diff --git a/README.md b/README.md index 6000e26..62db081 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ # data-science-ipython-notebooks -This repo is a collection of IPython Notebooks I reference while working with data. Although I developed and maintain most notebooks, some notebooks I reference were created by other authors, who are credited within their notebook(s) by providing their names and/or a link to their source. +This repo is a collection of IPython Notebooks I reference while working with data. Although I developed and maintain many notebooks, other notebooks I reference were created by various authors, who are credited within their notebook(s) by providing their names and/or a link to their source. For detailed instructions, scripts, and tools to more optimally set up your development environment for data analysis, check out the [dev-setup](https://github.com/donnemartin/dev-setup) repo. @@ -21,6 +21,7 @@ For detailed instructions, scripts, and tools to more optimally set up your deve * [amazon web services](#aws) * [kaggle-and-business-analyses](#kaggle-and-business-analyses) * [scikit-learn](#scikit-learn) +* [deep-learning](#deep-learning) * [statistical-inference-scipy](#statistical-inference-scipy) * [pandas](#pandas) * [matplotlib](#matplotlib) @@ -120,6 +121,25 @@ IPython Notebook(s) demonstrating scikit-learn functionality. | [gmm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-gmm.ipynb) | Gaussian mixture models. | | [validation](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-validation.ipynb) | Validation and model selection. | +
+

+ +

+ +## deep-learning + +IPython Notebook(s) demonstrating deep learning functionality. + +| Notebook | Description | +|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| [deep dream](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/deep-dream/dream.ipynb) | Caffe-based computer vision program which uses a convolutional neural network to find and enhance patterns in images. | +| [ts-not-mnist](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/1_notmnist.ipynb) | Learn simple data curation by creating a pickle with formatted datasets for training, development and testing in TensorFlow. | +| [ts-fully-connected](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/2_fullyconnected.ipynb) | Progressively train deeper and more accurate models using logistic regression and neural networks in TensorFlow. | +| [ts-regularization](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/3_regularization.ipynb) | Explore regularization techniques by training fully connected networks to classify notMNIST characters in TensorFlow. | +| [ts-convolutions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/4_convolutions.ipynb) | Create convolutional neural networks in TensorFlow. | +| [ts-word2vec](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/5_word2vec.ipynb) | Train a skip-gram model over Text8 data in TensorFlow. | +| [ts-lstm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/6_lstm.ipynb) | Train a LSTM character model over Text8 data in TensorFlow. | +

diff --git a/deep-learning/deep-dream/dream.ipynb b/deep-learning/deep-dream/dream.ipynb new file mode 100644 index 0000000..d8b4a5e --- /dev/null +++ b/deep-learning/deep-dream/dream.ipynb @@ -0,0 +1,597 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "RMhGdYHuOZM8" + }, + "source": [ + "# Deep Dreams (with Caffe)\n", + "\n", + "Credits: Forked from [DeepDream](https://github.com/google/deepdream) by Google\n", + "\n", + "This notebook demonstrates how to use the [Caffe](http://caffe.berkeleyvision.org/) neural network framework to produce \"dream\" visuals shown in the [Google Research blog post](http://googleresearch.blogspot.ch/2015/06/inceptionism-going-deeper-into-neural.html).\n", + "\n", + "It'll be interesting to see what imagery people are able to generate using the described technique. If you post images to Google+, Facebook, or Twitter, be sure to tag them with **#deepdream** so other researchers can check them out too.\n", + "\n", + "##Dependencies\n", + "This notebook is designed to have as few dependencies as possible:\n", + "* Standard Python scientific stack: [NumPy](http://www.numpy.org/), [SciPy](http://www.scipy.org/), [PIL](http://www.pythonware.com/products/pil/), [IPython](http://ipython.org/). Those libraries can also be installed as a part of one of the scientific packages for Python, such as [Anaconda](http://continuum.io/downloads) or [Canopy](https://store.enthought.com/).\n", + "* [Caffe](http://caffe.berkeleyvision.org/) deep learning framework ([installation instructions](http://caffe.berkeleyvision.org/installation.html)).\n", + "* Google [protobuf](https://developers.google.com/protocol-buffers/) library that is used for Caffe model manipulation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "cellView": "both", + "colab_type": "code", + "collapsed": false, + "id": "Pqz5k4syOZNA" + }, + "outputs": [], + "source": [ + "# imports and basic notebook setup\n", + "from cStringIO import StringIO\n", + "import numpy as np\n", + "import scipy.ndimage as nd\n", + "import PIL.Image\n", + "from IPython.display import clear_output, Image, display\n", + "from google.protobuf import text_format\n", + "\n", + "import caffe\n", + "\n", + "# If your GPU supports CUDA and Caffe was built with CUDA support,\n", + "# uncomment the following to run Caffe operations on the GPU.\n", + "# caffe.set_mode_gpu()\n", + "# caffe.set_device(0) # select GPU device if multiple devices exist\n", + "\n", + "def showarray(a, fmt='jpeg'):\n", + " a = np.uint8(np.clip(a, 0, 255))\n", + " f = StringIO()\n", + " PIL.Image.fromarray(a).save(f, fmt)\n", + " display(Image(data=f.getvalue()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AeF9mG-COZNE" + }, + "source": [ + "## Loading DNN model\n", + "In this notebook we are going to use a [GoogLeNet](https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet) model trained on [ImageNet](http://www.image-net.org/) dataset.\n", + "Feel free to experiment with other models from Caffe [Model Zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo). One particularly interesting [model](http://places.csail.mit.edu/downloadCNN.html) was trained in [MIT Places](http://places.csail.mit.edu/) dataset. It produced many visuals from the [original blog post](http://googleresearch.blogspot.ch/2015/06/inceptionism-going-deeper-into-neural.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "cellView": "both", + "colab_type": "code", + "collapsed": false, + "id": "i9hkSm1IOZNR" + }, + "outputs": [], + "source": [ + "model_path = '../caffe/models/bvlc_googlenet/' # substitute your path here\n", + "net_fn = model_path + 'deploy.prototxt'\n", + "param_fn = model_path + 'bvlc_googlenet.caffemodel'\n", + "\n", + "# Patching model to be able to compute gradients.\n", + "# Note that you can also manually add \"force_backward: true\" line to \"deploy.prototxt\".\n", + "model = caffe.io.caffe_pb2.NetParameter()\n", + "text_format.Merge(open(net_fn).read(), model)\n", + "model.force_backward = True\n", + "open('tmp.prototxt', 'w').write(str(model))\n", + "\n", + "net = caffe.Classifier('tmp.prototxt', param_fn,\n", + " mean = np.float32([104.0, 116.0, 122.0]), # ImageNet mean, training set dependent\n", + " channel_swap = (2,1,0)) # the reference model has channels in BGR order instead of RGB\n", + "\n", + "# a couple of utility functions for converting to and from Caffe's input image layout\n", + "def preprocess(net, img):\n", + " return np.float32(np.rollaxis(img, 2)[::-1]) - net.transformer.mean['data']\n", + "def deprocess(net, img):\n", + " return np.dstack((img + net.transformer.mean['data'])[::-1])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "UeV_fJ4QOZNb" + }, + "source": [ + "## Producing dreams" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "9udrp3efOZNd" + }, + "source": [ + "Making the \"dream\" images is very simple. Essentially it is just a gradient ascent process that tries to maximize the L2 norm of activations of a particular DNN layer. Here are a few simple tricks that we found useful for getting good images:\n", + "* offset image by a random jitter\n", + "* normalize the magnitude of gradient ascent steps\n", + "* apply ascent across multiple scales (octaves)\n", + "\n", + "First we implement a basic gradient ascent step function, applying the first two tricks:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "cellView": "both", + "colab_type": "code", + "collapsed": false, + "id": "pN43nMsHOZNg" + }, + "outputs": [], + "source": [ + "def objective_L2(dst):\n", + " dst.diff[:] = dst.data \n", + "\n", + "def make_step(net, step_size=1.5, end='inception_4c/output', \n", + " jitter=32, clip=True, objective=objective_L2):\n", + " '''Basic gradient ascent step.'''\n", + "\n", + " src = net.blobs['data'] # input image is stored in Net's 'data' blob\n", + " dst = net.blobs[end]\n", + "\n", + " ox, oy = np.random.randint(-jitter, jitter+1, 2)\n", + " src.data[0] = np.roll(np.roll(src.data[0], ox, -1), oy, -2) # apply jitter shift\n", + " \n", + " net.forward(end=end)\n", + " objective(dst) # specify the optimization objective\n", + " net.backward(start=end)\n", + " g = src.diff[0]\n", + " # apply normalized ascent step to the input image\n", + " src.data[:] += step_size/np.abs(g).mean() * g\n", + "\n", + " src.data[0] = np.roll(np.roll(src.data[0], -ox, -1), -oy, -2) # unshift image\n", + " \n", + " if clip:\n", + " bias = net.transformer.mean['data']\n", + " src.data[:] = np.clip(src.data, -bias, 255-bias) " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "nphEdlBgOZNk" + }, + "source": [ + "Next we implement an ascent through different scales. We call these scales \"octaves\"." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "cellView": "both", + "colab_type": "code", + "collapsed": false, + "id": "ZpFIn8l0OZNq" + }, + "outputs": [], + "source": [ + "def deepdream(net, base_img, iter_n=10, octave_n=4, octave_scale=1.4, \n", + " end='inception_4c/output', clip=True, **step_params):\n", + " # prepare base images for all octaves\n", + " octaves = [preprocess(net, base_img)]\n", + " for i in xrange(octave_n-1):\n", + " octaves.append(nd.zoom(octaves[-1], (1, 1.0/octave_scale,1.0/octave_scale), order=1))\n", + " \n", + " src = net.blobs['data']\n", + " detail = np.zeros_like(octaves[-1]) # allocate image for network-produced details\n", + " for octave, octave_base in enumerate(octaves[::-1]):\n", + " h, w = octave_base.shape[-2:]\n", + " if octave > 0:\n", + " # upscale details from the previous octave\n", + " h1, w1 = detail.shape[-2:]\n", + " detail = nd.zoom(detail, (1, 1.0*h/h1,1.0*w/w1), order=1)\n", + "\n", + " src.reshape(1,3,h,w) # resize the network's input image size\n", + " src.data[0] = octave_base+detail\n", + " for i in xrange(iter_n):\n", + " make_step(net, end=end, clip=clip, **step_params)\n", + " \n", + " # visualization\n", + " vis = deprocess(net, src.data[0])\n", + " if not clip: # adjust image contrast if clipping is disabled\n", + " vis = vis*(255.0/np.percentile(vis, 99.98))\n", + " showarray(vis)\n", + " print octave, i, end, vis.shape\n", + " clear_output(wait=True)\n", + " \n", + " # extract details produced on the current octave\n", + " detail = src.data[0]-octave_base\n", + " # returning the resulting image\n", + " return deprocess(net, src.data[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "QrcdU-lmOZNx" + }, + "source": [ + "Now we are ready to let the neural network reveal its dreams! Let's take a [cloud image](https://commons.wikimedia.org/wiki/File:Appearance_of_sky_for_weather_forecast,_Dhaka,_Bangladesh.JPG) as a starting point:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "cellView": "both", + "colab_type": "code", + "collapsed": false, + "executionInfo": null, + "id": "40p5AqqwOZN5", + "outputId": "f62cde37-79e8-420a-e448-3b9b48ee1730", + "pinned": false + }, + "outputs": [ + { + "data": { + "image/jpeg": 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KnhegNNeI8YYnHejXqV6CvbnO7ado9aYYjtG4cGr8RPlbW+YU8QcEY4NLmCxV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\nXH3vWpSflOKTQ0x6uR3x6VZhvGVshjx71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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "img = np.float32(PIL.Image.open('sky1024px.jpg'))\n", + "showarray(img)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Z9_215_GOZOL" + }, + "source": [ + "Running the next code cell starts the detail generation process. You may see how new patterns start to form, iteration by iteration, octave by octave." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab_type": "code", + "collapsed": false, + "executionInfo": null, + "id": "HlnVnDTlOZOL", + "outputId": "425dfc83-b474-4a69-8386-30d86361bbf6", + "pinned": false + }, + "outputs": [ + { + "data": { + "image/jpeg": 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loEavRl5iwDcE55IxVeSVvlHAwQcAYpskqSRqwZWVhkYNV2YhgA+R15HFJIqS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Higher layers produce complex features, while lower ones enhance edges and textures, giving the image an impressionist feeling:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab_type": "code", + "collapsed": false, + "executionInfo": null, + "id": "eHOX0t93OZOR", + "outputId": "0de0381c-4681-4619-912f-9b6a2cdec0c6", + "pinned": false + }, + "outputs": [ + { + "data": { + "image/jpeg": 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gqUsGb5cb/wC63BxQ9GN7WAZLAE7vRuKimkZpfIjPzH7zelSBtqFiAmB37VBbgtumxweF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\nU32ZSA8RHtzlfzFDjqYSi46CkHb+8hLp1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M5KkeTVbGlE/wC5AwAZHYN9OT/hUsZwQeR2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\nYgb5lPNAmtwGOCWYZ4XuD/8AWNQmDa+3A3HnF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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3 9 inception_3b/5x5_reduce (575, 1024, 3)\n" + ] + } + ], + "source": [ + "_=deepdream(net, img, end='inception_3b/5x5_reduce')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "rkzHz9E8OZOb" + }, + "source": [ + "We encourage readers to experiment with layer selection to see how it affects the results. Execute the next code cell to see the list of different layers. You can modify the `make_step` function to make it follow some different objective, say to select a subset of activations to maximize, or to maximize multiple layers at once. There is a huge design space to explore!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab_type": "code", + "collapsed": false, + "id": "OIepVN6POZOc" + }, + "outputs": [], + "source": [ + "net.blobs.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "vs2uUpMCOZOe" + }, + "source": [ + "What if we feed the `deepdream` function its own output, after applying a little zoom to it? It turns out that this leads to an endless stream of impressions of the things that the network saw during training. Some patterns fire more often than others, suggestive of basins of attraction.\n", + "\n", + "We will start the process from the same sky image as above, but after some iteration the original image becomes irrelevant; even random noise can be used as the starting point." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab_type": "code", + "collapsed": false, + "id": "IB48CnUfOZOe" + }, + "outputs": [], + "source": [ + "!mkdir frames\n", + "frame = img\n", + "frame_i = 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab_type": "code", + "collapsed": false, + "id": "fj0E-fKDOZOi" + }, + "outputs": [], + "source": [ + "h, w = frame.shape[:2]\n", + "s = 0.05 # scale coefficient\n", + "for i in xrange(100):\n", + " frame = deepdream(net, frame)\n", + " PIL.Image.fromarray(np.uint8(frame)).save(\"frames/%04d.jpg\"%frame_i)\n", + " frame = nd.affine_transform(frame, [1-s,1-s,1], [h*s/2,w*s/2,0], order=1)\n", + " frame_i += 1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "XzZGGME_OZOk" + }, + "source": [ + "Be careful running the code above, it can bring you into very strange realms!