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@NelsonMinar
Created January 6, 2018 02:17
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TensorFlow MNIST sigmoid recognizer
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An elaboration on MNIST digit recognizing, see https://www.tensorflow.org/get_started/mnist/pros\n",
"\n",
"Trying to reproduce Ng's coursera course week 5 exercise ([ex4.pdf](https://github.com/Borye/machine-learning-coursera-1/blob/master/Week%205%20Assignments/Neural%20Network%20Learning/ex4.pdf)) in Tensorflow.\n",
"The Coursera course describes a network with 3 layers\n",
"\n",
"1. Input layer of 20x20 pixel values (ie: 400 nodes)\n",
"2. Hidden layer of nodes 25 nodes with a sigmoid function\n",
"3. Output layer of 10 nodes, hot one representation\n",
"\n",
"The cost function is cross-entropy, I think. The exercise regularizes the input data and then does a \"sigmoid gradient\" for the backpropagation training. They use a function called `fmincg` to do the training, and train the network in 400(!) steps over the whole training set each time.\n",
"\n",
"For more info see https://nelsonslog.wordpress.com/2018/01/06/tensorflow-mnist-sigmoid-recognizer"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"from IPython.core.display import HTML"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def image_array(images):\n",
" \"Matplotlib function to display a group of images inline as a block\"\n",
" num_cols = 5\n",
" num_rows = len(images) / num_cols + 1\n",
" plt.figure(figsize=[num_cols * 1.5, num_rows * 1.5])\n",
" plt.gray()\n",
" for i, data in enumerate(images):\n",
" plt.subplot(num_rows, num_cols, i + 1)\n",
" plt.axis('off')\n",
" plt.imshow(images[i])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
" return f(*args, **kwds)\n"
]
},
{
"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": [
"from tensorflow.examples.tutorials.mnist import input_data\n",
"mnist = input_data.read_data_sets('MNIST_data', one_hot=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4f78ee84a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Display some samples from the training set\n",
"image_array(mnist.train.next_batch(10)[0].reshape(10, 28, 28))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Set up TensorFlow with an interactive session for notebook hacking\n",
"import tensorflow as tf\n",
"sess = tf.InteractiveSession()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Neural network definition"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Configurable parameters for the network\n",
"\n",
"# How many nodes in the hidden layer\n",
"num_hidden_nodes = 25\n",
"\n",
"# Probability we drop a hidden node during training\n",
"drop_probability = 0.1\n",
"\n",
"# What kind of training optimizer we use\n",
"# optimizer = tf.train.AdamOptimizer(1e-2)\n",
