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@mikigom
Last active April 26, 2017 09:50
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Project Nagne DL semina code
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I wanted to work through the [MNIST For ML Beginners](http://www.tensorflow.org/tutorials/mnist/beginners/index.html#mnist-for-ml-beginners) tutorial and remembered using iPython many years ago. It seemed like it would be a good fit for exploring both an unfamiliar API and data.\n",
"<!--more-->"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"# Lab 10 MNIST and NN\n",
"import tensorflow as tf\n",
"import random\n",
"# import matplotlib.pyplot as plt\n",
"\n",
"from tensorflow.examples.tutorials.mnist import input_data\n",
"\n",
"tf.set_random_seed(777) # reproducibility\n",
"\n",
"mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n",
"# Check out https://www.tensorflow.org/get_started/mnist/beginners for\n",
"# more information about the mnist dataset\n",
"\n",
"# parameters\n",
"learning_rate = 0.001\n",
"training_epochs = 15\n",
"batch_size = 100"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just to look at the data, I can grab the first array of image data and reshape it back into an 28x28 pixel image."
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
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EPQEWq7ZkEes+6xO22XSLFDX9HlaQ67gkIkuY4tscijBZCvNcgPY1pW0tGNFj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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f35bc272a50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n"
]
}
],
"source": [
"tmp = mnist.train.images[0]\n",
"tmp = tmp.reshape((28,28))\n",
"\n",
"plt.imshow(tmp, cmap = cm.Greys)\n",
"plt.show()\n",
"print(mnist.train.labels[0])"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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rQmwlXkj8r1i04mLILXXFN46iKB2Yy30XJU8PTa6lY+Mi7DRCC3DqdVm3Xl+2\nb1nqLGEWnzp/sNTOqHNFsJABVQFOZfJNp9OFxJCTkxMcHx9X/L/SOEFEr+dC1Oq+g5x/OyfC67h2\nNwEXYacxLCza0rQEuuSmXPeNm7Pkmg7QWQNzWoz5vYHFLDixfjn5glOlRYSPj48rjfssa1pn0OWS\nNeq+A8sfbFn4LrpXw0XYKYItXO6z9lvnMSzzN6XCm/MJ5+KErephOkQNQMUnzBawuBCsFGkRXYkH\n5qUe1LOWXHCprlkCXOKaKLkGuYpuNx0XYaeYJiKYK9d4lX3q4GNMlXjkfVPiw++jw8u43gT7ZUej\nEfr9fqXY+cXFRSXbLZcJNx6PF8LOZCCOY4QlDM2q3ZE7R/3Dof3e0m7fvo39/X3s7+9XJkmVqYzk\nx0Z/36XXxaniIuyslVxywrJLoGywTyqkWdXFtDhpNwIfv1XPVwR0NBot1NXlv5OQsbomFnFKgHW9\n4roZNLQ7RFdAs9Zl+fzzz+Pg4AC3b9/GrVu35jNW848L+7udq+Ei7KyNXB3dXF9uX8aKfODXdDiY\niBbvIwkmbAnr49duBPHljsfjBWGSv5P9T09Piwvy6IE5iZRIlcpMVWOT70HODXhtklae+FSsW90O\nDg5wcHCA5557bi7C2hrm78m5Gi7CztqwxNXKGmuyD1CegWclR+iUahETyxJOCTC7IUajkWkBy/48\nC7PVOKpBQtT0tEopS9gSYP4OBC5ByXPipZbPPfccnnvuuQVLuN/vVyx+t4RXg4uwsxYsa5ZTdHPp\nu7n9geogU25bW7LaomZrrs4doV0Sk8lkYVJLLcCTyWQuwroOMAuvHqRj3y9vyz4cemYJMLtbLEtY\npqFKtVu3buH27dtz3zBbwtrqd66Oi7CzNliIWQi1ZdrkNSAvwvq1lJXNj+x6cI6PX5qO751OpxXf\nKO8jr4u7Qk9HpAuysxCnfMZ1JSoZyz/OUxKJCIvA7u3tVdZv3bqF/f39+VJbwu4TXi0uws5asVwM\nEr9q1VGoa0A106tp6JQVOQCUW8IiluyCAFB5nV0VMgVULp5XC3KusQhbYptat2bEFsGVxsIrr0tz\nn/D6cBF21oIV1mU1FuKSdSBd98Dqk/31UvdpEWb3gpXpxpagRGGwBSy+106nUxHbumVKnPW