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab_type": "code", + "collapsed": false, + "executionInfo": null, + "id": "ZCZcz2p1OZOt", + "outputId": "d3773436-2b5d-4e79-be9d-0f12ab839fff", + "pinned": false + }, + "outputs": [ + { + "data": { + "image/jpeg": 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egPas3Nc/tE9EyJzU6d1quxZwfscq4zlSB+NZeoS2lnpMksxAjPyKc4wT0H9f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\nbC5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VrW1vuljUD/loXH5VHqsQfXZAXVNsYO4jOOOTiuKgv3kvmepWmro891bRxLqycMTE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quypQ+J7+hzGv65Ne3rzz2tuQGwibivlnqASMZHtyB+NZVrcyTRGWS22cZVYlCKo/H734Vu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\nsCGJHORUYmieLzi3ydjyAfzqbTo5TZb0vIEGNu8Y3Gmy2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FVjw6xkbgWHQE9h0/Wu0F6T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UquHa/wCBevNLGq6TYO5+aF9/Pcdq6qKbzdLsXlys8hS2CnuSeQfcKDXIa1eSWtgoTgYw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\nQs3dlXBP1q7bahCy7S+AfXiqcPiS5MQjvIoblF4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+ "text/plain": [ + "" + ] + }, + "execution_count": 189, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename='frames/0029.jpg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Controlling dreams\n", + "\n", + "The image detail generation method described above tends to produce some patterns more often the others. One easy way to improve the generated image diversity is to tweak the optimization objective. Here we show just one of many ways to do that. Let's use one more input image. We'd call it a \"*guide*\"." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/jpeg": 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UGxgqugfe74VSe2OpNTyaRf2MwEqK0fZ0bINWoU53TN4crdmjOu7ZLbHLEH1FXNFnlnvE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\no7iMBlVFRgByPf3FMijudK0QvE8bOrbmUKTn8a6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "guide = np.float32(PIL.Image.open('flowers.jpg'))\n", + "showarray(guide)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the neural network we use was trained on images downscaled to 224x224 size. So high resolution images might have to be downscaled, so that the network could pick up their features. The image we use here is already small enough.\n", + "\n", + "Now we pick some target layer and extract guide image features." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "end = 'inception_3b/output'\n", + "h, w = guide.shape[:2]\n", + "src, dst = net.blobs['data'], net.blobs[end]\n", + "src.reshape(1,3,h,w)\n", + "src.data[0] = preprocess(net, guide)\n", + "net.forward(end=end)\n", + "guide_features = dst.data[0].copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of maximizing the L2-norm of current image activations, we try to maximize the dot-products between activations of current image, and their best matching correspondences from the guide image." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/jpeg": 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wOfzq0gDDdgBT3NOaMPwQMU2C01RnWNqkES/JvOMln5bPfmr20gbo+B0IP86a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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3 9 inception_3b/output (575, 1024, 3)\n" + ] + } + ], + "source": [ + "def objective_guide(dst):\n", + " x = dst.data[0].copy()\n", + " y = guide_features\n", + " ch = x.shape[0]\n", + " x = x.reshape(ch,-1)\n", + " y = y.reshape(ch,-1)\n", + " A = x.T.dot(y) # compute the matrix of dot-products with guide features\n", + " dst.diff[0].reshape(ch,-1)[:] = y[:,A.argmax(1)] # select ones that match best\n", + "\n", + "_=deepdream(net, img, end=end, objective=objective_guide)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This way we can affect the style of generated images without using a different training set." + ] + } + ], + "metadata": { + "colabVersion": "0.3.1", + "default_view": {}, + "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": "3.4.3" + }, + "views": {} + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/deep-learning/deep-dream/flowers.jpg b/deep-learning/deep-dream/flowers.jpg new file mode 100644 index 0000000..5f5c358 Binary files /dev/null and b/deep-learning/deep-dream/flowers.jpg differ diff --git a/deep-learning/deep-dream/sky1024px.jpg b/deep-learning/deep-dream/sky1024px.jpg new file mode 100644 index 0000000..f4f4ec3 Binary files /dev/null and b/deep-learning/deep-dream/sky1024px.jpg differ diff --git a/deep-learning/tensor-flow-exercises/1_notmnist.ipynb b/deep-learning/tensor-flow-exercises/1_notmnist.ipynb new file mode 100644 index 0000000..d70ab8f --- /dev/null +++ b/deep-learning/tensor-flow-exercises/1_notmnist.ipynb @@ -0,0 +1,680 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "5hIbr52I7Z7U" + }, + "source": [ + "Deep Learning with TensorFlow\n", + "=============\n", + "\n", + "Credits: Forked from [TensorFlow](https://github.com/tensorflow/tensorflow) by Google\n", + "\n", + "Setup\n", + "------------\n", + "\n", + "Refer to the [setup instructions](https://github.com/donnemartin/data-science-ipython-notebooks/tree/feature/deep-learning/deep-learning/tensor-flow-exercises/README.md).\n", + "\n", + "Exercise 1\n", + "------------\n", + "\n", + "The objective of this exercise is to learn about simple data curation practices, and familiarize you with some of the data we'll be reusing later.\n", + "\n", + "This notebook uses the [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) dataset to be used with python experiments. This dataset is designed to look like the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset, while looking a little more like real data: it's a harder task, and the data is a lot less 'clean' than MNIST." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "collapsed": true, + "id": "apJbCsBHl-2A" + }, + "outputs": [], + "source": [ + "# These are all the modules we'll be using later. Make sure you can import them\n", + "# before proceeding further.\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import os\n", + "import tarfile\n", + "import urllib\n", + "from IPython.display import display, Image\n", + "from scipy import ndimage\n", + "from sklearn.linear_model import LogisticRegression\n", + "import cPickle as pickle" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jNWGtZaXn-5j" + }, + "source": [ + "First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The labels are limited to 'A' through 'J' (10 classes). The training set has about 500k and the testset 19000 labelled examples. Given these sizes, it should be possible to train models quickly on any machine." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 186058, + "status": "ok", + "timestamp": 1444485672507, + "user": { + "color": "#1FA15D", + "displayName": "Vincent Vanhoucke", + "isAnonymous": false, + "isMe": true, + "permissionId": "05076109866853157986", + "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", + "sessionId": "2a0a5e044bb03b66", + "userId": "102167687554210253930" + }, + "user_tz": 420 + }, + "id": "EYRJ4ICW6-da", + "outputId": "0d0f85df-155f-4a89-8e7e-ee32df36ec8d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found and verified notMNIST_large.tar.gz\n", + "Found and verified notMNIST_small.tar.gz\n" + ] + } + ], + "source": [ + "url = 'http://yaroslavvb.com/upload/notMNIST/'\n", + "\n", + "def maybe_download(filename, expected_bytes):\n", + " \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n", + " if not os.path.exists(filename):\n", + " filename, _ = urllib.urlretrieve(url + filename, filename)\n", + " statinfo = os.stat(filename)\n", + " if statinfo.st_size == expected_bytes:\n", + " print 'Found and verified', filename\n", + " else:\n", + " raise Exception(\n", + " 'Failed to verify' + filename + '. Can you get to it with a browser?')\n", + " return filename\n", + "\n", + "train_filename = maybe_download('notMNIST_large.tar.gz', 247336696)\n", + "test_filename = maybe_download('notMNIST_small.tar.gz', 8458043)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "cC3p0oEyF8QT" + }, + "source": [ + "Extract the dataset from the compressed .tar.gz file.\n", + "This should give you a set of directories, labelled A through J." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 186055, + "status": "ok", + "timestamp": 1444485672525, + "user": { + "color": "#1FA15D", + "displayName": "Vincent Vanhoucke", + "isAnonymous": false, + "isMe": true, + "permissionId": "05076109866853157986", + "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", + "sessionId": "2a0a5e044bb03b66", + "userId": "102167687554210253930" + }, + "user_tz": 420 + }, + "id": "H8CBE-WZ8nmj", + "outputId": "ef6c790c-2513-4b09-962e-27c79390c762" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['notMNIST_large/A', 'notMNIST_large/B', 'notMNIST_large/C', 'notMNIST_large/D', 'notMNIST_large/E', 'notMNIST_large/F', 'notMNIST_large/G', 'notMNIST_large/H', 'notMNIST_large/I', 'notMNIST_large/J']\n", + "['notMNIST_small/A', 'notMNIST_small/B', 'notMNIST_small/C', 'notMNIST_small/D', 'notMNIST_small/E', 'notMNIST_small/F', 'notMNIST_small/G', 'notMNIST_small/H', 'notMNIST_small/I', 'notMNIST_small/J']\n" + ] + } + ], + "source": [ + "num_classes = 10\n", + "\n", + "def extract(filename):\n", + " tar = tarfile.open(filename)\n", + " tar.extractall()\n", + " tar.close()\n", + " root = os.path.splitext(os.path.splitext(filename)[0])[0] # remove .tar.gz\n", + " data_folders = [os.path.join(root, d) for d in sorted(os.listdir(root))]\n", + " if len(data_folders) != num_classes:\n", + " raise Exception(\n", + " 'Expected %d folders, one per class. Found %d instead.' % (\n", + " num_folders, len(data_folders)))\n", + " print data_folders\n", + " return data_folders\n", + " \n", + "train_folders = extract(train_filename)\n", + "test_folders = extract(test_filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4riXK3IoHgx6" + }, + "source": [ + "---\n", + "Problem 1\n", + "---------\n", + "\n", + "Let's take a peek at some of the data to make sure it looks sensible. Each exemplar should be an image of a character A through J rendered in a different font. Display a sample of the images that we just downloaded. Hint: you can use the package IPython.display.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "PBdkjESPK8tw" + }, + "source": [ + "Now let's load the data in a more manageable format.\n", + "\n", + "We'll convert the entire dataset into a 3D array (image index, x, y) of floating point values, normalized to have approximately zero mean and standard deviation ~0.5 to make training easier down the road. The labels will be stored into a separate array of integers 0 through 9.\n", + "\n", + "A few images might not be readable, we'll just skip them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 30 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 399874, + "status": "ok", + "timestamp": 1444485886378, + "user": { + "color": "#1FA15D", + "displayName": "Vincent Vanhoucke", + "isAnonymous": false, + "isMe": true, + "permissionId": "05076109866853157986", + "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", + "sessionId": "2a0a5e044bb03b66", + "userId": "102167687554210253930" + }, + "user_tz": 420 + }, + "id": "h7q0XhG3MJdf", + "outputId": "92c391bb-86ff-431d-9ada-315568a19e59" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "notMNIST_large/A\n", + "Could not read: notMNIST_large/A/SG90IE11c3RhcmQgQlROIFBvc3Rlci50dGY=.png : cannot identify image file - it's ok, skipping.\n", + "Could not read: notMNIST_large/A/RnJlaWdodERpc3BCb29rSXRhbGljLnR0Zg==.png : cannot identify image file - it's ok, skipping.\n", + "Could not read: notMNIST_large/A/Um9tYW5hIEJvbGQucGZi.png : cannot identify image file - it's ok, skipping.\n", + "notMNIST_large/B\n", + "Could not read: notMNIST_large/B/TmlraXNFRi1TZW1pQm9sZEl0YWxpYy5vdGY=.png : cannot identify image file - it's ok, skipping.\n", + "notMNIST_large/C\n", + "notMNIST_large/D\n", + "Could not read: notMNIST_large/D/VHJhbnNpdCBCb2xkLnR0Zg==.png : cannot identify image file - it's ok, skipping.\n", + "notMNIST_large/E\n", + "notMNIST_large/F\n", + "notMNIST_large/G\n", + "notMNIST_large/H\n", + "notMNIST_large/I\n", + "notMNIST_large/J\n", + "Full dataset tensor: (529114, 28, 28)\n", + "Mean: -0.0816593\n", + "Standard deviation: 0.454232\n", + "Labels: (529114,)\n", + "notMNIST_small/A\n", + "Could not read: notMNIST_small/A/RGVtb2NyYXRpY2FCb2xkT2xkc3R5bGUgQm9sZC50dGY=.png : cannot identify image file - it's ok, skipping.\n", + "notMNIST_small/B\n", + "notMNIST_small/C\n", + "notMNIST_small/D\n", + "notMNIST_small/E\n", + "notMNIST_small/F\n", + "Could not read: notMNIST_small/F/Q3Jvc3NvdmVyIEJvbGRPYmxpcXVlLnR0Zg==.png : cannot identify image file - it's ok, skipping.\n", + "notMNIST_small/G\n", + "notMNIST_small/H\n", + "notMNIST_small/I\n", + "notMNIST_small/J\n", + "Full dataset tensor: (18724, 28, 28)\n", + "Mean: -0.0746364\n", + "Standard deviation: 0.458622\n", + "Labels: (18724,)\n" + ] + } + ], + "source": [ + "image_size = 28 # Pixel width and height.\n", + "pixel_depth = 255.0 # Number of levels per pixel.\n", + "\n", + "def load(data_folders, min_num_images, max_num_images):\n", + " dataset = np.ndarray(\n", + " shape=(max_num_images, image_size, image_size), dtype=np.float32)\n", + " labels = np.ndarray(shape=(max_num_images), dtype=np.int32)\n", + " label_index = 0\n", + " image_index = 0\n", + " for folder in data_folders:\n", + " print folder\n", + " for image in os.listdir(folder):\n", + " if image_index >= max_num_images:\n", + " raise Exception('More images than expected: %d >= %d' % (\n", + " num_images, max_num_images))\n", + " image_file = os.path.join(folder, image)\n", + " try:\n", + " image_data = (ndimage.imread(image_file).astype(float) -\n", + " pixel_depth / 2) / pixel_depth\n", + " if image_data.shape != (image_size, image_size):\n", + " raise Exception('Unexpected image shape: %s' % str(image_data.shape))\n", + " dataset[image_index, :, :] = image_data\n", + " labels[image_index] = label_index\n", + " image_index += 1\n", + " except IOError as e:\n", + " print 'Could not read:', image_file, ':', e, '- it\\'s ok, skipping.'\n", + " label_index += 1\n", + " num_images = image_index\n", + " dataset = dataset[0:num_images, :, :]\n", + " labels = labels[0:num_images]\n", + " if num_images < min_num_images:\n", + " raise Exception('Many fewer images than expected: %d < %d' % (\n", + " num_images, min_num_images))\n", + " print 'Full dataset tensor:', dataset.shape\n", + " print 'Mean:', np.mean(dataset)\n", + " print 'Standard deviation:', np.std(dataset)\n", + " print 'Labels:', labels.shape\n", + " return dataset, labels\n", + "train_dataset, train_labels = load(train_folders, 450000, 550000)\n", + "test_dataset, test_labels = load(test_folders, 18000, 20000)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "vUdbskYE2d87" + }, + "source": [ + "---\n", + "Problem 2\n", + "---------\n", + "\n", + "Let's verify that the data still looks good. Displaying a sample of the labels and images from the ndarray. Hint: you can use matplotlib.pyplot.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "GPTCnjIcyuKN" + }, + "source": [ + "Next, we'll randomize the data. It's important to have the labels well shuffled for the training and test distributions to match." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "collapsed": true, + "id": "6WZ2l2tN2zOL" + }, + "outputs": [], + "source": [ + "np.random.seed(133)\n", + "def randomize(dataset, labels):\n", + " permutation = np.random.permutation(labels.shape[0])\n", + " shuffled_dataset = dataset[permutation,:,:]\n", + " shuffled_labels = labels[permutation]\n", + " return shuffled_dataset, shuffled_labels\n", + "train_dataset, train_labels = randomize(train_dataset, train_labels)\n", + "test_dataset, test_labels = randomize(test_dataset, test_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "puDUTe6t6USl" + }, + "source": [ + "---\n", + "Problem 3\n", + "---------\n", + "Convince yourself that the data is still good after shuffling!\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "cYznx5jUwzoO" + }, + "source": [ + "---\n", + "Problem 4\n", + "---------\n", + "Another check: we expect the data to be balanced across classes. Verify that.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "LA7M7K22ynCt" + }, + "source": [ + "Prune the training data as needed. Depending on your computer setup, you might not be able to fit it all in memory, and you can tune train_size as needed.\n", + "\n", + "Also create a validation dataset for hyperparameter tuning." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 411281, + "status": "ok", + "timestamp": 1444485897869, + "user": { + "color": "#1FA15D", + "displayName": "Vincent Vanhoucke", + "isAnonymous": false, + "isMe": true, + "permissionId": "05076109866853157986", + "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", + "sessionId": "2a0a5e044bb03b66", + "userId": "102167687554210253930" + }, + "user_tz": 420 + }, + "id": "s3mWgZLpyuzq", + "outputId": "8af66da6-902d-4719-bedc-7c9fb7ae7948" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training (200000, 28, 28) (200000,)\n", + "Validation (10000, 28, 28) (10000,)\n" + ] + } + ], + "source": [ + "train_size = 200000\n", + "valid_size = 10000\n", + "\n", + "valid_dataset = train_dataset[:valid_size,:,:]\n", + "valid_labels = train_labels[:valid_size]\n", + "train_dataset = train_dataset[valid_size:valid_size+train_size,:,:]\n", + "train_labels = train_labels[valid_size:valid_size+train_size]\n", + "print 'Training', train_dataset.shape, train_labels.shape\n", + "print 'Validation', valid_dataset.shape, valid_labels.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tIQJaJuwg5Hw" + }, + "source": [ + "Finally, let's save the data for later reuse:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "collapsed": true, + "id": "QiR_rETzem6C" + }, + "outputs": [], + "source": [ + "pickle_file = 'notMNIST.pickle'\n", + "\n", + "try:\n", + " f = open(pickle_file, 'wb')\n", + " save = {\n", + " 'train_dataset': train_dataset,\n", + " 'train_labels': train_labels,\n", + " 'valid_dataset': valid_dataset,\n", + " 'valid_labels': valid_labels,\n", + " 'test_dataset': test_dataset,\n", + " 'test_labels': test_labels,\n", + " }\n", + " pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n", + " f.close()\n", + "except Exception as e:\n", + " print 'Unable to save data to', pickle_file, ':', e\n", + " raise" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 413065, + "status": "ok", + "timestamp": 1444485899688, + "user": { + "color": "#1FA15D", + "displayName": "Vincent Vanhoucke", + "isAnonymous": false, + "isMe": true, + "permissionId": "05076109866853157986", + "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", + "sessionId": "2a0a5e044bb03b66", + "userId": "102167687554210253930" + }, + "user_tz": 420 + }, + "id": "hQbLjrW_iT39", + "outputId": "b440efc6-5ee1-4cbc-d02d-93db44ebd956" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Compressed pickle size: 718193801\n" + ] + } + ], + "source": [ + "statinfo = os.stat(pickle_file)\n", + "print 'Compressed pickle size:', statinfo.st_size" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "gE_cRAQB33lk" + }, + "source": [ + "---\n", + "Problem 5\n", + "---------\n", + "\n", + "By construction, this dataset might contain a lot of overlapping samples, including training data that's also contained in the validation and test set! Overlap between training and test can skew the results if you expect to use your model in an environment where there is never an overlap, but are actually ok if you expect to see training samples recur when you use it.\n", + "Measure how much overlap there is between training, validation and test samples.\n", + "Optional questions:\n", + "- What about near duplicates between datasets? (images that are almost identical)\n", + "- Create a sanitized validation and test set, and compare your accuracy on those in subsequent exercises.\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "L8oww1s4JMQx" + }, + "source": [ + "---\n", + "Problem 6\n", + "---------\n", + "\n", + "Let's get an idea of what an off-the-shelf classifier can give you on this data. It's always good to check that there is something to learn, and that it's a problem that is not so trivial that a canned solution solves it.\n", + "\n", + "Train a simple model on this data using 50, 100, 1000 and 5000 training samples. Hint: you can use the LogisticRegression model from sklearn.linear_model.\n", + "\n", + "Optional question: train an off-the-shelf model on all the data!\n", + "\n", + "---" + ] + } + ], + "metadata": { + "colabVersion": "0.3.2", + "colab_default_view": {}, + "colab_views": {}, + "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": "3.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/deep-learning/tensor-flow-exercises/2_fullyconnected.ipynb b/deep-learning/tensor-flow-exercises/2_fullyconnected.ipynb new file mode 100644 index 0000000..df2e803 --- /dev/null +++ b/deep-learning/tensor-flow-exercises/2_fullyconnected.ipynb @@ -0,0 +1,611 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kR-4eNdK6lYS" + }, + "source": [ + "Deep Learning with TensorFlow\n", + "=============\n", + "\n", + "Credits: Forked from [TensorFlow](https://github.com/tensorflow/tensorflow) by Google\n", + "\n", + "Setup\n", + "------------\n", + "\n", + "Refer to the [setup instructions](https://github.com/donnemartin/data-science-ipython-notebooks/tree/feature/deep-learning/deep-learning/tensor-flow-exercises/README.md).\n", + "\n", + "Exercise 2\n", + "------------\n", + "\n", + "Previously in `1_notmnist.ipynb`, we created a pickle with formatted datasets for training, development and testing on the [notMNIST dataset](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html).\n", + "\n", + "The goal of this exercise is to progressively train deeper and more accurate models using TensorFlow." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "collapsed": true, + "id": "JLpLa8Jt7Vu4" + }, + "outputs": [], + "source": [ + "# These are all the modules we'll be using later. Make sure you can import them\n", + "# before proceeding further.\n", + "import cPickle as pickle\n", + "import numpy as np\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1HrCK6e17WzV" + }, + "source": [ + "First reload the data we generated in `1_notmist.ipynb`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 19456, + "status": "ok", + "timestamp": 1449847956073, + "user": { + "color": "", + "displayName": "", + "isAnonymous": false, + "isMe": true, + "permissionId": "", + "photoUrl": "", + "sessionId": "0", + "userId": "" + }, + "user_tz": 480 + }, + "id": "y3-cj1bpmuxc", + "outputId": "0ddb1607-1fc4-4ddb-de28-6c7ab7fb0c33" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training set (200000, 28, 28) (200000,)\n", + "Validation set (10000, 28, 28) (10000,)\n", + "Test set (18724, 28, 28) (18724,)\n" + ] + } + ], + "source": [ + "pickle_file = 'notMNIST.pickle'\n", + "\n", + "with open(pickle_file, 'rb') as f:\n", + " save = pickle.load(f)\n", + " train_dataset = save['train_dataset']\n", + " train_labels = save['train_labels']\n", + " valid_dataset = save['valid_dataset']\n", + " valid_labels = save['valid_labels']\n", + " test_dataset = save['test_dataset']\n", + " test_labels = save['test_labels']\n", + " del save # hint to help gc free up memory\n", + " print 'Training set', train_dataset.shape, train_labels.shape\n", + " print 'Validation set', valid_dataset.shape, valid_labels.shape\n", + " print 'Test set', test_dataset.shape, test_labels.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "L7aHrm6nGDMB" + }, + "source": [ + "Reformat into a shape that's more adapted to the models we're going to train:\n", + "- data as a flat matrix,\n", + "- labels as float 1-hot encodings." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 19723, + "status": "ok", + "timestamp": 1449847956364, + "user": { + "color": "", + "displayName": "", + "isAnonymous": false, + "isMe": true, + "permissionId": "", + "photoUrl": "", + "sessionId": "0", + "userId": "" + }, + "user_tz": 480 + }, + "id": "IRSyYiIIGIzS", + "outputId": "2ba0fc75-1487-4ace-a562-cf81cae82793" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training set (200000, 784) (200000, 10)\n", + "Validation set (10000, 784) (10000, 10)\n", + "Test set (18724, 784) (18724, 10)\n" + ] + } + ], + "source": [ + "image_size = 28\n", + "num_labels = 10\n", + "\n", + "def reformat(dataset, labels):\n", + " dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)\n", + " # Map 0 to [1.0, 0.0, 0.0 ...], 1 to [0.0, 1.0, 0.0 ...]\n", + " labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\n", + " return dataset, labels\n", + "train_dataset, train_labels = reformat(train_dataset, train_labels)\n", + "valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)\n", + "test_dataset, test_labels = reformat(test_dataset, test_labels)\n", + "print 'Training set', train_dataset.shape, train_labels.shape\n", + "print 'Validation set', valid_dataset.shape, valid_labels.shape\n", + "print 'Test set', test_dataset.shape, test_labels.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "nCLVqyQ5vPPH" + }, + "source": [ + "We're first going to train a multinomial logistic regression using simple gradient descent.\n", + "\n", + "TensorFlow works like this:\n", + "* First you describe the computation that you want to see performed: what the inputs, the variables, and the operations look like. These get created as nodes over a computation graph. This description is all contained within the block below:\n", + "\n", + " with graph.as_default():\n", + " ...\n", + "\n", + "* Then you can run the operations on this graph as many times as you want by calling `session.run()`, providing it outputs to fetch from the graph that get returned. This runtime operation is all contained in the block below:\n", + "\n", + " with tf.Session(graph=graph) as session:\n", + " ...\n", + "\n", + "Let's load all the data into TensorFlow and build the computation graph corresponding to our training:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "collapsed": true, + "id": "Nfv39qvtvOl_" + }, + "outputs": [], + "source": [ + "# With gradient descent training, even this much data is prohibitive.\n", + "# Subset the training data for faster turnaround.\n", + "train_subset = 10000\n", + "\n", + "graph = tf.Graph()\n", + "with graph.as_default():\n", + "\n", + " # Input data.\n", + " # Load the training, validation and test data into constants that are\n", + " # attached to the graph.\n", + " tf_train_dataset = tf.constant(train_dataset[:train_subset, :])\n", + " tf_train_labels = tf.constant(train_labels[:train_subset])\n", + " tf_valid_dataset = tf.constant(valid_dataset)\n", + " tf_test_dataset = tf.constant(test_dataset)\n", + " \n", + " # Variables.\n", + " # These are the parameters that we are going to be training. The weight\n", + " # matrix will be initialized using random valued following a (truncated)\n", + " # normal distribution. The biases get initialized to zero.\n", + " weights = tf.Variable(\n", + " tf.truncated_normal([image_size * image_size, num_labels]))\n", + " biases = tf.Variable(tf.zeros([num_labels]))\n", + " \n", + " # Training computation.\n", + " # We multiply the inputs with the weight matrix, and add biases. We compute\n", + " # the softmax and cross-entropy (it's one operation in TensorFlow, because\n", + " # it's very common, and it can be optimized). We take the average of this\n", + " # cross-entropy across all training examples: that's our loss.\n", + " logits = tf.matmul(tf_train_dataset, weights) + biases\n", + " loss = tf.reduce_mean(\n", + " tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n", + " \n", + " # Optimizer.\n", + " # We are going to find the minimum of this loss using gradient descent.\n", + " optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n", + " \n", + " # Predictions for the training, validation, and test data.\n", + " # These are not part of training, but merely here so that we can report\n", + " # accuracy figures as we train.\n", + " train_prediction = tf.nn.softmax(logits)\n", + " valid_prediction = tf.nn.softmax(\n", + " tf.matmul(tf_valid_dataset, weights) + biases)\n", + " test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "KQcL4uqISHjP" + }, + "source": [ + "Let's run this computation and iterate:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 9 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 57454, + "status": "ok", + "timestamp": 1449847994134, + "user": { + "color": "", + "displayName": "", + "isAnonymous": false, + "isMe": true, + "permissionId": "", + "photoUrl": "", + "sessionId": "0", + "userId": "" + }, + "user_tz": 480 + }, + "id": "z2cjdenH869W", + "outputId": "4c037ba1-b526-4d8e-e632-91e2a0333267" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initialized\n", + "Loss at step 0 : 17.2939\n", + "Training accuracy: 10.8%\n", + "Validation accuracy: 13.8%\n", + "Loss at step 100 : 2.26903\n", + "Training accuracy: 72.3%\n", + "Validation accuracy: 71.6%\n", + "Loss at step 200 : 1.84895\n", + "Training accuracy: 74.9%\n", + "Validation accuracy: 73.9%\n", + "Loss at step 300 : 1.60701\n", + "Training accuracy: 76.0%\n", + "Validation accuracy: 74.5%\n", + "Loss at step 400 : 1.43912\n", + "Training accuracy: 76.8%\n", + "Validation accuracy: 74.8%\n", + "Loss at step 500 : 1.31349\n", + "Training accuracy: 77.5%\n", + "Validation accuracy: 75.0%\n", + "Loss at step 600 : 1.21501\n", + "Training accuracy: 78.1%\n", + "Validation accuracy: 75.4%\n", + "Loss at step 700 : 1.13515\n", + "Training accuracy: 78.6%\n", + "Validation accuracy: 75.4%\n", + "Loss at step 800 : 1.0687\n", + "Training accuracy: 79.2%\n", + "Validation accuracy: 75.6%\n", + "Test accuracy: 82.9%\n" + ] + } + ], + "source": [ + "num_steps = 801\n", + "\n", + "def accuracy(predictions, labels):\n", + " return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))\n", + " / predictions.shape[0])\n", + "\n", + "with tf.Session(graph=graph) as session:\n", + " # This is a one-time operation which ensures the parameters get initialized as\n", + " # we described in the graph: random weights for the matrix, zeros for the\n", + " # biases. \n", + " tf.initialize_all_variables().run()\n", + " print 'Initialized'\n", + " for step in xrange(num_steps):\n", + " # Run the computations. We tell .run() that we want to run the optimizer,\n", + " # and get the loss value and the training predictions returned as numpy\n", + " # arrays.\n", + " _, l, predictions = session.run([optimizer, loss, train_prediction])\n", + " if (step % 100 == 0):\n", + " print 'Loss at step', step, ':', l\n", + " print 'Training accuracy: %.1f%%' % accuracy(\n", + " predictions, train_labels[:train_subset, :])\n", + " # Calling .eval() on valid_prediction is basically like calling run(), but\n", + " # just to get that one numpy array. Note that it recomputes all its graph\n", + " # dependencies.\n", + " print 'Validation accuracy: %.1f%%' % accuracy(\n", + " valid_prediction.eval(), valid_labels)\n", + " print 'Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "x68f-hxRGm3H" + }, + "source": [ + "Let's now switch to stochastic gradient descent training instead, which is much faster.\n", + "\n", + "The graph will be similar, except that instead of holding all the training data into a constant node, we create a `Placeholder` node which will be fed actual data at every call of `sesion.run()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "collapsed": true, + "id": "qhPMzWYRGrzM" + }, + "outputs": [], + "source": [ + "batch_size = 128\n", + "\n", + "graph = tf.Graph()\n", + "with graph.as_default():\n", + "\n", + " # Input data. For the training data, we use a placeholder that will be fed\n", + " # at run time with a training minibatch.\n", + " tf_train_dataset = tf.placeholder(tf.float32,\n", + " shape=(batch_size, image_size * image_size))\n", + " tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n", + " tf_valid_dataset = tf.constant(valid_dataset)\n", + " tf_test_dataset = tf.constant(test_dataset)\n", + " \n", + " # Variables.\n", + " weights = tf.Variable(\n", + " tf.truncated_normal([image_size * image_size, num_labels]))\n", + " biases = tf.Variable(tf.zeros([num_labels]))\n", + " \n", + " # Training computation.\n", + " logits = tf.matmul(tf_train_dataset, weights) + biases\n", + " loss = tf.reduce_mean(\n", + " tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n", + " \n", + " # Optimizer.\n", + " optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n", + " \n", + " # Predictions for the training, validation, and test data.