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Add TensorFlow basics notebook.
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Donne Martin
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@ -90,6 +90,12 @@ IPython Notebook(s) demonstrating deep learning functionality.
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<img src="https://avatars0.githubusercontent.com/u/15658638?v=3&s=100">
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<img src="https://avatars0.githubusercontent.com/u/15658638?v=3&s=100">
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</p>
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</p>
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### tensor-flow-tutorials
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| Notebook | Description |
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|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| [tsf-basics](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/1_intro/basic_operations.ipynb) | Learn basic operations in TensorFlow, a library for various kinds of perceptual and language understanding tasks from Google. |
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### tensor-flow-exercises
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### tensor-flow-exercises
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| Notebook | Description |
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| Notebook | Description |
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@ -0,0 +1,220 @@
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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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"# Basic Operations in TensorFlow\n",
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"\n",
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"Credits: Forked from [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien\n",
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"\n",
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"## Setup\n",
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"\n",
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"Refer to the [setup instructions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/Setup_TensorFlow.md)"
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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": true
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},
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"outputs": [],
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"source": [
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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": "code",
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"execution_count": 3,
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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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"# Basic constant operations\n",
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"# The value returned by the constructor represents the output\n",
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"# of the Constant op.\n",
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"a = tf.constant(2)\n",
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"b = tf.constant(3)"
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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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"a=2, b=3\n",
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"Addition with constants: 5\n",
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"Multiplication with constants: 6\n"
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]
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}
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],
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"source": [
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"# Launch the default graph.\n",
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"with tf.Session() as sess:\n",
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" print \"a=2, b=3\"\n",
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" print \"Addition with constants: %i\" % sess.run(a+b)\n",
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" print \"Multiplication with constants: %i\" % sess.run(a*b)"
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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": true
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},
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"outputs": [],
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"source": [
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"# Basic Operations with variable as graph input\n",
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"# The value returned by the constructor represents the output\n",
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"# of the Variable op. (define as input when running session)\n",
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"# tf Graph input\n",
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"a = tf.placeholder(tf.types.int16)\n",
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"b = tf.placeholder(tf.types.int16)"
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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": true
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},
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"outputs": [],
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"source": [
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"# Define some operations\n",
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"add = tf.add(a, b)\n",
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"mul = tf.mul(a, b)"
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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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"Addition with variables: 5\n",
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"Multiplication with variables: 6\n"
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]
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}
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],
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"source": [
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"# Launch the default graph.\n",
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"with tf.Session() as sess:\n",
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" # Run every operation with variable input\n",
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" print \"Addition with variables: %i\" % sess.run(add, feed_dict={a: 2, b: 3})\n",
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" print \"Multiplication with variables: %i\" % sess.run(mul, feed_dict={a: 2, b: 3})"
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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": true
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},
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"outputs": [],
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"source": [
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"# ----------------\n",
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"# More in details:\n",
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"# Matrix Multiplication from TensorFlow official tutorial\n",
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"\n",
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"# Create a Constant op that produces a 1x2 matrix. The op is\n",
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"# added as a node to the default graph.\n",
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"#\n",
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"# The value returned by the constructor represents the output\n",
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"# of the Constant op.\n",
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"matrix1 = tf.constant([[3., 3.]])"
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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": 9,
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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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"# Create another Constant that produces a 2x1 matrix.\n",
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"matrix2 = tf.constant([[2.],[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": 10,
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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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"# Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs.\n",
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"# The returned value, 'product', represents the result of the matrix\n",
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"# multiplication.\n",
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"product = tf.matmul(matrix1, matrix2)"
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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": 11,
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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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"[[ 12.]]\n"
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]
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}
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],
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"source": [
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"# To run the matmul op we call the session 'run()' method, passing 'product'\n",
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"# which represents the output of the matmul op. This indicates to the call\n",
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"# that we want to get the output of the matmul op back.\n",
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"#\n",
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"# All inputs needed by the op are run automatically by the session. They\n",
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"# typically are run in parallel.\n",
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"#\n",
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"# The call 'run(product)' thus causes the execution of threes ops in the\n",
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"# graph: the two constants and matmul.\n",
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"#\n",
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"# The output of the op is returned in 'result' as a numpy `ndarray` object.\n",
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"with tf.Session() as sess:\n",
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" result = sess.run(product)\n",
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" print result"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.4.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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