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eshmaapps/statefarm.ipynb

Created November 26, 2016 07:12
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statefarm.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using Convolutional Neural Networks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Welcome to the first week of the first deep learning certificate! We're going to use convolutional neural networks (CNNs) to allow our computer to see - something that is only possible thanks to deep learning."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"hidden": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using gpu device 0: Tesla K80 (CNMeM is disabled, cuDNN 5103)\n",
"/home/ubuntu/anaconda2/lib/python2.7/site-packages/theano/sandbox/cuda/__init__.py:600: UserWarning: Your cuDNN version is more recent than the one Theano officially supports. If you see any problems, try updating Theano or downgrading cuDNN to version 5.\n",
" warnings.warn(warn)\n"
]
}
],
"source": [
"%matplotlib inline\n",
"from theano.sandbox import cuda\n",
"cuda.use('gpu0')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"hidden": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using Theano backend.\n"
]
}
],
"source": [
"from __future__ import division,print_function\n",
"\n",
"import os, json\n",
"from glob import glob\n",
"import numpy as np\n",
"np.set_printoptions(precision=4, linewidth=100)\n",
"from matplotlib import pyplot as plt\n",
"from keras.preprocessing import image\n",
"from IPython.display import FileLink"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"We have created a file most imaginatively called 'utils.py' to store any little convenience functions we'll want to use. We will discuss these as we use them."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"hidden": true,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/ubuntu/nbs\n"
]
}
],
"source": [
"%cd ~/nbs/\n",
"import utils; reload(utils)\n",
"from utils import *"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"batch_size=64"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from keras.layers import BatchNormalization, Flatten, Input, Dense, Convolution2D, MaxPooling2D, UpSampling2D\n",
"from keras.models import Model, Sequential\n",
"from keras.optimizers import Adam, RMSprop"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Use a pretrained VGG model with our **Vgg16** class"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our first step is simply to use a model that has been fully created for us, which can recognise a wide variety (1,000 categories) of images. We will use 'VGG', which won the 2014 Imagenet competition, and is a very simple model to create and understand. The VGG Imagenet team created both a larger, slower, slightly more accurate model (*VGG 19*) and a smaller, faster model (*VGG 16*). We will be using VGG 16 since the much slower performance of VGG19 is generally not worth the very minor improvement in accuracy.\n",
"\n",
"We have created a python class, *Vgg16*, which makes using the VGG 16 model very straightforward. "
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"## The punchline: state of the art custom model in 7 lines of code\n",
"\n",
"Here's everything you need to do to get >97% accuracy on the Dogs vs Cats dataset - we won't analyze how it works behind the scenes yet, since at this stage we're just going to focus on the minimum necessary to actually do useful work."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Creating the data sets\n",
"Action Plan\n",
"Create Validation and Sample sets\n",
"Rearrange image files into their respective directories\n",
"Finetune and Train model\n",
"Generate predictions\n",
"Validate predictions\n",
"Submit predictions to Kaggle"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/ubuntu/nbs/statefarm\n"
]
}
],
"source": [
"%cd ~/nbs/statefarm\n",
"current_dir = os.getcwd()\n",
"DATA_HOME_DIR = current_dir\n",
"path = ''"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 20570 images belonging to 10 classes.\n",
"Found 2054 images belonging to 10 classes.\n"
]
}
],
"source": [
"gen_t = image.ImageDataGenerator(rotation_range=15, height_shift_range=0.05, \n",
" shear_range=0.1, channel_shift_range=20, width_shift_range=0.1)\n",
"batches = get_batches(path+'train', gen_t, batch_size=batch_size)\n",
"val_batches = get_batches(path+'valid', gen_t, batch_size=batch_size)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def conv1(batches):\n",
" model = Sequential([\n",
" BatchNormalization(axis=1, input_shape=(3,224,224)),\n",
" Convolution2D(32,3,3, activation='relu'),\n",
" BatchNormalization(axis=1),\n",
" MaxPooling2D(),\n",
" Convolution2D(64,3,3, activation='relu'),\n",
" BatchNormalization(axis=1),\n",
" MaxPooling2D(),\n",
" Convolution2D(128,3,3, activation='relu'),\n",
" BatchNormalization(axis=1),\n",
" MaxPooling2D(),\n",
" Flatten(),\n",
" Dense(200, activation='relu'),\n",
" BatchNormalization(),\n",
" Dropout(0.5),\n",
" Dense(200, activation='relu'),\n",
" BatchNormalization(),\n",
" Dropout(0.5),\n",
" Dense(10, activation='softmax')\n",
" ])\n",
"\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"model = conv1(batches)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/2\n",
"20570/20570 [==============================] - 304s - loss: 2.5351 - acc: 0.2721 - val_loss: 2.8202 - val_acc: 0.1913\n",
"Epoch 2/2\n",
"20570/20570 [==============================] - 296s - loss: 1.7200 - acc: 0.4558 - val_loss: 2.7658 - val_acc: 0.2157\n",
"Epoch 1/5\n",
"20570/20570 [==============================] - 300s - loss: 1.3294 - acc: 0.5709 - val_loss: 2.6332 - val_acc: 0.3087\n",
"Epoch 2/5\n",
"20570/20570 [==============================] - 298s - loss: 1.0503 - acc: 0.6531 - val_loss: 2.5455 - val_acc: 0.3350\n",
"Epoch 3/5\n",
"20570/20570 [==============================] - 303s - loss: 0.8257 - acc: 0.7301 - val_loss: 2.4909 - val_acc: 0.3627\n",
"Epoch 4/5\n",
"20570/20570 [==============================] - 297s - loss: 0.6994 - acc: 0.7716 - val_loss: 2.2866 - val_acc: 0.3836\n",
"Epoch 5/5\n",
"20570/20570 [==============================] - 299s - loss: 0.5847 - acc: 0.8102 - val_loss: 2.2987 - val_acc: 0.3793\n"
]
}
],
"source": [
"model.compile(Adam(lr=1e-4), loss='categorical_crossentropy', metrics=['accuracy'])\n",
"model.fit_generator(batches, batches.nb_sample, nb_epoch=2, validation_data=val_batches, \n",
" nb_val_samples=val_batches.nb_sample)\n",
"model.optimizer.lr = 0.001\n",
"model.fit_generator(batches, batches.nb_sample, nb_epoch=5, validation_data=val_batches, \n",
" nb_val_samples=val_batches.nb_sample)\n",
"model.save_weights(path+'results/conv1.h5')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"20570/20570 [==============================] - 307s - loss: 0.4848 - acc: 0.8447 - val_loss: 2.3181 - val_acc: 0.4041\n",
"Epoch 2/5\n",
"20570/20570 [==============================] - 299s - loss: 0.4359 - acc: 0.8603 - val_loss: 1.9574 - val_acc: 0.4357\n",
"Epoch 3/5\n",
"20570/20570 [==============================] - 399s - loss: 0.3950 - acc: 0.8742 - val_loss: 2.2340 - val_acc: 0.4051\n",
"Epoch 4/5\n",
"20570/20570 [==============================] - 302s - loss: 0.3477 - acc: 0.8894 - val_loss: 2.2810 - val_acc: 0.4075\n",
"Epoch 5/5\n",
"20570/20570 [==============================] - 346s - loss: 0.3216 - acc: 0.8970 - val_loss: 2.0573 - val_acc: 0.4489\n"
]
}
],
"source": [
"model.fit_generator(batches, batches.nb_sample, nb_epoch=5, validation_data=val_batches, \n",
" nb_val_samples=val_batches.nb_sample)\n",
"model.save_weights(path+'results/conv2.h5')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"20570/20570 [==============================] - 357s - loss: 0.3109 - acc: 0.9036 - val_loss: 2.0187 - val_acc: 0.4484\n",
"Epoch 2/5\n",
"20570/20570 [==============================] - 361s - loss: 0.2622 - acc: 0.9195 - val_loss: 1.9099 - val_acc: 0.4737\n",
"Epoch 3/5\n",
"20570/20570 [==============================] - 303s - loss: 0.2497 - acc: 0.9230 - val_loss: 2.4782 - val_acc: 0.3880\n",
"Epoch 4/5\n",
"20570/20570 [==============================] - 417s - loss: 0.2424 - acc: 0.9255 - val_loss: 2.2895 - val_acc: 0.4309\n",
"Epoch 5/5\n",
"20570/20570 [==============================] - 376s - loss: 0.2145 - acc: 0.9325 - val_loss: 1.9537 - val_acc: 0.4927\n"
]
}
],
"source": [
"model.fit_generator(batches, batches.nb_sample, nb_epoch=5, validation_data=val_batches, \n",
" nb_val_samples=val_batches.nb_sample)\n",
"model.save_weights(path+'results/conv3.h5')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/1\n",
"20570/20570 [==============================] - 306s - loss: 0.1995 - acc: 0.9378 - val_loss: 1.9990 - val_acc: 0.4971\n"
]
}
],
"source": [
"model.fit_generator(batches, batches.nb_sample, nb_epoch=1, validation_data=val_batches, \n",
" nb_val_samples=val_batches.nb_sample)\n",
"model.save_weights(path+'results/conv4.h5')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/4\n",
"20570/20570 [==============================] - 305s - loss: 0.1922 - acc: 0.9439 - val_loss: 2.4656 - val_acc: 0.3997\n",
"Epoch 2/4\n",
" 9280/20570 [============>.................] - ETA: 147s - loss: 0.1661 - acc: 0.9530"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-62e870ae2882>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'categorical_crossentropy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m model.fit_generator(batches, batches.nb_sample, nb_epoch=4, validation_data=val_batches, \n\u001b[0;32m----> 3\u001b[0;31m nb_val_samples=val_batches.nb_sample)\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'results/conv3.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/models.pyc\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, samples_per_epoch, nb_epoch, verbose, callbacks, validation_data, nb_val_samples, class_weight, max_q_size, nb_worker, pickle_safe, **kwargs)\u001b[0m\n\u001b[1;32m 872\u001b[0m \u001b[0mmax_q_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_q_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 873\u001b[0m \u001b[0mnb_worker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnb_worker\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 874\u001b[0;31m pickle_safe=pickle_safe)\n\u001b[0m\u001b[1;32m 875\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 876\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mevaluate_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_samples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_q_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnb_worker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_safe\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, samples_per_epoch, nb_epoch, verbose, callbacks, validation_data, nb_val_samples, class_weight, max_q_size, nb_worker, pickle_safe)\u001b[0m\n\u001b[1;32m 1441\u001b[0m outs = self.train_on_batch(x, y,\n\u001b[1;32m 1442\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1443\u001b[0;31m class_weight=class_weight)\n\u001b[0m\u001b[1;32m 1444\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1445\u001b[0m \u001b[0m_stop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m 1219\u001b[0m \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1220\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1221\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1222\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1223\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/backend/theano_backend.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 715\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 716\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 717\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 718\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 719\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/theano/compile/function_module.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 857\u001b[0m \u001b[0mt0_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 858\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 860\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 861\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'position_of_error'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"model.compile(optimizer='sgd' loss='categorical_crossentropy', metrics=['accuracy'])\n",
"model.fit_generator(batches, batches.nb_sample, nb_epoch=5, validation_data=val_batches, \n",
" nb_val_samples=val_batches.nb_sample)\n",
"model.save_weights(path+'results/conv4.h5')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"test_batches = get_batches(path+'test', shuffle=False, batch_size=64)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"preds = model.predict_generator(test_batches, test_batches.nb_sample)\n",
"preds[:5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"test_filenames=test_batches.filenames"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def do_clip(arr, mx): return np.clip(arr, (1-mx)/9, mx)\n",
"preds = bn_model.predict(conv_test_feat, batch_size=batch_size*2)\n",
"subm = do_clip(preds,0.93)\n",
"subm_name = path+'results/subm.gz'\n",
"classes = sorted(batches.class_indices, key=batches.class_indices.get)\n",
"submission = pd.DataFrame(subm, columns=classes)\n",
"submission.insert(0, 'img', [a[4:] for a in test_filenames])\n",
"submission.head()\n",
"submission.to_csv(subm_name, index=False, compression='gzip')\n",
"FileLink(subm_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"save_array(path+'results/test_preds.dat', preds)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def do_clip(arr, mx): return np.clip(arr, (1-mx)/9, mx)\n",
"subm = do_clip(preds,0.93)\n",
"subm_name = path+'results/subm.gz'\n",
"classes = sorted(batches.class_indices, key=batches.class_indices.get)\n",
"submission = pd.DataFrame(subm, columns=classes)\n",
"submission.insert(0, 'img', [a[4:] for a in test_filenames])\n",
"submission.head()\n",
"submission.to_csv(subm_name, index=False, compression='gzip')\n",
"FileLink(subm_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"last_conv_idx = [i for i,l in enumerate(model.layers) if type(l) is Convolution2D][-1]\n",
"conv_layers = model.layers[:last_conv_idx+1]\n",
"conv_model = Sequential(conv_layers)val_labels = onehot(val_batches.classes)\n",
"trn_labels = onehot(batches.classes)\n",
"val_classes = val_batches.classes\n",
"trn_classes = batches.classes\n",
"conv_feat = conv_model.predict_generator(batches, batches.nb_sample)\n",
"save_array(path+'results/my_conv_feat.dat', conv_feat)\n",
"conv_val_feat = conv_model.predict_generator(val_batches, val_batches.nb_sample)\n",
"save_array(path+'results/my_conv_val_feat.dat', conv_val_feat)\n",
"test_batches = get_batches(path+'test', shuffle=False, batch_size=64)\n",
"conv_test_feat = conv_model.predict_generator(test_batches, test_batches.nb_sample)\n",
"save_array(path+'results/my_conv_test_feat.dat', conv_test_feat)\n",
"def get_bn_layers(p):\n",
" return [\n",
" MaxPooling2D(input_shape=conv_layers[-1].output_shape[1:]),\n",
" Flatten(),\n",
" Dropout(p/2),\n",
" Dense(128, activation='relu'),\n",
" BatchNormalization(),\n",
" Dropout(p/2),\n",
" Dense(128, activation='relu'),\n",
" BatchNormalization(),\n",
" Dropout(p),\n",
" Dense(10, activation='softmax')\n",
" ]\n",
"\n",
"p=0.5\n",
"bn_model = Sequential(get_bn_layers(p))\n",
"bn_model.compile(Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])\n",
"bn_model.fit(conv_feat, trn_labels, batch_size=batch_size, nb_epoch=1, \n",
" validation_data=(conv_val_feat, val_labels))\n",
"bn_model.optimizer.lr=0.01\n",
"bn_model.fit(conv_feat, trn_labels, batch_size=batch_size, nb_epoch=2, \n",
" validation_data=(conv_val_feat, val_labels))\n",
"bn_model.save_weights(path+'results/conv8.h5')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#vgg = Vgg16()\n",
"#model=vgg.model\n",
"last_conv_idx = [i for i,l in enumerate(model.layers) if type(l) is Convolution2D][-1]\n",
"conv_layers = model.layers[:last_conv_idx+1]\n",
"conv_model = Sequential(conv_layers)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 20570 images belonging to 10 classes.\n",
"Found 2054 images belonging to 10 classes.\n"
]
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"conv_feat = conv_model.predict_generator(batches, batches.nb_sample)\n",
"save_array(path+'results/my_conv_feat.dat', conv_feat)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"conv_val_feat = conv_model.predict_generator(val_batches, val_batches.nb_sample)\n",
"save_array(path+'results/my_conv_val_feat.dat', conv_val_feat)\n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 79726 images belonging to 1 classes.\n"
]
},
{
"ename": "MemoryError",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mMemoryError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-46-bb2eaddbc7f2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mtest_batches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_batches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'test'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mconv_test_feat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconv_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_batches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_batches\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnb_sample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/models.pyc\u001b[0m in \u001b[0;36mpredict_generator\u001b[0;34m(self, generator, val_samples, max_q_size, nb_worker, pickle_safe)\u001b[0m\n\u001b[1;32m 943\u001b[0m \u001b[0mmax_q_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_q_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 944\u001b[0m \u001b[0mnb_worker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnb_worker\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 945\u001b[0;31m pickle_safe=pickle_safe)\n\u001b[0m\u001b[1;32m 946\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 947\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mpredict_generator\u001b[0;34m(self, generator, val_samples, max_q_size, nb_worker, pickle_safe)\u001b[0m\n\u001b[1;32m 1650\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mout\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1651\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mval_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1652\u001b[0;31m \u001b[0mall_outs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloatx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1653\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1654\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mMemoryError\u001b[0m: "