"optimizer = tf.train.GradientDescentOptimizer(0.5)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Initial weights for the neural network\n",
"\n",
"# Normal distribution: mean 0.0, SD 0.1, clamped to [-0.2, 0.2]\n",
"def weight_variable(shape):\n",
" initial = tf.truncated_normal(shape, stddev=0.1)\n",
" return tf.Variable(initial)\n",
" \n",
"# The constant value 0.1\n",
"def bias_variable(shape):\n",
" initial = tf.constant(0.1, shape=shape)\n",
" return tf.Variable(initial)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Placeholders for the input data; images and labels\n",
"x = tf.placeholder(tf.float32, shape=[None, 28*28])\n",
"y_ = tf.placeholder(tf.float32, shape=[None, 10])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Hidden sigmoid layer; squash 28x28 greyscale inputs to N features\n",
"W_hidden = weight_variable([28 * 28, num_hidden_nodes])\n",
"b_hidden = bias_variable([num_hidden_nodes])\n",
"\n",
"# Not positive if the b_hidden belongs in the sigmoid function or not.\n",
"# See https://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks\n",
"h_hidden = tf.nn.sigmoid(tf.matmul(x, W_hidden) + b_hidden)\n",
"\n",
"# For training we're going to conduct dropout on the hidden layer\n",
"# Cargo cult programming; in theory this helps avoid overfitting?\n",
"keep_prob = tf.placeholder(tf.float32)\n",
"h_hidden_drop = tf.nn.dropout(h_hidden, keep_prob)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Readout layer; a softmax of the hidden layer, reduced to 10 nodes\n",
"# The softmax part is done as part of the cost function definition\n",
"W_readout = weight_variable([num_hidden_nodes, 10])\n",
"b_readout = bias_variable([10])\n",
"y_conv = tf.matmul(h_hidden_drop, W_readout) + b_readout"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Cost function for the network\n",
"cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))\n",
"\n",
"# Optimization setup for training\n",
"train_step = optimizer.minimize(cross_entropy)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# Accuracy measure: do the y_conv predictions match the y_ inputs?\n",
"correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Training loop"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"step 0, training accuracy 0.08, test accuracy 0.1115\n",
"step 1000, training accuracy 0.94, test accuracy 0.9205\n",
"step 2000, training accuracy 0.92, test accuracy 0.9342\n",
"step 3000, training accuracy 0.9, test accuracy 0.9409\n",
"step 4000, training accuracy 0.98, test accuracy 0.9457\n",
"step 5000, training accuracy 0.92, test accuracy 0.9515\n",
"step 6000, training accuracy 0.92, test accuracy 0.9522\n",
"step 7000, training accuracy 0.92, test accuracy 0.9508\n",
"step 8000, training accuracy 0.94, test accuracy 0.9529\n",
"step 9000, training accuracy 0.96, test accuracy 0.9546\n",
