Commit d55d6e6d authored by Martin Karlsson's avatar Martin Karlsson
Browse files

hw3

parent d873eb66
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import sklearn.datasets\n",
"import tensorflow as tf\n",
"from tensorflow.python.framework.ops import reset_default_graph\n",
"\n",
"def onehot(t, num_classes):\n",
" out = np.zeros((t.shape[0], num_classes))\n",
" for row, col in enumerate(t):\n",
" out[row, col] = 1\n",
" return out"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Convolutional Neural networks 101\n",
"\n",
"Convolution neural networks are one of the most succesfull types of neural networks for image recognition and an integral part of reigniting the interest in neural networks. \n",
"\n",
"In this lab we'll experiment with inserting 2D-convolution layers in the fully connected neural networks introduced in LAB1. We'll furhter experiment with stacking of convolution layers, max pooling and strided convolutions which are all important techniques in current convolution neural network architectures. Lastly we'll try to visualize the learned convolution filters and try to understand what kind of features they learn to recognize.\n",
"\n",
"\n",
"If you are unfamilar with the the convolution operation https://github.com/vdumoulin/conv_arithmetic have a nice visualization of different convolution variants. For a more indept tutorial please see http://cs231n.github.io/convolutional-networks/ or http://neuralnetworksanddeeplearning.com/chap6.html. Lastly if you are ambitious and want implement a convolution neural network from scratch please see an exercise for our Deep Learning summer school last year https://github.com/DTU-deeplearning/day2-Conv"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Information on dataset\n",
"x_train (1000, 1, 28, 28)\n",
"targets_train (1000,)\n",
"x_valid (500, 1, 28, 28)\n",
"targets_valid (500,)\n",
"x_test (500, 1, 28, 28)\n",
"targets_test (500,)\n"
]
}
],
"source": [
"#LOAD the mnist data. To speed up training we'll only work on a subset of the data.\n",
"#Note that we reshape the data from (nsamples, num_features)= (nsamples, nchannels*rows*cols) -> (nsamples, nchannels, rows, cols)\n",
"# in order to retain the spatial arrangements of the pixels\n",
"data = np.load('mnist.npz')\n",
"num_classes = 10\n",
"nchannels,rows,cols = 1,28,28\n",
"x_train = data['X_train'][:1000].astype('float32')\n",
"x_train = x_train.reshape((-1,nchannels,rows,cols))\n",
"targets_train = data['y_train'][:1000].astype('int32')\n",
"\n",
"x_valid = data['X_valid'][:500].astype('float32')\n",
"x_valid = x_valid.reshape((-1,nchannels,rows,cols))\n",
"targets_valid = data['y_valid'][:500].astype('int32')\n",
"\n",
"x_test = data['X_test'][:500].astype('float32')\n",
"x_test = x_test.reshape((-1,nchannels,rows,cols))\n",
"targets_test = data['y_test'][:500].astype('int32')\n",
"\n",
"print \"Information on dataset\"\n",
"print \"x_train\", x_train.shape\n",
"print \"targets_train\", targets_train.shape\n",
"print \"x_valid\", x_valid.shape\n",
"print \"targets_valid\", targets_valid.shape\n",
"print \"x_test\", x_test.shape\n",
"print \"targets_test\", targets_test.shape"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
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Sk5NDTEwMMzMzXLhw4Ymvw21aDQ4OJiIigrm5OVlLXQmUSiXBwcEEBATI/hZvIoQgLi6O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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbe406c5250>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#plot a few MNIST examples\n",
"idx = 0\n",
"canvas = np.zeros((28*10, 10*28))\n",
"for i in range(10):\n",
" for j in range(10):\n",
" canvas[i*28:(i+1)*28, j*28:(j+1)*28] = x_train[idx].reshape((28, 28))\n",
" idx += 1\n",
"plt.figure(figsize=(7, 7))\n",
"plt.imshow(canvas, cmap='gray')\n",
"plt.title('MNIST handwritten digits')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Documentation on contrib layers\n",
"Check out the [github page](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/layers.py) for information on contrib layers (not well documented in their api)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from tensorflow.contrib.layers import fully_connected, convolution2d, flatten, batch_norm, max_pool2d, dropout\n",
"from tensorflow.python.ops.nn import relu, elu, relu6, sigmoid, tanh, softmax"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# define a simple feed forward neural network\n",
