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adding notebook update
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‎course_1_tf_lr.ipynb‎

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{
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"cells": [
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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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"linear regression experiment, hope you can know:\n",
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"1. how to design the learning model\n",
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"2. optimize the model\n",
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"3. dealing with the dataset\n",
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"\n",
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"Original Author: Aymeric Damien\n",
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"Edited by Wei Li for ChinaHadoop Deep learning course\n",
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"Project: https://github.com/aymericdamien/TensorFlow-Examples/\n",
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"'''\n",
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"\n",
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"\n",
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"import tensorflow as tf\n",
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"import numpy\n",
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"rng = numpy.random\n",
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"\n",
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"# model params\n",
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"learning_rate = 0.02\n",
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"training_epochs = 3000\n",
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"display_step=50\n",
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"# \n",
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"train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,\n",
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" 7.042,10.791,5.313,7.997,5.654,9.27,3.1])\n",
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"train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,\n",
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" 2.827,3.465,1.65,2.904,2.42,2.94,1.3])\n",
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"n_samples = train_X.shape[0]\n",
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"\n",
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"# tf Graph Input\n",
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"X = tf.placeholder(\"float\")\n",
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"Y = tf.placeholder(\"float\")\n",
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"\n",
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"# Set model weights\n",
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"W = tf.Variable(rng.randn(), name=\"weight\")\n",
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"b = tf.Variable(rng.randn(), name=\"bias\")\n",
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"\n",
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"# Construct a linear model\n",
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"pred = tf.add(tf.multiply(X, W), b)\n",
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"\n",
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"# Mean squared error\n",
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"cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)\n",
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"# Gradient descent\n",
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"optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n",
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"\n",
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"# Initializing the variables\n",
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"init = tf.global_variables_initializer()"
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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": 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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"('Epoch:', '0050', 'cost=', '0.178423569', 'W=', 0.42291793, 'b=', -0.4734658)\n",
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"('Epoch:', '0100', 'cost=', '0.156202286', 'W=', 0.40251526, 'b=', -0.32475927)\n",
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"('Epoch:', '0150', 'cost=', '0.138855815', 'W=', 0.38448787, 'b=', -0.19336548)\n",
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"('Epoch:', '0200', 'cost=', '0.125314981', 'W=', 0.36855927, 'b=', -0.077268951)\n",
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"('Epoch:', '0250', 'cost=', '0.114744954', 'W=', 0.35448512, 'b=', 0.025311502)\n",
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"('Epoch:', '0300', 'cost=', '0.106494129', 'W=', 0.34204948, 'b=', 0.11594942)\n",
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"('Epoch:', '0350', 'cost=', '0.100053802', 'W=', 0.3310616, 'b=', 0.19603507)\n",
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"('Epoch:', '0400', 'cost=', '0.095026731', 'W=', 0.32135299, 'b=', 0.26679745)\n",
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"('Epoch:', '0450', 'cost=', '0.091103002', 'W=', 0.31277463, 'b=', 0.3293213)\n",
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"('Epoch:', '0500', 'cost=', '0.088040523', 'W=', 0.30519509, 'b=', 0.38456526)\n",
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"('Epoch:', '0550', 'cost=', '0.085650302', 'W=', 0.29849792, 'b=', 0.43337804)\n",