]
}
],
"source": [
"test_batches = get_batches(path+'test', shuffle=False, batch_size=64)\n",
"conv_test_feat = conv_model.predict_generator(test_batches, test_batches.nb_sample)\n",
"save_array(path+'results/my_conv_test_feat.dat', conv_test_feat)\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"conv_val_feat = load_array(path+'results/conv_val_feat.dat')\n",
"conv_test_feat = load_array(path+'results/conv_test_feat.dat')\n",
"conv_feat = load_array(path+'results/conv_feat.dat')\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_bn_layers(p):\n",
" return [\n",
" MaxPooling2D(input_shape=conv_layers[-1].output_shape[1:]),\n",
" Flatten(),\n",
" Dropout(p/2),\n",
" Dense(128, activation='relu'),\n",
" BatchNormalization(),\n",
" Dropout(p/2),\n",
" Dense(128, activation='relu'),\n",
" BatchNormalization(),\n",
" Dropout(p),\n",
" Dense(10, activation='softmax')\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"p=0.5"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"val_labels = onehot(val_classes)\n",
"trn_labels = onehot(trn_classes)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 20624 samples, validate on 2000 samples\n",
"Epoch 1/1\n",
"20624/20624 [==============================] - 8s - loss: 1.5977 - acc: 0.5571 - val_loss: 0.0840 - val_acc: 0.9830\n",
"Train on 20624 samples, validate on 2000 samples\n",
"Epoch 1/2\n",
"20624/20624 [==============================] - 8s - loss: 0.3195 - acc: 0.9035 - val_loss: 0.0289 - val_acc: 0.9945\n",
"Epoch 2/2\n",
"20624/20624 [==============================] - 8s - loss: 0.1725 - acc: 0.9503 - val_loss: 0.0187 - val_acc: 0.9960\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f507dd69590>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bn_model = Sequential(get_bn_layers(p))\n",
"bn_model.compile(Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])\n",
"bn_model.fit(conv_feat, trn_labels, batch_size=batch_size, nb_epoch=1, \n",
" validation_data=(conv_val_feat, val_labels))\n",
"bn_model.optimizer.lr=0.01\n",
"bn_model.fit(conv_feat, trn_labels, batch_size=batch_size, nb_epoch=2, \n",
" validation_data=(conv_val_feat, val_labels))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bn_model.save_weights(path+'results/conv8.h5')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"test_filenames=test_batches.filenames"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<a href='results/subm.gz' target='_blank'>results/subm.gz</a><br>"
],
"text/plain": [
"/home/ubuntu/nbs/statefarm/results/subm.gz"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def do_clip(arr, mx): return np.clip(arr, (1-mx)/9, mx)\n",
"preds = bn_model.predict(conv_test_feat, batch_size=batch_size*2)\n",
"subm = do_clip(preds,0.93)\n",
"subm_name = path+'results/subm.gz'\n",
"classes = sorted(batches.class_indices, key=batches.class_indices.get)\n",
"submission = pd.DataFrame(subm, columns=classes)\n",
"submission.insert(0, 'img', [a[4:] for a in test_filenames])\n",
"submission.head()\n",
"submission.to_csv(subm_name, index=False, compression='gzip')\n",
"FileLink(subm_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 20624 images belonging to 10 classes.\n",
"Found 2000 images belonging to 10 classes.\n",
"Found 2000 images belonging to 10 classes.\n",
"Found 20624 images belonging to 10 classes.\n"
]
}
],
"source": [
"val_classes, trn_classes, onehot_val, onehot_trn = get_classes(path)\n",
"\n",
"val_batches = get_batches(path+'valid', batch_size=batch_size*2, shuffle=False)\n",
"batches = get_batches(path+'train', batch_size=batch_size*2, shuffle=False)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/2\n",
"1496/1496 [==============================] - 41s - loss: 2.6742 - acc: 0.0836 - val_loss: 6.9782 - val_acc: 0.1716\n",
"Epoch 2/2\n",
"1496/1496 [==============================] - 20s - loss: 2.1978 - acc: 0.2413 - val_loss: 6.0879 - val_acc: 0.1716\n",
"Epoch 1/5\n",
"1496/1496 [==============================] - 34s - loss: 2.0497 - acc: 0.3329 - val_loss: 5.0679 - val_acc: 0.1440\n",
"Epoch 2/5\n",
"1496/1496 [==============================] - 21s - loss: 1.9034 - acc: 0.4358 - val_loss: 3.1969 - val_acc: 0.2022\n",
"Epoch 3/5\n",
"1496/1496 [==============================] - 20s - loss: 1.7915 - acc: 0.4967 - val_loss: 2.2603 - val_acc: 0.3136\n",
"Epoch 4/5\n",
"1496/1496 [==============================] - 22s - loss: 1.6926 - acc: 0.5374 - val_loss: 1.7427 - val_acc: 0.4157\n",
"Epoch 5/5\n",
"1496/1496 [==============================] - 21s - loss: 1.6004 - acc: 0.5809 - val_loss: 1.5945 - val_acc: 0.4811\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f3cbfd52550>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = Sequential([\n",
" BatchNormalization(axis=1, input_shape=(3,224,224)),\n",
" Flatten(),\n",
" Dense(100, activation='relu'),\n",
" BatchNormalization(),\n",
" Dense(10, activation='softmax')\n",
" ])\n",
"model.compile(Adam(lr=1e-5), loss='categorical_crossentropy', metrics=['accuracy'])\n",
"model.fit_generator(batches, batches.nb_sample, nb_epoch=2, validation_data=val_batches, \n",
" nb_val_samples=val_batches.nb_sample)\n",
"\n",
"model.optimizer.lr = 0.01\n",
"model.fit_generator(batches, batches.nb_sample, nb_epoch=5, validation_data=val_batches, \n",
" nb_val_samples=val_batches.nb_sample)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def conv1(batches):\n",
" model = Sequential([\n",
" BatchNormalization(axis=1, input_shape=(3,224,224)),\n",
" Convolution2D(32,3,3, activation='relu'),\n",
" BatchNormalization(axis=1),\n",
" MaxPooling2D((3,3)),\n",
" Convolution2D(64,3,3, activation='relu'),\n",
" BatchNormalization(axis=1),\n",
" MaxPooling2D((3,3)),\n",
" Flatten(),\n",
" Dense(200, activation='relu'),\n",
" BatchNormalization(),\n",
" Dropout(0.5),\n",
" Dense(200, activation='relu'),\n",
" BatchNormalization(),\n",
" Dropout(0.5),\n",
" Dense(10, activation='softmax')\n",
" ])\n",
" model.compile(Adam(lr=1e-5), loss='categorical_crossentropy', metrics=['accuracy'])\n",
" model.fit_generator(batches, batches.nb_sample, nb_epoch=2, validation_data=val_batches, \n",
" nb_val_samples=val_batches.nb_sample)\n",
"\n",
" model.optimizer.lr = 0.01\n",
" model.fit_generator(batches, batches.nb_sample, nb_epoch=5, validation_data=val_batches, \n",
" nb_val_samples=val_batches.nb_sample)\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/2\n",
"20624/20624 [==============================] - 289s - loss: 3.5869 - acc: 0.1030 - val_loss: 2.4411 - val_acc: 0.1045\n",
"Epoch 2/2\n",
"12432/20624 [=================>............] - ETA: 99s - loss: 3.4990 - acc: 0.1008 "
]
},
{
"ename": "KeyboardInterrupt",
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"\u001b[0;32m<ipython-input-15-d30268a13e24>\u001b[0m in \u001b[0;36mconv1\u001b[0;34m(batches)\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'categorical_crossentropy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m model.fit_generator(batches, batches.nb_sample, nb_epoch=2, validation_data=val_batches, \n\u001b[0;32m---> 21\u001b[0;31m nb_val_samples=val_batches.nb_sample)\n\u001b[0m\u001b[1;32m 22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.01\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/models.pyc\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, samples_per_epoch, nb_epoch, verbose, callbacks, validation_data, nb_val_samples, class_weight, max_q_size, nb_worker, pickle_safe, **kwargs)\u001b[0m\n\u001b[1;32m 872\u001b[0m \u001b[0mmax_q_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_q_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 873\u001b[0m \u001b[0mnb_worker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnb_worker\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 874\u001b[0;31m pickle_safe=pickle_safe)\n\u001b[0m\u001b[1;32m 875\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 876\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mevaluate_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_samples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_q_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnb_worker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_safe\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, samples_per_epoch, nb_epoch, verbose, callbacks, validation_data, nb_val_samples, class_weight, max_q_size, nb_worker, pickle_safe)\u001b[0m\n\u001b[1;32m 1409\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1410\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1411\u001b[0;31m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwait_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1413\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__len__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"conv1(batches)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model.optimizer.lr=0.0000001"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/4\n",
"1496/1496 [==============================] - 71s - loss: 14.2973 - acc: 0.1130 - val_loss: 14.2906 - val_acc: 0.1134\n",
"Epoch 2/4\n",
"1496/1496 [==============================] - 61s - loss: 14.2973 - acc: 0.1130 - val_loss: 14.2906 - val_acc: 0.1134\n",
"Epoch 3/4\n",
"1496/1496 [==============================] - 60s - loss: 14.2973 - acc: 0.1130 - val_loss: 14.2906 - val_acc: 0.1134\n",
"Epoch 4/4\n",
"1496/1496 [==============================] - 61s - loss: 14.2973 - acc: 0.1130 - val_loss: 14.2906 - val_acc: 0.1134\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7fe53c1d7910>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit_generator(batches, batches.nb_sample, nb_epoch=4, validation_data=val_batches, nb_val_samples=val_batches.nb_sample)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=float32)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.round(model.predict_generator(batches, batches.N)[:10],2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model.optimizer.lr=0.001"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model.fit_generator(batches, batches.nb_sample, nb_epoch=4, validation_data=val_batches, nb_val_samples=val_batches.nb_sample)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": [
"# As large as you can, but no larger than 64 is recommended. \n",
"# If you have an older or cheaper GPU, you'll run out of memory, so will have to decrease this.\n",
"batch_size=64"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": [
"# Import our class, and instantiate\n",
"from vgg16 import Vgg16"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true,
"hidden": true
},
"outputs": [
{
"ename": "MemoryError",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m\u001b[0m",
"\u001b[0;31mMemoryError\u001b[0mTraceback (most recent call last)",
"\u001b[0;32m<ipython-input-21-4064c201af45>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvgg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVgg16\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDATA_HOME_DIR\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/sample'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# Grab a few images at a time for training and validation.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# NB: They must be in subdirectories named based on their category\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mbatches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_batches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'train'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mvgg16.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self)\u001b[0m\n",
"\u001b[0;32mvgg16.pyc\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(self)\u001b[0m\n",
"\u001b[0;32mvgg16.pyc\u001b[0m in \u001b[0;36mFCBlock\u001b[0;34m(self)\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/models.pyc\u001b[0m in \u001b[0;36madd\u001b[0;34m(self, layer)\u001b[0m\n\u001b[1;32m 306\u001b[0m output_shapes=[self.outputs[0]._keras_shape])\n\u001b[1;32m 307\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 308\u001b[0;31m \u001b[0moutput_tensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 309\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_tensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 310\u001b[0m raise Exception('All layers in a Sequential model '\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/engine/topology.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x, mask)\u001b[0m\n\u001b[1;32m 485\u001b[0m '`layer.build(batch_input_shape)`')\n\u001b[1;32m 486\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shapes\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 487\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shapes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 488\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 489\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shapes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/layers/core.pyc\u001b[0m in \u001b[0;36mbuild\u001b[0;34m(self, input_shape)\u001b[0m\n\u001b[1;32m 693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 694\u001b[0m self.W = self.init((input_dim, self.output_dim),\n\u001b[0;32m--> 695\u001b[0;31m name='{}_W'.format(self.name))\n\u001b[0m\u001b[1;32m 696\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 697\u001b[0m self.b = K.zeros((self.output_dim,),\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/initializations.pyc\u001b[0m in \u001b[0;36mglorot_uniform\u001b[0;34m(shape, name, dim_ordering)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0mfan_in\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfan_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_fans\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim_ordering\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdim_ordering\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6.\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfan_in\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfan_out\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0muniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/initializations.pyc\u001b[0m in \u001b[0;36muniform\u001b[0;34m(shape, scale, name)\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0muniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_uniform_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/backend/theano_backend.pyc\u001b[0m in \u001b[0;36mrandom_uniform_variable\u001b[0;34m(shape, low, high, dtype, name)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrandom_uniform_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhigh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_FLOATX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m return variable(np.random.uniform(low=low, high=high, size=shape),\n\u001b[0m\u001b[1;32m 141\u001b[0m dtype=dtype, name=name)\n\u001b[1;32m 142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mmtrand.pyx\u001b[0m in \u001b[0;36mmtrand.RandomState.uniform (numpy/random/mtrand/mtrand.c:17350)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mmtrand.pyx\u001b[0m in \u001b[0;36mmtrand.cont2_array_sc (numpy/random/mtrand/mtrand.c:3092)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mMemoryError\u001b[0m: "
]
}
],
"source": [
"vgg = Vgg16()\n",
"path = DATA_HOME_DIR + '/sample'\n",
"# Grab a few images at a time for training and validation.\n",
"# NB: They must be in subdirectories named based on their category\n",
"batches = vgg.get_batches(path+'train', batch_size=batch_size)\n",
"val_batches = vgg.get_batches(path+'valid', batch_size=batch_size*2)\n",
"vgg.finetune(batches)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/1\n",
" 8448/20487 [===========>..................] - ETA: 315s - loss: 14.3670 - acc: 0.1038"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-18-e18cdc14e2f7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_batches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnb_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mvgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'results/ft1.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mvgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_batches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnb_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mvgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'results/ft2.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/nbs/vgg16.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, batches, val_batches, nb_epoch)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_batches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnb_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 98\u001b[0m self.model.fit_generator(batches, samples_per_epoch=batches.nb_sample, nb_epoch=nb_epoch,\n\u001b[0;32m---> 99\u001b[0;31m validation_data=val_batches, nb_val_samples=val_batches.nb_sample)\n\u001b[0m\u001b[1;32m 100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/models.pyc\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, samples_per_epoch, nb_epoch, verbose, callbacks, validation_data, nb_val_samples, class_weight, max_q_size, nb_worker, pickle_safe, **kwargs)\u001b[0m\n\u001b[1;32m 872\u001b[0m \u001b[0mmax_q_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_q_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 873\u001b[0m \u001b[0mnb_worker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnb_worker\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 874\u001b[0;31m pickle_safe=pickle_safe)\n\u001b[0m\u001b[1;32m 875\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 876\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mevaluate_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_samples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_q_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnb_worker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_safe\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, samples_per_epoch, nb_epoch, verbose, callbacks, validation_data, nb_val_samples, class_weight, max_q_size, nb_worker, pickle_safe)\u001b[0m\n\u001b[1;32m 1441\u001b[0m outs = self.train_on_batch(x, y,\n\u001b[1;32m 1442\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1443\u001b[0;31m class_weight=class_weight)\n\u001b[0m\u001b[1;32m 1444\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1445\u001b[0m \u001b[0m_stop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m 1219\u001b[0m \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1220\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1221\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1222\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1223\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/backend/theano_backend.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 715\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 716\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 717\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 718\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 719\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/theano/compile/function_module.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 857\u001b[0m \u001b[0mt0_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 858\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 860\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 861\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'position_of_error'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"vgg.fit(batches, val_batches, nb_epoch=1)\n",