"\n",
"# hyperameters of the model\n",
"num_classes = 10\n",
"channels = x_train.shape[1]\n",
"height = x_train.shape[2]\n",
"width = x_train.shape[3]\n",
"num_filters_conv1 = 16\n",
"kernel_size_conv1 = [5, 5] # [height, width]\n",
"stride_conv1 = [1, 1] # [stride_height, stride_width]\n",
"num_l1 = 100\n",
"# resetting the graph ...\n",
"reset_default_graph()\n",
"\n",
"# Setting up placeholder, this is where your data enters the graph!\n",
"x_pl = tf.placeholder(tf.float32, [None, channels, height, width])\n",
"l_reshape = tf.transpose(x_pl, [0, 2, 3, 1]) # TensorFlow uses NHWC instead of NCHW\n",
"#is_training = tf.placeholder(tf.bool)#used for dropout\n",
"\n",
"# Building the layers of the neural network\n",
"# we define the variable scope, so we more easily can recognise our variables later\n",
"#l_conv1 = convolution2d(l_reshape, num_filters_conv1, kernel_size_conv1, stride_conv1, scope=\"l_conv1\")\n",
"l_flatten = flatten(l_reshape, scope=\"flatten\") # use l_conv1 instead of l_reshape\n",
"l1 = fully_connected(l_flatten, num_l1, activation_fn=relu, scope=\"l1\")\n",
"#l1 = dropout(l1, is_training=is_training, scope=\"dropout\")\n",
"y = fully_connected(l1, num_classes, activation_fn=softmax, scope=\"y\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# y_ is a placeholder variable taking on the value of the target batch.\n",
"y_ = tf.placeholder(tf.float32, [None, num_classes])\n",
"\n",
"# computing cross entropy per sample\n",
"cross_entropy = -tf.reduce_sum(y_ * tf.log(y+1e-8), reduction_indices=[1])\n",
"\n",
"# averaging over samples\n",
"cross_entropy = tf.reduce_mean(cross_entropy)\n",
"\n",
"# defining our optimizer\n",
"optimizer = tf.train.AdamOptimizer(learning_rate=0.001)\n",
"\n",
"# applying the gradients\n",
"train_op = optimizer.minimize(cross_entropy)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Test the forward pass\n",
"x = np.random.normal(0,1, (45, 1,28,28)).astype('float32') #dummy data\n",
"\n",
"# restricting memory usage, TensorFlow is greedy and will use all memory otherwise\n",
"gpu_opts = tf.GPUOptions(per_process_gpu_memory_fraction=0.2)\n",
"# initialize the Session\n",
"sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_opts))\n",
"sess.run(tf.initialize_all_variables())\n",
"res = sess.run(fetches=[y], feed_dict={x_pl: x})\n",
"#res = sess.run(fetches=[y], feed_dict={x_pl: x, is_training: False}) # for when using dropout\n",
"print \"y\", res[0].shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Training Loop\n",
"from confusionmatrix import ConfusionMatrix\n",
"batch_size = 100\n",
"num_epochs = 10\n",
"num_samples_train = x_train.shape[0]\n",
"num_batches_train = num_samples_train // batch_size\n",
"num_samples_valid = x_valid.shape[0]\n",
"num_batches_valid = num_samples_valid // batch_size\n",
"\n",
"train_acc, train_loss = [], []\n",
"valid_acc, valid_loss = [], []\n",
"test_acc, test_loss = [], []\n",
"cur_loss = 0\n",
"loss = []\n",
"try:\n",
" for epoch in range(num_epochs):\n",
" #Forward->Backprob->Update params\n",
" cur_loss = 0\n",
" for i in range(num_batches_train):\n",
" idx = range(i*batch_size, (i+1)*batch_size)\n",
" x_batch = x_train[idx]\n",
" target_batch = targets_train[idx]\n",
" feed_dict_train = {x_pl: x_batch, y_: onehot(target_batch, num_classes)}\n",
" #feed_dict_train = {x_pl: x_batch, y_: onehot(target_batch, num_classes), is_training: True}\n",
" fetches_train = [train_op, cross_entropy]\n",
" res = sess.run(fetches=fetches_train, feed_dict=feed_dict_train)\n",
" batch_loss = res[1] #this will do the complete backprob pass\n",
" cur_loss += batch_loss\n",
" loss += [cur_loss/batch_size]\n",
"\n",
" confusion_valid = ConfusionMatrix(num_classes)\n",
" confusion_train = ConfusionMatrix(num_classes)\n",
"\n",
" for i in range(num_batches_train):\n",
" idx = range(i*batch_size, (i+1)*batch_size)\n",
" x_batch = x_train[idx]\n",
" targets_batch = targets_train[idx]\n",
" # what to feed our accuracy op\n",
" feed_dict_eval_train = {x_pl: x_batch}\n",
" #feed_dict_eval_train = {x_pl: x_batch, is_training: False}\n",
" # deciding which parts to fetch\n",
" fetches_eval_train = [y]\n",
" # running the validation\n",
" res = sess.run(fetches=fetches_eval_train, feed_dict=feed_dict_eval_train)\n",
" # collecting and storing predictions\n",
" net_out = res[0] \n",
" preds = np.argmax(net_out, axis=-1) \n",