+IsJa\ncFPnyJZwv9+fW7/iehAf8O3bt+dhabKUpn3CzmpwEXbWimUF62Vq3eoDqrM4N123hCqXasxCzILI\nIp2KmJBgnCk+AAAgAElEQVRHdxbPXJMawLnvRM9dx4OLgvVUYFnCIsIyECdREYPBoBI9wUt3R6we\nF2FnbaSsYUtY6hqLMAtrbilNQtA4JE0LcGrE34oTns1mlX72Eeu2u7trnkdOXHODmLzkiAz5XmSp\nf3wsn7C4I27fvo2DgwO88MILeP755+cWvJ5VmX9Y3BJeHS7CztpIhZ1pH6tlEaa2gcWJKFlMtQDr\nFGRBCzCnHKcsYR54YwHWqcu8HkKodbHwNn93vEyt848KY/3IWANzYgkfHBzg+eefxwsvvIBer2dm\n0uk0bbeEV4OLsLMWcokOdT7PnI8UQFJ0LVFO1fzVfePxeJ4kISnCnM2mp5WXc9zZ2Zmfn1i92rWR\nEl6rWcKWEljLkreWu7u7c7+u9vdaS/b75upiOKvBRdhZC9oPrEf/dWiWtbT6gMXoiJxLImWh6uVk\nMsEXvvAFPHr0CEdHR/OKZZKxlqvPK+drhY2xCFtPBimsCAfdp61TPfEor3OomYSbccacrghn/bBZ\nA5rO1XERdtaGFdplpeyWZpOJLzY36KZf0xYcb/P6dDrFo0eP8OjRo/lU8mIVp4qkazHlHx7GShjR\nf8OURnVYFd7EZ6u3WYQ52kEPIlp1kUvD/5zlcBF21oK2hHkAq66QTW5b/J91ccKWf9hqIjrT6RSP\nHz+etzpLWI5DC6oMmGn/rh5Ys8SY0cJrbbO1y2JqtToRzk1Y6iK8XlyEnbVQUnfBmpbHKvvIS45s\nKGlaTFLbs9lsXixdavbyNPIpS5iXLMAsVDoNu06Ahbpz45oQujiPbpwRp90RbD2LsFvuHXdHrAcX\nYWdtWOm+WoS5ToKumaDbeDyuRAKkLLSURZxbnp2dmXO5cd0GLpyjRVawkiisCJE6v3CJlc8irON5\ndbMsYRFoEXHLH6w/U47NWR0uws5aSFnCWoB1wXJZ58b9bGWmfKW8nvMZaxHmWYv1ccnAYJ31ypyf\nn1dcFqUDc3oALufv1pawznCTdSnSzkKcG5jL/Qg4q8VF2FkLVnSEtoRZ9EqXnLpcuixpFxcXSfdI\nLjrC2tY/BNZAXGo7ddypyA8RT7aArdCzXHSETsJIuR9ciNeDi7CzFvSgnK5AxpYwNy5mbvXzgJcW\nA21BytISEr19cXFRW2pSD8zJe1iDdVqE5XX99yVhapYQc9gd+4M5Hliy4rhiWt3AHCe11D1xOKvB\nRdhZG5YlzINuenZhnlGCt7nJI36KlDCnlrLO/utcWrUVfqa3WXi1CFt/Z0VG5Cxibtodoauk6TKV\nIs6WCOfmj0utO1fHRdhZC7lkDZ7Y0pphWC95XVKXm2CJhtWX891aftxc4kYdJfukBuXYEs65I0SA\n2QrmspTaHcH+4NR3pI/PuTouws7asFKXrRRmnU2nrWVel6y5VSJiUiKMKUoG2Xg7ZZFb2X2pdRFX\ntnRT2+wfFgG2kjTkGJxnh4uws9FYInYVscy9/7reu26QixunHqfWZclWriXIuWgI7Qf2ehDt4SLs\nbB2rEsuUm2LV752LcNDL1HT01jpbu+wD1uscDWG5IDgawnn2uAg7W8lVxbJucG/V712SOr2zs1Ox\nVPW67stN1qkn8+TsORZy7Ypwnj0uws7GUjIwtIxYlojNqt5bD6qlSmpKpAMPsFlZcDojTk9BlGos\n5NoX7NZwu7gIO1tNU7FsIjKrem8rttcqPdnpdCqZbpzxlurjiAgrXVn6uaKatoS9RnC7uAg7W0+p\nWC4jMqt4bx1eZpWgFHHUyRapwuviYuDBtty6HuTTyRluBbeHi7CzkTQVhDqxvIrAXPW9tSuCRZet\nU8l448E1veT1lK/YWurZRKwZM1yE28FF2NkalvURr0Jcln3vlE9Y1/vltGMr0cJa6sk4c9t1A4Iu\nwu3hIuxcK7RYrlJYln3vnCuCBVgG2qTWg0zEmVpq14Ll4pDGoXB6nbedZ4+LsLMVLDOgtg5RWea9\nLUvYEmAr5fjWrVvzaellKeuWi8GaJZlTkeV4ckvn2eIi7KyVVM2DXLNERNqy9RqaRDnU7buMvzol\nwDrczCq8w1YxW8LsUqhbusBuLi7CzlqwwrJ0tS89jTy3VEUzqR1RUpEsVTaybsmCZQlyE0tYfoR0\n2Umr7u9wOJyLbK7kpISV1c2K7MK7HbgIO2uDLUDtAxUBzgmv1azZLVJ1eq3C6aWzW1ilKPX7N/UJ\nawuYXQ/sguAoiFzd39yEnM724CLsrIXcI7i2gEvEV5oUHM+JrO6zZjq21rmaGotvKjKiVIi59i9n\nxbH7oaTwjjUDhs+MvP24CDtrQQ9GaUuYJ83UroicZSyWcMqi1eJrfYaIp2xr9wMPvpX4mkvC1HLu\nCB6Es2pA6LngdKpxKurB2Q5chJ21ocOyut3uXEz1I35KdLUoWyJsTSVv1THmbQ3PmGEJcU6QLV8y\nk3NH6CgI9gVb9X85JC0VbubRDttFYxEOIbwZwA8AuAvgSwB8e4zx1+n19wF4m/qzj8cY33SVA3W2\ni1SCAlujsh8LcM4ClpZyJ+g+aTs7O5X3ls8FqhNz5lwSJVhWsX4aSFnCEn6maz5Y7oiUFexuiO1k\nGUt4H8DvAfhZAL+S2OcDAL4HgPwnTJf4HGeLsXzClgBrEc4NyMlSuxlSS3lfESptAbPVrMVX1uU4\nS8W4TohTlrBYw6kqatZccJbl62K8fTQW4RjjBwF8EABC+gpPYox/cpUDc7YbyyesoxHEXdFEhGez\nmenjtbZZgLUoscWsxUpnxTWxhuXvdXKENR+ctoRFhDmJQ2+zT1jem79vvc77OJvJunzCbw0hvArg\nEYCPAvhhF+WbB/uEWYC1QMe4ONOxtn5lfXd3NzmQZ4nw2dmZKbLS5EcgJVRNBZj/Tt6Tvwe2hK0U\nZV1TQteDYHeEfJe8tPqahNM5z551iPBvAPglAP8BwBsA/D0AvxVCuBtjnK3h85wNRAttql/cC3Ui\nzCFqVnKHFVmhM8X0oN26C9fwjw77hLvdrhkdcfv27doaEOyOsL5zZ/tYuQjHGH+ZNj8RQngZwGcA\nfAuAX1315zmbiSXCLEpsHWpr14qOEOFk37IWXd0nMzh3Op3KcjabYXd3d76Ugbu6mGLu0+QsZu0O\nsYr5aH+vVfPXK55dT9YeohZjfCWE8FkAb8ztd//+fRwcHFT67t27h3v37q3z8Jw1YokPW6MizqlM\nOn4fEa3ZbGb6gq24YxH22WxWu84DfhxJkRL5VKyyFbvMrpHZbIbpdIrJZILRaISTk5P54JtM9Kmb\nfHa3262IuHw3JdfBWR+Hh4c4PDys9B0dHRX//dpFOITwOgBfCuDzuf0ePHiAO3furPtwnGeMJcD6\ndUuArUf5TqdTCVFLDcxJHwtsqokQa7dHbj3lg7a2Acx93vJ5k8kE4/EYo9GoEgPME31KOz8/R6/X\nm38X7Ee3Bg0twXWf8HqxjMWHDx/i7t27RX+/TJzwPoCvwGvhZ18eQvhaAF942t4F4B/jUnTfAODd\nAP4YwPubfpaz3aQsYb2PWML9fn9hAE/H2Oo44Vy4mo4v5pZ6rYnVrOOatUUuwmdZwiLCEv0gIWfi\nK879ILG48/fIfS6628MylvDXAfgIgPi0/YOn/T8H4O0AvgbA3wLwPC6F+J8C+BsxxpMrH62zNVji\nK+t6PxFYFhwrwUEsw7okDVlPRVhYfeIrljadTpPr1vuIX1mQY+H1s7OzuStCRJiL8oQQKpY5izkP\nZHa73WQYnUdEbB/LxAl/FMBOZpe/uvzhONcRFl8rmUAG23IWsIR1sQhbwsutxLUg6+yrnU6nyXU9\nuCcCLKFwOv5Yh8tpnzBnv4kLhc8RqBYAsqxjvW4lizibi9eOcNaGTiBgd0TKV5wSYBG+JoNidUkg\nWoQnk8m8jcfjyrLb7WIymaDT6WAymcwHCVNhcCzClk9YV0OTv9Uiy9/FbDZDr9ervM7fNfuJraWz\nmbgIO2uFExZYbPXAUsr/qxsLkI5CsEQ4JcS6fzabYTQaYTwez10F4/EYvV4P4/G4kiSRmqWYhZ9F\nT4fMiU+Ypx3Sg3nynekfI+2mcKHdflyEnbVgCYIWDWlWinPKetWCK+t6qV0SVugZvzadTjEajTAa\njXB6eop+v7/gs03V7tWiz69rS3g6nVbifuV45Xj4u0rVYc5Zwvp7djYfF2FnrbA7Qlu/HEcrAlwS\nesZ/m1rXLgkr3pfFeDqd4vT0dC7AqYI5fF6WBazrVVg+YRZgfk2mbrIEOBcx4YNy242LsLM2LAEW\nWDh1Eoe8rpv+O2ub+y0RTyVfTKfTSuKEVTbSOjft8jg7O6uIrBwLizCLJbtDZJCPBbjf788HB2Uf\nbQl7eNp24yLcEinxuCqbcgNuwgh9ypq2LG0ZdGPL15q3jV0ovM0Wbc5fzAWFLBHWRX7G4zEGg0El\nNE5+QKxIE4tN+Z9wbFyEtwB/vFwOnVGmY5dFyHQdB4lJHgwGZgq1CKWu6WCJoa4DoaMhRJiByycC\nGbizGkdvTCaThZmW9bocr7PZuAhvGLnHa8ZvrnJ0qJwOm2OfNFc5Y4uZxVsX19EiDFQtZi7CY0VE\nyIBcCKFIfCVmmQcJ+Tg4vM1/wDcfF+ENodTX6f6/5WC/NAsvCxZbwla8rhZgS3wBu2C8VQFN3BGy\nb4zRFN+UKPOPwMXFxUKxd//f2A5chDeAupF+wUfCm2N9ZwDmwiXrPBimLWD9uo4ZrhNhPhZe6v1k\ngFCLr2UJj8fjyg8CD17KZ8ixO5uNi3DLpMKrrNcFjwltRuq74agMdkdYGWs8YCYVz1IuCI6YKIn6\n4Fhiy+q1xHg6nc4/h6eOkuPRlr6zubgIbwi5xAPATkt1mpGLHuAJSfk71wN2s9lsLsKMlSCiy25a\nReq5iU/YGpwT4eWlFTPMFrAL8HbgIrwB5AQ4Zwm7MJeRct1wRpv4e6VfJ0xwGrUUV2d0wgbXHpbt\nEF6b8VmHtkl/nfBy01EbfNxcY8PZbFyENwxLgFPZUS689aS+p9QPmbzGVcvOzs7mg3VSZD3nguAM\nuIuLi3kMsGCVuBSrOWcB6/WUD/j8/LwixM5m4yLcItr3a/kN9X4+ONecUiGWqmacPq0z6y4uLtDv\n9wFUi+7o9GMuBM/JGeKr5b+XZA0ZmCv1C8s5cPQFH7sL8HbgItwyVmha3WCdHol3Aa6nLpuMIyFy\ng2dirWoLmC1arpYmIgxU60TIZ3I6szUwl4uQkOPnCA7OqHN3xHbgIrxilvmnr7vpedS7LlW1iShf\nd/FeVxqvRCZo/69YvjywNpvNKhav1Idgy5gt6FzGnG7i/+VZN7hAvlvD24GL8ApJhZalLNvcI68O\nc9JpsWL9XGX7uovwOtERFVLrod/vYzgczgU5xpjMkuOKalacseVrZsGWiU85ttldEduHi/AasPy7\nsi5LtoCsmR54W/yILKS5pVV8XC89iH952AfL0RMySSfPE8dPJvq660JBsg/va/mc9fx47IJwK3j7\ncBFeMSl3gn7NeoRNLUWErSItuo+tM72UGNhOp+NW8BWxLOFc3V8dPzybzUwRln2tuGNtCdcJsPuE\ntwMX4TVgibC+MSzfoQ7Ul3UpFJ4q2KL7pRi5WGhWFphbwlcjZQlrAebrr/2+dSJsWcI6AiMlxC6+\n24OL8IrRVq/OjtI+wVQMKPdxse+Uu4GtYLHKrDnJeF9nOTgpgi3h1MwXfL3lmqeKxlv/P9onbAkx\njy1YT2HO5uIivEJSN5Bl1ejpz2WCSatxoXCunGWtdzod0yIDqpNp+s15NbQlfHZ2Ni9/yd+3ZMnJ\n/HJyvfXcdSWWsA6D08KsB+b8Gm8HLsIrJuWK0FEQPP25zO6bajKKXtJkanQ9DQ6n4fqgzdXQlnC3\n260U6+HXQwgLAszz1y0THWH5hS0B9mu8HbgIr4GcAMtS35gixCcnJ/MJJ09PT3FyclIRYS4mbm2L\nIOi6ArpUo9+gV4MtYUlltp44QgiVJx49iajljsj9iKcEmJ969ICgs9m4CK+YuhAjDjXim1OmWj89\nPcXx8TFOTk7mS5kmPRXxwH29Xi9pActjs4vw1dGWsCXAkgbN17hOhIXUoFxqYM4t4e3FRXgNpCwZ\nfTOJCIvvVyzf4+NjHB8f48mTJzg+Pp5PQsnCqyel5HKL+pE4N2OE0xxOftGlL3n2DfHly5POYDDA\nYDBo7I7QP97W0n3C24uL8IopEd+UJSzuCBHgx48f4/HjxwszAesmN7QILVAVBClGzjes36BXQ0eZ\n8PctWWwiwuJqGgwGSUtYKPn/SVnCHh2xnbgI19Dkn1gPuuWW4/F4bvGenp7OrWEJSRO3AbD4iMvx\nvzzbg8Sq7u/vY29vL3vTe7LGIqXXWocZ5q7zaDTCkydP5r7+0Wg0Dz2Uoj3+ZHKzcRFeEuumsQLy\nU208HuPk5MS8OXORDSK6UquAY4KldoGI8N7eHobD4Xw/HsRzEa7SRIB1xqNOruE2Go3w+PHjuX9/\nNBphPB5XrrU/mdxsXIQbom8W3hYLSSdh8OSMvC7iK20ymcwD+rnugI5JFcEVS1ea+ByHw+FcgGWf\nVHKAU491zVMJN/o6n56ezn37MvAqr+saE87NxEW4AVpw9bqOfODQM17qdb5xLUvYmmySRZfFl5fS\n2BKWEXsX4XosYUxZwvq6ypJFmC1hsZw9WsVxES7EEl3LCtbZUXJDitWrrSE9e4JlHVnuCBHh4XBY\nsXzF/cDWMfuF3RIuo+SJR1xPVpihrJ+cnJgi7JawI7gIN0QLsE5VTlnCckNKk0w4HsjhbXZHWKFm\nXLt2b2+v4gdmP7E0H5grp06AtTsid5053lvEWSxhfurh5BrnZuEi3IA6AdaWMPt+OfTs+Ph4XhPC\nqiWsb8ycT5hF+NatWxgOhwvRExxRIULsIlxG6gkol3AjUS8c/SL/A+4TdjQuwgXkXBEciyk3p1i0\nOh1ZRPjJkycYjUbZAj+WT9gqIK5FeG9vL5tR55ZwOanrzu4I9glrF4Q0XRvEfcIO4yK8BPqGtAbm\nLJ+wiPDR0RHG43GRn9kqFpOzhPf394vKXroI26QG4/TSio7QTz2PHz+ei7COmnBL2BFchAux/ITa\nCk5FR2h3xOPHjzEajbJzv+m54XIDc5YIp+aW43UnzbK+fyvjMRUr7skaDuAi3IhUKij3Wz7hlCWs\nLdbcfHClPuFbt24tHLcL7tVIWcIpn7C4I46OjvD48WNz4FUPwnq9h5uLi3ABOp+fl7zONxrPopBa\n6jnfuIaAni1Dl6zMibVzSUrQUi6HnI+e+8bj8dzVICnJOvpBhxyy4FrFdgBUnlD0k0zu/8Gab9Bn\n1N4eXIRrSBVUsW5SLbRW4xtSbpKLi8uJPHWGXMkN5+6FMkoTbVIzX3PfeDzG0dERjo6OKkKsRZiz\nH5vMBZcSX6uSni7q7/8T24eLcCHW7Bi6ehX7+rQYc00JuZnlhtE+Qe0Htm44bfHI3zmL5Py7srQK\nL6XWxRIWd0OqEJO2fnPVzgRrPED/IFulTFOWsLP5uAgXoC1hbdnITcXim7KGuQyhNeebHoyzirmz\nC8JvuDypUMLcQJseQOPMRol4YXdErkpa6VxwlhDrqBYtxJYVzIOv8l7OZuMiXENOgHVh7Zz4WkIs\nVrC2hPVgXMoS9kfPMizR1c0aUE0tR6PR3PpNZcTpKmn8v5NzR+golpRrSv9PuCW8vTQaxQkh/FAI\n4V+GEB6HEF4NIbw/hPCVxn7vCiH8UQjhNITwkRDCV63ukJ89lhCze0FbT3UWsDVIk7OE68TXb7Yy\nLPFlqzQVWijhZo8ePcKjR4/wxS9+EY8ePZqHoHGVNCsZQ09Nbw3uCnUDcpZLwn3C203TofQ3A3gP\ngL8A4K/g0pL+UAhhKDuEEH4QwDsAvB3A1wF4BcCHQwj7KzniFkhZw1qILV+w5VfUN6X2Udb5hH0k\nvBk54U1ZwjrcTHzAX/ziF+cizD5h7Y7IDczlfMJAuTuiToD9f2I7aOSOiDF+M2+HEL4XwB8DuAvg\nd552vwPAu2OMv/Z0n7cBeBXAdwD4mase8LOmiQDr4t65yIgSS1iPiucsYscm54awRDiVZMPuh5OT\nk/kAXKpEKVvCqZBG/QOsfbl1A3Op/wcPVdwuruoTfh5ABPAFAAghvAHA6wF8WHaIMU5DCB8F8CZs\noQgD5ULcNERNP54KqRvPLZ7lKBFicUfoFGSu98GDcLpgv25cJS3lh85ZwlpQtTuC5xX0/4vt5qoi\n/ADAb8cYP/F0+/W4FOVX1X6vAvizV/ysVrmqAGt/cCpTykpV1jda6oZzquh4YMslIeu68BJPvMru\niMePH+Pk5CSZhqy3RYT5GPjYSkLU+LrnBubcJ7ydLC3CIYT3AvhqAF+/usPZPKwbt06IU9uWT9iq\nHZAKUUs9evLfOVW0yKXEuK4EqYjwo0ePcHx8bMYPW32p4jxWn1A6MJeqjuc/zNvFUiIcQngPgG8F\n8OYY4+fppVcABAAvPl0X9PYC9+/fx8HBQaXv3r17uHfv3jKHuBZS4UO5gTJ9I6QGiKzsLBFqLd5a\n/Hm70+nMj9U6/tx2rr/pDZ0Tmdx+WjBz63WWZSqpRi8vLi4wGo3mlq6OepBBN45+sK5Hia9f+3x5\ne3d3dz47ijQ9hZWeV5DrROvkDRfiZ8Ph4SEODw8rfUdHR8V/31iEQwg/CeCvA3hLjPGz/FqM8dMh\nhFcAfAOAf/N0/x6AtwD4gdz7PnjwAHfu3Gl6OGvHskosy+Ti4iIbyWAFz+csbK4v0el0zMffbre7\n8PirRZg/T/elljHGlQf711mD2j+aWtdRBVa4l/Wd5tKRJRWZxdeKetBhZ7nkC6buR5tjwqUSnsyS\nwrNmW0JszZziIvxssYzFhw8f4u7du0V/30iEQwg/BeAegG8DcBJCePHpS0cxxvHT9Z8A8M4QwqcA\nfArAOwGcADjU77dN5Ear5ebTN0IuiiH1OMwWLQuxni1Dmhbm3d1d09rKWWDWumyzIC9LyorV30Uq\nciH19FDX+DvlxBprfTweV2Y+0VawlYBRKsRi5VoRLrzd7Xaxv78/byzAlmXMAszWsEfObBdNLeHv\nx+XA2z9T/d8L4OcBIMb490MIAwDvBfACgN8F8I0xxpOrHWp7WAIslq8sY4yN8/k5PMryM4uopixh\na1BIRFi7Tuoan6dlPS+L5S7Qy5RVa1m5ugaDFX+rv0s9IGolz0wmk4W54Xg+QF2Ux/r8VNSDNdBq\ntV6vZ4pwSoitaay0JexsPk3jhIuCD2OMPwbgx5Y6og3DGq3WMbyCZQmnHg3rXBF60KXX69UKsFjN\nlgCn1rmv7ntoSp3wWt+DVZ3Osm5TS71eFyrIERHidtCzY0sfz4aR8i2nLGFxN6SeaGTKKnZHyJJn\n02Z3BFu/OmTNLeHtwWtHFJASYC0mKZ+wFj5BC4y2zviRdTKZzAu614mx9jXqwcSLiwszlMkS5FVa\nwql1PYCW27YGKlNWrhU6aCXUcGGeXGN3RO5Hg8+NrWCO8WV/rrR+v19rCbM7oi55w33C24GLcCEs\nUCzAbPGUpBcL+jFcW8JaQEV8J5NJRYh1bCoXiLeaCLB8th40lNdke1U+Ycv9oKMYtHVpRTPkQgKt\n9HGr6apouj+3lPfW7hJrnf932BJm0eVBtsFgMLeELZ+wzKQif8NPSyz0bgVvFy7CBbAAi3CJEMvr\nALJZTHWWMIuwvpHEEk4JsRZhbbFrIdZCKJavvMZCvcob2fKZWj9EuZYqmFQnuDqbzcpus2K8LZcG\nZ8GlojUsERbRZAFmYR0Oh6YVnLKE9fW11l2INx8X4Rq0AMcYsbu7u/B6CKEoiF6wBEjKW+r9xRLO\niS+LB1tGIqjcp0OoNHyuVyEnutb5Wz9Iej1ntVp9PMOxtc4Dbinfsl5akRup85XryK4IcUfwHIE8\nV6B2SaRih3MuJ3dHbA8uwoVoMbZeSw2O5KIjRHR2d3crQsz77ezszAU4J8RiCYvwXlxczI9Dfjz4\nR8QSWXFBWP7bJlh/kxPhVDaijnJgwS0RWO3TTfl7p9Npo/A3fY6pQUi5ftoS7vV6FRGWpt0RLNJ6\nYK4k6sVFePNxES5AD1jpR00RvZKBOUa7I+Rmnc1mC5+vBTglxvzZ8t7sw2Yr3jrPi4uLBSG+Kjlr\nkX2pqaxBS4QtIeVqZrpfXuMlr8t3nho81H2l6LEEtoT7/f5cXC0LOOeKGAwGlc/Qn+lsDy7ChdSF\nc/EjJ49a6zAksYLOzs4qgi03DovT2dkZgEtLih/Du91usl6AWNV1iQG516x960LYNJbIppap9F8r\nXZsFOCXG3K+F2RLqyWRSKbTT5P9B/29oC7TT6cxFlC1bblZIGg/ASSSFftJyrgcuwgWIZSjrlhiL\nxWkJMD9+yiMoD/Cx8MnNKxapDEaJAFs+ZvYpj8fjyvtZn1EnyqnWhFzUgF7P1WCwLGF2PeQa7yc/\nYvze1gBaqRBbESjWIGin08H+/j5u3bo1dzWwy8ESYBFf+b/xJIzrjYtwDVqAebBF+ji6QMeDcjgS\nj4iLC0MnULDLg0OddOSDHAuL2Gx2ORNwyai5FmG+0VPrTdC+3lxcbUp0rW0rzCy3rqMfrAI7TYVY\nRztYP1jSz6nIWnzrLGBt/cp1c64XLsKFsOgCWIi5lT7LEuaqV2wJpz4DuBSx8/PzeZ9OSdZRFWIt\n9/v9ZIywZbXx8Wp3in6tCXXiq6MhctEIvKxLuLD6dAQJJ1vkBidz/wv6u9NZa7KuU5FTAiwtVSXN\nLeHri4twAdoaFkSIZV1bwVZgvoiwFeaUix5gK4gFmNNuZbDOelROJXBosbWavN6Ekiw4q9ZD3Xou\n9bikpUpN5q65BYuwXGdr2e/3F0LPtB+YhVgsYO2O4LEDF+HrhYtwIal/fI6pzQmxtoRzIqVDt8Qi\n1hYwxwb3ej2MRiN0u91k7KgVS5oSXl2jdhkRtjLeUstcmUn9emrQLjegZxXUt9wR+pqnLGUd+8vC\nybBu0tAAABIsSURBVINpg8GgMgCnLV+9zSKuK6TxdXWuDy7CDbD++Tmcy3pE5doAHF6UsvjYFyz9\n/NmWC0JX0tIRE5bvWdZTj9G8LssmpNKPrQpopdlyliBbLosStwb/+NVdc0uILUuYn3bErWBFQ6Si\nI4bDYeU6enW0m4GLcCE8KGf1SQyuvjlzlrDUiJBQNBFgXmeLjS3gnOugNIifRdi68fV6E5qK7DKC\nfdX1nDvCuva8rZMv+NpazRJf3T8cDmvdQi7C1w8X4QK02LJrgJcpAbZ8wpycwW4GjheWyAcWEKmu\nlovxTcWw6u0QwoIVnSu12IQSC9YS15KlFe5WGolh9S0rxClLWJIvLLHVGXDcx5XRUvHc7hO+frgI\nF5JzRXDEREl0xGQymYuw9vNyn4iWCFeuRoD2F5YstQizD9Lqa4IluKnBNhbX1LrlL08NYuYGOK3+\nJv8D/IPLTxI6DZkH4rTgpratojxWaKFzvXARbogWY97WoV2WJSyhUtrtoGtG6PAt/XnaLVJ3bFaf\niHBqZF8vm1Dil7X8syXWrFWfIVe7oWS7FOsHV6chi/jevn27IsJ1TcILLf+93nauDy7CNTT5h7f8\nrCzAuhQi31AiLiJO2gcoosEDSSkxKj3mEMJCUoSkVHe7XcxmM/R6Pcxms6XdESWDZU3dB02wfqxk\nPfdDpl04vOx2u2aYmQ5BY0uYS1bywJ2OgtDuIj4W3e9cD1yEV0hqwIbjU0VEddyuFmMeREo9XvP+\nloVYerPqcDhtgS1DiS84Z+mWuAxKji0naHo75ebRfb1eb56GrNOReZtjf61UZJ0JZ33nLrrXHxfh\nFZMKXeI6tAAWbmwgPe8cW4Xav2lZxrxddwPr9zo/PzfFoKkF2iQUTR9D7odFvrscOQvSesznH8+6\ngkY7OztzEbay4KxSlDwmwFlw+mnH+nEoPWdne3ERXiFy42hfIVvAsp/l39PiKy4Cy2IMIczfM/eY\nXmcV5xJDeJ+6eFqNHlzLRTqkfmBS/l0Lyx/exMK1EldSmYS9Xs+sfpZKRdZzyXFNCCv+13KF5K6h\ns924CK8Qyx3Blizvw6PclhBySq4lYFokSyzenBCzuJ+fn5uv5c5bk4