"step 10000, training accuracy 0.94, test accuracy 0.9549\n",
"step 11000, training accuracy 0.94, test accuracy 0.9559\n",
"step 12000, training accuracy 0.94, test accuracy 0.9569\n",
"step 13000, training accuracy 1, test accuracy 0.9588\n",
"step 14000, training accuracy 0.96, test accuracy 0.9577\n",
"step 15000, training accuracy 0.92, test accuracy 0.9575\n",
"step 16000, training accuracy 0.94, test accuracy 0.9574\n",
"step 17000, training accuracy 0.98, test accuracy 0.9596\n",
"step 18000, training accuracy 0.94, test accuracy 0.957\n",
"step 19000, training accuracy 0.96, test accuracy 0.9587\n",
"step 20000, training accuracy 0.98, test accuracy 0.9608\n",
"test accuracy 0.9607\n",
"CPU times: user 35.7 s, sys: 12.2 s, total: 47.9 s\n",
"Wall time: 25.1 s\n"
]
}
],
"source": [
"%%time\n",
"sess.run(tf.global_variables_initializer())\n",
"\n",
"for i in range(20001):\n",
" # Grab a bit of training data. Note this batching function is quite slow.\n",
" batch = mnist.train.next_batch(50)\n",
" \n",
" # Report training progress every 1000 steps\n",
" if i % 1000 == 0:\n",
" train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.9})\n",
" test_accuracy = accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})\n",
" print('step %d, training accuracy %g, test accuracy %g' % (i, train_accuracy, test_accuracy))\n",
" \n",
" # Run the training step\n",
" train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1 - drop_probability})\n",
"\n",
"# Report on final accuracy\n",
"test_accuracy = accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})\n",
"print('test accuracy %g' % accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<big>Accuracy <b>0.9607</b></big>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(HTML(f\"<big>Accuracy <b>{test_accuracy:.4}</b></big>\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualize info about the trained network"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4f76249a58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Visualize the hidden layer weights\n",
"\n",
"# Get the weights out of the tensor\n",
"weights = sess.run(W_hidden, feed_dict={x: batch[0], y_: batch[1], keep_prob: 1 - drop_probability})\n",
"# Recast this as an array of 28x28 sets of numbers. \n",
"weights = weights.transpose().reshape(num_hidden_nodes, 28, 28)\n",
"# Render\n",
"image_array(weights)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4f74979390>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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KljH0l6kO6A1TXm4bVIifQ4aXZKVTTVVxbVk6iTc0o2UAlXUugnRMi/GOiUL1PtoXvehFyzXUqZcXLrnkkiTbQBHv/0JnnfQcasnUofsYissJyq+//vrlGtrOY+MlL3lJkm0wz+tf//rlGnrQY5RlgcMGkiRb3YASmyhUjym3n3NZXX+odu+D43/JqnvWWwKknEzc+56h+CbKMVll4fH1vOc9L8mWqvXyCnrlPmQ5wu3dAwfp8EzPXwThHJR8HZ12QBh94aUm6xpzqXUWutW0KbqfrOPJFKmXqFhicN2gy70k4aUuaNsp6f6Utet0qAdZFEVRFAP6gSyKoiiKAXfZeZBEODmCCffdNKTTZfE7UajJuufOdKhpC2gER0FCF/h/pqPAhRdeuFxP9LDvhw4wBeFoXCg20x/QBIeNWnPEFtSNy6inaVO3B4rCkXnUzXS223v77bcn2e71hJqBIky2fY6M3BdvfOMbl+v3v//9SbZ7zHim7zH9RXv9O207F2cWmmKFanNUHu815TjtyTSdCrXoNHymaJGTaTxoZfeb948hM0dNOkUX73/hC1+4lLF/z5S26WP63hQWlJ2pzr2YokaduJp3TanukvWAgle84hVLGXQeFGeyXUphb98XvvCFpYw5xHSo38O4MYXqRN3UczqIwLSs0xFOhwpYhw8DPwd5Wif53ZHIX/nKV5Zr/uvfGXPu989//vPLNW33UsEnP/nJJOsZnH5Osu4vtdw8nzCXOpJ8Wo6yfkILOxKeb4PffaaoB1kURVEUA47Ug7QVM+3Tw6K0F+aFVRbgbelhKdgL86IvFpQXnLEeX/e61y1l075AvKRkGyRCUID3tmEJ2kvz3jzqPJ3sPe0vPBtYrlhTDgpBnraKbb3j/dhDpL72RO0l4dG4f6YMNm4vXpL7wov2b33rW5MkH/vYx5Yy/mt9cf/Tb64nVua5SKg9HR00HeflIC/rzeQFYYX7OWZNuDYrghfkjDv2kPFqD5L985///CRbrxS9sP64vQRhuD14t1MQ19nCYw5vdmIHnNHFoD8+85nPLGUkwP7whz+8lFlON9xwQ5JtNiSYgTe/+c1L2bT/1UF51jfmHtcTj93BgZ4PyPjiYKJzpbfuY57v+REWa2IGkrVfpiPYHDR5uuDCz33uc0m2rIDHMd6+5wPPhdTJ9YRB8bzja3TG75nmvzNFPciiKIqiGNAPZFEURVEMOFKK1bQM7rOpDFzlW265ZbwH+sQL39AsdsO9QA895IAI9um98pWvXMrs2vNfL3b7nQQHmDpgQdtuvKkr9l9NZ70dNpjE1CiY0s+5vg4OmAIGoJX8bCfChsLwMznjzoFXpkOpkwNNTNkQfOW9ePSV/2c6krqb7qHMddsLU2DomOUE5eg6TYEurgu6ZtmYaobOMg3Ofk/Tkg40YVnA1N90IrzpSOrsul111VXLNZS4g1PYX+ZgjL3wWOEdToIO3frFL35xKTMVCFVoio6gPpcxXpOVqvbY5ixZy9t79xg/loPHBc9yfyI778dzUBX9YeoTvdoTTGJ4jpn2W0I5er+y51lodusx+wu9BxnaPlmXRUxDv+1tb0uyTRXnpQh01rpvGdHXljt1s25b55mjTLFSdwdrnSnqQRZFURTFgCP1IG39kUXBlh7Wlq0qZ2vAwrJFgiXx6le/eilzloVPfOITSbYZHrDGHWTjEGwsKy9S2wLD4rGVSai8rWJ7Slh1rhsW557FY2M67speDpaxLa3pyCh7J4Sx2+qaPGJvW8Fzc6CHvU6CB+zNuw/43VlxkKuf42Ah2mEGgPYcNvgp2coRL9WeAjpry9fAK/bWEDxRBxfYiqYfzIrwHuuPtxqgq+9+97uXsle96lXLNSH5k1d57NixpcwJ0vES/Z6JPdkL6wnsgutHWz1veJyiJw7qO3HiRJLtdhezC8gJrzFZt75Y3u4vAm7cX/bSmK9Iiu32WJftCVEnMyPMdXsyvhj2WnmPZUTdvGXD2+kYa/aySZbvcQhj5P/ag8SLsyxdN+YOswbWNcaBvxHMzX6m5zXGq8cj9diT/akeZFEURVEM6AeyKIqiKAYcKcVqygvXf0oCbvrP7jPuud14KAoHSZgqec973pNkuwhNZge74d6jBH3iBWXfD73kxXToAlMZpotYtDctyyL1udhTdnLdTQNSJ1OxlhHtNG0ErWEqwwvf0ECmLaDETIOZipuCT0zzQKWZRpyymDiTEjSgAwr8/MPCbYYCtuzQ6UmPT64rgOJy9hzfzzmMzlTCnl3TXh/60IeWa/rhoosuWsoc7EY/eo8cfUNGk2QblDTtS6a9e05nPxnWR+hejxmWHqa9tsna964/euLAN2dVueaaa5Jsz9Ik45DlbX2jbzx2LSeWG0w/cr/npelgBi9RQDkfNgDKSyXTu1kucnssdwJxvJQ1JRY3RY6MPZdxPS2JJWsw34033riUOdG/qXVw/PjxJHPi92Sdk01to/NTMOPpUA+yKIqiKAb0A1kURVEUA46UYnUEExSNaUquTTuY3pmSJ0O3mt6AVk3WJM5O3MzzTRGY4oJ+sevuaEZoXd8PXet7JprGCYChG/zuPXA9kJHpCWgpU3+myKBEp8gw0zCuJ2m1brvttqUMynhKcpysVIv7d9oD6qhD3m8qdpKrqVBoICfhPheYouHYY3UQTU5UqGljxoEjKV1X2uxoXyIp3YdTMnLvAXYfoxemuNjH5ihB9yd9bLoKvTJVuxeO3p7aB7XqMWW9Rs+st+Atb3nLcu0UcOiRI+WnqHVHpE7j03MQlLj7mGULt8d6T9ut10TeOmH3Hkxp5SxrItOtH75GDl4S437rgqlt3uk2Mja8lOBnQnk6paLPioUWNsXOPG9Ze9mBMer5mvd4njxT1IMsiqIoigF32XFXWCT2BrCcpiwVyepN2krB4rDF4P05V199dZKt5YNF6awQXkhmL46DeGx9kIDYVv+UANgBKlhD9siwgB1Qswf2zLHWLCOu7a1ZXgQcTIEXlr8tebwOByvgudmit6fB7wdZyLxrsmZtRbov2b9lzwKvbUqIf7awx0Vdpqwj9pStv3jvU/Jke8qTRexjwzg6yO+xh8l4ss5eccUVyzVBIe4b9Ne67TFKO+wB4D173O6FdYs6TF6c9RavJFm9CdeZYCZ715OHMh2C4CPJ3IfI3LrgoBfmGwcUErRy0FyG52gPkzHpwJ498F5W2uvgGrw4z1/2ktER6xrjzHON78eDtIwYx67PxLB5LnI9mSv9HgLPfI/nOuCxxZw8MQ2nQz3IoiiKohjQD2RRFEVRDLjLzoPEfTdlAh1gqsKLtpSbCsH99j2+hnoyJQKF4cAe1w06yxSpaT0oAwdZ8B7X17QZVMqUzNyL6ntgugH6z+3h+W7vlMbMAVHQQd53ZuoH6tQUKzBl7L2kUC4H7aGDBnTdqLtpNt/P+01lcb1n39PJMPU07ScF3n9oKo2T2i0nfnewjxOPQ795DyX/Ne1lvaFvTMs6hRd7/Ex30U+WLQEnyRrwY9qMPXreN7wXplOh25wiDirQFKnfy5jyoQO0yc92GjWoVS91MF84qblTIEKhXnzxxUuZ6W3mlukgAgesWR+pn6l39P+we0zdNmRkapQx7XHGQRDJ2sceu9CTprNNszs4DNBeL3mZ+mQcuE89tzP/etnAcxTwnMrymdvG/dO9p0M9yKIoiqIY0A9kURRFUQw4UorV9A60jekoKAbTAY6CnOg0oikn+iJZaShHrELrOvLRZxhOmfh9dhrUlykGqFW3x/Qkz3JKNFPBh4HphP/pd0d7WV6maAFUhSnjKQrZ+5qgI03v+pp+dcSw+xo6yJF9vh+4X4DpyukMvL1wm9E/U5vosctME6IDpr0oM+1qqo19uI7eYz+vKVb/TtSm99VZztTP72Efm1OxmYZH5y3H6SSSvZhOx5j2PLqdd9xxx3KNHB1VzP0uc/3Zw+z+4vmea6xPpD/zWLDeTmfSEvnr8eHfido1vcjvhz0pxUsA03ID9fVc5KUEosyn+k5noSbzaR7IyHJzGj3q6T2nUypER70z7xwUoc7c7XmY+WSKdj0d6kEWRVEUxYAj9SBtRWOZ22LlS2/r7nTeEZaRLSAvGLPYbg+SzDFeMLaHiBV+0HlpLFizHzJZrTEvGPv5eItetMfKPOyivGWE92zPB0vbgQUXXnjhcs1/HYyBBW6ra9ojZ48GS8+L4faisaZtZU7J6G39USdb2tPvDgyibv7fXlgXYSvcrwS1OBDBlvm01xAP8brrrlvK3vCGNyzXWMfuLwLHrrrqqqXshhtuWK7Zu+vAMM45TNZ+cN3x4Oxp+n7GlD0SxuhB51+eDdxnXNszo889pnxeIP1sHWUsTfsLkzUYxR7Vxz/+8STbdtq75x632eOYACmPQ8aPx7Y9Q+p0++23L2W002NmDxwshBduTxadtrfud3KPxyZ1d6CevexpPzDeoAOe3L/omr1B9yUy9jiYPG+3lyAf9wWM4cSUnQ71IIuiKIpiQD+QRVEURTHgLjsPEnfXlA+0h6k803Hcb3qEPVx+tmkYyk2hElxz0LmEPN/7olwP3HjfA21hGtn70KZzDQk2Oew+SLd9SgEGBeFgB9OPUyATVIgXzb1nlXssQ2ga18fUItSr+3dKc2aahUAA05Ze9KcepoPol3ORUNvtQ47WP+gh7yNzkBhtcr9A1To44SMf+chyTTot761EpgSZJNs+fO9735tku2fQYws6zNQgARN33nnnUuYUbfSNKViesyfx88nwnjdg2U5nrBoslXh5hXF60Fmb6JGpQKhm06oO2oPic5tJ9O53TUsY7mP3xxQwwpg7bHCZx9c0FvjdQVLWT2Rjeph7rOd+JnWfgtEcVGc6lL7yPlzXnef7jEjGjufMKZDTujUtw5wp6kEWRVEUxYAj9SBtKWAR29vA+vDisRdwWTy2JUBAzhS+nqxWm5/JFgZbd1PSbS8E+xoLywvWWEmumxeFp5B0rJzDZiWZwuVtIQMHRE3Js33P5Ck4iIdrZxdBbg52sDzweKbjeJLVOpyyixx0NBn9a7myQH8uttFYJvTxFKDhPnCGJTLY4O0k63FD/p+9j1tvvTXJ1nOi/Q4UsQdJgMcHP/jBpeyaa6455fcpNN9tvP7665frKUsS4+hcyNZ6j2dslmJiei644ILlesoSwzMnj+igZ6Kv9ti9nQQds/djHea/Hgvo4EGBMFx7jsAT2pPxxZgClFxf6mRGyVmb0EXLkPFu/fPYvummm5JsjxZDrmbvrrzyyuUa/TsoSTwy9BFsUwCePVTKrec8f4/O1oMsiqIoigH9QBZFURTFgCOlWE2fQDE4+IK9NqYi7NJDrXk/0YkTJ5Kse8uSORuH3XCCEkwHmD7BpfciNVRZsgZhmIKgzqZ7TBdAJbu9UJ6Hpau852dK+D1ln3A9p9PAoSW818m0BbJx3aFUTBuZjpxO+/a+Oz8fIEP3hekiyr1Xjt8Pu7/05OdO76d9pvG8PxYa37qPfk17z5J1T6TPr0P/rIeWLUElrq+pKf7r+6ErvcxhQHmbSpsCK/bC1BpjyvQi49i6DD2dzGcQskzj8ex72BvqADr6xjS4lyMYF6acvX+R+60DyNS67r6ZAmUYH6ZI92Da7+d3806Xea5iXrRekEzftKxx6aWXJtnqBfP0Qe+hr90X3k/M3OL+5RtguZlupa+8b52+3BMMWQ+yKIqiKAb0A1kURVEUA46UYvV+Ktx402BQKaZDTXUQTWY3HnrDrrn380GLmJaFcvG+JCIHXacpkXayuv6uG26+aRjvz2HvnttLajbvm9sDUyrT/kQoMlMVjgrlv6aloCpMu04Jgh3JizxMeZgeo56WmxMRQ7n4furp9piGo48mKvSwSZ+Tbd9Ag/pdUNqus2ljqB7T+URLmpIzjYkue08Z+gPteTKuvfbaJNtISteJ+9we6m4a3TQUKcL8HOrssbMXpttMFwPGmZdZPLaJHjU9zfjzfjrrAWNyomotB4995i1HZ5sWZIy4Pbz/IFoP2Tv6GtrQ43QPPGbZo+mxzVKG9xObOp32D3OP5/BpX7XnZmToMX7zzTcv1/SfzyC1XiEHR63zHs95nssonyJ5D0pw/j+hHmQvA+EUAAAEMElEQVRRFEVRDDhSD9JWIl91B3ngbfi4IH/1sdxtbWOdeE8VRwMlqxVsy4RsEA6SsPV4ySWXJNl6Ag6yIDuFs5rYygG2YqinrWEsTi8oHzt27JTnnA5TIIA9N9o57ZdMVhl7UZ5r94+Dn5DHlAnnoOTDeI5Tdh3/1/Wg7r5nSoBuL2JKsr0X3sMFs2HZTvt5bYFPRyGxz8yema1sglfcr7TpsssuW8qmhMweY7bm8VjMzuAFWZ4eW8jUgWV4EpPHfhgQIOMxg0wdrGSvh4xZ3kM6ZdKx18MYcKYcdHwar36mvUE/E303I4B83IfuD/ppCj5ztpo98Dilv+2VwvrYszYDhz64j9EVy8BHV6EX7iv0x31hnccDtRftOiEv153fncXMcyqBk1OmIr/nTFEPsiiKoigG9ANZFEVRFAOOlGL1wjdus+koaA27wg4CgXIx3QaNZgp1CkQwvQGdalrMych5p4NJTJ/gvp9//vlLGXTplBrPv09n4B323ELTODzLz4SeM1Vmeg55TOc4+n+mbnjmtJfNVK7rBtXi/rW8oH1NyaAzUxo819mU8bQXbi+sS+iLaUxoZdfJwTXQVKbSoKuss6b80AvrOePEsnNAGPptiso04PHjx5NsdZL2+DlOXE6d3TbG27mQrel7rt1m+t4UmvUJffb5r/SN2+RgEmTv5ZNpj7KDoehD66Dnsmk5gj60fk/JwU1587upyz3wO5HXFLDm/3leQqd9D203tTktn3i5CBl5HvX9zC2mUD220DvLmt8dhOWAKeaDaTzuWRaoB1kURVEUA47Ug7QHwvUU3DAdeZOsXovLsPpsJdoimaxDnmOrydbhlFTbi89YePZK8TRsWds6xKJxkAHW3Z7FY2MKspgCXabk1snazikDjmUwHaVlDwvr35a0nwlDYLm5L/mvA56mJO/2atEV68y5OjYo2QZnsTXA9ecdtnxdf+RnvaCPpnYka1utnzzfz/Y9eB+24KesJw5m4/n2NN2f6Ijfg/6a2dmLKejL4xCZWZ/sjeBFuD/wyj3ep8TiDsihHi6z1zJ57/bEpy1R3GP9cdAY3pv1ht+ngL+9QF5mKyZmwLpG8NMUVDQxack6N9hLxlv0vOF5CS99mgOStf+nY7cOOu4KnbWHOXmiZ4p6kEVRFEUxoB/IoiiKohhw3pTYtiiKoij+r6MeZFEURVEM6AeyKIqiKAb0A1kURVEUA/qBLIqiKIoB/UAWRVEUxYB+IIuiKIpiQD+QRVEURTGgH8iiKIqiGNAPZFEURVEM6AeyKIqiKAb0A1kURVEUA/qBLIqiKIoB/UAWRVEUxYB+IIuiKIpiQD+QRVEURTGgH8iiKIqiGNAPZFEURVEM6AeyKIqiKAb0A1kURVEUA/qBLIqiKIoB/UAWRVEUxYB+IIuiKIpiQD+QRVEURTHg/wHntCyUzModogAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4f7498d208>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4f74842a20>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4f74765cf8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4f71e11710>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4f71eeb7f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4f71cb3cc0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4f74899e80>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4f76ca3ba8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4f76e185c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Display the hidden weights, but this time sorted by how important they are for each digit\n",
"# Ie: first row is the top 10 hidden weights that identify the number 0\n",
"# I'm not certain this is right; should consider role of bias terms\n",
"readout, biases = sess.run((W_readout, b_readout), feed_dict={x: batch[0], y_: batch[1], keep_prob: 1 - drop_probability})\n",
"for identified_number in range(0, 10):\n",
" top = list(enumerate(readout[:,identified_number]))\n",
" top.sort(key=lambda a: -a[1])\n",
" top_images = [weights[n[0]] for n in top[:5]]\n",
" image_array(top_images)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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