" confusion_train.batch_add(targets_batch, preds)\n",
"\n",
" confusion_valid = ConfusionMatrix(num_classes)\n",
" for i in range(num_batches_valid):\n",
" idx = range(i*batch_size, (i+1)*batch_size)\n",
" x_batch = x_valid[idx]\n",
" targets_batch = targets_valid[idx]\n",
" # what to feed our accuracy op\n",
" feed_dict_eval_train = {x_pl: x_batch}\n",
" #feed_dict_eval_train = {x_pl: x_batch, is_training: False}\n",
" # deciding which parts to fetch\n",
" fetches_eval_train = [y]\n",
" # running the validation\n",
" res = sess.run(fetches=fetches_eval_train, feed_dict=feed_dict_eval_train)\n",
" # collecting and storing predictions\n",
" net_out = res[0]\n",
" preds = np.argmax(net_out, axis=-1) \n",
"\n",
" confusion_valid.batch_add(targets_batch, preds)\n",
"\n",
" train_acc_cur = confusion_train.accuracy()\n",
" valid_acc_cur = confusion_valid.accuracy()\n",
"\n",
" train_acc += [train_acc_cur]\n",
" valid_acc += [valid_acc_cur]\n",
" print \"Epoch %i : Train Loss %e , Train acc %f, Valid acc %f \" \\\n",
" % (epoch+1, loss[-1], train_acc_cur, valid_acc_cur)\n",
"except KeyboardInterrupt:\n",
" pass\n",
" \n",
"\n",
"#get test set score\n",
"confusion_test = ConfusionMatrix(num_classes)\n",
"# what to feed our accuracy op\n",
"feed_dict_eval_train = {x_pl: x_test}\n",
"#feed_dict_eval_train = {x_pl: x_test, is_training: False}\n",
"# deciding which parts to fetch\n",
"fetches_eval_train = [y]\n",
"# running the validation\n",
"res = sess.run(fetches=fetches_eval_train, feed_dict=feed_dict_eval_train)\n",
"# collecting and storing predictions\n",
"net_out = res[0] \n",
"preds = np.argmax(net_out, axis=-1) \n",
"confusion_test.batch_add(targets_test, preds)\n",
"print \"\\nTest set Acc: %f\" %(confusion_test.accuracy())\n",
"\n",
"\n",
"epoch = np.arange(len(train_acc))\n",
"plt.figure()\n",
"plt.plot(epoch,train_acc,'r',epoch,valid_acc,'b')\n",
"plt.legend(['Train Acc','Val Acc'])\n",
"plt.xlabel('Epochs'), plt.ylabel('Acc'), plt.ylim([0.75,1.03])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Assignments 1\n",
"\n",
" 1) Note the performance of the standard feedforward neural network. Add a 2D convolution layer before the dense hidden layer and confirm that it increases the generalization performance of the network (try num_filters=16 and filter_size=5 as a starting point). \n",
" \n",
" 2) Can the performance be increases even further by stacking more convolution layers ?\n",
" \n",
" 3) Maxpooling is a technique for decreasing the spatial resolution of an image while retaining the important features. Effectively this gives a local translational invariance and reduces the computation by a factor of four. In the classification algorithm which is usually desirable. Try to either: \n",
" \n",
" a) add a maxpool layer(add arguement pool_size=2) after the convolution layer or\n",
" b) set add stride=2 to the arguments of the convolution layer. \n",
" Verify that this decreases spatial dimension of the image. (print l_conv.output_shape or print l_maxpool.output_shape). Does this increase the performance of the network (you may need to stack multiple layers or increase the number of filters to increase performance) ?\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualization of filters\n",
"Convolution filters can be interpreted as spatial feature detectors picking up different image features such as edges, corners etc. Below we provide code for visualization of the filters. The best results are obtained with fairly large filters of size 9 and either 16 or 36 filters. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# to start with we print the names of the weights in our graph\n",
"# to see what operations we are allowed to perform on the variables in our graph, try:\n",
"#print(dir(tf.all_variables()[0]))\n",
"# you will notice it has \"name\" and \"value\", which we will build a dictionary from\n",
"names_and_vars = {var.name: sess.run(var.value()) for var in tf.all_variables()}\n",
"print(names_and_vars.keys())\n",
"# getting the name was easy, just use .name on the variable object\n",
"# getting the value in a numpy array format is slightly more tricky\n",
"# we need to first get a variable object, then turn it into a tensor with .value()\n",
"# and the evaluate the tensor with sess.run(...)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"### ERROR - If you get a key error, then you need to define l_conv1 in your model!\n",