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"('Epoch:', '0600', 'cost=', '0.083784848', 'W=', 0.29258049, 'b=', 0.47650799)\n",
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"('Epoch:', '0650', 'cost=', '0.082329050', 'W=', 0.28735185, 'b=', 0.51461679)\n",
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"('Epoch:', '0700', 'cost=', '0.081192940', 'W=', 0.28273201, 'b=', 0.54828918)\n",
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"('Epoch:', '0750', 'cost=', '0.080306433', 'W=', 0.27865005, 'b=', 0.57804072)\n",
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"('Epoch:', '0800', 'cost=', '0.079614699', 'W=', 0.27504328, 'b=', 0.60432887)\n",
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"('Epoch:', '0850', 'cost=', '0.079074971', 'W=', 0.27185646, 'b=', 0.6275565)\n",
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"('Epoch:', '0900', 'cost=', '0.078653932', 'W=', 0.26904064, 'b=', 0.64807951)\n",
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"('Epoch:', '0950', 'cost=', '0.078325450', 'W=', 0.26655263, 'b=', 0.66621393)\n",
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"('Epoch:', '1000', 'cost=', '0.078069247', 'W=', 0.26435426, 'b=', 0.68223649)\n",
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"('Epoch:', '1050', 'cost=', '0.077869445', 'W=', 0.26241186, 'b=', 0.69639373)\n",
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"('Epoch:', '1100', 'cost=', '0.077713616', 'W=', 0.26069549, 'b=', 0.70890343)\n",
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"('Epoch:', '1150', 'cost=', '0.077592134', 'W=', 0.25917912, 'b=', 0.71995574)\n",
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"('Epoch:', '1200', 'cost=', '0.077497423', 'W=', 0.2578392, 'b=', 0.72972184)\n",
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"('Epoch:', '1250', 'cost=', '0.077423617', 'W=', 0.25665545, 'b=', 0.73834991)\n",
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"('Epoch:', '1300', 'cost=', '0.077366099', 'W=', 0.2556093, 'b=', 0.74597466)\n",
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"('Epoch:', '1350', 'cost=', '0.077321291', 'W=', 0.25468507, 'b=', 0.75271124)\n",
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"('Epoch:', '1400', 'cost=', '0.077286400', 'W=', 0.25386831, 'b=', 0.75866407)\n",
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"('Epoch:', '1450', 'cost=', '0.077259235', 'W=', 0.25314665, 'b=', 0.76392406)\n",
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"('Epoch:', '1500', 'cost=', '0.077238098', 'W=', 0.252509, 'b=', 0.76857102)\n",
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"('Epoch:', '1550', 'cost=', '0.077221632', 'W=', 0.25194564, 'b=', 0.77267736)\n",
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"('Epoch:', '1600', 'cost=', '0.077208854', 'W=', 0.25144795, 'b=', 0.77630514)\n",
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"('Epoch:', '1650', 'cost=', '0.077198923', 'W=', 0.25100803, 'b=', 0.77951139)\n",
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"('Epoch:', '1700', 'cost=', '0.077191189', 'W=', 0.25061971, 'b=', 0.78234196)\n",
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"('Epoch:', '1750', 'cost=', '0.077185199', 'W=', 0.25027612, 'b=', 0.78484607)\n",
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"('Epoch:', '1800', 'cost=', '0.077180564', 'W=', 0.24997255, 'b=', 0.78705853)\n",
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"('Epoch:', '1850', 'cost=', '0.077176966', 'W=', 0.2497045, 'b=', 0.78901207)\n",
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"('Epoch:', '1900', 'cost=', '0.077174187', 'W=', 0.24946776, 'b=', 0.79073763)\n",
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"('Epoch:', '1950', 'cost=', '0.077172041', 'W=', 0.24925858, 'b=', 0.79226238)\n",
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"('Epoch:', '2000', 'cost=', '0.077170387', 'W=', 0.24907368, 'b=', 0.7936098)\n",
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"('Epoch:', '2050', 'cost=', '0.077169113', 'W=', 0.24891038, 'b=', 0.79480028)\n",
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"('Epoch:', '2100', 'cost=', '0.077168114', 'W=', 0.24876596, 'b=', 0.79585338)\n",
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"('Epoch:', '2150', 'cost=', '0.077167362', 'W=', 0.24863829, 'b=', 0.79678357)\n",
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"('Epoch:', '2200', 'cost=', '0.077166796', 'W=', 0.24852541, 'b=', 0.79760629)\n",
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"('Epoch:', '2250', 'cost=', '0.077166334', 'W=', 0.24842578, 'b=', 0.79833227)\n",
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"('Epoch:', '2300', 'cost=', '0.077165999', 'W=', 0.2483376, 'b=', 0.79897529)\n",
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"('Epoch:', '2350', 'cost=', '0.077165760', 'W=', 0.24825987, 'b=', 0.79954147)\n",
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"('Epoch:', '2400', 'cost=', '0.077165581', 'W=', 0.24819092, 'b=', 0.80004394)\n",
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"('Epoch:', '2450', 'cost=', '0.077165432', 'W=', 0.24813022, 'b=', 0.80048668)\n",
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"('Epoch:', '2500', 'cost=', '0.077165321', 'W=', 0.24807698, 'b=', 0.80087441)\n",
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"('Epoch:', '2550', 'cost=', '0.077165253', 'W=', 0.24802969, 'b=', 0.80121905)\n",
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"('Epoch:', '2600', 'cost=', '0.077165186', 'W=', 0.24798796, 'b=', 0.80152339)\n",
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"('Epoch:', '2650', 'cost=', '0.077165157', 'W=', 0.2479513, 'b=', 0.8017906)\n",
