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justheuristicjustheuristic
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py3 fix (ty stamax@)
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+35
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‎week7/captioning_theano.ipynb

Lines changed: 33 additions & 79 deletions
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@@ -2,9 +2,7 @@
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"source": [
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"DeepLarning Couse HSE 2016 fall: \n",
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"* Arseniy Ashuha, you can text me ```ars.ashuha@gmail.com```,\n",
@@ -22,9 +20,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"!wget https://www.dropbox.com/s/3hj16b0fj6yw7cc/data.tar.gz?dl=1 -O data.tar.gz\n",
@@ -41,9 +37,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
@@ -58,9 +52,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"print \"each image code is a 1000-unit vector:\", img_codes.shape\n",
@@ -73,9 +65,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"#split descriptions into tokens\n",
@@ -88,9 +78,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# Build a Vocabulary\n",
@@ -144,9 +132,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"#try it out on several descriptions of a random image\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# network shapes. \n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# Input Variable\n",
@@ -215,9 +197,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"#network inputs\n",
@@ -233,9 +213,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# input layer for image features\n",
@@ -251,9 +229,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# Concatinate image features and word embedings in one sequence \n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# Decoding of rnn hiden states\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"next_word_probas = <get network output>\n",
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"predictions_flat = next_word_probas[:,:-1].reshape((-1,n_tokens))\n",
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"reference_answers = sentences[:,1:].reshape((-1,))\n",
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"\n",
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"#write symbolic loss function to train NN for\n",
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"loss = <compute elementwise loss function>\n",
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"\n",
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"#mean over non-PAD tokens\n",
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"output_mask = sentence_mask[:,1:]\n",
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"loss = (loss.reshape(reference_answers.shape)*output_mask).sum() / output_mask.sum()"
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"#write symbolic loss function to minimize over NN params\n",
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"loss = <compute elementwise loss function>"
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]
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},
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{
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"#compile a functions for training and evaluation\n",
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"source": [
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"# Training\n",
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"\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"generate_batch(img_codes,captions,3)"
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"scrolled": false
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},
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"outputs": [],
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"source": [
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"### apply trained model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"#the same kind you did last week, but a bit smaller\n",
@@ -520,6 +477,9 @@
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"\n",
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"#load weights\n",
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"lenet_weights = pickle.load(open('data/blvc_googlenet.pkl'))['param values']\n",
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"#python3: pickle.load(open('data/blvc_googlenet.pkl', 'rb'), encoding='latin1')['param values']\n",
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"\n",
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"\n",
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"set_all_param_values(lenet[\"prob\"], lenet_weights)\n",
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"\n",
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"#compile get_features\n",
@@ -531,9 +491,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"from matplotlib import pyplot as plt\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"#deprocess and show, one line :)\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"for i in range(10):\n",
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [Root]",
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"display_name": "Python 3",
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"language": "python",
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"name": "Python [Root]"
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.12"
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"pygments_lexer": "ipython3",
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"version": "3.6.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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"nbformat_minor": 1
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}

‎week7/pretrained_lenet.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -101,9 +101,9 @@ def preprocess(im):
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# Resize so smallest dim = 224, preserving aspect ratio
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h, w, _ = im.shape
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if h < w:
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im = skimage.transform.resize(im, (224, w*224/h), preserve_range=True)
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im = skimage.transform.resize(im, (224, w*224//h), preserve_range=True)
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else:
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im = skimage.transform.resize(im, (h*224/w, 224), preserve_range=True)
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im = skimage.transform.resize(im, (h*224//w, 224), preserve_range=True)
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# Central crop to 224x224
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h, w, _ = im.shape

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