"vgg.model.save_weights(path+'results/ft1.h5')\n",
"\n",
"vgg.fit(batches, val_batches, nb_epoch=1)\n",
"vgg.model.save_weights(path+'results/ft2.h5')\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/1\n",
"10560/21728 [=============>................] - ETA: 289s - loss: 14.3796 - acc: 0.1079"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-16-575f378d0b08>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mvgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.01\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mvgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_batches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnb_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mvgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'results/ft2_1.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/nbs/vgg16.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, batches, val_batches, nb_epoch)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_batches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnb_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 98\u001b[0m self.model.fit_generator(batches, samples_per_epoch=batches.nb_sample, nb_epoch=nb_epoch,\n\u001b[0;32m---> 99\u001b[0;31m validation_data=val_batches, nb_val_samples=val_batches.nb_sample)\n\u001b[0m\u001b[1;32m 100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/models.pyc\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, samples_per_epoch, nb_epoch, verbose, callbacks, validation_data, nb_val_samples, class_weight, max_q_size, nb_worker, pickle_safe, **kwargs)\u001b[0m\n\u001b[1;32m 872\u001b[0m \u001b[0mmax_q_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_q_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 873\u001b[0m \u001b[0mnb_worker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnb_worker\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 874\u001b[0;31m pickle_safe=pickle_safe)\n\u001b[0m\u001b[1;32m 875\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 876\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mevaluate_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_samples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_q_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnb_worker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_safe\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, samples_per_epoch, nb_epoch, verbose, callbacks, validation_data, nb_val_samples, class_weight, max_q_size, nb_worker, pickle_safe)\u001b[0m\n\u001b[1;32m 1441\u001b[0m outs = self.train_on_batch(x, y,\n\u001b[1;32m 1442\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1443\u001b[0;31m class_weight=class_weight)\n\u001b[0m\u001b[1;32m 1444\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1445\u001b[0m \u001b[0m_stop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m 1219\u001b[0m \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1220\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1221\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1222\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1223\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/keras/backend/theano_backend.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 715\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 716\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 717\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 718\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 719\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/ubuntu/anaconda2/lib/python2.7/site-packages/theano/compile/function_module.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 857\u001b[0m \u001b[0mt0_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 858\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 860\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 861\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'position_of_error'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"vgg.model.optimizer.lr = 0.01\n",
"\n",
"vgg.fit(batches, val_batches, nb_epoch=1)\n",
"vgg.model.save_weights(path+'results/ft2_1.h5')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/1\n",
"22778/22778 [==============================] - 657s - loss: 0.7015 - acc: 0.9543 - val_loss: 0.3211 - val_acc: 0.9797\n"
]
}
],
"source": [
"vgg.fit(batches, val_batches, nb_epoch=1)\n",
"vgg.model.save_weights(path+'results/ft2_2.h5')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Submission"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"vgg = Vgg16()\n",
"path = \"statefarm/\"\n",
"import utils; reload(utils)\n",
"vgg.model.load_weights(path+'results/ft2.h5')\n",
"gen=image.ImageDataGenerator()\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 12500 images belonging to 1 classes.\n"
]
},
{
"data": {
"text/plain": [
"array([[ 1., 0.],\n",
" [ 1., 0.],\n",
" [ 1., 0.],\n",
" [ 1., 0.],\n",
" [ 0., 1.]], dtype=float32)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_batches = gen.flow_from_directory(path+'test', target_size=(224,224), class_mode=None, shuffle=False, batch_size=batch_size*2) \n",
"preds = vgg.model.predict_generator(test_batches, test_batches.nb_sample)\n",
"preds[:5]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'save_array' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-11-998930efc78d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msave_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'results/test_preds.dat'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'save_array' is not defined"
]
}
],
"source": [
"save_array(path+'results/test_preds.dat', preds)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'save_array' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-298473960ba3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfilenames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_batches\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilenames\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msave_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'results/filenames.dat'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilenames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mfilenames\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'save_array' is not defined"
]
}
],
"source": [
"filenames = test_batches.filenames\n",
"save_array(path+'results/filenames.dat', filenames) \n",
"filenames[:5]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['unknown/9292.jpg',\n",
" 'unknown/12026.jpg',\n",
" 'unknown/9688.jpg',\n",
" 'unknown/4392.jpg',\n",
" 'unknown/779.jpg']"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<a href='subm98.csv' target='_blank'>subm98.csv</a><br>"
],
"text/plain": [
"/home/ubuntu/nbs/subm98.csv"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"RULES OF COMPETITION\n",
"The 10 classes to predict are:\n",
"\n",
"c0: normal driving\n",
"c1: texting - right\n",
"c2: talking on the phone - right\n",
"c3: texting - left\n",
"c4: talking on the phone - left\n",
"c5: operating the radio\n",
"c6: drinking\n",
"c7: reaching behind\n",
"c8: hair and makeup\n",
"c9: talking to passenger"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<a href='subm2.csv' target='_blank'>subm2.csv</a><br>"
],
"text/plain": [
"/home/ubuntu/nbs/subm2.csv"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#preds = load_array('results/test_preds.dat')\n",
"#filenames = load_array('results/filenames.dat’)\n",
"from PIL import Image\n",
"Image.open('statefarm/test/'+filenames[0])\n",
" \n",
"c0 = np.clip(preds[:,1], 0.05, 0.95) \n",
"c0[:5]\n",
"c1 = np.clip(preds[:,2], 0.05, 0.95) \n",
"c1[:5]\n",
"c2 = np.clip(preds[:,3], 0.05, 0.95) \n",
"c2[:5]\n",
"c3 = np.clip(preds[:,4], 0.05, 0.95) \n",
"c3[:5]\n",
"c4 = np.clip(preds[:,5], 0.05, 0.95) \n",
"c4[:5]\n",
"c5 = np.clip(preds[:,6], 0.05, 0.95) \n",
"c5[:5]\n",
"c6 = np.clip(preds[:,7], 0.05, 0.95) \n",
"c6[:5]\n",
"c7 = np.clip(preds[:,8], 0.05, 0.95) \n",
"c7[:5]\n",
"c8 = np.clip(preds[:,9], 0.05, 0.95) \n",
"c8[:5]\n",
"c9 = np.clip(preds[:,10], 0.05, 0.95) \n",
"c9[:5]\n",
"#ids = [int(f[8:f.find('.')]) for f in filenames]\n",
"#ids[:5]\n",
"subm = np.stack([f,isdog], axis=1)\n",
"subm[:5]\n",
"np.savetxt('subm1.csv', subm, fmt='%d,%.5f', header='img,c0,c1,c2,c3,c4,c5,c6,c7,c8,c9', comments='')\n",
"from IPython.display import FileLink\n",
"FileLink('subm1.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"The code above will work for any image recognition task, with any number of categories! All you have to do is to put your images into one folder per category, and run the code above.\n",
"\n",
"Let's take a look at how this works, step by step..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use Vgg16 for basic image recognition\n",
"\n",
"Let's start off by using the *Vgg16* class to recognise the main imagenet category for each image.\n",
"\n",
"We won't be able to enter the Cats vs Dogs competition with an Imagenet model alone, since 'cat' and 'dog' are not categories in Imagenet - instead each individual breed is a separate category. However, we can use it to see how well it can recognise the images, which is a good first step.\n",
"\n",
"First, create a Vgg16 object:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"vgg = Vgg16()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vgg16 is built on top of *Keras* (which we will be learning much more about shortly!), a flexible, easy to use deep learning library that sits on top of Theano or Tensorflow. Keras reads groups of images and labels in *batches*, using a fixed directory structure, where images from each category for training must be placed in a separate folder.\n",
"\n",
"Let's grab batches of data from our training folder:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 23000 images belonging to 2 classes.\n"
]
}
],
"source": [
"batches = vgg.get_batches(path+'train', batch_size=4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(BTW, when Keras refers to 'classes', it doesn't mean python classes - but rather it refers to the categories of the labels, such as 'pug', or 'tabby'.)\n",
"\n",
"*Batches* is just a regular python iterator. Each iteration returns both the images themselves, as well as the labels."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"imgs,labels = next(batches)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, the labels for each image are an array, containing a 1 in the first position if it's a cat, and in the second position if it's a dog. This approach to encoding categorical variables, where an array containing just a single 1 in the position corresponding to the category, is very common in deep learning. It is called *one hot encoding*. \n",
"\n",
"The arrays contain two elements, because we have two categories (cat, and dog). If we had three categories (e.g. cats, dogs, and kangaroos), then the arrays would each contain two 0's, and one 1."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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87nWv47S165DWMzU+wUkrTzrs3nSITykK9+Lkk0/mBS94AV/70pfZunUrUaS65DJJEr72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\n+ZE2ZF2ctQjrwwI6KfCd9oB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aDSnDKV1Ij1ADrOiRJIt/f5P1hvH1hG3XPoDXt8V3008Q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8vzVQ35ZMbS6gpOQ6U1+VxS1yF1GSLSLs6kOKMRoQIMzlUoZRd2ex/HcbZltnAsaPhgH6QptGT/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C+Y14Gz2vHxWUtaTDG2om1XW5TAe0/oEpQuccIihUBnfaePRxAPO5zpRQ+xCfEhhqWU5RiJ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Tc8tC13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\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UqkIV6lCEAgiTHB+CTQjuPjh65ym84J/84T9h89knqGaNDvAP6yURuN5YVFExGwzf+OYv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\nrCiKoiiKopSW/wHjHE9GbDCmBwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2fcab46b10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plots(imgs, titles=labels)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can now pass the images to Vgg16's predict() function to get back probabilities, category indexes, and category names for each image's VGG prediction."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 0.8441, 0.4584, 0.3954, 0.4223], dtype=float32),\n",
" array([200, 205, 236, 208]),\n",
" [u'Tibetan_terrier',\n",
" u'flat-coated_retriever',\n",
" u'Doberman',\n",
" u'Labrador_retriever'])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vgg.predict(imgs, True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The category indexes are based on the ordering of categories used in the VGG model - e.g here are the first four:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[u'tench', u'goldfish', u'great_white_shark', u'tiger_shark']"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vgg.classes[:4]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(Note that, other than creating the Vgg16 object, none of these steps are necessary to build a model; they are just showing how to use the class to view imagenet predictions.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use our Vgg16 class to finetune a Dogs vs Cats model\n",
"\n",
"To change our model so that it outputs \"cat\" vs \"dog\", instead of one of 1,000 very specific categories, we need to use a process called \"finetuning\". Finetuning looks from the outside to be identical to normal machine learning training - we provide a training set with data and labels to learn from, and a validation set to test against. The model learns a set of parameters based on the data provided.\n",
"\n",
"However, the difference is that we start with a model that is already trained to solve a similar problem. The idea is that many of the parameters should be very similar, or the same, between the existing model, and the model we wish to create. Therefore, we only select a subset of parameters to train, and leave the rest untouched. This happens automatically when we call *fit()* after calling *finetune()*.\n",
"\n",
"We create our batches just like before, and making the validation set available as well. A 'batch' (or *mini-batch* as it is commonly known) is simply a subset of the training data - we use a subset at a time when training or predicting, in order to speed up training, and to avoid running out of memory."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"batch_size=64"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 23000 images belonging to 2 classes.\n",
"Found 2000 images belonging to 2 classes.\n"
]
}
],
"source": [
"batches = vgg.get_batches(path+'train', batch_size=batch_size)\n",
"val_batches = vgg.get_batches(path+'valid', batch_size=batch_size)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calling *finetune()* modifies the model such that it will be trained based on the data in the batches provided - in this case, to predict either 'dog' or 'cat'."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"vgg.finetune(batches)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we *fit()* the parameters of the model using the training data, reporting the accuracy on the validation set after every epoch. (An *epoch* is one full pass through the training data.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/1\n",
"23000/23000 [==============================] - 350s - loss: 0.5581 - acc: 0.9644 - val_loss: 0.3130 - val_acc: 0.9795\n"
]
}
],
"source": [
"vgg.fit(batches, val_batches, nb_epoch=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That shows all of the steps involved in using the Vgg16 class to create an image recognition model using whatever labels you are interested in. For instance, this process could classify paintings by style, or leaves by type of disease, or satellite photos by type of crop, and so forth.\n",
"\n",
"Next up, we'll dig one level deeper to see what's going on in the Vgg16 class."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create a VGG model from scratch in Keras\n",
"\n",
"For the rest of this tutorial, we will not be using the Vgg16 class at all. Instead, we will recreate from scratch the functionality we just used. This is not necessary if all you want to do is use the existing model - but if you want to create your own models, you'll need to understand these details. It will also help you in the future when you debug any problems with your models, since you'll understand what's going on behind the scenes."