\n", + " train_prediction = tf.nn.softmax(logits)\n", + " valid_prediction = tf.nn.softmax(\n", + " tf.matmul(tf_valid_dataset, weights) + biases)\n", + " test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "XmVZESmtG4JH" + }, + "source": [ + "Let's run it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 6 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 66292, + "status": "ok", + "timestamp": 1449848003013, + "user": { + "color": "", + "displayName": "", + "isAnonymous": false, + "isMe": true, + "permissionId": "", + "photoUrl": "", + "sessionId": "0", + "userId": "" + }, + "user_tz": 480 + }, + "id": "FoF91pknG_YW", + "outputId": "d255c80e-954d-4183-ca1c-c7333ce91d0a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initialized\n", + "Minibatch loss at step 0 : 16.8091\n", + "Minibatch accuracy: 12.5%\n", + "Validation accuracy: 14.0%\n", + "Minibatch loss at step 500 : 1.75256\n", + "Minibatch accuracy: 77.3%\n", + "Validation accuracy: 75.0%\n", + "Minibatch loss at step 1000 : 1.32283\n", + "Minibatch accuracy: 77.3%\n", + "Validation accuracy: 76.6%\n", + "Minibatch loss at step 1500 : 0.944533\n", + "Minibatch accuracy: 83.6%\n", + "Validation accuracy: 76.5%\n", + "Minibatch loss at step 2000 : 1.03795\n", + "Minibatch accuracy: 78.9%\n", + "Validation accuracy: 77.8%\n", + "Minibatch loss at step 2500 : 1.10219\n", + "Minibatch accuracy: 80.5%\n", + "Validation accuracy: 78.0%\n", + "Minibatch loss at step 3000 : 0.758874\n", + "Minibatch accuracy: 82.8%\n", + "Validation accuracy: 78.8%\n", + "Test accuracy: 86.1%\n" + ] + } + ], + "source": [ + "num_steps = 3001\n", + "\n", + "with tf.Session(graph=graph) as session:\n", + " tf.initialize_all_variables().run()\n", + " print \"Initialized\"\n", + " for step in xrange(num_steps):\n", + " # Pick an offset within the training data, which has been randomized.\n", + " # Note: we could use better randomization across epochs.\n", + " offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n", + " # Generate a minibatch.\n", + " batch_data = train_dataset[offset:(offset + batch_size), :]\n", + " batch_labels = train_labels[offset:(offset + batch_size), :]\n", + " # Prepare a dictionary telling the session where to feed the minibatch.\n", + " # The key of the dictionary is the placeholder node of the graph to be fed,\n", + " # and the value is the numpy array to feed to it.\n", + " feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n", + " _, l, predictions = session.run(\n", + " [optimizer, loss, train_prediction], feed_dict=feed_dict)\n", + " if (step % 500 == 0):\n", + " print \"Minibatch loss at step\", step, \":\", l\n", + " print \"Minibatch accuracy: %.1f%%\" % accuracy(predictions, batch_labels)\n", + " print \"Validation accuracy: %.1f%%\" % accuracy(\n", + " valid_prediction.eval(), valid_labels)\n", + " print \"Test accuracy: %.1f%%\" % accuracy(test_prediction.eval(), test_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "7omWxtvLLxik" + }, + "source": [ + "---\n", + "Problem\n", + "-------\n", + "\n", + "Turn the logistic regression example with SGD into a 1-hidden layer neural network with rectified linear units (nn.relu()) and 1024 hidden nodes. This model should improve your validation / test accuracy.\n", + "\n", + "---" + ] + } + ], + "metadata": { + "colabVersion": "0.3.2", + "colab_default_view": {}, + "colab_views": {}, + "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": "3.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/deep-learning/tensor-flow-exercises/3_regularization.ipynb b/deep-learning/tensor-flow-exercises/3_regularization.ipynb new file mode 100644 index 0000000..08547df --- /dev/null +++ b/deep-learning/tensor-flow-exercises/3_regularization.ipynb @@ -0,0 +1,323 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kR-4eNdK6lYS" + }, + "source": [ + "Deep Learning with TensorFlow\n", + "=============\n", + "\n", + "Credits: Forked from [TensorFlow](https://github.com/tensorflow/tensorflow) by Google\n", + "\n", + "Setup\n", + "------------\n", + "\n", + "Refer to the [setup instructions](https://github.com/donnemartin/data-science-ipython-notebooks/tree/feature/deep-learning/deep-learning/tensor-flow-exercises/README.md).\n", + "\n", + "Exercise 3\n", + "------------\n", + "\n", + "Previously in `2_fullyconnected.ipynb`, you trained a logistic regression and a neural network model.\n", + "\n", + "The goal of this exercise is to explore regularization techniques." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "collapsed": true, + "id": "JLpLa8Jt7Vu4" + }, + "outputs": [], + "source": [ + "# These are all the modules we'll be using later. Make sure you can import them\n", + "# before proceeding further.\n", + "import cPickle as pickle\n", + "import numpy as np\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1HrCK6e17WzV" + }, + "source": [ + "First reload the data we generated in _notmist.ipynb_." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 11777, + "status": "ok", + "timestamp": 1449849322348, + "user": { + "color": "", + "displayName": "", + "isAnonymous": false, + "isMe": true, + "permissionId": "", + "photoUrl": "", + "sessionId": "0", + "userId": "" + }, + "user_tz": 480 + }, + "id": "y3-cj1bpmuxc", + "outputId": "e03576f1-ebbe-4838-c388-f1777bcc9873" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training set (200000, 28, 28) (200000,)\n", + "Validation set (10000, 28, 28) (10000,)\n", + "Test set (18724, 28, 28) (18724,)\n" + ] + } + ], + "source": [ + "pickle_file = 'notMNIST.pickle'\n", + "\n", + "with open(pickle_file, 'rb') as f:\n", + " save = pickle.load(f)\n", + " train_dataset = save['train_dataset']\n", + " train_labels = save['train_labels']\n", + " valid_dataset = save['valid_dataset']\n", + " valid_labels = save['valid_labels']\n", + " test_dataset = save['test_dataset']\n", + " test_labels = save['test_labels']\n", + " del save # hint to help gc free up memory\n", + " print 'Training set', train_dataset.shape, train_labels.shape\n", + " print 'Validation set', valid_dataset.shape, valid_labels.shape\n", + " print 'Test set', test_dataset.shape, test_labels.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "L7aHrm6nGDMB" + }, + "source": [ + "Reformat into a shape that's more adapted to the models we're going to train:\n", + "- data as a flat matrix,\n", + "- labels as float 1-hot encodings." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 11728, + "status": "ok", + "timestamp": 1449849322356, + "user": { + "color": "", + "displayName": "", + "isAnonymous": false, + "isMe": true, + "permissionId": "", + "photoUrl": "", + "sessionId": "0", + "userId": "" + }, + "user_tz": 480 + }, + "id": "IRSyYiIIGIzS", + "outputId": "3f8996ee-3574-4f44-c953-5c8a04636582" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training set (200000, 784) (200000, 10)\n", + "Validation set (10000, 784) (10000, 10)\n", + "Test set (18724, 784) (18724, 10)\n" + ] + } + ], + "source": [ + "image_size = 28\n", + "num_labels = 10\n", + "\n", + "def reformat(dataset, labels):\n", + " dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)\n", + " # Map 2 to [0.0, 1.0, 0.0 ...], 3 to [0.0, 0.0, 1.0 ...]\n", + " labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\n", + " return dataset, labels\n", + "train_dataset, train_labels = reformat(train_dataset, train_labels)\n", + "valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)\n", + "test_dataset, test_labels = reformat(test_dataset, test_labels)\n", + "print 'Training set', train_dataset.shape, train_labels.shape\n", + "print 'Validation set', valid_dataset.shape, valid_labels.shape\n", + "print 'Test set', test_dataset.shape, test_labels.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "collapsed": true, + "id": "RajPLaL_ZW6w" + }, + "outputs": [], + "source": [ + "def accuracy(predictions, labels):\n", + " return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))\n", + " / predictions.shape[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "sgLbUAQ1CW-1" + }, + "source": [ + "---\n", + "Problem 1\n", + "---------\n", + "\n", + "Introduce and tune L2 regularization for both logistic and neural network models. Remember that L2 amounts to adding a penalty on the norm of the weights to the loss. In TensorFlow, you can compue the L2 loss for a tensor `t` using `nn.l2_loss(t)`. The right amount of regularization should improve your validation / test accuracy.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "na8xX2yHZzNF" + }, + "source": [ + "---\n", + "Problem 2\n", + "---------\n", + "Let's demonstrate an extreme case of overfitting. Restrict your training data to just a few batches. What happens?\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ww3SCBUdlkRc" + }, + "source": [ + "---\n", + "Problem 3\n", + "---------\n", + "Introduce Dropout on the hidden layer of the neural network. Remember: Dropout should only be introduced during training, not evaluation, otherwise your evaluation results would be stochastic as well. TensorFlow provides `nn.dropout()` for that, but you have to make sure it's only inserted during training.\n", + "\n", + "What happens to our extreme overfitting case?\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-b1hTz3VWZjw" + }, + "source": [ + "---\n", + "Problem 4\n", + "---------\n", + "\n", + "Try to get the best performance you can using a multi-layer model! The best reported test accuracy using a deep network is [97.1%](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html?showComment=1391023266211#c8758720086795711595).\n", + "\n", + "One avenue you can explore is to add multiple layers.\n", + "\n", + "Another one is to use learning rate decay:\n", + "\n", + " global_step = tf.Variable(0) # count the number of steps taken.\n", + " learning_rate = tf.train.exponential_decay(0.5, step, ...)\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)\n", + " \n", + " ---\n" + ] + } + ], + "metadata": { + "colabVersion": "0.3.2", + "colab_default_view": {}, + "colab_views": {}, + "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": "3.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/deep-learning/tensor-flow-exercises/4_convolutions.ipynb b/deep-learning/tensor-flow-exercises/4_convolutions.ipynb new file mode 100644 index 0000000..5062c0b --- /dev/null +++ b/deep-learning/tensor-flow-exercises/4_convolutions.ipynb @@ -0,0 +1,489 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4embtkV0pNxM" + }, + "source": [ + "Deep Learning with TensorFlow\n", + "=============\n", + "\n", + "Credits: Forked from [TensorFlow](https://github.com/tensorflow/tensorflow) by Google\n", + "\n", + "Setup\n", + "------------\n", + "\n", + "Refer to the [setup instructions](https://github.com/donnemartin/data-science-ipython-notebooks/tree/feature/deep-learning/deep-learning/tensor-flow-exercises/README.md).\n", + "\n", + "Exercise 4\n", + "------------\n", + "\n", + "Previously in `2_fullyconnected.ipynb` and `3_regularization.ipynb`, we trained fully connected networks to classify [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) characters.\n", + "\n", + "The goal of this exercise is make the neural network convolutional." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "collapsed": true, + "id": "tm2CQN_Cpwj0" + }, + "outputs": [], + "source": [ + "# These are all the modules we'll be using later. Make sure you can import them\n", + "# before proceeding further.\n", + "import cPickle as pickle\n", + "import numpy as np\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 11948, + "status": "ok", + "timestamp": 1446658914837, + "user": { + "color": "", + "displayName": "", + "isAnonymous": false, + "isMe": true, + "permissionId": "", + "photoUrl": "", + "sessionId": "0", + "userId": "" + }, + "user_tz": 480 + }, + "id": "y3-cj1bpmuxc", + "outputId": "016b1a51-0290-4b08-efdb-8c95ffc3cd01" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training set (200000, 28, 28) (200000,)\n", + "Validation set (10000, 28, 28) (10000,)\n", + "Test set (18724, 28, 28) (18724,)\n" + ] + } + ], + "source": [ + "pickle_file = 'notMNIST.pickle'\n", + "\n", + "with open(pickle_file, 'rb') as f:\n", + " save = pickle.load(f)\n", + " train_dataset = save['train_dataset']\n", + " train_labels = save['train_labels']\n", + " valid_dataset = save['valid_dataset']\n", + " valid_labels = save['valid_labels']\n", + " test_dataset = save['test_dataset']\n", + " test_labels = save['test_labels']\n", + " del save # hint to help gc free up memory\n", + " print 'Training set', train_dataset.shape, train_labels.shape\n", + " print 'Validation set', valid_dataset.shape, valid_labels.shape\n", + " print 'Test set', test_dataset.shape, test_labels.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "L7aHrm6nGDMB" + }, + "source": [ + "Reformat into a TensorFlow-friendly shape:\n", + "- convolutions need the image data formatted as a cube (width by height by #channels)\n", + "- labels as float 1-hot encodings." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 11952, + "status": "ok", + "timestamp": 1446658914857, + "user": { + "color": "", + "displayName": "", + "isAnonymous": false, + "isMe": true, + "permissionId": "", + "photoUrl": "", + "sessionId": "0", + "userId": "" + }, + "user_tz": 480 + }, + "id": "IRSyYiIIGIzS", + "outputId": "650a208c-8359-4852-f4f5-8bf10e80ef6c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training set (200000, 28, 28, 1) (200000, 10)\n", + "Validation set (10000, 28, 28, 1) (10000, 10)\n", + "Test set (18724, 28, 28, 1) (18724, 10)\n" + ] + } + ], + "source": [ + "image_size = 28\n", + "num_labels = 10\n", + "num_channels = 1 # grayscale\n", + "\n", + "import numpy as np\n", + "\n", + "def reformat(dataset, labels):\n", + " dataset = dataset.reshape(\n", + " (-1, image_size, image_size, num_channels)).astype(np.float32)\n", + " labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\n", + " return dataset, labels\n", + "train_dataset, train_labels = reformat(train_dataset, train_labels)\n", + "valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)\n", + "test_dataset, test_labels = reformat(test_dataset, test_labels)\n", + "print 'Training set', train_dataset.shape, train_labels.shape\n", + "print 'Validation set', valid_dataset.shape, valid_labels.shape\n", + "print 'Test set', test_dataset.shape, test_labels.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "collapsed": true, + "id": "AgQDIREv02p1" + }, + "outputs": [], + "source": [ + "def accuracy(predictions, labels):\n", + " return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))\n", + " / predictions.shape[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "5rhgjmROXu2O" + }, + "source": [ + "Let's build a small network with two convolutional layers, followed by one fully connected layer. Convolutional networks are more expensive computationally, so we'll limit its depth and number of fully connected nodes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "collapsed": true, + "id": "IZYv70SvvOan" + }, + "outputs": [], + "source": [ + "batch_size = 16\n", + "patch_size = 5\n", + "depth = 16\n", + "num_hidden = 64\n", + "\n", + "graph = tf.Graph()\n", + "\n", + "with graph.as_default():\n", + "\n", + " # Input data.\n", + " tf_train_dataset = tf.placeholder(\n", + " tf.float32, shape=(batch_size, image_size, image_size, num_channels))\n", + " tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n", + " tf_valid_dataset = tf.constant(valid_dataset)\n", + " tf_test_dataset = tf.constant(test_dataset)\n", + " \n", + " # Variables.\n", + " layer1_weights = tf.Variable(tf.truncated_normal(\n", + " [patch_size, patch_size, num_channels, depth], stddev=0.1))\n", + " layer1_biases = tf.Variable(tf.zeros([depth]))\n", + " layer2_weights = tf.Variable(tf.truncated_normal(\n", + " [patch_size, patch_size, depth, depth], stddev=0.1))\n", + " layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]))\n", + " layer3_weights = tf.Variable(tf.truncated_normal(\n", + " [image_size / 4 * image_size / 4 * depth, num_hidden], stddev=0.1))\n", + " layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]))\n", + " layer4_weights = tf.Variable(tf.truncated_normal(\n", + " [num_hidden, num_labels], stddev=0.1))\n", + " layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]))\n", + " \n", + " # Model.\n", + " def model(data):\n", + " conv = tf.nn.conv2d(data, layer1_weights, [1, 2, 2, 1], padding='SAME')\n", + " hidden = tf.nn.relu(conv + layer1_biases)\n", + " conv = tf.nn.conv2d(hidden, layer2_weights, [1, 2, 2, 1], padding='SAME')\n", + " hidden = tf.nn.relu(conv + layer2_biases)\n", + " shape = hidden.get_shape().as_list()\n", + " reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])\n", + " hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)\n", + " return tf.matmul(hidden, layer4_weights) + layer4_biases\n", + " \n", + " # Training computation.