
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"[[1,2,3], [4,5,6]]: a rank 2 tensor- matrix with shape [2,3]\n",
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"[[[1,2,3]], [[7,8,9]]]: a rank 3 tensor shape [2,1,3]\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Computational Graph\n",
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"``` \n",
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"Series of tensorflow operations arranged into graph of nodes. To actually evaluate the nodes, we must run the computational graph within a session. A session encapsulates the control and state of the TensorFlow runtime.```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"b'TensorFlow Playground'\n"
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]
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}
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],
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"source": [
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"#add a constant to the graph\n",
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"hello = tf.constant(\"TensorFlow Playground\")\n",
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"\n",
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"#create tf session\n",
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"sess = tf.Session()\n",
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"\n",
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"#run the session\n",
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"print(sess.run(hello))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"3.0\n",
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"5 <dtype: 'int32'>\n"
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]
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}
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],
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"source": [
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"#tf.constant\n",
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"a = tf.constant(3.0, tf.float32) #to specify a constant right away\n",
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"b = tf.constant(5)\n",
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"\n",
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"sess = tf.Session()\n",
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"\n",
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"print(sess.run(a))\n",
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"print(sess.run(b), b.dtype)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"mat1 shape: (1, 2)\n",
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"mat2 shape: (2, 1)\n",
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"[[-3.]]\n",
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"finally shape (1, 1)\n"
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]
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}
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],
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"source": [
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"#tf.constant for matrix multiplications\n",
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"mat1 = tf.constant([[6., 0.]])\n",
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"print(\"mat1 shape:\", mat1.shape)\n",
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"\n",
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"mat2 = tf.constant([[-0.5], [9]])\n",
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"print(\"mat2 shape:\", mat2.shape)\n",
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"\n",
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"with tf.Session() as sess:\n",
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" prod = tf.matmul(mat1, mat2)\n",
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" print(sess.run(prod))\n",
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" print(\"finally shape\", prod.shape)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"8.0\n",
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"30.0\n"
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]
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}
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],
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"source": [
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"#tf.placeholder\n",
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"#to specify a placeholder and value will be provided later\n",
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"c = tf.placeholder(tf.float32) \n",
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"d = tf.placeholder(tf.float32)\n",
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"\n",
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"#operation \n",
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"addition = tf.add(c,d)\n",
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"product = tf.multiply(c,d)\n",
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"\n",
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"sess = tf.Session()\n",
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"\n",
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"print(sess.run(addition, feed_dict={c: 10, d: -2}))\n",
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"print(sess.run(product, {c: 25, d: 1.2}))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[ 0. 0.30000001 0.60000002 0.90000004]\n",
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"23.66\n"
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]
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}
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],
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"source": [
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"#tf.Variables allow us to add trainable parameters to a graph. \n",
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"#They are constructed with a type and initial value:\n",
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"w = tf.Variable([.3], tf.float32)\n",
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"b = tf.Variable([-.3], tf.float32)\n",
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"x = tf.placeholder(tf.float32)\n",
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"\n",
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"model = w*x + b\n",
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"\n",
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"#To initialize all the variables in a TensorFlow program, you must explicitly call a special operation as follows:\n",
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"init = tf.global_variables_initializer()\n",
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"sess = tf.Session()\n",
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"sess.run(init)\n",
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"\n",
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"#Since x is a placeholder, we can evaluate linear_model for several values of x simultaneously as follows:\n",
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"print(sess.run(model, {x: [1,2,3,4]}))\n",
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"\n",
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"#We've created a model, but we don't know how good it is yet. \n",
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"#To evaluate the model on training data, we need a y placeholder to provide the desired values, \n",
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"#and we need to write a loss function.\n",
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"y = tf.placeholder(tf.float32)\n",
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"\n",
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"#squaring the error\n",
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"squared_deltas = tf.square(model - y)\n",
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"\n",
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"#sum all the sqaured errors\n",
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"loss = tf.reduce_sum(squared_deltas)\n",
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"\n",
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"print(sess.run(loss, {x:[1,2,3,4], y:[0,-1,-2,-3]}))\n",
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"\n",
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"sess.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.0\n"
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]
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}
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],
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"source": [
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"#after getting the optimal parameteres we can assign the final optimal values to our tf.Variable using tf.assign\n",
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"fixw = tf.assign(w, [-1])\n",
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"fixb = tf.assign(b, [1])\n",
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"\n",
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"sess = tf.Session()\n",
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"sess.run([fixw, fixb])\n",
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"\n",
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"print(sess.run(loss,{x:[1,2,3,4], y:[0,-1,-2,-3]}))\n",
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"\n",
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"sess.close()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### complete program"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"w: [-0.99999791] b: [ 0.99999392] loss: 2.52847e-11\n"
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]
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}
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],
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"source": [
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"#imports\n",
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"import numpy as np\n",
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"import tensorflow as tf\n",
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"\n",
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"#model parameters\n",
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"w = tf.Variable([.3], tf.float32)\n",
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"b = tf.Variable([.3], tf.float32)\n",
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"\n",
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"#model input and output\n",
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"x = tf.placeholder(tf.float32)\n",
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"model = w * x + b\n",
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"y = tf.placeholder(tf.float32)\n",
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"\n",
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"#loss\n",
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"loss = tf.reduce_sum(tf.square(model-y))\n",
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"\n",
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"#optimiser\n",
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"optimiser = tf.train.GradientDescentOptimizer(0.01)\n",
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"train = optimiser.minimize(loss)\n",
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"\n",
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"#trainings data\n",
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"x_train = [1,2,3,4]\n",
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"y_train = [0,-1,-2,-3]\n",
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"\n",
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"#initialise the variables\n",
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"init = tf.global_variables_initializer()\n",
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"sess = tf.Session()\n",
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"sess.run(init)\n",
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"\n",
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"#training loop\n",
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"for i in range(1000):\n",
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" sess.run(train, {x:x_train, y:y_train})\n",
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"\n",
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"#accuracy\n",
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"final_w, final_b, final_loss = sess.run([w,b,loss], {x:x_train, y:y_train})\n",
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"print(\"w: %s b: %s loss: %s\" %(final_w, final_b, final_loss))\n",
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"sess.close()"
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]
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}
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],
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"file_extension": ".py",
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