pwsPpLoxcsMc6JlBbZuloa\nufA8PSuyFXZmbcvU9FbkiWUJ83mkBlyd64WL8IrR7ggtwBxBIfCgHFvAuuDMzs7OXIC1UHLMcgot\nxFrs+D3ldRbOJtTF8nJ/yg/cJJQsZTmyEOsSn7yusxut2ZD5tdKIh8FgkMxAZBHWSRguyDcHF+EV\noi1hFmAdV7q7+9psFyJGWnx1DVxLQMV6Zb9ykxuV30f3S4EisVybYPl5U8u6AccUdUKlBThXR7nT\n6SxUMOPYbr3NTUc96GmIrIp0vC5ZjtY56HXn+uEivEJYhDmkS/f3ej3s7u5WBE5bwDyF0Ww2M/20\nLJRMzg9siXRKgKVJxETJ+fN7WCKr+zhems9L99V9prW03BE6G022UzMgp7ZFYOuWEvubKpSvI2Qs\nl4q17lwfXIRXjLgcLBdEt9udh2qJpcwCrC1gKU+ZE2FxU5QKru5noROBlEG/XKhaiQ86ZdnWRT/k\n+qzPTw1oWe6IVK1kFuFcerE0rgfCVdOsSIgSf7SV7Vj6XTvbjYvwCpEbXm93Op2FMC3uk5oQbAFP\nJpP5jT2dTufvqV0YdTG9dUIs4iZuDTlu8Tvn3ruOEuu2buCtJDKCj5nXtdBpC1gXX9cTb+pIBw47\nS/mKrSgIK1Ijta3Pybn+uAivEH1TiQBbvtBOp7PgfmARHo/HlRsZsKMoUlZwE1gkrfcqfX9tqevP\nyG0vQ0q4dLMsYT1Qpgvw8PTzerm3t2fW1rDWO52OD7g5WVyEV4zcTHLj5x7B2d8oc6FZ86Kdn5/P\npzfSo+lNRDMXopZbap9xSkDY6l7Gqivdp8SiZB98XYnOTqezMO28zoLjdSk5matAt2wdZufm4f8h\na4If9WWb0bGp7I/UKc67u7uVSSytiS1FvLnYj3VMmiZRDE0eq5v+EJRagqVxv5x8kStUz0k1qSmH\neCBO/70Oe7uK+8a5mbgIrwEWYD0AxpaiVRTcqjHR6XQqQsut1+thMpnMxXw2myWPycLKYNN9HIdc\nKoC5R3DreJqIsBVhkAo7SzUW4U6nU0nAsJaDwWAh282K9XUhdpriIrxitACnfK06s07qS7DwiVB3\nu9256I7H48q6noesqQhblc0kLE2wig6lwq2suNc6P2gTsdICq8U291ouJbnX61XC0KzKZ3r2Yx3u\nVucmchwLF+E1YYkxL1lgrRoTbCmLCI/HY/T7fYzHY4zH44XBILGYU8djwbMWiwBLYghbwPIefFyp\ncC8ZjNJ/l9suRft5c8ucMKdC1FKxvhyGJiJsZeC5ADtNcRFeMyy8jFVjIhVb3O/3MRqNKkIgbTQa\nVcTEqvubG5CT0Ljd3d2FeGSuJyHvw5ZwTgCtKAXLZ9xUtHgArCQyQVus1jpbw/z9plKXLV+wW8PO\nsrgIrwHL8gWw0Mc1C/QjP4dOiUU2Go3MSlwsOJPJZOFYUsQY575mEQ9+TWKYtQiziKWmQ8oN2KUE\nuQSu8ZCr76At1pTPuORcrMI7qYQLF2CnKS7Ca8YK3wKqlrD4gC0/scxJlyqFqAeHOCSqTghijBVB\nYReE1LKwBtm0aFkCqAU4td5UsHgqIStDzcpWq/NfWwN5+vu1vms+Fx+Uc5bFRXhNpKIiLJ8wUB31\nF2HjRI46AWYRLhWAGON8EE27IM7Pz82wK8sdwckOXC9Bi5R+j2VFWNdusOo5SPUyy12Q6qtreuBR\nfy9X8XU7NxcX4TWTEmO5oaWPLWA9FZAlwjxKr0W4FLG+eZtrWbAIy3Fa7gi22mWpQ7Zy603gKAYu\nF6nXpY6vdhXocDK9bR2n1SffR27A0XFKcBF+Rlg3plhUu7uvVVSzavDKhJu6GLiVKCDCXgL7qNkF\nIRZ4SoR1IoSOLBgOh9n4Yd3XBB3Pa8X0ynpJ8Rx9HLmBw5SVq6+ti7DTBBfhFdL05mM/7O7ua/WF\ndeNawtbNz31NLGGOfBB0mUn+YeAY2pw1mhPhlEVcikwtlJrZgsXYEmHrR6DpMTjOKnERbhntruA+\n3tZ+WI4r5noUy7gj2BWiq4nt7e3h9PQUo9Fowf9qNbGGxcrPCeBV3BEi/Dp+Vz8l5KIzHGcTcBHe\nIHKRFFqEeaZi9jdbccIpxALXg2uDwQCj0Qh7e3sYjUYYjUbzRJHUbBO6aaG1fMHLirAekBMRttKI\nc9EYLsTOJuAi3CLiikita18s+2H1vG/iD05lzFlIiBpbwJPJBMPhcJ4eLdl5HCaXS2iQ7VRUhNXX\nBCtEzfKXays4JcSO0zYuwhuAFl0rxVm7DTilmEW61+sVf26MccEC1lXZuGJbKjPNWqaEL+UeKIV/\nNFKx01Ycr/VZLsbOJuAivIFYPmEOC2MXBEcr5KqopRAB5mmVeJ23czV59XrKD3tV36zObNMhe9on\nvKrPdZx10UiEQwg/BOB/APDnAYwAfAzAD8YY/x3t8z4Ab1N/+vEY45uueKzXErZ4U9Yw+4TZ58sx\nuyJCuXrCFlI7ggv56HXZzlUhszLK5PxSAriMGOqaFakMN+2OsI7FcTaBppbwmwG8B8C/fvq3/yeA\nD4UQXooxjmi/DwD4HgDyn17uqLzBpAbmgGpyh+zL1jGXoyxFkjMkNpjX9bZk0JW2lNhedXBMrH6r\nCI9OXNE/BLl1x2mLRiIcY/xm3g4hfC+APwZwF8Dv0EuTGOOfXP3wbgY8KCfbQFWMRXzYtypz1eki\n7KVILDBn5+W267LN9IAYn0vdssl3ZaUfW0se9Lvq5zrOuriqT/h5ABHAF1T/W0MIrwJ4BOCjAH7Y\nRTkPi4JVCJ6tSxFkK8uuyQSaqaQM3uY+K8IhF/GgB8GsdWu77nvSn5sKhVvl5zrOuriqCD8A8Nsx\nxk9Q328A+CUA/wHAGwD8PQC/FUK4G2NsNmp0Q7HEgstf6qnk9XYT6t5Lv2fu0V731Z3XMuTcGrlj\ncMF1NpWlRTiE8F4AXw3g67k/xvjLtPmJEMLLAD4D4FsA/Oqyn3fdWOYx3HGc68dSIhxCeA+AbwXw\n5hjj53P7xhhfCSF8FsAbc/vdv38fBwcHlb579+7h3r17yxyi4