multi_value.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"multi_value.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"pyvHmfVd0gyY","colab_type":"code","colab":{}},"source":["import tensorflow as tf\n","import numpy as np"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ltCBvoyM0kIo","colab_type":"code","colab":{}},"source":["#imports MNIST data from their tutorial\n","from tensorflow.examples.tutorials.mnist import input_data"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"njsj6ePr0pjv","colab_type":"code","outputId":"1f7aad76-b2d8-460f-9138-64b27762e004","executionInfo":{"status":"ok","timestamp":1560293150541,"user_tz":300,"elapsed":3134,"user":{"displayName":"Rahul Krishnan","photoUrl":"","userId":"09931310336604586969"}},"colab":{"base_uri":"https://localhost:8080/","height":441}},"source":["mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From <ipython-input-3-a839aeb82f4b>:1: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please write your own downloading logic.\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use tf.data to implement this functionality.\n","Extracting MNIST_data/train-images-idx3-ubyte.gz\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use tf.data to implement this functionality.\n","Extracting MNIST_data/train-labels-idx1-ubyte.gz\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use tf.one_hot on tensors.\n","Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n","Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Kbb-ZuF00xk8","colab_type":"code","outputId":"e8798029-0ff8-4032-cf2b-c22df5684cdb","executionInfo":{"status":"ok","timestamp":1560293150543,"user_tz":300,"elapsed":3122,"user":{"displayName":"Rahul Krishnan","photoUrl":"","userId":"09931310336604586969"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["type(mnist)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensorflow.contrib.learn.python.learn.datasets.base.Datasets"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"36UcGg0c09pm","colab_type":"code","outputId":"93aed24d-4cd9-42b4-d6e6-32477e9907fc","executionInfo":{"status":"ok","timestamp":1560293150675,"user_tz":300,"elapsed":3240,"user":{"displayName":"Rahul Krishnan","photoUrl":"","userId":"09931310336604586969"}},"colab":{"base_uri":"https://localhost:8080/","height":146}},"source":["#array containing all the training images\n","mnist.train.images"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0., 0., 0., ..., 0., 0., 0.],\n","       [0., 0., 0., ..., 0., 0., 0.],\n","       [0., 0., 0., ..., 0., 0., 0.],\n","       ...,\n","       [0., 0., 0., ..., 0., 0., 0.],\n","       [0., 0., 0., ..., 0., 0., 0.],\n","       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"NvuaSBr-1Bme","colab_type":"code","outputId":"a3e0ccbd-b3d1-40d3-d89c-77fb90139f9e","executionInfo":{"status":"ok","timestamp":1560293150677,"user_tz":300,"elapsed":3226,"user":{"displayName":"Rahul Krishnan","photoUrl":"","userId":"09931310336604586969"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["mnist.train.num_examples"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["55000"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"id":"7X7CZ0Rv1DZM","colab_type":"code","outputId":"c3fb4f75-18dd-4883-dbd2-52358e886ec3","executionInfo":{"status":"ok","timestamp":1560293150678,"user_tz":300,"elapsed":3213,"user":{"displayName":"Rahul Krishnan","photoUrl":"","userId":"09931310336604586969"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["mnist.test.num_examples"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["10000"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"u5dSy_lF1GY6","colab_type":"code","colab":{}},"source":["import matplotlib.pyplot as plt\n","%matplotlib inline"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"4XDkwTAp1L4w","colab_type":"code","colab":{}},"source":["#converts from 1x724 to 28x28 image\n","single_image = mnist.train.images[1].reshape(28, 28)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"W0_-4pI61NBG","colab_type":"code","outputId":"cd6e709d-1776-4714-9289-f0be990a8f7b","executionInfo":{"status":"ok","timestamp":1560293150941,"user_tz":300,"elapsed":3444,"user":{"displayName":"Rahul Krishnan","photoUrl":"","userId":"09931310336604586969"}},"colab":{"base_uri":"https://localhost:8080/","height":287}},"source":["plt.imshow(single_image, cmap='gist_gray')"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fee7ec3a048>"]},"metadata":{"tags":[]},"execution_count":10},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"I1bDy4jQ1g1o","colab_type":"code","outputId":"55e5c8c5-cf3d-4767-f735-f773b1c52adf","executionInfo":{"status":"ok","timestamp":1560293150948,"user_tz":300,"elapsed":3435,"user":{"displayName":"Rahul Krishnan","photoUrl":"","userId":"09931310336604586969"}},"colab":{"base_uri":"https://localhost:8080/","height":92}},"source":["#PLACEHOLDER(S)\n","x = tf.placeholder(tf.float32, shape=[None,784])\n","\n","#PGD VARIABLES\n","l = tf.Variable(tf.zeros([784, 10]), dtype=tf.float32)                           # left bounds for weight matrix W\n","r = tf.Variable(tf.random.uniform([784, 10], 1, 2), dtype=tf.float32)            # right bounds for weight matrix W\n","\n","#ANALYSIS VARIABLES\n","eps = tf.Variable((r-l)/1000, dtype=tf.float32) # for round-off errors\n","\n","leps = l - eps #comparison left analysis bounds\n","reps = r + eps #comparison right analysis bounds"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Colocations handled automatically by placer.