"if not u'l_conv1/weights:0' in names_and_vars:\n",
" print \"You need to go back and define a convolutional layer in the network.\"\n",
"else:\n",
" np_W = names_and_vars[u'l_conv1/weights:0'] # get the filter values from the first conv layer\n",
" print np_W.shape, \"i.e. the shape is filter_size, filter_size, num_channels, num_filters\"\n",
" filter_size, _, num_channels, num_filters = np_W.shape\n",
" n = int(num_filters**0.5)\n",
"\n",
" # reshaping the last dimension to be n by n\n",
" np_W_res = np_W.reshape(filter_size, filter_size, num_channels, n, n)\n",
" fig, ax = plt.subplots(n,n)\n",
" print \"learned filter values\"\n",
" for i in range(n):\n",
" for j in range(n):\n",
" ax[i,j].imshow(np_W_res[:,:,0,i,j], cmap='gray',interpolation='none')\n",
" ax[i,j].xaxis.set_major_formatter(plt.NullFormatter())\n",
" ax[i,j].yaxis.set_major_formatter(plt.NullFormatter())\n",
"\n",
"\n",
" idx = 1\n",
" plt.figure()\n",
" plt.imshow(x_train[idx,0],cmap='gray',interpolation='none')\n",
" plt.title('Inut Image')\n",
" plt.show()\n",
"\n",
" #visalize the filters convolved with an input image\n",
" from scipy.signal import convolve2d\n",
" np_W_res = np_W.reshape(filter_size, filter_size, num_channels, n, n)\n",
" fig, ax = plt.subplots(n,n,figsize=(9,9))\n",
" print \"Response from input image convolved with the filters\"\n",
" for i in range(n):\n",
" for j in range(n):\n",
" ax[i,j].imshow(convolve2d(x_train[1,0],np_W_res[:,:,0,i,j],mode='same'),\n",
" cmap='gray',interpolation='none')\n",
" ax[i,j].xaxis.set_major_formatter(plt.NullFormatter())\n",
" ax[i,j].yaxis.set_major_formatter(plt.NullFormatter())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Assignment 2\n",
"\n",
"The visualized filters will likely look most like noise due to the small amount of training data.\n",
"\n",
" 1) Try to use 10000 traning examples instead and visualise the filters again\n",
" \n",
" 2) Dropout is a very usefull technique for preventing overfitting. Try to add a DropoutLayer after the convolution layer and hidden layer. This should increase both performance and the \"visual appeal\" of the filters\n",
" \n",
" 3) Batch normalization is a recent innovation for improving generalization performance. Try to insert batch normalization layers into the network to improve performance. \n",
" \n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# More Fun with convolutional networks\n",
"### Get the data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!wget -N https://s3.amazonaws.com/lasagne/recipes/datasets/mnist_cluttered_60x60_6distortions.npz"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the data the each mnist digit (20x20 pixels) has been placed randomly in a 60x60 canvas. To make the task harder each canvas has then been cluttered with small pieces of digits. In this task it is helpfull for a network if it can focus only on the digit and ignore the rest.\n",
"\n",
"The ``TransformerLayer`` lets us do this. The transformer layer learns an affine transformation which lets the network zoom, rotate and skew. If you are interested you should read the paper, but the main idea is that you can let a small convolutional network determine the the parameters of the affine transformation. You then apply the affine transformation to the input data. Usually this also involves downsampling which forces the model to zoom in on the relevant parts of the data. After the affine transformation we can use a larger conv net to do the classification. \n",
"This is possible because you can backprop through a an affine transformation if you use bilinear interpolation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"import matplotlib\n",
"import numpy as np\n",
"np.random.seed(123)\n",
"import matplotlib.pyplot as plt\n",
"import tensorflow as tf\n",
"from tensorflow.contrib.layers import fully_connected, convolution2d, flatten, max_pool2d\n",
"pool = max_pool2d\n",
"conv = convolution2d\n",
"dense = fully_connected\n",
"from tensorflow.python.ops.nn import relu, softmax\n",
"from tensorflow.python.framework.ops import reset_default_graph\n",
"\n",
"from spatial_transformer import transformer\n",
"\n",
"def onehot(t, num_classes):\n",
" out = np.zeros((t.shape[0], num_classes))\n",
" for row, col in enumerate(t):\n",
" out[row, col] = 1\n",
" return out\n",
"\n",
"NUM_EPOCHS = 500\n",
"BATCH_SIZE = 256\n",
"LEARNING_RATE = 0.001\n",
"DIM = 60\n",
"NUM_CLASSES = 10\n",