zjOM+Hw8BCHh4eVvqOjo+K/D00f\nX0MIPwngrwN4S4zxDwv2fx2AzwH4vhjjLxiv3wHw8ssvv4w7d+40OhbHcZxN5OHDh7h79y4A3I0x\nPszt2yhnNITwUwC+E8B3ADgJIbz4tA2evr4fQvjxEMJfDCF8WQjhrQB+DZcRFO9f4lwcx3GuNU3d\nEd+Py2iIf6b6vxfAzwM4B/A1AP4WLiMnPg/gnwL4GzHGkysdqeM4zjWkaZxw1nKOMY4B/NUrHZHj\nOM4NolkJK8dxHGeluAg7juO0iIuw4zhOi7gIO47jtIiLsOM4Tou4CDuO47SIi7DjOE6LuAg7juO0\niIuw4zhOi7gIO47jtIiLsOM4Tou4CDuO47SIi7DjOE6LuAg7juO0iIuw4zhOi7gIO47jtMhGi7Ce\nPO86cZ3PDbje5+fntr1s4vm5CLfEdT434Hqfn5/b9rKJ57fRIuw4jnPdcRF2HMdpERdhx3GcFmk6\n5f06GADAJz/5yYUXjo6O8PDhw2d+QM+C63xuwPU+Pz+37eVZnR/p2aBu3xBjXO/R1B1ACN8B4B+1\nehCO4zjr4TtjjL+Y22ETRPhPA/gmAJ8BMG71YBzHcVbDAMCfA/CbMcb/ktuxdRF2HMe5yfjAnOM4\nTou4CDuO47SIi7DjOE6LuAg7juO0yEaKcAjh7SGEPwwhjEII/yqE8JfaPqZVEEL40RDChWr/qe3j\nWoYQwptDCL8eQvijp+fxbcY+73r6+mkI4SMhhK9q41iXoe78QgjvM67lx9o63lJCCD8UQviXIYTH\nIYRXQwjvDyF8pbHfVl67kvPbtGu3cSIcQvibAB4A+D8A/LcAfgfAB0IIf6bVA1sdvw/gRQCvf9q+\npt3DWZp9AL8H4O0AFkJsQgg/COAdT1//OgCvAPhwCGH/WR7kFcie31M+gOq1/OZnc2hX4s0A3gPg\nLwD4K7hM2PpQCGEoO2z5tas9v6dszrWLMW5UA/BxAD+p+j4B4N1tH9sKzu1HATxs+zjWcF4XAL5N\n9f0nAH+HtnsAvgjg+9o+3hWd3/sA/Erbx7aCc3vd0/P7S9f02lnnt1HXbqMs4RBCF8BdAB9WL30I\nwJue/RGthTc+fcz7wxDCYQjhDW0f0Kp5ek6vB13HGOMUwEdxfa4jALz16SPv/xtC+L9CCP9V2we0\nBM/j0tL/AnAtr13l/IiNuXYbJcK4/NXaBfCq6n8Vl/8Y287HAXw3gG8E8D/h8pw+FkJ4odWjWj2v\nx+U//nW9jgDwGwC+E8BfBvC/AfjvAPzWU0Nim3gA4LdjjJ94un3drp0+P2DDrt0mFPC5McQYf5M2\n/yCE8HEA/x7A2wD8RDtH5SxDjPGXafMTIYSXcZl6/y0AfrWVg2pICOG9AL4awNe3fSzrIHV+m3bt\nNs0S/s8AznHpMGdexOXgwLUixngK4N8CeGPbx7JiXgEQcEOuIwDEGF8B8FlsybUMIbwHwLcCeGuM\n8fP00rW4dpnzW6Dta7dRIhxjnAF4GcA3qJe+AcDGh/80JYTQB/ASgOw/ybYRY/w0Lm/Y+XUMIfQA\nvAXAP2/ruNZJCOF1AL4UW3AtQwg/CeDbAfzlGONn+bXrcO1y55fYv9Vrt4nuiH8I4OefPiL8CwB/\nG5df0E+3elQrIITw4wD+CS5/dV8E8HcB3Abwc20e1zI8DVf6ClxaTQDw5SGErwXwhRjj53DpXnln\nCOFTAD4F4J0ATgBs3iRfBrnze9reBeAf4/LGfQOAdwP4YwDvf+YH24AQwk8BuAfg2wCchBDE4j2K\nMUoVw629dnXn9/S6vgubdO3aDs9IhJV8P4A/BDAC8K8AfH3bx7Si8zoE8B9xWbLzcwB+GcCfb/u4\nljyXt+Ay9Odctf+b9vnfAfwRgFMAHwHwVW0f9yrOD5dlCj+IS4txDODTAH4WwH/T9nEXnJd1TucA\nvlvtt5XXru78NvHaeSlLx3GcFtkon7DjOM5Nw0XYcRynRVyEHcdxWsRF2HEcp0VchB3HcVrERdhx\nHKdFXIQdx3FaxEXYcRynRVyEHcdxWsRF2HEcp0VchB3HcVrERdhxHKdF/n/YcQmuNjyCXgAAAABJ\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f35bc0c4c90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n"
]
}
],
"source": [
"tmp = mnist.train.images[1]\n",
"tmp = tmp.reshape((28,28))\n",
"\n",
"plt.imshow(tmp, cmap = cm.Greys)\n",
"plt.show()\n",
"print(mnist.train.labels[1])"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# input place holders\n",
"X = tf.placeholder(tf.float32, [None, 784])\n",
"Y = tf.placeholder(tf.float32, [None, 10])\n",
"\n",
"# weights & bias for nn layers\n",
"W1 = tf.Variable(tf.random_normal([784, 256]))\n",
"b1 = tf.Variable(tf.random_normal([256]))\n",
"L1 = tf.nn.relu(tf.matmul(X, W1) + b1)\n",
"\n",
"W2 = tf.Variable(tf.random_normal([256, 256]))\n",
"b2 = tf.Variable(tf.random_normal([256]))\n",
"L2 = tf.nn.relu(tf.matmul(L1, W2) + b2)\n",
"\n",
"W3 = tf.Variable(tf.random_normal([256, 10]))\n",
"b3 = tf.Variable(tf.random_normal([10]))\n",
"hypothesis = tf.matmul(L2, W3) + b3"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# define cost/loss & optimizer\n",