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"beyFAlIL6diT","colab_type":"code","colab":{}},"source":["#OPERATION PLACEHOLDERS\n","a = tf.placeholder(tf.float32)\n","b = tf.placeholder(tf.float32)\n","add_op = a + b\n","\n","delta = tf.constant(1.0)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"O1yR9LeP1udl","colab_type":"code","colab":{}},"source":["#VARIABLES\n","current_scope = tf.contrib.framework.get_name_scope()\n","\n","# HERE'S THE MAGIC:\n","# placing an element-wise interval (box) constraint using: `constraint=lambda x: tf.clip_by_value(x, l, r)`\n","# will automatically clip values after each optimization step\n","with tf.variable_scope(current_scope, reuse=tf.AUTO_REUSE):\n","  W = tf.get_variable(name=\"W\", initializer=tf.zeros([784, 10]), constraint=lambda x: tf.clip_by_value(x, l, r))\n","b = tf.Variable(tf.zeros([10]))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"BfuntJ411vT6","colab_type":"code","colab":{}},"source":["#CREATE GRAPH OPERATIONS\n","y = tf.matmul(x, W) + b"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Yw1rYt7n1w03","colab_type":"code","colab":{}},"source":["y_true = tf.placeholder(tf.float32, [None, 10])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"2Db3RBYd2S1r","colab_type":"code","outputId":"cb350e53-4341-4266-c073-bfb7574c253b","executionInfo":{"status":"ok","timestamp":1560293151096,"user_tz":300,"elapsed":3527,"user":{"displayName":"Rahul Krishnan","photoUrl":"","userId":"09931310336604586969"}},"colab":{"base_uri":"https://localhost:8080/","height":164}},"source":["#LOSS FUNCTION\n","cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=y))\n","#creates a loss function (cross entropy) where labels are true values and logits are predictions"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From <ipython-input-16-566a1efcaee8>:1: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","\n","Future major versions of TensorFlow will allow gradients to flow\n","into the labels input on backprop by default.\n","\n","See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"fv6czV1x1xYU","colab_type":"code","colab":{}},"source":["#OPTIMIZER\n","optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.5)\n","\n","train = optimizer.minimize(cross_entropy)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"3FgMRnucrmcd","colab_type":"code","colab":{}},"source":["#DEFINE UPDATES TO BOUNDS\n","leftUpdate = tf.Variable(0.1, dtype=tf.float32)\n","rightUpdate = tf.Variable(0.1, dtype=tf.float32)\n","\n","updatel = tf.assign(l, tf.add(l, leftUpdate))\n","updater = tf.assign(r, tf.add(r, rightUpdate))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"MzpuWC1t1yOD","colab_type":"code","colab":{}},"source":["#CREATE SESSION\n","init = tf.global_variables_initializer()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"6IHwSqmI2qmq","colab_type":"code","outputId":"a0e5adc8-b8a3-4309-88c4-9318e9111355","executionInfo":{"status":"ok","timestamp":1560293153837,"user_tz":300,"elapsed":6233,"user":{"displayName":"Rahul Krishnan","photoUrl":"","userId":"09931310336604586969"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["with tf.Session() as sess:\n","  sess.run(init)\n","  \n","  for step in range(1000):\n","    #first create batch and run step update\n","    batch_x, batch_y = mnist.train.next_batch(100)\n","    sess.run(train, feed_dict={x:batch_x, y_true:batch_y})\n","    #tensorflow has already automatically updated/projected values inside interval\n","    \n","    #optional analysis to check that projected within bounds\n","    lv = sess.run(leps)\n","    rv = sess.run(reps)\n","    \n","    matrix = sess.run(W)\n","    assert (matrix <= rv).all() #right bounds checking\n","    assert (matrix >= lv).all() #left bounds checking\n","    \n","    #update projection bounds\n","    sess.run([updatel, updater])\n","    \n","  #evaluate the model\n","  correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_true, 1))\n","  \n","  #returns: [True, False, True, True,...] where maximum probability matches true value\n","  #now convert to --> [1, 0, 1, 1, ...]\n","  acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) #reduce_mean returns percentage of correct\n","  \n","  print(\"Accuracy:\", sess.run(acc, feed_dict={x:mnist.test.images, y_true:mnist.test.labels}))\n","  \n","  \n","  \n","  "],"execution_count":0,"outputs":[{"output_type":"stream","text":["Accuracy: 0.1135\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"yKhDgnDV26cq","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}