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"('Epoch:', '2700', 'cost=', '0.077165119', 'W=', 0.24791868, 'b=', 0.80202842)\n",
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"('Epoch:', '2750', 'cost=', '0.077165097', 'W=', 0.24789007, 'b=', 0.80223686)\n",
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"('Epoch:', '2800', 'cost=', '0.077165097', 'W=', 0.24786451, 'b=', 0.80242288)\n",
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"('Epoch:', '2850', 'cost=', '0.077165082', 'W=', 0.24784194, 'b=', 0.80258781)\n",
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"('Epoch:', '2900', 'cost=', '0.077165082', 'W=', 0.24782193, 'b=', 0.80273348)\n",
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"('Epoch:', '2950', 'cost=', '0.077165082', 'W=', 0.24780463, 'b=', 0.80285954)\n",
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"('Epoch:', '3000', 'cost=', '0.077165082', 'W=', 0.24778947, 'b=', 0.80296975)\n",
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"('Training cost=', 0.077165082, 'W=', 0.24778947, 'b=', 0.80296975, '\\n')\n",
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"Tssting...\n",
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"('Test LOSS=', 0.079976395)\n",
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"('Final Loss:', 0.0028113127)\n"
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]
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}
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],
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"source": [
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"\n",
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"# Launch the graph\n",
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"with tf.Session() as sess:\n",
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" sess.run(init)\n",
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"\n",
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" # Fit all training data\n",
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" for epoch in range(training_epochs):\n",
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" for (x, y) in zip(train_X, train_Y):\n",
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" sess.run(optimizer, feed_dict={X: x, Y: y})\n",
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"\n",
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" # Display logs per epoch step\n",
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" if (epoch+1) % display_step == 0:\n",
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" c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})\n",
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" print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(c), \\\n",
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" \"W=\", sess.run(W), \"b=\", sess.run(b))\n",
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"\n",
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"\n",
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" training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})\n",
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" print(\"Training cost=\", training_cost, \"W=\", sess.run(W), \"b=\", sess.run(b), '\\n')\n",
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"\n",
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" \n",
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"\n",
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" # the testing data\n",
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" test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])\n",
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" test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])\n",
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"\n",
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" print(\"Tssting...\")\n",
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" testing_cost = sess.run(\n",
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" tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]),\n",
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" feed_dict={X: test_X, Y: test_Y}) # same function as cost above\n",
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" print(\"Test LOSS=\", testing_cost)\n",
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" print(\"Final Loss:\", abs(\n",
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" training_cost - testing_cost))"
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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": null,
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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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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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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": 2
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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": "ipython2",
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"version": "2.7.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎course_2_tf_nn.ipynb‎

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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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"collapsed": false,
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"editable": true
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},
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"outputs": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"collapsed": true,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": []

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