\n", + " logits = model(tf_train_dataset)\n", + " loss = tf.reduce_mean(\n", + " tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n", + " \n", + " # Optimizer.\n", + " optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss)\n", + " \n", + " # Predictions for the training, validation, and test data.\n", + " train_prediction = tf.nn.softmax(logits)\n", + " valid_prediction = tf.nn.softmax(model(tf_valid_dataset))\n", + " test_prediction = tf.nn.softmax(model(tf_test_dataset))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 37 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 63292, + "status": "ok", + "timestamp": 1446658966251, + "user": { + "color": "", + "displayName": "", + "isAnonymous": false, + "isMe": true, + "permissionId": "", + "photoUrl": "", + "sessionId": "0", + "userId": "" + }, + "user_tz": 480 + }, + "id": "noKFb2UovVFR", + "outputId": "28941338-2ef9-4088-8bd1-44295661e628" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initialized\n", + "Minibatch loss at step 0 : 3.51275\n", + "Minibatch accuracy: 6.2%\n", + "Validation accuracy: 12.8%\n", + "Minibatch loss at step 50 : 1.48703\n", + "Minibatch accuracy: 43.8%\n", + "Validation accuracy: 50.4%\n", + "Minibatch loss at step 100 : 1.04377\n", + "Minibatch accuracy: 68.8%\n", + "Validation accuracy: 67.4%\n", + "Minibatch loss at step 150 : 0.601682\n", + "Minibatch accuracy: 68.8%\n", + "Validation accuracy: 73.0%\n", + "Minibatch loss at step 200 : 0.898649\n", + "Minibatch accuracy: 75.0%\n", + "Validation accuracy: 77.8%\n", + "Minibatch loss at step 250 : 1.3637\n", + "Minibatch accuracy: 56.2%\n", + "Validation accuracy: 75.4%\n", + "Minibatch loss at step 300 : 1.41968\n", + "Minibatch accuracy: 62.5%\n", + "Validation accuracy: 76.0%\n", + "Minibatch loss at step 350 : 0.300648\n", + "Minibatch accuracy: 81.2%\n", + "Validation accuracy: 80.2%\n", + "Minibatch loss at step 400 : 1.32092\n", + "Minibatch accuracy: 56.2%\n", + "Validation accuracy: 80.4%\n", + "Minibatch loss at step 450 : 0.556701\n", + "Minibatch accuracy: 81.2%\n", + "Validation accuracy: 79.4%\n", + "Minibatch loss at step 500 : 1.65595\n", + "Minibatch accuracy: 43.8%\n", + "Validation accuracy: 79.6%\n", + "Minibatch loss at step 550 : 1.06995\n", + "Minibatch accuracy: 75.0%\n", + "Validation accuracy: 81.2%\n", + "Minibatch loss at step 600 : 0.223684\n", + "Minibatch accuracy: 100.0%\n", + "Validation accuracy: 82.3%\n", + "Minibatch loss at step 650 : 0.619602\n", + "Minibatch accuracy: 87.5%\n", + "Validation accuracy: 81.8%\n", + "Minibatch loss at step 700 : 0.812091\n", + "Minibatch accuracy: 75.0%\n", + "Validation accuracy: 82.4%\n", + "Minibatch loss at step 750 : 0.276302\n", + "Minibatch accuracy: 87.5%\n", + "Validation accuracy: 82.3%\n", + "Minibatch loss at step 800 : 0.450241\n", + "Minibatch accuracy: 81.2%\n", + "Validation accuracy: 82.3%\n", + "Minibatch loss at step 850 : 0.137139\n", + "Minibatch accuracy: 93.8%\n", + "Validation accuracy: 82.3%\n", + "Minibatch loss at step 900 : 0.52664\n", + "Minibatch accuracy: 75.0%\n", + "Validation accuracy: 82.2%\n", + "Minibatch loss at step 950 : 0.623835\n", + "Minibatch accuracy: 87.5%\n", + "Validation accuracy: 82.1%\n", + "Minibatch loss at step 1000 : 0.243114\n", + "Minibatch accuracy: 93.8%\n", + "Validation accuracy: 82.9%\n", + "Test accuracy: 90.0%\n" + ] + } + ], + "source": [ + "num_steps = 1001\n", + "\n", + "with tf.Session(graph=graph) as session:\n", + " tf.initialize_all_variables().run()\n", + " print \"Initialized\"\n", + " for step in xrange(num_steps):\n", + " offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n", + " batch_data = train_dataset[offset:(offset + batch_size), :, :, :]\n", + " batch_labels = train_labels[offset:(offset + batch_size), :]\n", + " feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n", + " _, l, predictions = session.run(\n", + " [optimizer, loss, train_prediction], feed_dict=feed_dict)\n", + " if (step % 50 == 0):\n", + " print \"Minibatch loss at step\", step, \":\", l\n", + " print \"Minibatch accuracy: %.1f%%\" % accuracy(predictions, batch_labels)\n", + " print \"Validation accuracy: %.1f%%\" % accuracy(\n", + " valid_prediction.eval(), valid_labels)\n", + " print \"Test accuracy: %.1f%%\" % accuracy(test_prediction.eval(), test_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "KedKkn4EutIK" + }, + "source": [ + "---\n", + "Problem 1\n", + "---------\n", + "\n", + "The convolutional model above uses convolutions with stride 2 to reduce the dimensionality. Replace the strides a max pooling operation (`nn.max_pool()`) of stride 2 and kernel size 2.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "klf21gpbAgb-" + }, + "source": [ + "---\n", + "Problem 2\n", + "---------\n", + "\n", + "Try to get the best performance you can using a convolutional net. Look for example at the classic [LeNet5](http://yann.lecun.com/exdb/lenet/) architecture, adding Dropout, and/or adding learning rate decay.\n", + "\n", + "---" + ] + } + ], + "metadata": { + "colabVersion": "0.3.2", + "colab_default_view": {}, + "colab_views": {}, + "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": "3.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/deep-learning/tensor-flow-exercises/5_word2vec.ipynb b/deep-learning/tensor-flow-exercises/5_word2vec.ipynb new file mode 100644 index 0000000..31257ac --- /dev/null +++ b/deep-learning/tensor-flow-exercises/5_word2vec.ipynb @@ -0,0 +1,889 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "D7tqLMoKF6uq" + }, + "source": [ + "Deep Learning with TensorFlow\n", + "=============\n", + "\n", + "Credits: Forked from [TensorFlow](https://github.com/tensorflow/tensorflow) by Google\n", + "\n", + "Setup\n", + "------------\n", + "\n", + "Refer to the [setup instructions](https://github.com/donnemartin/data-science-ipython-notebooks/tree/feature/deep-learning/deep-learning/tensor-flow-exercises/README.md).\n", + "\n", + "Exercise 5\n", + "------------\n", + "\n", + "The goal of this exercise is to train a skip-gram model over [Text8](http://mattmahoney.net/dc/textdata) data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "collapsed": true, + "id": "0K1ZyLn04QZf" + }, + "outputs": [], + "source": [ + "# These are all the modules we'll be using later. Make sure you can import them\n", + "# before proceeding further.\n", + "import collections\n", + "import math\n", + "import numpy as np\n", + "import os\n", + "import random\n", + "import tensorflow as tf\n", + "import urllib\n", + "import zipfile\n", + "from matplotlib import pylab\n", + "from sklearn.manifold import TSNE" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aCjPJE944bkV" + }, + "source": [ + "Download the data from the source website if necessary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 14640, + "status": "ok", + "timestamp": 1445964482948, + "user": { + "color": "#1FA15D", + "displayName": "Vincent Vanhoucke", + "isAnonymous": false, + "isMe": true, + "permissionId": "05076109866853157986", + "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", + "sessionId": "2f1ffade4c9f20de", + "userId": "102167687554210253930" + }, + "user_tz": 420 + }, + "id": "RJ-o3UBUFtCw", + "outputId": "c4ec222c-80b5-4298-e635-93ca9f79c3b7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found and verified text8.zip\n" + ] + } + ], + "source": [ + "url = 'http://mattmahoney.net/dc/'\n", + "\n", + "def maybe_download(filename, expected_bytes):\n", + " \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n", + " if not os.path.exists(filename):\n", + " filename, _ = urllib.urlretrieve(url + filename, filename)\n", + " statinfo = os.stat(filename)\n", + " if statinfo.st_size == expected_bytes:\n", + " print 'Found and verified', filename\n", + " else:\n", + " print statinfo.st_size\n", + " raise Exception(\n", + " 'Failed to verify ' + filename + '. Can you get to it with a browser?')\n", + " return filename\n", + "\n", + "filename = maybe_download('text8.zip', 31344016)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Zqz3XiqI4mZT" + }, + "source": [ + "Read the data into a string." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 28844, + "status": "ok", + "timestamp": 1445964497165, + "user": { + "color": "#1FA15D", + "displayName": "Vincent Vanhoucke", + "isAnonymous": false, + "isMe": true, + "permissionId": "05076109866853157986", + "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", + "sessionId": "2f1ffade4c9f20de", + "userId": "102167687554210253930" + }, + "user_tz": 420 + }, + "id": "Mvf09fjugFU_", + "outputId": "e3a928b4-1645-4fe8-be17-fcf47de5716d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data size 17005207\n" + ] + } + ], + "source": [ + "def read_data(filename):\n", + " f = zipfile.ZipFile(filename)\n", + " for name in f.namelist():\n", + " return f.read(name).split()\n", + " f.close()\n", + " \n", + "words = read_data(filename)\n", + "print 'Data size', len(words)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Zdw6i4F8glpp" + }, + "source": [ + "Build the dictionary and replace rare words with UNK token." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 28849, + "status": "ok", + "timestamp": 1445964497178, + "user": { + "color": "#1FA15D", + "displayName": "Vincent Vanhoucke", + "isAnonymous": false, + "isMe": true, + "permissionId": "05076109866853157986", + "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", + "sessionId": "2f1ffade4c9f20de", + "userId": "102167687554210253930" + }, + "user_tz": 420 + }, + "id": "gAL1EECXeZsD", + "outputId": "3fb4ecd1-df67-44b6-a2dc-2291730970b2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most common words (+UNK) [['UNK', 418391], ('the', 1061396), ('of', 593677), ('and', 416629), ('one', 411764)]\n", + "Sample data [5243, 3083, 12, 6, 195, 2, 3136, 46, 59, 156]\n" + ] + } + ], + "source": [ + "vocabulary_size = 50000\n", + "\n", + "def build_dataset(words):\n", + " count = [['UNK', -1]]\n", + " count.extend(collections.Counter(words).most_common(vocabulary_size - 1))\n", + " dictionary = dict()\n", + " for word, _ in count:\n", + " dictionary[word] = len(dictionary)\n", + " data = list()\n", + " unk_count = 0\n", + " for word in words:\n", + " if word in dictionary:\n", + " index = dictionary[word]\n", + " else:\n", + " index = 0 # dictionary['UNK']\n", + " unk_count = unk_count + 1\n", + " data.append(index)\n", + " count[0][1] = unk_count\n", + " reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys())) \n", + " return data, count, dictionary, reverse_dictionary\n", + "\n", + "data, count, dictionary, reverse_dictionary = build_dataset(words)\n", + "print 'Most common words (+UNK)', count[:5]\n", + "print 'Sample data', data[:10]\n", + "del words # Hint to reduce memory." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "lFwoyygOmWsL" + }, + "source": [ + "Function to generate a training batch for the skip-gram model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 113, + "status": "ok", + "timestamp": 1445964901989, + "user": { + "color": "#1FA15D", + "displayName": "Vincent Vanhoucke", + "isAnonymous": false, + "isMe": true, + "permissionId": "05076109866853157986", + "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", + "sessionId": "2f1ffade4c9f20de", + "userId": "102167687554210253930" + }, + "user_tz": 420 + }, + "id": "w9APjA-zmfjV", + "outputId": "67cccb02-cdaf-4e47-d489-43bcc8d57bb8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 3083 -> 5243\n", + "originated -> anarchism\n", + "3083 -> 12\n", + "originated -> as\n", + "12 -> 3083\n", + "as -> originated\n", + "12 -> 6\n", + "as -> a\n", + "6 -> 12\n", + "a -> as\n", + "6 -> 195\n", + "a -> term\n", + "195 -> 6\n", + "term -> a\n", + "195 -> 2\n", + "term -> of\n" + ] + } + ], + "source": [ + "data_index = 0\n", + "\n", + "def generate_batch(batch_size, num_skips, skip_window):\n", + " global data_index\n", + " assert batch_size % num_skips == 0\n", + " assert num_skips <= 2 * skip_window\n", + " batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n", + " labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n", + " span = 2 * skip_window + 1 # [ skip_window target skip_window ]\n", + " buffer = collections.deque(maxlen=span)\n", + " for _ in range(span):\n", + " buffer.append(data[data_index])\n", + " data_index = (data_index + 1) % len(data)\n", + " for i in range(batch_size / num_skips):\n", + " target = skip_window # target label at the center of the buffer\n", + " targets_to_avoid = [ skip_window ]\n", + " for j in range(num_skips):\n", + " while target in targets_to_avoid:\n", + " target = random.randint(0, span - 1)\n", + " targets_to_avoid.append(target)\n", + " batch[i * num_skips + j] = buffer[skip_window]\n", + " labels[i * num_skips + j, 0] = buffer[target]\n", + " buffer.append(data[data_index])\n", + " data_index = (data_index + 1) % len(data)\n", + " return batch, labels\n", + "\n", + "batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)\n", + "for i in range(8):\n", + " print batch[i], '->', labels[i, 0]\n", + " print reverse_dictionary[batch[i]], '->', reverse_dictionary[labels[i, 0]]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Ofd1MbBuwiva" + }, + "source": [ + "Train a skip-gram model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "collapsed": true, + "id": "8pQKsV4Vwlzy" + }, + "outputs": [], + "source": [ + "batch_size = 128\n", + "embedding_size = 128 # Dimension of the embedding vector.\n", + "skip_window = 1 # How many words to consider left and right.\n", + "num_skips = 2 # How many times to reuse an input to generate a label.\n", + "# We pick a random validation set to sample nearest neighbors. here we limit the\n", + "# validation samples to the words that have a low numeric ID, which by\n", + "# construction are also the most frequent. \n", + "valid_size = 16 # Random set of words to evaluate similarity on.\n", + "valid_window = 100 # Only pick dev samples in the head of the distribution.\n", + "valid_examples = np.array(random.sample(xrange(valid_window), valid_size))\n", + "num_sampled = 64 # Number of negative examples to sample.\n", + "\n", + "graph = tf.Graph()\n", + "\n", + "with graph.as_default():\n", + "\n", + " # Input data.\n", + " train_dataset = tf.placeholder(tf.int32, shape=[batch_size])\n", + " train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n", + " valid_dataset = tf.constant(valid_examples, dtype=tf.int32)\n", + " \n", + " # Variables.\n", + " embeddings = tf.Variable(\n", + " tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))\n", + " softmax_weights = tf.Variable(\n", + " tf.truncated_normal([vocabulary_size, embedding_size],\n", + " stddev=1.0 / math.sqrt(embedding_size)))\n", + " softmax_biases = tf.Variable(tf.zeros([vocabulary_size]))\n", + " \n", + " # Model.\n", + " # Look up embeddings for inputs.\n", + " embed = tf.nn.embedding_lookup(embeddings, train_dataset)\n", + " # Compute the softmax loss, using a sample of the negative labels each time.\n", + " loss = tf.reduce_mean(\n", + " tf.nn.sampled_softmax_loss(softmax_weights, softmax_biases, embed,\n", + " train_labels, num_sampled, vocabulary_size))\n", + "\n", + " # Optimizer.\n", + " optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)\n", + " \n", + " # Compute the similarity between minibatch examples and all embeddings.