"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n",
" logits=hypothesis, labels=Y))\n",
"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# initialize\n",
"sess = tf.Session()\n",
"sess.run(tf.global_variables_initializer())"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('Epoch:', '0001', 'cost =', '177.273160444')\n",
"('Epoch:', '0002', 'cost =', '43.718642770')\n",
"('Epoch:', '0003', 'cost =', '27.303833560')\n",
"('Epoch:', '0004', 'cost =', '18.842648612')\n",
"('Epoch:', '0005', 'cost =', '13.762048228')\n",
"('Epoch:', '0006', 'cost =', '10.120526491')\n",
"('Epoch:', '0007', 'cost =', '7.660729609')\n",
"('Epoch:', '0008', 'cost =', '5.626483637')\n",
"('Epoch:', '0009', 'cost =', '4.186760860')\n",
"('Epoch:', '0010', 'cost =', '3.160262699')\n",
"('Epoch:', '0011', 'cost =', '2.334184935')\n",
"('Epoch:', '0012', 'cost =', '1.768244919')\n",
"('Epoch:', '0013', 'cost =', '1.306207328')\n",
"('Epoch:', '0014', 'cost =', '1.006527935')\n",
"('Epoch:', '0015', 'cost =', '0.907672952')\n",
"Learning Finished!\n"
]
}
],
"source": [
"# train my model\n",
"for epoch in range(training_epochs):\n",
" avg_cost = 0\n",
" total_batch = int(mnist.train.num_examples / batch_size)\n",
"\n",
" for i in range(total_batch):\n",
" batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
" feed_dict = {X: batch_xs, Y: batch_ys}\n",
" c, _ = sess.run([cost, optimizer], feed_dict=feed_dict)\n",
" avg_cost += c / total_batch\n",
"\n",
" print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))\n",
"\n",
"print('Learning Finished!')"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('Accuracy:', 0.94520015)\n"
]
}
],
"source": [
"# Test model and check accuracy\n",
"correct_prediction = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y, 1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
"print('Accuracy:', sess.run(accuracy, feed_dict={\n",
" X: mnist.test.images, Y: mnist.test.labels}))"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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LUkbz09PT0B1RN3Fh92Xvtgyg4k/lqATfMcNKSvqmn9E277rwk1FnCbMLwkdYRD3rfL3i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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f35bb9c5ed0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"('Label: ', array([0]))\n",
"('Prediction: ', array([0]))\n"
]
}
],
"source": [
"# Get one and predict\n",
"r = random.randint(0, mnist.test.num_examples - 1)\n",
"\n",
"tmp = mnist.test.images[r:r+1]\n",
"tmp = tmp.reshape((28,28))\n",
"plt.imshow(tmp, cmap = cm.Greys)\n",
"plt.show()\n",
"\n",
"print(\"Label: \", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))\n",
"print(\"Prediction: \", sess.run(\n",
" tf.argmax(hypothesis, 1), feed_dict={X: mnist.test.images[r:r + 1]}))"
]
}
],
"metadata": {
"date": "2015-12-02T20:03:30-06:00",
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
},
"tags": [
"ipython",
"machine-learning"
],
"title": "TensorFlow MNIST with iPython"
},
"nbformat": 4,
"nbformat_minor": 0
}
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