\n", + " # We use the cosine distance:\n", + " norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))\n", + " normalized_embeddings = embeddings / norm\n", + " valid_embeddings = tf.nn.embedding_lookup(\n", + " normalized_embeddings, valid_dataset)\n", + " similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 23 + }, + { + "item_id": 48 + }, + { + "item_id": 61 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 436189, + "status": "ok", + "timestamp": 1445965429787, + "user": { + "color": "#1FA15D", + "displayName": "Vincent Vanhoucke", + "isAnonymous": false, + "isMe": true, + "permissionId": "05076109866853157986", + "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", + "sessionId": "2f1ffade4c9f20de", + "userId": "102167687554210253930" + }, + "user_tz": 420 + }, + "id": "1bQFGceBxrWW", + "outputId": "5ebd6d9a-33c6-4bcd-bf6d-252b0b6055e4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initialized\n", + "Average loss at step 0 : 8.58149623871\n", + "Nearest to been: unfavourably, marmara, ancestral, legal, bogart, glossaries, worst, rooms,\n", + "Nearest to time: conformist, strawberries, sindhi, waterfall, xia, nominates, psp, sensitivity,\n", + "Nearest to over: overlord, panda, golden, semigroup, rawlings, involved, shreveport, handling,\n", + "Nearest to not: hymenoptera, reintroducing, lamiaceae, because, davao, omnipotent, combustion, debilitating,\n", + "Nearest to three: catalog, koza, gn, braque, holstein, postgresql, luddite, justine,\n", + "Nearest to if: chilled, vince, fiddler, represented, sandinistas, happiness, lya, glands,\n", + "Nearest to there: coast, photosynthetic, kimmei, legally, inner, illyricum, formats, fullmetal,\n", + "Nearest to between: chuvash, prinz, suitability, wolfe, guideline, computability, diminutive, paulo,\n", + "Nearest to from: tanganyika, workshop, elphinstone, spearhead, resurrected, kevlar, shangri, loves,\n", + "Nearest to state: sextus, wuppertal, glaring, inches, unrounded, courageous, adler, connie,\n", + "Nearest to on: gino, phocas, rhine, jg, macrocosm, jackass, jays, theorie,\n", + "Nearest to and: standings, towed, reyes, willard, equality, juggling, wladislaus, faked,\n", + "Nearest to eight: gresham, dogg, moko, tennis, superseded, telegraphy, scramble, vinod,\n", + "Nearest to they: prisons, divisor, coder, ribeira, willingness, factional, nne, lotta,\n", + "Nearest to more: blues, fur, sterling, tangier, khwarizmi, discouraged, cal, deicide,\n", + "Nearest to other: enemies, bogged, brassicaceae, lascaux, dispense, alexandrians, crimea, dou,\n", + "Average loss at step 2000 : 4.39983723116\n", + "Average loss at step 4000 : 3.86921076906\n", + "Average loss at step 6000 : 3.72542127335\n", + "Average loss at step 8000 : 3.57835536212\n", + "Average loss at step 10000 : 3.61056993055\n", + "Nearest to been: glossaries, legal, unfavourably, be, hadad, wore, scarcity, were,\n", + "Nearest to time: strawberries, conformist, gleichschaltung, waterfall, molality, nominates, baal, dole,\n", + "Nearest to over: golden, semigroup, catus, motorways, brick, shehri, mussolini, overlord,\n", + "Nearest to not: hinayana, it, often, they, boots, also, noaa, lindsey,\n", + "Nearest to three: four, seven, six, five, nine, eight, two, zero,\n", + "Nearest to if: glands, euros, wallpaper, redefine, toho, confuse, unsound, shepherd,\n", + "Nearest to there: it, they, fullmetal, pace, legally, harpsichord, mma, bug,\n", + "Nearest to between: chuvash, wandering, from, kirsch, pursuant, eurocents, suitability, jackie,\n", + "Nearest to from: into, in, workshop, to, at, misogynist, elphinstone, spearhead,\n", + "Nearest to state: sextus, glaring, connie, adler, esoteric, didactic, handedness, presidents,\n", + "Nearest to on: in, at, for, ruminants, wakefulness, torrey, foley, gino,\n", + "Nearest to and: or, who, but, zelda, of, for, thirst, chisel,\n", + "Nearest to eight: nine, six, seven, five, four, three, zero, two,\n", + "Nearest to they: he, prisons, there, we, hydrate, it, not, cumbersome,\n", + "Nearest to more: skye, blues, trypomastigotes, deicide, most, readable, used, sterling,\n", + "Nearest to other: trochaic, hush, surveyors, joachim, differentiation, attackers, reverence, attestation,\n", + "Average loss at step 12000 : 3.66169466591\n", + "Average loss at step 14000 : 3.60342905837\n", + "Average loss at step 16000 : 3.57761328053\n", + "Average loss at step 18000 : 3.57667332476\n", + "Average loss at step 20000 : 3.53310145146\n", + "Nearest to been: be, become, was, hadad, unfavourably, were, wore, partido,\n", + "Nearest to time: gleichschaltung, strawberries, year, nominates, conformist, etch, admittedly, treasuries,\n", + "Nearest to over: golden, semigroup, motorways, rawlings, triangle, trey, ustawa, mattingly,\n", + "Nearest to not: they, boots, often, dieppe, still, hinayana, nearly, be,\n", + "Nearest to three: two, four, five, seven, eight, six, nine, one,\n", + "Nearest to if: wallpaper, euros, before, toho, unsound, so, bg, pfc,\n", + "Nearest to there: they, it, he, usually, which, we, not, transactions,\n", + "Nearest to between: from, with, about, near, reactance, eurocents, wandering, voltaire,\n", + "Nearest to from: into, workshop, by, between, in, on, elphinstone, under,\n", + "Nearest to state: glaring, esoteric, succeeding, sextus, vorarlberg, presidents, depends, connie,\n", + "Nearest to on: in, at, upon, during, from, janis, foley, nubian,\n", + "Nearest to and: or, thirst, but, where, s, who, pfaff, including,\n", + "Nearest to eight: nine, seven, six, five, four, three, zero, one,\n", + "Nearest to they: there, he, we, not, it, you, prisons, who,\n", + "Nearest to more: less, most, deicide, skye, trypomastigotes, interventionism, toed, drummond,\n", + "Nearest to other: such, joachim, hush, attackers, surveyors, trochaic, differentiation, reverence,\n", + "Average loss at step 22000 : 3.59519316927\n", + "Average loss at step 24000 : 3.55378576797\n", + "Average loss at step 26000 : 3.56455037558\n", + "Average loss at step 28000 : 3.5040882225\n", + "Average loss at step 30000 : 3.39208897972\n", + "Nearest to been: become, be, were, was, spotless, hadad, by, hausdorff,\n", + "Nearest to time: gleichschaltung, year, day, nominates, jesus, strawberries, way, admittedly,\n", + "Nearest to over: golden, semigroup, motorways, rawlings, interventionism, counternarcotics, adaption, brick,\n", + "Nearest to not: often, they, it, never, still, nor, boots, pki,\n", + "Nearest to three: four, six, two, eight, five, seven, nine, zero,\n", + "Nearest to if: when, before, so, should, toho, where, bg, wallpaper,\n", + "Nearest to there: they, it, which, usually, he, that, also, now,\n", + "Nearest to between: with, from, in, panasonic, presupposes, churchmen, hijacking, where,\n", + "Nearest to from: into, elphinstone, workshop, between, through, speculates, sosa, in,\n", + "Nearest to state: esoteric, glaring, presidents, vorarlberg, atmosphere, succeeding, lute, connie,\n", + "Nearest to on: upon, in, janis, during, torrey, against, infield, catalans,\n", + "Nearest to and: or, thirst, in, but, of, sobib, cleaves, including,\n", + "Nearest to eight: nine, six, four, seven, three, zero, five, one,\n", + "Nearest to they: we, there, he, you, it, these, who, i,\n", + "Nearest to more: less, most, deicide, faster, toed, very, skye, tonic,\n", + "Nearest to other: different, attackers, joachim, various, such, many, differentiation, these,\n", + "Average loss at step 32000 : 3.49501452419\n", + "Average loss at step 34000 : 3.48593705952\n", + "Average loss at step 36000 : 3.50112806576\n", + "Average loss at step 38000 : 3.49244426501\n", + "Average loss at step 40000 : 3.3890105716\n", + "Nearest to been: become, be, were, was, jolie, hausdorff, spotless, had,\n", + "Nearest to time: year, way, gleichschaltung, period, day, stanislav, stage, outcome,\n", + "Nearest to over: through, semigroup, rawlings, golden, about, brick, on, motorways,\n", + "Nearest to not: they, radiated, never, pki, still, omnipotent, hinayana, really,\n", + "Nearest to three: four, six, five, two, seven, eight, one, nine,\n", + "Nearest to if: when, before, where, then, bg, because, can, should,\n", + "Nearest to there: they, it, he, usually, this, typically, still, often,\n", + "Nearest to between: with, in, from, about, against, churchmen, johansen, presupposes,\n", + "Nearest to from: into, through, elphinstone, in, workshop, between, suing, under,\n", + "Nearest to state: esoteric, presidents, atmosphere, vorarlberg, lute, succeeding, glaring, didactic,\n", + "Nearest to on: upon, at, in, during, unitarians, under, catalans, batavians,\n", + "Nearest to and: or, but, s, incapacitation, including, while, of, which,\n", + "Nearest to eight: nine, six, seven, four, five, three, one, two,\n", + "Nearest to they: we, he, there, you, she, i, not, it,\n", + "Nearest to more: less, most, deicide, toed, greater, faster, quite, longer,\n", + "Nearest to other: various, different, attackers, joachim, clutter, nz, trochaic, apulia,\n", + "Average loss at step 42000 : 3.45294014364\n", + "Average loss at step 44000 : 3.47660055941\n", + "Average loss at step 46000 : 3.47458503014\n", + "Average loss at step 48000 : 3.47261548793\n", + "Average loss at step 50000 : 3.45390708435\n", + "Nearest to been: become, be, had, was, were, hausdorff, prem, remained,\n", + "Nearest to time: way, year, period, stv, day, gleichschaltung, stage, outcome,\n", + "Nearest to over: through, golden, semigroup, about, brick, counternarcotics, theremin, mattingly,\n", + "Nearest to not: they, still, never, really, sometimes, it, kiwifruit, nearly,\n", + "Nearest to three: five, four, six, seven, two, eight, one, nine,\n", + "Nearest to if: when, before, where, because, connexion, though, so, whether,\n", + "Nearest to there: they, it, he, this, now, often, usually, still,\n", + "Nearest to between: with, from, fashioned, churchmen, panasonic, explores, within, racial,\n", + "Nearest to from: into, through, under, elphinstone, between, workshop, circumpolar, idiom,\n", + "Nearest to state: atmosphere, vorarlberg, esoteric, presidents, madhya, majority, moulin, bowmen,\n", + "Nearest to on: upon, in, catalans, tezuka, minotaurs, wakefulness, batavians, guglielmo,\n", + "Nearest to and: or, but, thirst, signifier, which, however, including, unattractive,\n", + "Nearest to eight: six, nine, seven, five, four, three, zero, two,\n", + "Nearest to they: we, there, he, you, it, she, these, not,\n", + "Nearest to more: less, most, quite, very, further, faster, toed, deicide,\n", + "Nearest to other: various, different, many, attackers, are, joachim, nihilo, reject,\n", + "Average loss at step 52000 : 3.43597227755\n", + "Average loss at step 54000 : 3.25126817495\n", + "Average loss at step 56000 : 3.35102432287\n", + "Average loss at step 58000 : 3.44654818082\n", + "Average loss at step 60000 : 3.4287913968\n", + "Nearest to been: become, be, was, prem, had, remained, hadad, stanislavsky,\n", + "Nearest to time: year, way, period, stv, barely, name, stage, restoring,\n", + "Nearest to over: about, through, golden, adaption, counternarcotics, up, mattingly, brick,\n", + "Nearest to not: still, never, nor, kiwifruit, they, nearly, therefore, rarely,\n", + "Nearest to three: two, five, four, six, seven, eight, one, nine,\n", + "Nearest to if: when, though, before, where, although, because, can, could,\n", + "Nearest to there: they, it, he, still, she, we, this, often,\n", + "Nearest to between: with, from, churchmen, among, ethical, within, vma, panasonic,\n", + "Nearest to from: through, into, under, during, between, in, suing, across,\n", + "Nearest to state: atmosphere, infringe, madhya, vorarlberg, government, bowmen, vargas, republic,\n", + "Nearest to on: upon, through, within, ridiculous, janis, in, under, over,\n", + "Nearest to and: or, while, including, but, of, like, whose, bannister,\n", + "Nearest to eight: nine, six, five, four, seven, zero, three, two,\n", + "Nearest to they: we, there, you, he, it, these, she, prisons,\n", + "Nearest to more: less, most, quite, further, toed, very, faster, rather,\n", + "Nearest to other: different, various, many, nihilo, these, amour, including, screenplays,\n", + "Average loss at step 62000 : 3.38358767056\n", + "Average loss at step 64000 : 3.41693099326\n", + "Average loss at step 66000 : 3.39588000977\n", + "Average loss at step 68000 : 3.35567189544\n", + "Average loss at step 70000 : 3.38878934443\n", + "Nearest to been: become, be, was, prem, remained, were, being, discounts,\n", + "Nearest to time: year, way, day, period, barely, ethos, stage, reason,\n", + "Nearest to over: about, through, fortunately, semigroup, theremin, off, loudest, up,\n", + "Nearest to not: still, nor, never, they, actually, nearly, unelected, therefore,\n", + "Nearest to three: five, two, four, six, seven, eight, nine, zero,\n", + "Nearest to if: when, though, before, where, because, then, after, since,\n", + "Nearest to there: they, it, he, often, she, we, usually, still,\n", + "Nearest to between: among, with, within, from, ethical, churchmen, racial, prentice,\n", + "Nearest to from: through, into, within, during, under, until, between, across,\n", + "Nearest to state: city, atmosphere, desks, surrounding, preservation, bohr, principal, republic,\n", + "Nearest to on: upon, tezuka, through, within, wakefulness, catalans, at, ingeborg,\n", + "Nearest to and: or, but, while, including, thirst, jerzy, massing, abadan,\n", + "Nearest to eight: seven, six, nine, five, four, three, two, zero,\n", + "Nearest to they: we, you, he, there, she, it, prisons, who,\n", + "Nearest to more: less, most, quite, very, faster, smaller, further, larger,\n", + "Nearest to other: various, different, some, screenplays, lab, many, including, debugging,\n", + "Average loss at step 72000 : 3.41103189731\n", + "Average loss at step 74000 : 3.44926435578\n", + "Average loss at step 76000 : 3.4423020488\n", + "Average loss at step 78000 : 3.41976813722\n", + "Average loss at step 80000 : 3.39511853886\n", + "Nearest to been: become, be, remained, was, grown, were, prem, already,\n", + "Nearest to time: year, way, period, reason, barely, distance, stage, day,\n", + "Nearest to over: about, fortunately, through, semigroup, further, mattingly, rawlings, golden,\n", + "Nearest to not: still, they, nor, never, we, kiwifruit, noaa, really,\n", + "Nearest to three: five, two, seven, four, eight, six, nine, zero,\n", + "Nearest to if: when, where, though, before, since, because, although, follows,\n", + "Nearest to there: they, it, he, we, she, still, typically, actually,\n", + "Nearest to between: with, among, within, in, racial, around, from, serapeum,\n", + "Nearest to from: into, through, in, within, under, using, during, towards,\n", + "Nearest to state: city, atmosphere, ferro, vorarlberg, surrounding, republic, madhya, national,\n", + "Nearest to on: upon, poll, in, from, tezuka, janis, through, within,\n", + "Nearest to and: or, but, including, while, s, which, thirst, although,\n", + "Nearest to eight: nine, seven, six, five, four, three, zero, two,\n", + "Nearest to they: we, you, there, he, she, it, these, not,\n", + "Nearest to more: less, most, smaller, very, faster, quite, rather, larger,\n", + "Nearest to other: various, different, joachim, including, theos, smaller, individual, screenplays,\n", + "Average loss at step 82000 : 3.40933967865\n", + "Average loss at step 84000 : 3.41618054378\n", + "Average loss at step 86000 : 3.31485116804\n", + "Average loss at step 88000 : 3.37068593091\n", + "Average loss at step 90000 : 3.2785516749\n", + "Nearest to been: become, be, was, prem, remained, grown, recently, already,\n", + "Nearest to time: year, way, period, day, barely, battle, buds, name,\n", + "Nearest to over: through, about, fortunately, off, theremin, semigroup, extraterrestrial, mattingly,\n", + "Nearest to not: nor, still, never, otherwise, generally, separately, gown, hydrate,\n", + "Nearest to three: four, five, six, two, eight, seven, nine, zero,\n", + "Nearest to if: when, where, before, though, because, since, then, while,\n", + "Nearest to there: they, it, he, we, she, still, typically, fiorello,\n", + "Nearest to between: with, among, within, from, churchmen, prentice, racial, panasonic,\n", + "Nearest to from: through, into, across, during, towards, until, at, within,\n", + "Nearest to state: bohr, city, atmosphere, ferro, bowmen, republic, retaliation, vorarlberg,\n", + "Nearest to on: upon, in, tezuka, at, during, within, via, catalans,\n", + "Nearest to and: or, including, but, while, like, thirst, with, schuman,\n", + "Nearest to eight: seven, nine, six, five, four, three, zero, two,\n", + "Nearest to they: we, there, he, you, she, it, prisons, these,\n", + "Nearest to more: less, most, very, faster, larger, quite, smaller, better,\n", + "Nearest to other: different, various, tamara, prosthetic, including, individual, failing, restaurants,\n", + "Average loss at step 92000 : 3.40355363208\n", + "Average loss at step 94000 : 3.35647508007\n", + "Average loss at step 96000 : 3.34374570692\n", + "Average loss at step 98000 : 3.4230104093\n", + "Average loss at step 100000 : 3.36909827\n", + "Nearest to been: become, be, grown, was, being, already, remained, prem,\n", + "Nearest to time: way, year, day, period, years, days, mothersbaugh, separators,\n", + "Nearest to over: through, about, semigroup, further, fortunately, off, into, theremin,\n", + "Nearest to not: never, nor, still, dieppe, really, unelected, actually, now,\n", + "Nearest to three: four, two, five, seven, six, eight, nine, zero,\n", + "Nearest to if: when, though, where, before, is, abe, then, follows,\n", + "Nearest to there: they, it, he, we, still, she, typically, often,\n", + "Nearest to between: within, with, among, churchmen, around, explores, from, reactance,\n", + "Nearest to from: into, through, within, across, in, between, using, workshop,\n", + "Nearest to state: atmosphere, bohr, national, ferro, germ, desks, city, unpaid,\n", + "Nearest to on: upon, in, within, tezuka, janis, batavians, about, macrocosm,\n", + "Nearest to and: or, but, purview, thirst, sukkot, epr, including, honesty,\n", + "Nearest to eight: seven, nine, six, four, five, three, zero, one,\n", + "Nearest to they: we, there, you, he, she, prisons, it, these,\n", + "Nearest to more: less, most, very, quite, faster, larger, rather, smaller,\n", + "Nearest to other: various, different, tamara, theos, some, cope, many, others,\n" + ] + } + ], + "source": [ + "num_steps = 100001\n", + "\n", + "with tf.Session(graph=graph) as session:\n", + " tf.initialize_all_variables().run()\n", + " print \"Initialized\"\n", + " average_loss = 0\n", + " for step in xrange(num_steps):\n", + " batch_data, batch_labels = generate_batch(\n", + " batch_size, num_skips, skip_window)\n", + " feed_dict = {train_dataset : batch_data, train_labels : batch_labels}\n", + " _, l = session.run([optimizer, loss], feed_dict=feed_dict)\n", + " average_loss += l\n", + " if step % 2000 == 0:\n", + " if step > 0:\n", + " average_loss = average_loss / 2000\n", + " # The average loss is an estimate of the loss over the last 2000 batches.\n", + " print \"Average loss at step\", step, \":\", average_loss\n", + " average_loss = 0\n", + " # note that this is expensive (~20% slowdown if computed every 500 steps)\n", + " if step % 10000 == 0:\n", + " sim = similarity.eval()\n", + " for i in xrange(valid_size):\n", + " valid_word = reverse_dictionary[valid_examples[i]]\n", + " top_k = 8 # number of nearest neighbors\n", + " nearest = (-sim[i, :]).argsort()[1:top_k+1]\n", + " log = \"Nearest to %s:\" % valid_word\n", + " for k in xrange(top_k):\n", + " close_word = reverse_dictionary[nearest[k]]\n", + " log = \"%s %s,\" % (log, close_word)\n", + " print log\n", + " final_embeddings = normalized_embeddings.eval()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "collapsed": true, + "id": "jjJXYA_XzV79" + }, + "outputs": [], + "source": [ + "num_points = 400\n", + "\n", + "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n", + "two_d_embeddings = tsne.fit_transform(final_embeddings[1:num_points+1, :])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 4763, + "status": "ok", + "timestamp": 1445965465525, + "user": { + "color": "#1FA15D", + "displayName": "Vincent Vanhoucke", + "isAnonymous": false, + "isMe": true, + "permissionId": "05076109866853157986", + "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", + "sessionId": "2f1ffade4c9f20de", + "userId": "102167687554210253930" + }, + "user_tz": 420 + }, + "id": "o_e0D_UezcDe", + "outputId": "df22e4a5-e8ec-4e5e-d384-c6cf37c68c34" + }, + "outputs": [ + { + "data": { + "image/png": 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8/FZhZmaka1cPChZM//3l0pPBYCCTzOBPpWLF16hY8TWMRmPKKJ2XlzfNmrXK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\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Onl6pPDL3uSlIJGTHjq0yIZQxY4YhFktQUFBg7NiJ30QwFxS0iZ07t2JmZo6LS14LhmrV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\nAAAAJSTmAAAASuj/AKSSWUR2kw4CAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot(embeddings, labels):\n", + " assert embeddings.shape[0] >= len(labels), 'More labels than embeddings'\n", + " pylab.figure(figsize=(15,15)) # in inches\n", + " for i, label in enumerate(labels):\n", + " x, y = embeddings[i,:]\n", + " pylab.scatter(x, y)\n", + " pylab.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',\n", + " ha='right', va='bottom')\n", + " pylab.show()\n", + "\n", + "words = [reverse_dictionary[i] for i in xrange(1, num_points+1)]\n", + "plot(two_d_embeddings, words)" + ] + } + ], + "metadata": { + "colabVersion": "0.3.2", + "colab_default_view": {}, + "colab_views": {}, + "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": "3.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/deep-learning/tensor-flow-exercises/6_lstm.ipynb b/deep-learning/tensor-flow-exercises/6_lstm.ipynb new file mode 100644 index 0000000..4417bd2 --- /dev/null +++ b/deep-learning/tensor-flow-exercises/6_lstm.ipynb @@ -0,0 +1,1064 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "D7tqLMoKF6uq" + }, + "source": [ + "Deep Learning with TensorFlow\n", + "=============\n", + "\n", + "Credits: Forked from [TensorFlow](https://github.com/tensorflow/tensorflow) by Google\n", + "\n", + "Setup\n", + "------------\n", + "\n", + "Refer to the [setup instructions](https://github.com/donnemartin/data-science-ipython-notebooks/tree/feature/deep-learning/deep-learning/tensor-flow-exercises/README.md).\n", + "\n", + "Exercise 6\n", + "------------\n", + "\n", + "After training a skip-gram model in `5_word2vec.ipynb`, the goal of this exercise is to train a LSTM character model over [Text8](http://mattmahoney.net/dc/textdata) data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "collapsed": true, + "id": "MvEblsgEXxrd" + }, + "outputs": [], + "source": [ + "# These are all the modules we'll be using later. Make sure you can import them\n", + "# before proceeding further.\n", + "import os\n", + "import numpy as np\n", + "import random\n", + "import string\n", + "import tensorflow as tf\n", + "import urllib\n", + "import zipfile" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 5993, + "status": "ok", + "timestamp": 1445965582896, + "user": { + "color": "#1FA15D", + "displayName": "Vincent Vanhoucke", + "isAnonymous": false, + "isMe": true, + "permissionId": "05076109866853157986", + "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", + "sessionId": "6f6f07b359200c46", + "userId": "102167687554210253930" + }, + "user_tz": 420 + }, + "id": "RJ-o3UBUFtCw", + "outputId": "d530534e-0791-4a94-ca6d-1c8f1b908a9e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found and verified text8.zip\n" + ] + } + ], + "source": [ + "url = 'http://mattmahoney.net/dc/'\n", + "\n", + "def maybe_download(filename, expected_bytes):\n", + " \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n", + " if not os.path.exists(filename):\n", + " filename, _ = urllib.urlretrieve(url + filename, filename)\n", + " statinfo = os.stat(filename)\n", + " if statinfo.st_size == expected_bytes:\n", + " print 'Found and verified', filename\n", + " else:\n", + " print statinfo.st_size\n", + " raise Exception(\n", + " 'Failed to verify ' + filename + '. Can you get to it with a browser?')\n", + " return filename\n", + "\n", + "filename = maybe_download('text8.zip', 31344016)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 5982, + "status": "ok", + "timestamp": 1445965582916, + "user": { + "color": "#1FA15D", + "displayName": "Vincent Vanhoucke", + "isAnonymous": false, + "isMe": true, + "permissionId": "05076109866853157986", + "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", + "sessionId": "6f6f07b359200c46", + "userId": "102167687554210253930" + }, + "user_tz": 420 + }, + "id": "Mvf09fjugFU_", + "outputId": "8f75db58-3862-404b-a0c3-799380597390" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data size 100000000\n" + ] + } + ], + "source": [ + "def read_data(filename):\n", + " f = zipfile.ZipFile(filename)\n", + " for name in f.namelist():\n", + " return f.read(name)\n", + " f.close()\n", + " \n", + "text = read_data(filename)\n", + "print \"Data size\", len(text)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ga2CYACE-ghb" + }, + "source": [ + "Create a small validation set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 6184, + "status": "ok", + "timestamp": 1445965583138, + "user": { + "color": "#1FA15D", + "displayName": "Vincent Vanhoucke", + "isAnonymous": false, + "isMe": true, + "permissionId": "05076109866853157986", + "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", + "sessionId": "6f6f07b359200c46", + "userId": "102167687554210253930" + }, + "user_tz": 420 + }, + "id": "w-oBpfFG-j43", + "outputId": "bdb96002-d021-4379-f6de-a977924f0d02" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "99999000 ons anarchists advocate social relations based upon voluntary as\n", + "1000 anarchism originated as a term of abuse first used against earl\n" + ] + } + ], + "source": [ + "valid_size = 1000\n", + "valid_text = text[:valid_size]\n", + "train_text = text[valid_size:]\n", + "train_size = len(train_text)\n", + "print train_size, train_text[:64]\n", + "print valid_size, valid_text[:64]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Zdw6i4F8glpp" + }, + "source": [ + "Utility functions to map characters to vocabulary IDs and back." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 6276, + "status": "ok", + "timestamp": 1445965583249, + "user": { + "color": "#1FA15D", + "displayName": "Vincent Vanhoucke", + "isAnonymous": false, + "isMe": true, + "permissionId": "05076109866853157986", + "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", + "sessionId": "6f6f07b359200c46", + "userId": "102167687554210253930" + }, + "user_tz": 420 + }, + "id": "gAL1EECXeZsD", + "outputId": "88fc9032-feb9-45ff-a9a0-a26759cc1f2e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 26 0 Unexpected character: ï\n", + "0\n", + "a z \n" + ] + } + ], + "source": [ + "vocabulary_size = len(string.ascii_lowercase) + 1 # [a-z] + ' '\n", + "first_letter = ord(string.ascii_lowercase[0])\n", + "\n", + "def char2id(char):\n", + " if char in string.ascii_lowercase:\n", + " return ord(char) - first_letter + 1\n", + " elif char == ' ':\n", + " return 0\n", + " else:\n", + " print 'Unexpected character:', char\n", + " return 0\n", + " \n", + "def id2char(dictid):\n", + " if dictid > 0:\n", + " return chr(dictid + first_letter - 1)\n", + " else:\n", + " return ' '\n", + "\n", + "print char2id('a'), char2id('z'), char2id(' '), char2id('ï')\n", + "print id2char(1), id2char(26), id2char(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "lFwoyygOmWsL" + }, + "source": [ + "Function to generate a training batch for the LSTM model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 1 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 6473, + "status": "ok", + "timestamp": 1445965583467, + "user": { + "color": "#1FA15D", + "displayName": "Vincent Vanhoucke", + "isAnonymous": false, + "isMe": true, + "permissionId": "05076109866853157986", + "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", + "sessionId": "6f6f07b359200c46", + "userId": "102167687554210253930" + }, + "user_tz": 420 + }, + "id": "d9wMtjy5hCj9", + "outputId": "3dd79c80-454a-4be0-8b71-4a4a357b3367" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['ons anarchi', 'when milita', 'lleria arch', ' abbeys and', 'married urr', 'hel and ric', 'y and litur', 'ay opened f', 'tion from t', 'migration t', 'new york ot', 'he boeing s', 'e listed wi', 'eber has pr', 'o be made t', 'yer who rec', 'ore signifi', 'a fierce cr', ' two six ei', 'aristotle s', 'ity can be ', ' and intrac', 'tion of the', 'dy to pass ', 'f certain d', 'at it will ', 'e convince ', 'ent told hi', 'ampaign and', 'rver side s', 'ious texts ', 'o capitaliz', 'a duplicate', 'gh ann es d', 'ine january', 'ross zero t', 'cal theorie', 'ast instanc', ' dimensiona', 'most holy m', 't s support', 'u is still ', 'e oscillati', 'o eight sub', 'of italy la', 's the tower', 'klahoma pre', 'erprise lin', 'ws becomes ', 'et in a naz', 'the fabian ', 'etchy to re', ' sharman ne', 'ised empero', 'ting in pol', 'd neo latin', 'th risky ri', 'encyclopedi', 'fense the a', 'duating fro', 'treet grid ', 'ations more', 'appeal of d', 'si have mad']\n", + "['ists advoca', 'ary governm', 'hes nationa', 'd monasteri', 'raca prince', 'chard baer ', 'rgical lang', 'for passeng', 'the nationa', 'took place ', 'ther well k', 'seven six s', 'ith a gloss', 'robably bee', 'to recogniz', 'ceived the ', 'icant than ', 'ritic of th', 'ight in sig', 's uncaused ', ' lost as in', 'cellular ic', 'e size of t', ' him a stic', 'drugs confu', ' take to co', ' the priest', 'im to name ', 'd barred at', 'standard fo', ' such as es', 'ze on the g', 'e of the or', 'd hiver one', 'y eight mar', 'the lead ch', 'es classica', 'ce the non ', 'al analysis', 'mormons bel', 't or at lea', ' disagreed ', 'ing system ', 'btypes base', 'anguages th', 'r commissio', 'ess one nin', 'nux suse li', ' the first ', 'zi concentr', ' society ne', 'elatively s', 'etworks sha', 'or hirohito', 'litical ini', 'n most of t', 'iskerdoo ri', 'ic overview', 'air compone', 'om acnm acc', ' centerline', 'e than any ', 'devotional ', 'de such dev']\n", + "[' a']\n", + "['an']\n" + ] + } + ], + "source": [ + "batch_size=64\n", + "num_unrollings=10\n", + "\n", + "class BatchGenerator(object):\n", + " def __init__(self, text, batch_size, num_unrollings):\n", + " self._text = text\n", + " self._text_size = len(text)\n", + " self._batch_size = batch_size\n", + " self._num_unrollings = num_unrollings\n", + " segment = self._text_size / batch_size\n", + " self._cursor = [ offset * segment for offset in xrange(batch_size)]\n", + " self._last_batch = self._next_batch()\n", + " \n", + " def _next_batch(self):\n", + " \"\"\"Generate a single batch from the current cursor position in the data.\"\"\"\n", + " batch = np.zeros(shape=(self._batch_size, vocabulary_size), dtype=np.float)\n", + " for b in xrange(self._batch_size):\n", + " batch[b, char2id(self._text[self._cursor[b]])] = 1.0\n", + " self._cursor[b] = (self._cursor[b] + 1) % self._text_size\n", + " return batch\n", + " \n", + " def next(self):\n", + " \"\"\"Generate the next array of batches from the data. The array consists of\n", + " the last batch of the previous array, followed by num_unrollings new ones.\n", + " \"\"\"\n", + " batches = [self._last_batch]\n", + " for step in xrange(self._num_unrollings):\n", + " batches.append(self._next_batch())\n", + " self._last_batch = batches[-1]\n", + " return batches\n", + "\n", + "def characters(probabilities):\n", + " \"\"\"Turn a 1-hot encoding or a probability distribution over the possible\n", + " characters back into its (mostl likely) character representation.\"\"\"\n", + " return [id2char(c) for c in np.argmax(probabilities, 1)]\n", + "\n", + "def batches2string(batches):\n", + " \"\"\"Convert a sequence of batches back into their (most likely) string\n", + " representation.\"\"\"\n", + " s = [''] * batches[0].shape[0]\n", + " for b in batches:\n", + " s = [''.join(x) for x in zip(s, characters(b))]\n", + " return s\n", + "\n", + "train_batches = BatchGenerator(train_text, batch_size, num_unrollings)\n", + "valid_batches = BatchGenerator(valid_text, 1, 1)\n", + "\n", + "print batches2string(train_batches.next())\n", + "print batches2string(train_batches.next())\n", + "print batches2string(valid_batches.next())\n", + "print batches2string(valid_batches.next())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "collapsed": true, + "id": "KyVd8FxT5QBc" + }, + "outputs": [], + "source": [ + "def logprob(predictions, labels):\n", + " \"\"\"Log-probability of the true labels in a predicted batch.\"\"\"\n", + " predictions[predictions < 1e-10] = 1e-10\n", + " return np.sum(np.multiply(labels, -np.log(predictions))) / labels.shape[0]\n", + "\n", + "def sample_distribution(distribution):\n", + " \"\"\"Sample one element from a distribution assumed to be an array of normalized\n", + " probabilities.\n", + " \"\"\"\n", + " r = random.uniform(0, 1)\n", + " s = 0\n", + " for i in xrange(len(distribution)):\n", + " s += distribution[i]\n", + " if s >= r:\n", + " return i\n", + " return len(distribution) - 1\n", + "\n", + "def sample(prediction):\n", + " \"\"\"Turn a (column) prediction into 1-hot encoded samples.\"\"\"\n", + " p = np.zeros(shape=[1, vocabulary_size], dtype=np.float)\n", + " p[0, sample_distribution(prediction[0])] = 1.0\n", + " return p\n", + "\n", + "def random_distribution():\n", + " \"\"\"Generate a random column of probabilities.\"\"\"\n", + " b = np.random.uniform(0.0, 1.0, size=[1, vocabulary_size])\n", + " return b/np.sum(b, 1)[:,None]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "K8f67YXaDr4C" + }, + "source": [ + "Simple LSTM Model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "collapsed": true, + "id": "Q5rxZK6RDuGe" + }, + "outputs": [], + "source": [ + "num_nodes = 64\n", + "\n", + "graph = tf.Graph()\n", + "with graph.as_default():\n", + " \n", + " # Parameters:\n", + " # Input gate: input, previous output, and bias.\n", + " ix = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))\n", + " im = tf.Variable(tf.truncated_normal([num_nodes, num_nodes], -0.1, 0.1))\n", + " ib = tf.Variable(tf.zeros([1, num_nodes]))\n", + " # Forget gate: input, previous output, and bias.\n", + " fx = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))\n", + " fm = tf.Variable(tf.truncated_normal([num_nodes, num_nodes], -0.1, 0.1))\n", + " fb = tf.Variable(tf.zeros([1, num_nodes]))\n", + " # Memory cell: input, state and bias. \n", + " cx = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))\n", + " cm = tf.Variable(tf.truncated_normal([num_nodes, num_nodes], -0.1, 0.1))\n", + " cb = tf.Variable(tf.zeros([1, num_nodes]))\n", + " # Output gate: input, previous output, and bias.\n", + " ox = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))\n", + " om = tf.Variable(tf.truncated_normal([num_nodes, num_nodes], -0.1, 0.1))\n", + " ob = tf.Variable(tf.zeros([1, num_nodes]))\n", + " # Variables saving state across unrollings.\n", + " saved_output = tf.Variable(tf.zeros([batch_size, num_nodes]), trainable=False)\n", + " saved_state = tf.Variable(tf.zeros([batch_size, num_nodes]), trainable=False)\n", + " # Classifier weights and biases.\n", + " w = tf.Variable(tf.truncated_normal([num_nodes, vocabulary_size], -0.1, 0.1))\n", + " b = tf.Variable(tf.zeros([vocabulary_size]))\n", + " \n", + " # Definition of the cell computation.\n", + " def lstm_cell(i, o, state):\n", + " \"\"\"Create a LSTM cell. See e.g.: http://arxiv.org/pdf/1402.1128v1.pdf\n", + " Note that in this formulation, we omit the various connections between the\n", + " previous state and the gates.\"\"\"\n", + " input_gate = tf.sigmoid(tf.matmul(i, ix) + tf.matmul(o, im) + ib)\n", + " forget_gate = tf.sigmoid(tf.matmul(i, fx) + tf.matmul(o, fm) + fb)\n", + " update = tf.matmul(i, cx) + tf.matmul(o, cm) + cb\n", + " state = forget_gate * state + input_gate * tf.tanh(update)\n", + " output_gate = tf.sigmoid(tf.matmul(i, ox) + tf.matmul(o, om) + ob)\n", + " return output_gate * tf.tanh(state), state\n", + "\n", + " # Input data.\n", + " train_data = list()\n", + " for _ in xrange(num_unrollings + 1):\n", + " train_data.append(\n", + " tf.placeholder(tf.float32, shape=[batch_size,vocabulary_size]))\n", + " train_inputs = train_data[:num_unrollings]\n", + " train_labels = train_data[1:] # labels are inputs shifted by one time step.\n", + "\n", + " # Unrolled LSTM loop.\n", + " outputs = list()\n", + " output = saved_output\n", + " state = saved_state\n", + " for i in train_inputs:\n", + " output, state = lstm_cell(i, output, state)\n", + " outputs.append(output)\n", + "\n", + " # State saving across unrollings.\n", + " with tf.control_dependencies([saved_output.assign(output),\n", + " saved_state.assign(state)]):\n", + " # Classifier.\n", + " logits = tf.nn.xw_plus_b(tf.concat(0, outputs), w, b)\n", + " loss = tf.reduce_mean(\n", + " tf.nn.softmax_cross_entropy_with_logits(\n", + " logits, tf.concat(0, train_labels)))\n", + "\n", + " # Optimizer.\n", + " global_step = tf.Variable(0)\n", + " learning_rate = tf.train.exponential_decay(\n", + " 10.0, global_step, 5000, 0.1, staircase=True)\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n", + " gradients, v = zip(*optimizer.compute_gradients(loss))\n", + " gradients, _ = tf.clip_by_global_norm(gradients, 1.25)\n", + " optimizer = optimizer.apply_gradients(\n", + " zip(gradients, v), global_step=global_step)\n", + "\n", + " # Predictions.\n", + " train_prediction = tf.nn.softmax(logits)\n", + " \n", + " # Sampling and validation eval: batch 1, no unrolling.\n", + " sample_input = tf.placeholder(tf.float32, shape=[1, vocabulary_size])\n", + " saved_sample_output = tf.Variable(tf.zeros([1, num_nodes]))\n", + " saved_sample_state = tf.Variable(tf.zeros([1, num_nodes]))\n", + " reset_sample_state = tf.group(\n", + " saved_sample_output.assign(tf.zeros([1, num_nodes])),\n", + " saved_sample_state.assign(tf.zeros([1, num_nodes])))\n", + " sample_output, sample_state = lstm_cell(\n", + " sample_input, saved_sample_output, saved_sample_state)\n", + " with tf.control_dependencies([saved_sample_output.assign(sample_output),\n", + " saved_sample_state.assign(sample_state)]):\n", + " sample_prediction = tf.nn.softmax(tf.nn.xw_plus_b(sample_output, w, b))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "output_extras": [ + { + "item_id": 41 + }, + { + "item_id": 80 + }, + { + "item_id": 126 + }, + { + "item_id": 144 + } + ] + }, + "colab_type": "code", + "collapsed": false, + "executionInfo": { + "elapsed": 199909, + "status": "ok", + "timestamp": 1445965877333, + "user": { + "color": "#1FA15D", + "displayName": "Vincent Vanhoucke", + "isAnonymous": false, + "isMe": true, + "permissionId": "05076109866853157986", + "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", + "sessionId": "6f6f07b359200c46", + "userId": "102167687554210253930" + }, + "user_tz": 420 + }, + "id": "RD9zQCZTEaEm", + "outputId": "5e868466-2532-4545-ce35-b403cf5d9de6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initialized\n", + "Average loss at step 0 : 3.29904174805 learning rate: 10.0\n", + "Minibatch perplexity: 27.09\n", + "================================================================================\n", + "srk dwmrnuldtbbgg tapootidtu xsciu sgokeguw hi ieicjq lq piaxhazvc s fht wjcvdlh\n", + "lhrvallvbeqqquc dxd y siqvnle bzlyw nr rwhkalezo siie o deb e lpdg storq u nx o\n", + "meieu nantiouie gdys qiuotblci loc hbiznauiccb cqzed acw l tsm adqxplku gn oaxet\n", + "unvaouc oxchywdsjntdh zpklaejvxitsokeerloemee htphisb th eaeqseibumh aeeyj j orw\n", + "ogmnictpycb whtup otnilnesxaedtekiosqet liwqarysmt arj flioiibtqekycbrrgoysj\n", + "================================================================================\n", + "Validation set perplexity: 19.99\n", + "Average loss at step 100 : 2.59553678274 learning rate: 10.0\n", + "Minibatch perplexity: 9.57\n", + "Validation set perplexity: 10.60\n", + "Average loss at step 200 : 2.24747137785 learning rate: 10.0\n", + "Minibatch perplexity: 7.68\n", + "Validation set perplexity: 8.84\n", + "Average loss at step 300 : 2.09438110709 learning rate: 10.0\n", + "Minibatch perplexity: 7.41\n", + "Validation set perplexity: 8.13\n", + "Average loss at step 400 : 1.99440989017 learning rate: 10.0\n", + "Minibatch perplexity: 6.46\n", + "Validation set perplexity: 7.58\n", + "Average loss at step 500 : 1.9320810616 learning rate: 10.0\n", + "Minibatch perplexity: 6.30\n", + "Validation set perplexity: 6.88\n", + "Average loss at step 600 : 1.90935629249 learning rate: 10.0\n", + "Minibatch perplexity: 7.21\n", + "Validation set perplexity: 6.91\n", + "Average loss at step 700 : 1.85583009005 learning rate: 10.0\n", + "Minibatch perplexity: 6.13\n", + "Validation set perplexity: 6.60\n", + "Average loss at step 800 : 1.82152368546 learning rate: 10.0\n", + "Minibatch perplexity: 6.01\n", + "Validation set perplexity: 6.37\n", + "Average loss at step 900 : 1.83169809818 learning rate: 10.0\n", + "Minibatch perplexity: 7.20\n", + "Validation set perplexity: 6.23\n", + "Average loss at step 1000 : 1.82217029214 learning rate: 10.0\n", + "Minibatch 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4.61\n", + "Average loss at step 3000 : 1.6495274055 learning rate: 10.0\n", + "Minibatch perplexity: 4.53\n", + "================================================================================\n", + "ject covered in belo one six six to finsh that all di rozial sime it a the lapse\n", + "ble which the pullic bocades record r to sile dric two one four nine seven six f\n", + " originally ame the playa ishaps the stotchational in a p dstambly name which as\n", + "ore volum to bay riwer foreal in nuily operety can and auscham frooripm however \n", + "kan traogey was lacous revision the mott coupofiteditey the trando insended frop\n", + "================================================================================\n", + "Validation set perplexity: 4.76\n", + "Average loss at step 3100 : 1.63705502152 learning rate: 10.0\n", + "Minibatch perplexity: 5.50\n", + "Validation set perplexity: 4.76\n", + "Average loss at step 3200 : 1.64740695596 learning rate: 10.0\n", + "Minibatch 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perplexity: 4.91\n", + "Validation set perplexity: 4.54\n", + "Average loss at step 4000 : 1.65179730773 learning rate: 10.0\n", + "Minibatch perplexity: 4.77\n", + "================================================================================\n", + "k s rasbonish roctes the nignese at heacle was sito of beho anarchys and with ro\n", + "jusar two sue wletaus of chistical in causations d ow trancic bruthing ha laters\n", + "de and speacy pulted yoftret worksy zeatlating to eight d had to ie bue seven si\n", + "s fiction of the feelly constive suq flanch earlied curauking bjoventation agent\n", + "quen s playing it calana our seopity also atbellisionaly comexing the revideve i\n", + "================================================================================\n", + "Validation set perplexity: 4.58\n", + "Average loss at step 4100 : 1.63794238806 learning rate: 10.0\n", + "Minibatch perplexity: 5.47\n", + "Validation set perplexity: 4.79\n", + "Average loss at step 4200 : 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"Validation set perplexity: 4.27\n", + "Average loss at step 6200 : 1.53433164835 learning rate: 1.0\n", + "Minibatch perplexity: 4.77\n", + "Validation set perplexity: 4.27\n", + "Average loss at step 6300 : 1.54773445129 learning rate: 1.0\n", + "Minibatch perplexity: 4.76\n", + "Validation set perplexity: 4.25\n", + "Average loss at step 6400 : 1.54021131516 learning rate: 1.0\n", + "Minibatch perplexity: 4.56\n", + "Validation set perplexity: 4.24\n", + "Average loss at step 6500 : 1.56153374553 learning rate: 1.0\n", + "Minibatch perplexity: 5.43\n", + "Validation set perplexity: 4.27\n", + "Average loss at step 6600 : 1.59556478739 learning rate: 1.0\n", + "Minibatch perplexity: 4.92\n", + "Validation set perplexity: 4.28\n", + "Average loss at step 6700 : 1.58076951623 learning rate: 1.0\n", + "Minibatch perplexity: 4.77\n", + "Validation set perplexity: 4.30\n", + "Average loss at step 6800 : 1.6070714438 learning rate: 1.0\n", + "Minibatch perplexity: 4.98\n", + "Validation set perplexity: 4.28\n", + "Average loss at step 6900 : 1.58413293839 learning rate: 1.0\n", + "Minibatch perplexity: 4.61\n", + "Validation set perplexity: 4.29\n", + "Average loss at step 7000 : 1.57905534983 learning rate: 1.0\n", + "Minibatch perplexity: 5.08\n", + "================================================================================\n", + "jague are officiencinels ored by film voon higherise haik one nine on the iffirc\n", + "oshe provision that manned treatists on smalle bodariturmeristing the girto in s\n", + "kis would softwenn mustapultmine truativersakys bersyim by s of confound esc bub\n", + "ry of the using one four six blain ira mannom marencies g with fextificallise re\n", + " one son vit even an conderouss to person romer i a lebapter at obiding are iuse\n", + "================================================================================\n", + "Validation set perplexity: 4.25\n" + ] + } + ], + "source": [ + "num_steps = 7001\n", + "summary_frequency = 100\n", + "\n", + "with tf.Session(graph=graph) as session:\n", + " tf.initialize_all_variables().run()\n", + " print 'Initialized'\n", + " mean_loss = 0\n", + " for step in xrange(num_steps):\n", + " batches = train_batches.next()\n", + " feed_dict = dict()\n", + " for i in xrange(num_unrollings + 1):\n", + " feed_dict[train_data[i]] = batches[i]\n", + " _, l, predictions, lr = session.run(\n", + " [optimizer, loss, train_prediction, learning_rate], feed_dict=feed_dict)\n", + " mean_loss += l\n", + " if step % summary_frequency == 0:\n", + " if step > 0:\n", + " mean_loss = mean_loss / summary_frequency\n", + " # The mean loss is an estimate of the loss over the last few batches.\n", + " print 'Average loss at step', step, ':', mean_loss, 'learning rate:', lr\n", + " mean_loss = 0\n", + " labels = np.concatenate(list(batches)[1:])\n", + " print 'Minibatch perplexity: %.2f' % float(\n", + " np.exp(logprob(predictions, labels)))\n", + " if step % (summary_frequency * 10) == 0:\n", + " # Generate some samples.\n", + " print '=' * 80\n", + " for _ in xrange(5):\n", + " feed = sample(random_distribution())\n", + " sentence = characters(feed)[0]\n", + " reset_sample_state.run()\n", + " for _ in xrange(79):\n", + " prediction = sample_prediction.eval({sample_input: feed})\n", + " feed = sample(prediction)\n", + " sentence += characters(feed)[0]\n", + " print sentence\n", + " print '=' * 80\n", + " # Measure validation set perplexity.\n", + " reset_sample_state.run()\n", + " valid_logprob = 0\n", + " for _ in xrange(valid_size):\n", + " b = valid_batches.next()\n", + " predictions = sample_prediction.eval({sample_input: b[0]})\n", + " valid_logprob = valid_logprob + logprob(predictions, b[1])\n", + " print 'Validation set perplexity: %.2f' % float(np.exp(\n", + " valid_logprob / valid_size))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "pl4vtmFfa5nn" + }, + "source": [ + "---\n", + "Problem 1\n", + "---------\n", + "\n", + "You might have noticed that the definition of the LSTM cell involves 4 matrix multiplications with the input, and 4 matrix multiplications with the output. Simplify the expression by using a single matrix multiply for each, and variables that are 4 times larger.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4eErTCTybtph" + }, + "source": [ + "---\n", + "Problem 2\n", + "---------\n", + "\n", + "We want to train a LSTM over bigrams, that is pairs of consecutive characters like 'ab' instead of single characters like 'a'. Since the number of possible bigrams is large, feeding them directly to the LSTM using 1-hot encodings will lead to a very sparse representation that is very wasteful computationally.\n", + "\n", + "a- Introduce an embedding lookup on the inputs, and feed the embeddings to the LSTM cell instead of the inputs themselves.\n", + "\n", + "b- Write a bigram-based LSTM, modeled on the character LSTM above.\n", + "\n", + "c- Introduce Dropout. For best practices on how to use Dropout in LSTMs, refer to this [article](http://arxiv.org/abs/1409.2329).\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Y5tapX3kpcqZ" + }, + "source": [ + "---\n", + "Problem 3\n", + "---------\n", + "\n", + "(difficult!)\n", + "\n", + "Write a sequence-to-sequence LSTM which mirrors all the words in a sentence. For example, if your input is:\n", + "\n", + " the quick brown fox\n", + " \n", + "the model should attempt to output:\n", + "\n", + " eht kciuq nworb xof\n", + " \n", + "Reference: http://arxiv.org/abs/1409.3215\n", + "\n", + "---" + ] + } + ], + "metadata": { + "colabVersion": "0.3.2", + "colab_default_view": {}, + "colab_views": {}, + "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": "3.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/deep-learning/tensor-flow-exercises/Dockerfile b/deep-learning/tensor-flow-exercises/Dockerfile new file mode 100644 index 0000000..59ae4ab --- /dev/null +++ b/deep-learning/tensor-flow-exercises/Dockerfile @@ -0,0 +1,6 @@ +FROM b.gcr.io/tensorflow/tensorflow:latest +MAINTAINER Vincent Vanhoucke +RUN pip install scikit-learn +ADD *.ipynb /notebooks/ +WORKDIR /notebooks +CMD ["/run_jupyter.sh"] diff --git a/deep-learning/tensor-flow-exercises/README.md b/deep-learning/tensor-flow-exercises/README.md new file mode 100644 index 0000000..b857c7f --- /dev/null +++ b/deep-learning/tensor-flow-exercises/README.md @@ -0,0 +1,12 @@ +Exercises +=========================================================== + +Building the Docker container +----------------------------- + + docker build -t $USER/exercises . + +Running the container +--------------------- + + docker run -p 8888:8888 -it --rm $USER/exercises