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"## Model setup\n",
"\n",
"We need to import all the modules we'll be using from numpy, scipy, and keras:"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false,
"hidden": true
},
"outputs": [],
"source": [
"from numpy.random import random, permutation\n",
"from scipy import misc, ndimage\n",
"from scipy.ndimage.interpolation import zoom\n",
"\n",
"import keras\n",
"from keras import backend as K\n",
"from keras.utils.data_utils import get_file\n",
"from keras.models import Sequential, Model\n",
"from keras.layers.core import Flatten, Dense, Dropout, Lambda\n",
"from keras.layers import Input\n",
"from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D\n",
"from keras.optimizers import SGD, RMSprop\n",
"from keras.preprocessing import image"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Let's import the mappings from VGG ids to imagenet category ids and descriptions, for display purposes later."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"hidden": true
},
"outputs": [],
"source": [
"FILES_PATH = 'http://www.platform.ai/models/'; CLASS_FILE='imagenet_class_index.json'\n",
"# Keras' get_file() is a handy function that downloads files, and caches them for re-use later\n",
"fpath = get_file(CLASS_FILE, FILES_PATH+CLASS_FILE, cache_subdir='models')\n",
"with open(fpath) as f: class_dict = json.load(f)\n",
"# Convert dictionary with string indexes into an array\n",
"classes = [class_dict[str(i)][1] for i in range(len(class_dict))]"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Here's a few examples of the categories we just imported:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"hidden": true
},
"outputs": [
{
"data": {
"text/plain": [
"[u'tench', u'goldfish', u'great_white_shark', u'tiger_shark', u'hammerhead']"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"classes[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"## Model creation\n",
"\n",
"Creating the model involves creating the model architecture, and then loading the model weights into that architecture. We will start by defining the basic pieces of the VGG architecture.\n",
"\n",
"VGG has just one type of convolutional block, and one type of fully connected ('dense') block. Here's the convolutional block definition:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": [
"def ConvBlock(layers, model, filters):\n",
" for i in range(layers): \n",
" model.add(ZeroPadding2D((1,1)))\n",
" model.add(Convolution2D(filters, 3, 3, activation='relu'))\n",
" model.add(MaxPooling2D((2,2), strides=(2,2)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"...and here's the fully-connected definition."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": [
"def FCBlock(model):\n",
" model.add(Dense(4096, activation='relu'))\n",
" model.add(Dropout(0.5))"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"When the VGG model was trained in 2014, the creators subtracted the average of each of the three (R,G,B) channels first, so that the data for each channel had a mean of zero. Furthermore, their software that expected the channels to be in B,G,R order, whereas Python by default uses R,G,B. We need to preprocess our data to make these two changes, so that it is compatible with the VGG model:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"hidden": true
},
"outputs": [],
"source": [
"# Mean of each channel as provided by VGG researchers\n",
"vgg_mean = np.array([123.68, 116.779, 103.939]).reshape((3,1,1))\n",
"\n",
"def vgg_preprocess(x):\n",
" x = x - vgg_mean # subtract mean\n",
" return x[:, ::-1] # reverse axis bgr->rgb"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Now we're ready to define the VGG model architecture - look at how simple it is, now that we have the basic blocks defined!"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": [
"def VGG_16():\n",
" model = Sequential()\n",
" model.add(Lambda(vgg_preprocess, input_shape=(3,224,224)))\n",
"\n",
" ConvBlock(2, model, 64)\n",
" ConvBlock(2, model, 128)\n",
" ConvBlock(3, model, 256)\n",
" ConvBlock(3, model, 512)\n",
" ConvBlock(3, model, 512)\n",
"\n",
" model.add(Flatten())\n",
" FCBlock(model)\n",
" FCBlock(model)\n",
" model.add(Dense(1000, activation='softmax'))\n",
" return model"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"We'll learn about what these different blocks do later in the course. For now, it's enough to know that:\n",
"\n",
"- Convolution layers are for finding patterns in images\n",
"- Dense (fully connected) layers are for combining patterns across an image\n",
"\n",
"Now that we've defined the architecture, we can create the model like any python object:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"hidden": true
},
"outputs": [],
"source": [
"model = VGG_16()"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"As well as the architecture, we need the weights that the VGG creators trained. The weights are the part of the model that is learnt from the data, whereas the architecture is pre-defined based on the nature of the problem. \n",
"\n",
"Downloading pre-trained weights is much preferred to training the model ourselves, since otherwise we would have to download the entire Imagenet archive, and train the model for many days! It's very helpful when researchers release their weights, as they did here."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": [
"fpath = get_file('vgg16.h5', FILES_PATH+'vgg16.h5', cache_subdir='models')\n",
"model.load_weights(fpath)"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"## Getting imagenet predictions\n",
"\n",
"The setup of the imagenet model is now complete, so all we have to do is grab a batch of images and call *predict()* on them."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": [
"batch_size = 4"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Keras provides functionality to create batches of data from directories containing images; all we have to do is to define the size to resize the images to, what type of labels to create, whether to randomly shuffle the images, and how many images to include in each batch. We use this little wrapper to define some helpful defaults appropriate for imagenet data:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false,
"hidden": true
},
"outputs": [],
"source": [
"def get_batches(dirname, gen=image.ImageDataGenerator(), shuffle=True, \n",
" batch_size=batch_size, class_mode='categorical'):\n",
" return gen.flow_from_directory(path+dirname, target_size=(224,224), \n",
" class_mode=class_mode, shuffle=shuffle, batch_size=batch_size)"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"From here we can use exactly the same steps as before to look at predictions from the model."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"hidden": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 23000 images belonging to 2 classes.\n",
"Found 2000 images belonging to 2 classes.\n"
]
},
{
"data": {
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jteAEVN02uBsMb9UbrsZ4PRx/42/9L/zZP/GDxzff/PsNznXDHa8z1jg8yl335+uNT5rt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\n448/5p133mG5XDIejxHCLfDNZsPp6WlPrLfWslqtePPNN/F9n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5gM+uRIUm2oLCg0oe88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ZfkVU0YJ1y9foswSjlbLFnmGQdHJxwcHbI52ubq7i7bu7vORq1xNjiC8w7W5VgA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\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T/Ks/+PdM7tzi5Tc/SlNkRhl7ZiwUv/Odd3jnu9/n8PCQ/Zu3OT8/ZzabcffebYbDPpPJ\nhPv37xL7EavVqgPcLRvD933mi6VZu40NQJ0rlEcpJVlmLKGiKMJ1XR69+15Xndvd3e28sKWUeL7R\nXbEU1tlsRr/fIwh80qTg2bNnvPzyy2w2G+7du2fOoaYhCDzyPDVVbV3juIK6rgDVVeEPDw+3qspm\nT+33TbFgOp2yWCxMnOG61IATGFX7RjedJooQgv5gQN7allnw//T0lKqqWJ6dtWys0vShZ+ZPWRhK\nbywDokGf3mSE0BpHC6osJ61K8roiq0pk5FPVFdQVdV7gujHPnj3j4dFjwndjbt67xcffepNRHFPV\nCpQmdAPyzZrY9wjCEEdI+q7ZX6oso8xzLlZLVGvRo4MJe7tjJmEMN25RXJwR+hFFWVHVCq0lwvF5\n/OQBZZ7hD0Oqpmr36ktGG/oyBBdC/Bm4WP9yhmV+Warzdt+rjV1sj3Oatiyh1m3Gzu1tWq8FuS17\nEKBsjG976JlYWdUNvn/Zs9zFqq7bVcttnLCtySKlJEtTpGuUn22sVJYlURSZ/ll5qY5tcwVThTc/\nP5lMup5xz/OI+r2uncFqIljWqsTcG9MzHnT08yAI2NRFd6YMh0OklDx79qxzNLDU9etJ7Dbd276f\n/by22r89thPk7Sq8ve91U+NZBXqhabYq5B9UUf5pVfCrLNMPLt5c/3x2WFaSzVOufyZbHNkutljl\nefsMzXvrLhbeppJvV6ttxdt1HCplkCqhNQhjy+yCYeTUWwwB22R9+deVb13/108bH6rTWyDY39/v\n+kmVUq2QgYPr+vzRl77M4cEtfvM3f4s8K0nWGbPZGUKotkfJRQiHQW+MagQfff0tJpMdfuu3fos0\n2wBG+fu9994zVMbd3U6terVaEY0iSl2xe7DPpz/9GaKoR7oumJ0tiXsuWlcEgWM2CiUY9Efce/Ge\n8fSrchSKwXjAZHfCcrPs6Fn379/vgoSdnR3ee+89Tk5OcBynk/EvypJlWTFPEs43Kcpx0aGP2/aI\nWuGUi4uLbnIppUiThDrPiaREVSU0NT6ag8kEvxdRaUXeVJS6oUKB60Dj4DQung4o0wrVCKTj0QhJ\nWi1YFYLGGSH8AV4volRrZDjjYvEuezsxNw+m9AZ9Kh8y5ZNpD4IhaaXBDYh6Ma7nEfUiiiqjTFeU\n6xNcleHJCk8qUBLhNig/JdofIoYj/IMXEXv3OXjxLi+/dpvdu4eM9u+SLCqOHz3k2dPHlHXFIk1p\nNBTJhs3ZOdnFDIoSV9TEvmRvMkHVkne++33KoiYMYppac3hwk/lsCVriBi448Mlf+TTPT46pFfhh\nD9ePWCUpX/ryH1NraBBUSpMWJVlRkOY5wnEI47j7d38w4nx2YTbfIidPVzi6IpCaBz94h1v7N/nN\nf/pbPHjwCCkdFA3LPOV4fsG6zKnUJbPAcRxCP2DQ6zMZjQn9AKGhF8UgPjw4mUUHdSs25yCQjcZp\n+65tZdNuoNbOR1QatxE4SFwcfMcn8EOKqka6nmk7qDW+cOkHMT4OIQ59GSB1jm4a8kZQKo0vCvpu\njm7WVI7kpBYkuaLOClSWodMcXWr64YRB74BU+aigh9cb0B+ZPSjwQqgkgRORFgocn1orXM9hbzci\nDis+/tZt3nr9BW7dmpDWK57MjtiIklVdUWQVJ7MNy01FUUmSQrFOSpbLBJB4jk+ZF9RljW40yWbD\n7OKC46NnJJsFTV0wyytO1gnrKqWSNefJgmVVMa803vQGfu+A3s49oul9an+MDgfoqI8IAnZuHOD3\n+hRag+fgRQGVqmhomOxOmQwnhF5olM2FS9wb4IUR/ZGpNje1aJN5wc2btxEYy7gwjMjqnKRI6PUi\n4jBiGMYEQhA2gnKxopotOByOqJIEV8J8OePw3k1eefMj9IOY4yfP+eM//P8ok5x0ueH9dx9QbDJ6\nfoRbewyjIetlwve/966xOXME77z3fU6fnbC6mJNsZsSRA7Kg0TkNFXmV4zttj7/0OJutSXJNFI+g\nkZTrhE1esckrpB/hxwNwA2olqLSk1z+gF+8SuiYRT5RDGPS5Pw55PVREqsRtKnRdg3Sp8V/awQAA\nIABJREFUlETLDD8scKRGiIZKFdSiQjkNtS7xPUGMps426CrDo0Hy43vbPgzjRyogLSBmKZFRbBwt\nLKXYAoRg2gTW63XnuHD//n0j2tU6ZiRJwmKx6CjORVFQliXL5ZLRcEw/jvEcie8K4tAj8hz6YZ9h\n1GPcN44MQkqGw6HpzW9BouVyyVe/+jZFUTAajXj99Y+xs7PDrVu32N3d7fQMxmPj/hGEoenpbtlz\n050d+oMBTRskWgss24usVHMFRLS9ohZAm06nXdXbVgV7vZ7RN5DOFQDXJgiWuWeD1fV63Vk+5kWO\ndACh2359jeeZ1qxGVd2zCYItIE1KQwVvk4RtBlD7A11wbPdltgB9y6RSSnW97EmStJTqjKooqcvK\n7GdFhdAQ+gGe79Pr9xkMBl0Rw1Q4jfiQEibhquqauqmp2x5aK5yWJAlPHj3mm29/nQc/fNgmTsZi\nx5cCT0oiCaGoUes15XzOyXsPOX34iLMHjzl58JjTB++TzOfErs9mtuD546cUmWHqlarCjXyiXkjZ\nlBRFbu6pVghdfyBN96clIn+Rhr3vFuCxrVnbVVObyFiL2O2iwXYibiu02/PdvvbH6fpsFySuA3P2\nNfa6OgDPda7EB1fbGcWVxAssnf2yihqGIYOWAWvn/fbXLGhmE2D7ntvvbz2ubSHAghK2wGVb037a\nfbf3brvqu129vk4Ht79zG7x0pHPl/5bufj2h/9OM7ef884zrc+j692xlGn7U//o6OHB9rW1X8beF\n0BqlkFojWqBLaEBpdKM70OYXNT48kTgwGJhK7PZmL4To1GaDIOA73/kOX/jCF8iyzPQTlcYLzSJA\nYRjy/OkzcnRXqbQCJADL5bI72CyKJNsKIcJQDc7OznjnnXeMSq1wul4/izDbBWYP1yiKOiXDIAx4\n9uwZZ2dn3L5haMfvvvsut27dIm6tgvb39zuxqyRJmE6nLJdL0osLAGNt0vb9Xh+6pX53KKJFgFyX\nshXUEO0GWpalUcQTV/srrGDf9gFqK84AQmnqukEKidM0oDRl2XTJfRhHnXic1nQH9XafRPcMNTSq\noixr0BopXFxXIZX9fZIgDHA9D+n44HsMh0OCIGgpHIAGx2lFwcKIuqlANWzWG8q2ZxouacPL5ZJX\nX7nZCcEcHR1xcnLC4eFhZ81kEdAHDx50FPumaVplVoP2b6NwFiWz97EsS+I47g7/vb09elGMg2a5\nmnPj4ADXcbh//z5FUXQI/yuvvHJFDdE+k20BDFv9y/Oc4XDYUZ7+jISEf0nDzAHP9dAtQFbl5j6s\nkk1nPWdR4qYx8y1qD/SqVQf3g4BGG1XSLMtwhURoIyyStc/KQRCHEY6UVK3Ni+M4aKXQQuFIh7K9\n36puyPMSRxp6YyMaPN/4ZHuei99qJxRFgapqvLa1wj5728+ptVEBN0G4x51bkkGvz7uPHlKVBVmW\nkVYbpmGEpz2Wy6Vh4Ug4Pj5hb2di6FB+gOcFaARPnx21B6TA9Xw8z0c6HqJK6cURUS+k1xvgSI80\nq42fbhBRVQqlNEmaEwyHJJsVQmh68ZDRZIjnmb66qmrQygQFVdkwc2ZkUjEaj3Fdo0ugtEbWqtOL\nSFPDlBBCGDG9WuO1lD237VmzLQzz2TnSkbi+j+sF9KIeju/x/tMnTPZ2eOutt4yI03pNvswMTb5l\nmBwfH5t+7SdP+Ft/62/hOj7vP3rAO++8w/n5mbFHqo0FEqroAnSz9k3lWEgbIJnDPIoihjtT1stV\np0IdRRG5brUMWuqaEMLsoxoyK5TjOUxGE05Pz4kOJZ/45Kf5d7/zu8hJjKM0YRzhBgFZUVHUFbWq\ncbRJFjQNWpv9uaxyhJbIRlE1lWmVcU3Q+GEbXWvMlgJu0zRUtaHVu21l+smTJ92/m6YhCge4jmzP\nWVP93Ww23Lp1q6sK/e7v/A6b3FSPxuMxe3t7rUBk2lV/B+MR1XpFL3bZ2xmyurgg8hXNKiXu9dBa\n8fWvfZ2/8mt/lXsvvcwmyTrBpb29PY6PT/ncZz9PEEQ8efLEKPP3exRlwXg8ZLVatJW6kMVqjed5\nHWOk+6xVRVXWpuqLFVMy6yNoGWJW7CgIgu7nr9s+9vv9S20VjKCUjTN2d3c7muTRc7NGZrNZqyHj\nd21LjgNSOpycnXLzzm2KokYIjefJLrG3IkGWjuq6Lpq605OxQX9nWdWKJyIdFLC7d2ho6C07y14r\ntE4s6zVNVZOnWfesPM/DEZLB0AiIetKjFtqAz1UNdUO+SSiUUZ2Ogh51q5vhIwn6PWI3pFQNA3dA\nTUOSZ7z3/R/y8N0H7E7/E27vT3BQxK4gPz/l5OyIPFnCusQVLq70aCpFz/VAS5pCUecZX/ujP+L0\n7ITId5COy90XXuDJ2TPKsiYrc7Rb4/mCbDMnGo9xlaBxHISwCczletBaU6Px+HAMKxBmWXN1XZuE\ns74U/7KsS0sLt62FcJVebM9vO+y/L/uWFVpdVqmvx7LXk0Zb8d5ec4502r72y+S3q+hyFdyzfwtB\nd5bZM8L2elfKfA4bb2z3druO21VGbSzrOE7nOGLjtW0Bv16vx+npaXdPf9LYBhA+aFxPMrf7iu3/\n7T6kte7s7/4kifBPGj9LxXo7kbf/tgzN7ed5/Z4YEFJfitjyo7meTUK2QZzreYW9jqaqEK7RagCT\nWAv7PWWa237c57n+tZ/l2VwfH6rEerVaEQYhu7u73eS5uLjoqClBEDAej1uqVN/QL1olTVWaBKgs\nS+M72dRdUvTWW2919Nw0zXnjjTc6OpQVH7B9Gt/+9re5ffsue3tGLTfPyivefknbW2s9L7XWfOlL\nX+JTrbdhr9fj8PDwCpVkZ8dYedkDyQYdFhW0IilWkGV3dxcNOEHQofZ20VslcbsxSinJpKRpzIai\nGhPgzOZzSq3xW19LpRRem8zr8JJWYf90NGfXbER5muG5LoXbHhsb46OdZAUvvfIyQWCUrwNHMp8v\nOqsQ2zsehVEHbMwvZlxcXLSTXaOliydM/5dGUVQl1XLNnRdfRjmOOeQ2cy7OzhkNhniWClOVrKqS\nqshJ1kt6UUiWJmTrFVVdEoUhURR1iuxvv/02v/7rvw7Aa6+9xmazuRJw2Hn0rW99i5s3bwImeBwM\nBvzwhz/k3r17XQBuAIrLDdJ6F9Z1TSlKJtMRi9mcF+/dpWyVhvM859btGyyXS/723/7b/D+/82/4\nv/7Pf81nf/VzJElypXph56OhOvW6loXNZkNRFJ04z4dlWLTZot+2AgNcoQanadpRtQDqwoidRP2e\nuYdFYWxgQmMBE/kBRWYAh/HIWOklK1MBc6XxovSlQ+wHBJ4PdYYjIFmv6QUDlNZ4UcBqtUG7IY7j\nGeE5FL3QBTTpam4SbKWoSkhXCwD8MMB3wEHhesZKqEiXqEZxf2eE2hlxd2/M6WLO0+MTzhdz5tMp\nXpZzcWZU5sfjIeiG85NToijixuG+oS0HMX4Q47pmn2hUQboyno91skaGAb/y+b9CjaFh51nJt77x\nbdarNcPRHoPxLosk596Nl6iVi+f5eI6gbGC+SlsaudlDojAy63KzQvoezx8/YffggLOTc+J+D92C\nibTAEoAQRbdnNE3DbDaDRuFol7JQhHEPNwiJhhN83+Xea6+Ccnjy7CkHd29xfH7GV7/xdfqjoRGM\nUoat88UvfhHf93nllVeo67qb77/7b38brTVfe/uPAcXOzg5aNzSqwJPmMLUJQdMYCplnk1VtTtjV\nakXomgBI1WU3/ywdvaMctmBbvz8g1MaW6dnjZ7z22mvUZcFsXdNkKX/5r/6HlIuUpxfnNOmaZDOH\nOEIGkmWeMqhMn1+RpawSk8gbmrpENBrV5EipcR2HwPuwEEgvh+2js61R9u/Y80Bk5JW5x3Hbw2iU\nXT2EUIzHYwaDQSfql6YpT58+7c7EyWTCC7svMx6Pu2pqmqZdFff27dtczGfc2dvltU+9xauv3OPZ\nwwf80Zf+gO995Qf86q/+KptkznfefZe/99/9A3KgVDXrcyNElmwKtBL8/u//IWFolMhPZzOMjZzk\n/PyUMPTbSnfEOLysrFraYtcOVCmjYr5O2wr4lKqqrpzvluZugdd1kqCU6sDYzWbTxR0Ai5NFl5TG\ncdwl5U3T8PLLL3eg7De/+U3iODZVN0cxn8+REgaDHrt7OwRtO1PgGsbAeDzu3ETMvbykX1p6e0en\nlZeCQq4f4AUB5+fnBFGIJ0UHgtnX2wLDwcEN7ty514lWWeDfJkhZlpFVBfuTIUWaocoKd7pD7WqO\njo5w2mpU4PskaYrQ0CQrlFLkZdkJrQ4Dcy7+3u/+PtNBxO4gwFMFe4MAnxJPaLzRgDTLiFyjDq7b\naqcQksxTvPDqq7wcfoy4F/Hk6fv88R8nDFijFPTCEUmqmKff4+nTR+zeuG16+rUDiA7E2yaCCkfy\nZ2G39csYUkru3L3Ler3m4uKCwcAIvTbXhG5tS4tNdpum6c7s7Vi0E6TdSj6hnT/SQWOKXVZE1lae\n7Xtu9wXbfmX7ntZvWjiy+53b1dmyKrtrtKJkFkTyWyDZ9vHa6rqozVlmAVrbblLXNVlbzQZo6opm\nq7Kv22sVQnRrtmkajo+PybKMF154gcePH//Ye75dKIGr1dfrzJ/tYYs7vu8TteuuaZquHSPLMory\n0obqgxL7n2Vs09B/3PVc/9r1Yp29p/b5bgOJdo6Y11+2S1lGjc3bzGuajr2zDSjYFgYrFGmVyB1V\n4+sGD2E82jE96VI40ILbV+6N5ucSKftJ40OVWOct0ntxccHu7m7nNQxw69Yt/tJf8thsEr773e/y\niY9/ylAxyrWpmLS9B1mWUeYF/d0dgz5HQdevOhiPcAODxFkvaUuV8jyPtNzwiU98giCI+Ef/6H/n\n/v37NLUmjmOiyPgMT6fTToTMoi2f/vSnO6TNbiLL5ZJbhzfQWvPOO+/whS98oRNMMAFGyWKxYNFa\nN1maWK/XI50v0Bhkv6oVcWSUue3PzedzqqpiZ2fnEmnOMzxX0tTGQ9fQfC4PQ7tJwiVKZDfKbdRI\n1w3aaVEirSlqA04Enrm3tYJ3332X8WTXABctDQ6tMR6colsoSZLQ5CmLhQkaHKBubWiKuiFwHZq6\n4uT4jOGuwHV98sZslqPh0FSny4pkveb8zKgxohrqokSrmvnFOelmjS89HFeglEk6hsMhw+GQz3zm\nM52C+u7ubrcR2wq71pqXXnqpYz/YwKiuDeXOJhXGF9mgp9JxCFtkLt5Sr5/P5+zu7PDlL3+Zz3z2\n02RJws50inRcisIHIfjrf/2vc3Z2htaiOwxsFQFMwG+r7hcXF4a50G4o25vzh2X0ej28VqBEa43U\nLWpeV91nsp8ZQKvLw9Nu0tvif1VVEbRAT57nLLlUh9eqFa9Con1Dr65yF0cqhOvSj3vM85ow9Ejz\nklpLpCMp2vsqdIVoD88wDJCtLUOV58Sx2T9WqyWuM0F4DrWuiH3PiLs4RregririKOBueIMiyymy\nnBs3biArzVnb+lEUpr9X1w2TybTt09Scnl8gZ/MrqGyWmWqxLzzqQrFOG4JeTNW4KKF57fWP8v6D\nhzx9tkCcnzDZv4kWUDcwn5+SbRImuzsoVYN2Oj2IKIxxHdPX6UkHJwip8rLbn+q6MFZVrtMh/kKY\nHvZU5ZyfnxNFEav5yoicFTnLtbH0Gg6HLNcLaiF48vQxQggWyyWj0QgvDAjDkJ2dHUbxiFdeeYXJ\nZGL2uVZNNssykiTh5PSI3d1der2IJEnY39/tKnxuaKjrjis61wi4rGbkRcHh3j6qMQGIKx0Cr52D\njkP9Y4JBuxdu8qoVwnGpG410A4qyZtCf8P33HrPjeKBKHM+lH/usi4xBFJMkRhSorjW6budGK8yj\nmophEFNr8D1DS9b1z6UK/hdi2DXrbDGSLHhmz1AbSFn6seM4NO3PWb0U2wZycHDA7du3OTk5YW9/\nn4ObNzg9PWW9XuM4jhEechySJOHx48fs7O2yMxrw/vNn3Lt7j52DQ1558y3+4en/xDe++112Dg5Z\nlhm4Hk1RsF6tmc1mnByf4Xket27dYTKZIoTg4mJOGIbcu3fXADRNiRC6q9jVSnf9pUoZb+wkSQzz\nxI86YLADaRzJYDAwCv/tHr49nwf9fsf4sp/P0roXi8WVBMCy5rIs48UXX+wsOW0R4MaNGy3Dx2U8\nmvL86ISbN28b1ltitCh6odvRam18YdkZdWOSaxtPbDabrhpnExj7ZzQa4XguTWtraNcN0BUH3Mb0\nNi43pujQIEgLo7btuB5RT+CogKypcHyPpqpJs4yz5QXz2YzA9RAaotGQfq9HWVUISqgUDhpHSYq6\nxvdCwOG9d77HU9/l5sGU24d7eEHI7Zt32N/dQfmSm3GMoxWy1ZFxhAHQUgmu57HONhw9OabMc/ww\nYiMEaEGS5bhOj7feeovKcWkwgqWXibS4whrTaFTTmNjgQzCElKRp2lU989wAt1LTFRuAri/ZxnKe\n53VntE1kbYECrlarrUp/EAS40kG1DLLBYGCeQZp2saithtuq7LZQqe1Ztl+z89LzPLI86/5vwVUb\nI/i+TxiYuMwmgrZi7bZaBvazWGCpqiojEFa1FHTPNaJ6qp1/jtNpvGzfA8/zSNOU09PTK+tiO3m2\n4/r/t6/v+tcsmGcV1W3BbbtPuWOfbAEi233v1/vnt5/TB40Pqlhvv9f1v7c/43Zyvv25zfPiShXe\nAhaXn4MrffZWG8d+Vlult98LXQdPgVANTVPhaAXasDCkuCoQJ4RAOtKqlXXjkqb/swNkH6rE+tnT\np7zy0std9Vhr3VGs5/M54/GYi4sZx8fHHB0dtbZWRj05HBkrG8Hlg1uv10zGQz7ykY+wWs7NIall\nd1jaAMCiJlprTk9PmU6NBchoNGK5MNWwKBDt96Zd8Ku1SbpXq1V3UNkF8NJLL3VonaWRz+fzbqJY\nxDgMQ87Ozjg7O+s+c1VVSMfB9Vw0qkvAHMdhuVx29ycIArOpYCfHZY+LAFAmkDTnRksZ0aY3YZua\ns91jouq6VYC1tHG7ONr3rWvW6zUI02fmottD/qovX61NkL5paX/GDkcCCi0ExgvcRWlNkqVEpUGX\nPDegqU2Srh0ompKyaK07pEA4HrqpkLjMLzLzeZqmpafmnUhZnucdCGI3KUvTs/fNUusODw+BS4Rz\nm5K9ndw1ddN9PluJ2Gw2DIdD/MAFpbu2g2fPnjEZj/F82foOJ+we7PPw4SM+9/kbLNv+PCuOZ4Ea\nI55hKmu2L98mN37g/9LW4p92yHb+VFWFaimUrq34O7KrRF2nSdkNtanK7pAIgoBqS2HSbVU+0zQ1\na6ZucITEd120EuimrZIkAukq/CgE5RC4Pi41jeNSqwqkR0NNU5XU7e8TgGrnE42Zq2EY4nseq2SD\n4wjKKid0vHb9RyCMZV2TVDhaomvYG40J3IDl6Yw33ngDlDn4LmZnrBZLdG2SVd/12rloEkhbCTBB\njqCuKnTWIIOQ8/MNdyc3qYHDGzucHz/jlVdfZ775Bo0ocAOJ6wuSZM1qvUagOL9YmjkV9FhvcqbT\nMU+fP8NxHHZ2duj3YvKiIN1sUFpQ5gVaSKPYi9cKmuXdvlW0ateTyYTl+YLNJsXzHNI04/TsgtPZ\nBXmeMxgNcQKf3d1dpvt7vPHGG51on1KKG7s3un18Mpl0lc4sy/jt3/5t3n33XYbDfrtvXpDlCevN\nkqaxdn2XB7dVgLcHuBXHqZT523c90G0FrZYQeB3NzgYxvV7PrNPVxsxHz+H0YsaoP8AVkuPzOVp6\nPD8+onEbvCjEdWO0KvCakBCJg6aqDfgnHWkEVbSiF4WUWc60Z3rybu3t8+LdAf/0t37nl78w/xTj\nOvWzo/01DUV9KURo1eLtWRgEvS5xVMp+Leju/507d5jP57z99tt4nke/3++sbR4+fMhgMAAMBVn6\nAYXwOF0lCCHYPbzDf/Zf/z3++W/+C/71v/ltfvUv/wccn5yys79H6Pu8+MLLvP6Rj5FlBUmS8vjx\nY5JNhuN43Lp/s6MvO67A912CwEcISZWX6LLGkQKpBedHJ7iuy8F0l9ly1bbmmL3L80KkkFTNJbvN\n7u02ILT0besaYdsSrMDZIrE09NgItbV08TyruuDaVKelacloGoaHN3j27H1euP8yjvQpipqirAlC\nkyDZCpytBtngPw7ijg2w7REspUS3+6++fNA0TX2lohXHcUfzd12XMstphCAeDg3QCTRCUNVGYTz0\njB1mUhXopiHdbFgt/n/u3vTHkuw87/ydJda75VpVWUt3dbfY3Wo2RZOiaFuEbBNjfxFgwTIwkD5Y\n1l/gf8jfxoAx/4EhYDAyx5IXWbYlUWSTIqu3WrNyu3vscc58OHHiRiarKZLSUOw5QCGz8m5xI+Kc\n877P+zzPu0CGijgIUdb1n7VZSUmLikLiUQJ1SygVyiqiqsEKjZCaO8cOjLtcrLlYrhlPJ9xf1/yS\nSXnt3m02W8NsHNPWOVVT0NYV1rakYUK2WTObTJiGMUJFnNaKl7lbf00rMUZghAShqa11a+Fn5hoC\nlOiS71/8IcXO8dtTvf3+OXTW9uaufs33ki1/D/n7eehyDfReCwJXuDJNy8Hefg/Y+oTL//SJY5K4\nvQ+cK/4wvh7qq7380reSHSZMHtQryxKB6mWbfi1SSvXxgj8GTwc3xiC16hNuL9Xz3gIy2CWwsDMS\ndp0vnAGazzl+pEL6Uwz/vX3c54sMTVn1LJZAOuO4Msv77+DPhX/+8Ly86jN+3Of/JMf4WWOoI/f3\nh88z/HcLuhaEwyp277nA9eT2Gpgy8OTxbN0wCJwjuBIEWGjMYK6+etL+bZWmPleJdZqmrNfrvlKn\ntebs7Iw0TZ1hyOaCtm155513WK1WPWqcpimhUASJS/ay9Ybx0SH379/HWsP/8e/+Hb/7O/87QRDw\n+JMnvZ52b28Pb8SRJAlpmnJ1ucBay7/5N/+GKIr49JMnvYNpHMdsNhsmE9fC5ujoiLxyKPRoNOoR\nfT+h08gh1vfu3aMsXRVoMpmQP3naa5z8pNBau2DW938UgraqsJ37rDd88gtQGIaO6tih3EEQgHWT\nqy7aDqHeoUG+36BfPP1i6E0shujREEX0i0TvnqhdhfHs7IyyqYmUW4jbZoeKFUVBEjs69tX8kqJw\nm3jZNmitMLZ1FCoBQRhhjEMPTdeWoe+L2QW9jt6jCbSmKhx1WHU6SqxFaUVZVYxSt9DUVc3Lly95\n+PAhV1dXGGNYrVaUZdkvpEMH+KOjIy4vL3ut/GaTMZlMrl3Xtm2RCNLRCIng+PCI0WjEo0ePAKf5\nTeOkp+p7+vPFxUVfgZ3NZnzwwQe89Utv9yjocLPx18b/7qtofgMLg89PYq28sYbEucAbg+6AgaLr\n4eurJD7INtYtjF6/HkURWZ73PX+9g34a7zZ9KQRCa4eOFzlW7HoWe0CkaRrqRiB0h7yLEDqjQyWd\nHmi9XLj+9F2HgTLfEocRKgxRWNq2YTxKCJQkDmMQBkxDFCiquqWkRcYKbZzhSSAksQrIs4Kzs3O+\n9KVf4fT0hZvDGxcYn59dkiaRc1/NS5qmBRybYpsVPSXvKJqx2da8OF0RTBedVnXCNqvZrFZEseDp\ni5cUVrCuLCpUHN/aZ7lcsl5WnJ2dUhYVm+2ak5PbfOlXfpkk2TkW225uj5IRV1dX7B8d9wGJMXRV\nYScFaWrD0dFR5wzr1s6yrrmYn5NXJZvtljd/6Q3u3r1L3ckonj5+0htEHuzvo6XixQunJz84OGAy\nmfD8+XP+4i/+gjiOefToEefnLzk/P2e5nPPa6/d59uwZee6SeB/gW+NNcgxaS3Sg+iBwuHa45zm9\nfFnkaC37Nd1ay2Qy6duHpVFM0VG6P/3kCe+9956TsAjNow9/QFKv0KOAkWpJW4UShlhqojBA6Bal\nBFEU0HbBg0QTBZrD2T623vDuw4fcuXOHT5+f/d1MzL/BGCbTQ2ojQBSG1J38yrdp8/+sqXs6XxTF\nLBaLPoHuwWFrCdOEzWbTB5dFUbC3t+f6WCcJ070pVd2gdMvV2lVH47Lml7/y9/j185f80Z/+Cccn\nt3l5cU6YxEipUcpVv87PL1gslrSNpx+Kzsm67HpTBwSBRintQN9u3Wg74HPSAfz1IDCXQnfAq+2r\nwZ6O6imbnvYY3gAQvQ7R//MVxPF4zHK57DuXeGBxs9m4jgpdwuP2uZbNOuPW7RMuL+eYFgSKtt45\n8w6DXbfvuarXbDbj+fPn1yi2cRSTTpxkbtMF7qrTs0Zdkn6zaqe1pgk0UmusFDTGtfJsndMAZVMT\nSE3TtlhjeqDeGMNqvmKSjjia7kHtnM6LfE2gFEpZhNXIwKKtQgtN0RqEVOyNR4ShJq8NNghY1w3f\n++QZJp4Rx2NmkxFXy4wwdL258WBBXTE/v2B9donGMhuNGevISVpQIAJag9NkW0PdWmrbEOtXh9HC\nXci/FWrpz2P4hNDHd72LdjffPBDk/92sXA+rk75SfFMHLKWkbmsCFVB1zKeh3nbYJ3t4XMMiVA+u\nt62j9Xav9clXGIaU2+paJdYfnwfcfdGr70ygVD/PbibuQgoMLqkTQsDg/jWmJTC7GK1PZO3OkM2b\novmY7bPGMDF/1WM319Y+UR/E5/44lFKIgWZ8OI//riSDQ7BjqKP241Xff6grH1bBh9/Jn/fhc/x9\nqYRFS4lqBdK60mJ3lrD2M87DACT5WVmgn6vEerF0/SI9DdcHSb4SfHQ45fLyim9+85vs7x0ShiHr\nzLWy2m637E1nSOnaW8V7M4QQHB7s8dZbbzl9rYC9vT2WyyX379/vKbe+Eqxjd8NeXV3xx3/8X/hn\n/+yfcXV1xZtvvkkYutf6ftVZljma2cuXHB4e9m0Koiji9PSUjz76iF/76q8ipeTjjz/m7bff7tHs\n+/fvMxqNODk54ckTl+iv12suVivm87nbrNuWdDrBItHqOnVnWG0eVmQDLUmTBGHKDaZ8AAAgAElE\nQVRajHVa054m093AaoBGD6l80N201gn/67omiEKkFEgEWLWjZdiQLC8RWoHaVcl3DqmuYlw3Ncvl\nHLpJYizYxlA3zhyobayr9EqJjiKyoiCOU6RQlEXNarVhvVwRSEmsA9JRzNo0VEVOXhaO+u7cChCC\nnfupkpiy4fnz5zx69Ihf+ZVf6b+rN7JZbV2gsl6vsdb+CCLrz7GvvPhqmk+2z8/P+fTTT7l79y6T\nyYTF8qoHBbIs4wtf+ALLxYJ07DTfYRwxny/5R//onxBFCWeXp0ynU4Cuf2J4zUhuf3+fq6sr4jgm\nyzLu3LnzI+0ZfpFHLBT7I0c9zvOc3NKzFjzFa0jt8Rve6GDab8b5ZkmgA5SWaOEcTINgTGtrVBAi\nm8Zpua0lkALbzLB1AUVObVKY3CIaj4i1oLw4RxYLXmxaoolCjVLysiKcxAipQCqqTUFhHShiVUtW\n5ygcaCUSga4NWlmCOESHmuV6QRRpgmCEXFyhhKJuWtbrDcd3b1E/ec7dcMbmyvLRoxe8+8tf4ODg\nkO9+8L/I8i3nL8+xUlCULabcMk6dIaDSEUXZUkiNlRM+XJ0TpyPKv/pz8q1bH84eP2Y0mdJaQ7Na\nkF294Nnjj/jCVyTvf+XXSEZjksWak2XGP/z6l/j4448p9xPqtuaD732v17zWxvbyiW1RIoOwP/+f\nPndg02QyJstynj59ShRFmNPn1HXN6SefcHFxwcuXz9hma9774jt8+b13ee+9d3n06BGHd1/jyZNn\nfOWrX2M6niKtJM9LjIFa1XznL79Nnq1YLS74k//x35FBwJvvfpEffPpDEixaQKgVxTajLgq3cUqB\nVAalXXtFaw3WGpSKiMKIKIopyprlZsPh3h5F0yBNA6alKLeEWmHKhiSOXVu2pmaz2bLYliy2rvKm\nAk0LbFYLPv6rD5iNJqjGEJmKSWqZzkYk4xHZ1vVizdmiAk1gQ9J4RBI5+Q2tq0q+9uCBC97bnPl8\nzicffcp3f/DR3+0E/RnGkNrn56w39DJi11Km6qh74OZ8KLv+zEqhddCD1F7qFUUR+/v7lBfnRFHE\narXi4uKC2WzG/fv3ubq6om1blsslnz57xq3bJxzeus1IBXz8/Izlp5/y5rtv85v/4l9QNTWnp6d8\n+pFbm7ebOfv7zuNkOp1yWc1ZLpdEccLLly8ZjZzm2bGXdLeXttAa0jCiMO46tl0SEYYhZuQAL4F3\nLXbVvrqtrgHZQyMnfy58EuKrYWVZ9vv6/v4+q9XKAVAHB0ipGI1C5wI+MLK8uLjg9u3bvHx5zuHh\nMUEQ0dQG08J0utcBsq59F+x6FkvpTKsQOyMyz9jzyYdou2PXutdcW2vZbDb9a+B6O8WqdcDp+sIV\nReqmJisduBImMXXpYoGqbTB1Q92xTg739gmVY5RMxzOnBX/xhMY4E0ApHEAbWI2wLWhBZQxxpGkF\n1KLh2cWC/ZMH5G3LB49PCSrJ/Qd3eOONQ4xtaFoQ0hlCjnTEWIUUmy1NVfH82RlmkxOHiWPqiU4X\nHEY8P1/wBs7U8DPnA12P4s8JFbzukkwf4ziJwgQdhD1jzmvjrbXXWnz6e9QXgcqy5OjoiKZprrWa\n8v+fHIwosrxP3n3S6ZPnHSuL3hDM076HlGMvdWw6puR47FhMZn3dHXzomSFw8hF/3/vYOUzia/PR\nf98wCMnqnSmZB8NgB0YMKcv+2Hyxavj7j0vUhgWtYQX85vB0dV9Y2R9NqMuKfJv1VVst1bX43R/v\nUJv8dzH8+fLH4n96qWvT7CSAw+TWX0dxw6Xea+6v6eurqmfjYFosIE2LsAZhQeKMF4eA1/CeYpCc\n/6zj8xOJA6vlkv39/Z4qIKXsk1U/ubz++uzsjKOjo/4ieN2sUoqDw8PuwrnE82tf+xrWuATz5OSk\nv5B1Xfe6pfF4zHgvZbXc0raGk5MTkiThnXfe6YL/oqeveqrIcrkkz3MWi4V7fVf1GI/HnJyc7JDZ\n1aqng6875BnoEzZv4uInrzd+iqKIprXEUdAvbN7EyqPh/vemacAKmqCz6cfpmP0NCyC6G353E++Q\nMEfh6ehC7IKoFovqkOvWaxOtuza6S2SNcZv6EN30lXclnIHIcBIZa2mtRekQgcS1SXOmIH7h1kL2\nlBjbtAShu5Vt66oJPcggJHVl+gXHt1jx18m3M/MJnF/YfSIHDjDxTrb+7x5BHZoseCqzFpJJOiIJ\nI0Klef78OUfHrp2KiSKatuo0uSsOjma0jTvHl1cLDg8Padsd8uhpSn5zSpLkmsukvyeG7pyfh9Ea\nwzpzm1vV1LTYazwcr/vz1WrPnJjMJlxeXhIojcRVAwLlrkOeZRSmRmrX/1kq9zrbbYTZdkuoDOA2\nx812izYtlcRRqpuWJEmJ05RVUXVBvQuWA+Wq6e4epnc5Fo0l75DyuiyJ44ggCnvwpe4CxTzPaaqa\nOE57Ey5XDTCEouHi9DmPRwF/7+9/FRNpXr48pW4sVZZhOsTbWLeJnF9ecvfBWzQiYL7O++qVilMa\nWvK85OPv/DkP33yLy6sF5OestyVlZfj4448Jkwn7h8eMx1OU1iwWC87OzvjK136VP/2f/4O9/X22\n2y1nZ2dsCxcg+bXUa0nrumY2dbKG7cY9t64q5ldXWGtZrVY8+/QT5vNLRqMR3/iNf8zde3c4PDzA\nELJ3cAutE05u3yWJY/Ltlu3atei5PDvn8uIl86sLHn/yiM16wXa1xEjF/PwM1VEKHcunJo7Hg4BB\nInG6SSHASglIrLHk2xwGGq6maUAJwkDT1AYpnYbL6fNbjGnIq5poNOnne2sbJM5LQUcCa1uKMmMW\nJ0jpgKCTkxOCOOKjT5+hgxDbradecyukxNSuC8Jms+HZs2cU2wxZ5/067wOyX/QhWoNsLWKoLRUC\n2+0dbds6Gq+1mLyk3G45nMy4qi8Aw3icUm4zTCtoGzCyJR6P0Eqhu6TOaRW3HEzHbCRo3N7aljlV\ntmEcO7Cnyiu++OY7BElKmo4oiobtes18kxFfbUClrK9e8sYDxWg6dj2s0yM264rz86cURUFRZEgF\nX/3qV9nb29tVS4yFRtIaS10YjDC0tnU00BaCKEBK3cmYKkBhLd1a3tA0FXVV0daNA2lli2kcy0ki\nnLSpByUcyIwOKLOcommo2oowjLBCE8YjgigljFOa+YaRDLg122ezXDq/mLYhKwtGo0633VZkC5dI\nBx1rQwcJi9XSMV66ud22rUtqpSQ0gjAeub3QWhojaBqL7vTIUccKqquiTyC9bMnTYOPOLPQocuay\nsTUsFguquqbJMhpAB5qycOtJEiQ0SFSYYiNDEGviOEFGEcumxZQZIggJRchkFLNcLqnaFiEbgkRj\njUUjma+3ritEXiA2c5oXFUkQUFcViygniFaMD98iCRWp1sSthtqyDC11FGHKCpqSOJI8/+GHtKtL\nNnVNsD8jnR5j8oY4y8hWS+TtO0TW9xX2oXpXKQXX3cT8aAXyF3LYXTsnHy/VTU0YOwDGg10+bh1W\nSWGXtMGu8jxMQEVn8Ob7sfvkZ5iQDunSw+qmX689o7D/DLtLgn0Vffh+YrAO+ePQHYjk4/ohA3N3\nKna+LcPkfFgtvXns/nXdA9fez7/2x1HBh5VZceP1/vwOqe/+uIZgiH/v1uxi+aGs6VXV8J/38NfS\nH5e/1sP8wJ/joS7dfZeBMaDYtQb0DFD/d2OMu85YrPHAB58p2/gssOFV1+EnGZ+rxHoymbDsqtbe\n1Mgv5FVVcXkx5+tf/zqr1dq1ddGa1Wrlnidkz8evOh1fWZZsN2s++Mtv8w//wa8RhiF/8Ad/wMOH\nDxmPx71pyB//8R/zz3/rn7PZbNhuN0RRwpe//GXW6zX/z7f+iH/5L/8lVWfC5TavoN+sGmv6Ce8p\nwGVZ8uTJE24fOWfx3/zN3+zpwGEQYHB0aa8h8zeaTwCzzZa0cz1vDYxHu4VKStlXTr3mpO0oPIGW\nrNcb188aejOxMAgIYoeq5wjqjjI+pM/4my4QCqtF3+bAWreZ9MiYcD91RylzSaqnBQ0MgZxRL23b\n0DROixVGiWurVVVss4IwilxP0lFKko47/di6R7OrsmY8mlAVBXVjqMuKuq5wDsCSIqtp6golY6Ry\n9M7pdMpsNiMKnZzg6OiIJEmYz+ckSdK1EHK6Oq+N8QjsZDLhxYsXTCYz9vb2euTST/okSfr2UKPR\niOVyydXVFTqKd1X6DjTxTt4AQaj6RXI2nXF6dtbf31nmDJ8mkwl5nne9UU0vTfAyBW/w9HkZOgzJ\n8hzVobiiW2C9Zt0n037jsNaSjlKKoujdcQGk2Dk3+81VKOfc6V3ajXUmMlGgiIOAMLA0VUFRFKyM\noZGG0ELTtMio+9yq6VFQJd3PyrSdNtf0fS6lvI6aGmNoTUtdeECkpGkbpNSYrn+sf55PVCNRYW3D\n6cunLPIvc/LOV9B7z2isZfH0Q9p8hRQxpmmJwpCHb7xF1UrOrlY8uHeHvf2Esm6ohHJVB1qiyYij\nu3e48/A1Nue3mC6WCKWpWkG2WSOxKGEZqTHf/e53KeqKDz/80AFL1hApySbPqOuWly9fuhY7OHdm\nT2s7O7votWrLywuklFycvmC5XPL8+XPiNOLk3l2+9KUv8eabbzp9u7UoNWI8lgQ6ZX2x4lv/+f/i\nan7B/PKCpi7Zbrf80sPXOX36KfnqCm0NcSDYlgXlds0kDijyihcvXlCWJXfu3OHly929NQx6/BoU\niF2bwel02gNoAolOQkZpQlMVrJcLsEG3tyhkp882Hc1W6sqBiUqC1Ni6Yr1ccXv2OumdAw7GGhlo\n51MRBgglMQKsdJVu332i6gKhwBvfYGlsSythPJ3SXs1/vhPyZxxDfeRNuqIxzuXaO2YPmSdeZjCk\nBFprCQZUU2+o5SniLV1HjK7iu1wue68TZxyaEsUhYezWzu1qQ1FWZJsNWk97UGinwbRUed4fm7XO\nbTgdxQSDa+WP2XT7mPM4sRhraFuDkkG3FrhkQkkFdgd6ejq7/xzPxrlZ4ffnZ+im7PdfaxyAbdqW\n8cgBD01dUzd17wVisy2mSyB0R8Me9vqeTCZ9xa8e+IH4wNxX48AB6NYYmo66b63t++T6PdGD9n0g\n211PDyJFUdRX6jy1v6oq2o4F5sF/136uS8hUCLYlTUcEsb7eDgzV30/bbU7b2m7fUCAUQlrapqGu\nW6qqdtdGBbStJY4DCATPnz6haSre+dI7lHWLthYlBFq6llm37pwQHN/G5hnZ5RkvP/whZi3Z5IUD\nxFSClgLTtJiqZliL3sXfg0rY35pq8+cwBomJ34vruoY4cXOzk8cNacW+Cjus2IJLwv38HFKrwZmW\nSuGKQFVR9p85LOh4avZwPfHzwT8/CAKyIu+TMM8aHOqZ/fDGXv53/9187uB9lIbH4uM7H4cMvZeG\nx/UjSbX7j2sRN6gY30wW//rLsbt3hp9xkxVU1jsW5W4dGRhyDc7HkFr98x6vSlKH9HQPjAPXzvMQ\nJLl5bYdM2J6RIAaO5Nat09flGD/+/P9tVPQ/V4n1t7/9bf7p//ZP+xsIdvRe15MYnj175kw/Qtcy\n5vHjx+zt7fGFN968JpT3WuTDA6fRWq83hFrz4MEDxuNxv4EeHx/zzjvvMEpHhKnm8mJOGMZ873vf\n44033uDg4MCZFAROB1wURW+2MBqNSNO0p5vArhm8F+/nuaP/nZyc9Nq/4cLlKVjLTjPuN/kizyHQ\nxMmo12d7ms5QayGAOElolaQssv77uxtxN9l8xV9KiWIXGPnAQinlNlvrKotKKUxXOfI3rTvmLsCS\nGsOubcJN9E7gJ/2uYu4rFEopirIztwnd30ajUb+RV1XFbDzpJ1Kapsyv1kiJ08Cprmf2jYXYa76M\ncW1IHjx40OuGptMpRVFcO/9+MfYghXdo3ds76IMkv/AqKVGR7o0U/GsAHj9+TJyE/Nqvfo060KzW\nO71dlm2I43F/TVwP4YZW7PRM/lqC27yH1eowDPu2bL8IaORPOozAUfKlc2LUShN0Tsm6m99hGPYu\nwd7IbzIew8Fhv6D6BDzbbNFBgEBjaDvAre7N0TSQRBGhsmjt+1g7HaNRTjKwWm9Io9m1zdm1pokI\ndIhQzhFUdtCnqRuQLgiVQtLgrk0URs6cr3UyjDRKCXREHCa92YtbszTWtgR2wzgZsagrHr885wv3\n3yI+FHzhS5LNfky5eMliZSi7Fk1FVVFUDbdv3+Lo9gnGVkyUIj445vjePZ68eMHhyQmj2R55UTEe\n7XO3C4Y/ffwx6/XaOWJWJS/WW9bZ1oFGL09BCO51Tsjj6YQoiK9JGJ49e9abjFFbis7Uq6oqLi8v\nXbBcFNw6OCTeT7lz+y4GyaOPHlOVNXXdEgQRZ2dn7IVjPvjed/jT//6f0bJhs10ySkPu379Lvpmz\nujxFmJqqKtjMr1hsM07u3EN0ycl8Pmc0Tvr1qTVdH2UhsNp1MhUW4jCiqZtr+lNwa0IYOoDGuTnr\nrv9wSLZZIiU97bXonEKTcUprDda21E1OFGsCo1Cq6Y1j8jzHCuGSH+hYPLaXdFR1RVGVYGHT6bbT\nJEGFEVQ1p+cXnF1c/l1My596eNaQT6yGVQMPMvq111e8XCXYJcc+EfT71kRPr4FUvrVmWZbcv3PH\nMTbOzxmPx31wfHp66hhiqzmnZy8ZT/dROgYrScdjbr/xBh988B3W6zVZlnUmX+74qkbh3b6TJGY0\nTnjw4H4HHvl+0+HgvtlV01yALlAy6BLnzhl4ABJ61pHfY6qqugaw+nWs6PZmL9vabDa9eZOUCt22\nlNuMJncdTpq8RMddxap27BrPeBslXbutwMUT8/mc27dv91Rxf2593OTnt/cL8evt5eVlf3w94MnO\nidnHGX7/iaKoB/f7vayuCTF9jJJlWU9B9/tta2qcn0WAkiHYBhnHhImbM17O5tedxWJBqGUfb7Vt\ny3g87t2pi6qialrCKGI0mTiQLHTMkaosOH36nP/yx3/K/fv3eXDnHq1WJFGENoYwiCjbgiBO2bv3\nOsfvXBBOJ9izU1Z1xfnlknJ9hVEj2rzkKAhp87KPcYYyPD8P+BsG6T+vEUiFsGCNoSqrTlvuYs0o\niig68FrgGF6mAw+bprlGozbGOLNPDGAclboz5wuEos5Lss6It5cfQL/fwk6j7+UQwyrtdrvtAZum\ncqBeqIOuK4zl8vwCJV3RqDUtbWOxVmC7zhBbW3RxRUW9WbkuHhhstYsX/bH1LcUw1xLTIRjYtK0D\nXYyBYcLdndch/XmYVA8TRrhe1X7VGCbyvvjQti1WOr1wICRSQFmXoARa7PTqHoz0BsjDJPdmwnsT\nKBh+35vP+evGkNHwKl11r+OHjiGsiONwwA5o3BqrBNYapIxoW9vnDdYKkmSEaQzWSqSW7jFjCKqG\ng9rNR4mhxoCwSNw6Psy1/bw11qLCzpwOg+yYbwBN+/9TV/Df+ue/TaAjjg5vUZQZV1cXzOfn7O8f\n9z0hv/f97/DWW2/x5ptvESeKW8fHzGYz18IHN+l1GpNEMfv7+yyWV/zf3/qP/P7v/x7bpuTDR0/5\n9V+/T9PUHBxOWa4u+E9/9Ie89957yEDx7//9/8nv//7v8/rrD5hOx7z3xbdp25JN2WAMhGGM1iFZ\nVpAkI0bxiPV67ZL9IEILjakNX37/y6gwoi4KkvEEIyQyCJFCcvX8grfeeosoWjGbTXjy9FMuLl5w\nVWzIqg15WSFUxCweMZscoBpXIQ5wPfU0krJuoamZpSPKfE3ZGspiQ6hdxTnQAa0Maeqapm765DoM\nQ6yiT6CDIHAJdYea56btNQxxHNOaFtGC1Z1JWtfWzE/iGpcgKy3QAQjpFsK6Khx1vHRJkA40UaCY\nTh2tM9+swUridI8o3SdJp2xXa9YvnqO1ZCUaROAqcEomrFrBdDLDFA3bzQZ0QDqK2K4KkCUWDVax\n3VRk25Lj42OKuuGvHn2IEIL333+fURhxtXQmZqNx1Lf6MQbG4z1G6ZTZ1KH72+22X7QODg66TUZg\nREMratb5kvHe2LmK37vD2dkZeb5lsVg4KnBW8MYbbyAVrFdbktGMaWl5/PgxWgU07W6BHVLVlZLU\nrXGtIZauJdx0NKbcZuTF6u9yev5Uo6pKdBojpFu1sjwjaLoWRF212D2v6uUN1hjmF5d90qukJN9m\n5NbpHJu2JitydKhojDOKKoqCUEratiEyrmWKjSwmnTr3+jQijCKkCkB1vd/bltq0HUDmgsxRMqbB\nEgpBmCTQlGghqZuSOAjRgWsbJkSXJAiD1C54z8sCpWKiUCOFpsjKbmOEpinZmwRYBEFrePLJp9x9\n64tM9vcJxyH3b++zuXzG+UXG4uIlVVE4k7zWko5nRNMxQZoynsw4vv+Ap2eX3Lr3kMn+IUJqrKgI\nx2MA6rLgzTd/mbzY9gFyVi6Z7s2YzmYorblz94SsyKnbljCJUVb15pBlWbK/v09RFOzv71PnGYuF\nc/lO05QkDThIpjx58gSlFP/pP36Ld975ZaIw4eLiiqqqwUq2Wwcm3p4dcHr6nLJYIEWDoOTZ08d8\n8598nf/x3/4XZb4lCgJsU3J8uM/B4S3mV1cU27wHotbrdR+Y+SAtsBbbIdhKu84CgVLYDpR0icGk\nCwJddwhrWgLlpAVRqPoAKE0SWqFoqpKmLVGhAwMNAo2CpiIKBLYtaGrLupLUXWIltaJpW4wKsFIg\ntWbbVf6DzqjPYtkWORfzK87OTpnP5xweHhKORj/X+fizjptaYZ9gePDYB1YefPEMsizLmI4njlKv\nnazDs8+8zClJkl4/7D0lfJD94MED1us1m42TDyiluJq/ZLZ3TFvV2EZS1YYyr/joh+fs7U05PDzk\nvXffptxuUEpirOUgnbnWcKsFSRq59bWuqeuS4+PbPZgL9Ou91hpESF07HahWTg/YNC4JzTvplac8\n+uosDCmNO3lPGIbXqLPGSKbTcZ+YZtmGs+cvODg4IO6qwEm3/56tViRJggoDsnXBs2fPePPNNyk3\nGeG+a8U4Ho95/fXXe7DXJ+95lzB5Xao3QC1zxwqqy6q/HhhLttn2/cM902vI1vHr8rCK7SnSrbVE\nSYLqOmWoICAEZNNQlDlSSerakJcFUgRYC8a0/bnxcjav6726WvSSqCAIKIqKpvFmUT6RkhwcOJ3v\ndrtF0BDqiLppePTBRzz6/qe89/6XODg44u7tO+ztBSgNgpBwFHJ6/pwsiallSCkCwlHKUSQoo4rt\n1pJfzQlbyLu54Ctq14b9/NSsrTF92X3o2Fx3RZe+uMLAEEvudPY99Vop2srtcaq7H4aVaF9U8Qmd\n7PZnXzn2z/Nrh49L/X0wXGNgV6zaOUdbJpMpq9UK015vzdQ0DUEY932wfUGK1nUOGdLH/bFJ6To4\nvKriepMGfpPODdd7Ov+4SujwvT7reTffd/g3ay11t4Z4wGz4vsP/D7/jZx3/zfHjHvtJxmdR24ef\n66jeu8rzsMp/kxbunq+6/UeAlBgEqn+OT54dPdY5Kfj/X/9ePcuqS56Hz/hZvu3nKrF++fKUb/z6\nr7tNui66k+sWs/l87tw5JxMODg4IAucAvr+/z2g04vLykuPjY7TWXM2vuHPnLp988gkP33iN3/md\n3wEgSVK+8Y1vkGUbjm/tEcWK8/OC3/u933MmInXFb/3Wb1GWZd9D8/z8nK985StoIXvtSFEUfP/7\n3+dXf/VX+0rO/fv3WcwXPdL+0UcfkYzdZvYf/sN/4Ld/+7e7TTtgNBo5VLZzNJ/NZq66eXVOlheU\nZc3JvTuAYrVaMIsnfauOpqlomoq92R5KS9brNW2dk+egAk1eFIRag+o0JYPKrkd8bUeVUwjqunLG\nZp1OyGD7KuoOhRMopftepT7odclgQ9u6G1zJANOCkoq6LnpqvJQSHQbO/G3c6bGiiLLTo4+mEwSG\nFy9eIIuc7XbDx59+yPGd2ygZcnR8QhKHKAGjNEaZKdvlnKxp0DpAao3AGX5NJhOiKOLw8BChFO+/\n/z5/8id/0iNnvtKglGKxWLhq+HzJ4eExm3XG3bv3ePb8U2azmXNrzjK22y1HR0dMpklfoUjTlPF4\n7Fr0LDfcunWLy8tLDg4OOD09JUkSzs/PUVogcP2LPTIPXKOa+4XFG5XpwAEiQ92+Voo0nfzc5+TP\nOtqqIRopTOOCsjR1gIo3MTLG9JVjH9wtlkuCQcAmlaLsKhYHBwfOnVkD0pKXOdY6qrVoW4JAk9Q1\nMogYJZokisBa5yI8mZLlJTpyxoPj6RRrXeu2IIhoK3efp7MJcRBSVzWxlJiqIQlCaC11U9IIZ75R\nNTXpJKFtLUHgKnWYiMqUTCZ7XJxdEiQCS8vx8QG1jCiXK2QrqJ78kMW3/ysnX/s64f4RhZiQixnK\nPOfByes0dUmkJGEYI3VEFKdkOiKNR9RGcOdkv5N4KIQVHI1GNNIF6G08YrXNiCNXFTR1w6wRrNZr\nirri1v4ez148pzYt09mMyXRKm9e9LMK3XPH9dmUkaAJAaOK9MenhlNPTU9ZNzic//IT16YL2JKOQ\nBX/2n/8T/+Affp3Hjz/i/PycINR8uDljkiacPnvO8dEe73/pPX7w/Q/4o//639guNozHY3QUEUQR\nq3WJUTFXV3N0NKOp1gB9sFVVlQM3tEZUda9dNQMfCSUkQsouGVKojjVihGFvNiVQgkBJNmtXqWxa\nZ16IDtluC6wKGE0PCKKQNI3ZzF8iLETScvvgmB9+//s8eOcLWCHJy5KybWksyMg5JzfCoj1bQbt1\n8vT0lPVyxYMHDxgd7nG4WpGmKecXnw8quPfVeFWlxVcwvZTDa+G8ZMrT8cMwpGjcXmDErn/pYrHo\n38cBZKI3NNpsNijl+ij7z5juj7h4OWe5nJNnFaEK2d8/5N69e+zvz7ouG06mA74tYsJ4MiJONMvl\ngrqLK+6c3CIMdZdsVj2jy69FShtcu7uGIq862vOPdmXwiWaSJD0g65k2Q0OrlVEAACAASURBVF+M\npqnxZnttZ/a12azZbrdsNhtOT0956623+j6/y+XSVX87s1S6hCdNEuJOl57nOW3bcv++c83XWjOZ\nTPrvMPQJkVJydXVFlmUsL67cfiglSeho7HVR0pQuGffyD29QdZMx5St0uwSpxgJ5WdDUNVIrdBgQ\np45ivN4AVlIWhnpTUFQFYZf8DNkLPp6LoohiW/R7hTeUBbi8vOzZhrWxvDg779sBtm3LKA4oqpai\nrFE65E//2/8kGo3ZPzjgtddu8dabDzm5fcgiyxgnM3IZUomU+dYw2Ys42t/D6oan25cszy+xpdOY\n35RC+CGF/swk6RdtFEXhkmt2jBNrLbKbc0N5ggeEpJAUdcNqs+0lCf71w3Ph7w9fhYZdIrdarRDW\nFXG2262T3HQJ11BGURRF59Dvkkbfessn1OPx2LWmWyx6BoyUsmeRgtsvhIVRkoLZuYR7RgXQG9X6\nOTqZTLhYzvt47FVVZ3/v+6qsl3sMqck3E92/7r541eM3adE+7veggWdgDs+/f6736/lpxquAgp91\n3KyK+3V+OIbtZf0Y6sc9Y2AI5kkpUYHz9mhNg21bAgDp1yTPqrXXQK6bVHvHMnASIf/YkDr/0+AJ\nn6vE+uTE9TYdj8fdxrPpA1efGL3zzjtMp9MeNfUUX69n9YZk3siqqipGoxFxHGCt4c/+7M94//33\nANtPzjRNwbp2EX/wB3/Av/7X/7rvlfbw4UOw0LRNT61qmobDw8P+uC4vL/uEGuD4+JjtdtsjS++8\n806v/RuidEqLXofkbyIArZ1DdNvsen6WlWsN4m84h6p3E7DbQB09pnMmtLbrG/2jN3xDtylKhWna\njsq0UykMjRM8rdU7LL7q/aRwLYZuLjTXEEGx00/4ieTRfi2hrStXua0rqrrAtDWb1YLZ/jFVXSCD\nmDzLaaudZkdKCUHQ9eoWTCaTnpovhOs7/vrrr3Pnzp2eVug/t211rxHTWpPnOU+ePOHOndvXKEH+\n2Id0dR9Q+O93enrK+++/37/f/v4+UkpevHjB0fEBWIMDTdvuOrkqkG98P9ShGGMIlXJtSQZUaCUl\ncZT8fzTz/vZHGkaMk7SnzjbWtdMbHSZsckeL9JuwpzGFYUjTterpaZZ9xaJDnwkI4wAQ/YashUAE\nmtV8SToKmU1ipFRUdY7oWCxN44AgiaSpa1ScIpVGSkWDC9TSbsNM4hiKrGNzWJACKRRBpF2AjEVK\nRV5mFEXDeDqlqgyBjsg72vVm6xg2RVmQo5jMjlCrJW1e8vzDH3D/tdeJZEg7maHiPcaHNaIqCOPY\n6cTDkKo2VMZiVAhhSEjkOPYNYCDUEdZI57TZNFR105lpGZqqommbXq5yfnVJUTqQ4sHrr7nqUF5g\nGkehThIXBPsWfovFAtMaRhNXDZ/sTTsKNCAFf/nBd/jiydsc7h/xgx9+wGw6ZrNeoKQhjgRZtsYm\nE2ojObl/h7feeMhyvaK1lvPLObcOjgmkICsrJAKhNOvtliQdk5erPskJ2HlB6MFG6/8NDf2EEMju\nGuZFgTAGJeFg6uQ04SgZ0G8l0giSdMSmqMizjHS2T9UalBEEkXPzr9YNkzRiu91ycuL6HiMVQiq0\nktR11wmhssRhQhCFBMq1imprt2fde3DfmdlFQd9K0n5ODI8EOzDSaxF9AO73YO+F4mUdQK9fVkpR\nViWz2ayn8nvgxq+9PrjWWvagmn8P71MwnU6p24Y4GBGHZyyvVkgp2Z8ljlbcgdQSw+FsStz1cr6Y\nzwHL5eVFH0ukoxhrW7bZuu/xDGAxuLbFbecLErhKa152YKozUrXduuX37iAInPxCSqbTaV+F9xVf\nay3G1t13EygtyDc5SeqMLvNCcHR8zGq9dvdmd67DMMQogW0bpHJU8/VqhUKgjAMftNZ8+umn7O3t\ndXGOM/mRKrpWSVuv131V2IMIcRz3e6LWmr29PVrsrq1hx2TxGmp/Xfz79Hs9Tc84qpq6k5s5Cm1Z\nllgTumRLSIQWKAQyNGSZc4dXSvW0b79faKGvJTFVVfVssDiOGY/HfQu/YQW9aGsCqRh11OWmzLF1\nwWp5yQ8/XHF4uE8cSkZRQGVbIhWgkgMOZrc5PrlFqgKCBDbrkrPc0dp3Gv3PqEp+ThLr4XH6NdAY\n09OchxXGYSLpEzzvHxCFIULra+fDrwk+wfav9+8pETeSGPf4TV21T/bbdtdj2t+fwIAls0u+hrGl\nj1eH8aefB3Hs2t8OY2jPkLjpBTGMYW9WmV9VVR4m5DeTyVeNz6KE36Rw+/cbti0bAnb+/HxWYjy8\nX29KNm9+3t90DN9n+LnDz7iZFwxf6/YLee08u7Wz2xOUs7P1Q4off9w38xQfXyshf+Q+/Kzr8Vnj\nc5VYf/jhR7z/xfcpioLZ3oQsi1FKsFwuuXv3LqPRiE8++aRHsj0a9vTpU77+9a/3ifLz589pasOb\nb77JkydPGI0S6DbNP/zDP+Tv//2vEUUxiKZLzEviKGSTZ3zzm9/sg4Rnz57x7Nkz/tFv/AZK7JqU\nT6fTnrp2cXHB66+/zuuvv967ED558oQPPviA3/gn/5imaXj33XfZbDZEUcRsNkNr7dp0HTl3Xk9/\nC3SCNRukUlxcXDAajYmSmLIsKMqiQ5FzwtAhVk1bd63JSnQYE4Y5WLcglWVJMECF/MKZpimlqZw2\nO4yodUDbNLSVM3posb1xlEctoygCoa4tUsOk2wMZfgEc0nistX3bjjzPaYwh22ZYBgl421DVBXW+\noSpymqbC2IYoCogCjWlqgiimynPKfEuebdmu186gxbSE3WcfHR1xeHjYJ7iHh4csFgs++eQTgiDo\nWphIorjrmyxMD9iUpaOPR1GMWboNIAhcIBwEQd/D3E8+b64znU55/fXXmc9dBerRo0e8/fbbrFYr\nxmMX6GzWGUI4vb21ljwvIHCL+d7eXh/seKqUp8Clseur/eLpM2bTKWEY/fwm499wlLSUNJR1gRUW\nHQU0Tc1qtew0Q7bfDD3aGkURt++csFwue7Q6TtK+dZtSCmNrsqJB6pBAaieNMA0KS3I4IlQSoTVS\nhKRp4oJSEWFsRpZtSUcHaB2zyTKiJEFKBRh0oEk7vLPebhmPneOu6AxdojBEBZIgSTuWRktbG4Ig\nYnm5QouConXmHE25gaakJSSOUww19eqSFE1lNY+fnXKyXHB4cMisSUithDDBaJeM5KYlyw2T8Zg4\nDIlUSKw1m80apQLqpnL9am1JJCPHKLEQIsiyDU1Z0NY1ozBkU5Wcz88pq5KnTx9zcHCAxqKVJExj\n9NStJR7gCAqnGUxS155ISkmajNFac/70nG//6Xd4eXpOIiYslSU8TFl/sOXdL71N27Y8ffmCbW3J\nrCKtGjZ5wd/7lff4y7/4c+IkINYSFUjWzRJtIDSSlJBxNKEiZ222TGeCyoYgHV20KjPSKERiUKah\nER0MaFrXHkcprJTU1jFvZANZXnI4m5BEAW1TE2rJdr0iiUMqqTBhRNk0NGXFKAl5eHuPJImZt1tU\nVmIU3Nq/x/mmdb0xTQPUqCZFaKcHU9IQKwEiIIqcb4cOAkzTUpcVFxcX0LRsl2vSOO71+7M0Ivp8\ndOi5NoaB4xBgHIKE3hOiKLK+OuGD2LqoaW3bB7HepBTo94xhYu2T1yBwjtqNccFPGIbM9kaslyvK\ncoMKdjp8L4EYentcXFz0AWmSxl1CWbl7q7vv/XF4czHvTwL0YLpnHJkbAZuvHvtE2xtT3qwoDQEJ\nHyi7CnWLjkK2Rd6Dit4Qz7WOcW7dAE1ZUZcV62IFYdBX9Pwe3CfTcveZN7uHaKXAuupOXdfOUFRc\np7D76+glIsPr4s+R//5K77xW/M+mu35FUYAJXYyVhGgtcZdlV6n21UR/D7gkQWKMpapKNpsNm83G\nuYALQVGUuMSq6xIgfWxiKIUlCkMiEaCUpGoq2qYkFgGLxYL1es2d4z2qqmIvnaKFJMsrRqMJWjmD\nuqp1sZaVwoEt4rqJ1c9S4fqFGHbn1tyzEYVb74eJjp+3vuCTJgnVgLrt7jNP8d65cXug3M81oO9p\nbzozPQ+W+RGGYV+p9gyXoZ/DsDoshNj525id/GI2m/XrhDGGQCryjYsRgyAg0gEisjQdw80DRR5k\nXS6XyED1if8wfoXrCe4wERsm9v7xV592288tf5//JNVs/3nDNcpfJ39OPPDhz9twGGN6+U2fpHZ/\n8/N3eE2H7MmfJQEffqch4DIEHW6CU5454NepNA374/LXo20aGtMgWtelg7YlCQICObgW1iKExQo/\nLx2L1r93Lyu4kVC/qqr+k4zPVWJ9cHjQt65ar9f9BPO6DCllX5n2FLOjoyMuLy97Kvh6vXa03cnE\n9bbe20MpQV07/eDv/u7vkucFOsD1Qx0khEEQ8JWvfIU8d21RXP/qkCdPn/LWwzd6arOnbz18+JDz\nc0dF8ii1r5r66rR37vYJmkeA9/f3erq5/7vTSLWE8U5z5JCWXT84R591gURZdf0DuyQVqVB6V1n2\ntG1rbU+XcpRJ97jWGts63Y2VvvH9bhIMK8xC7jZeGCJPmiDY6WOcQ3hLXTc0jSHUuy5LbdtC53Ta\nI21dX7uqLGmqCtGZnRjTsJhf0raWg6M7UBa0dUlZdCh1UdA2DaZt+sDLL+ZCOFda2ThN2fvvv9/r\nz4IgQCtNHEds6gwpJQcHB8znK8JA9wCIv888ou4XgiG9zi/Mo9GIR48ecXBw0Lf5ury85N69e+hA\nIkR+repTV22PyCZJ4uioDAyB8sK1DROS2WzGRz98xGQ8pig/Hy16gM64yGJai+yCIQ/emI7dMDRD\nAjenT8/PXLAaRx141GKwGOEc5i0glGMF5HmOlhJRl5gyp60KysYiJmMqa9CNwXYB9ygdIQSoIEII\ni9Iaa93GL4wljUesVouu6jRDSIuQ0JoahKUxNbQab4QkZddqSEvatiYIBMa0NI1zrYcd82MyScjb\nDaasiRNNXIdsNmtua4ltnXdDFAe9xrOu3abXtC3KGFeB9i2/Osd/X23K8qyvErh5u+sNXtVVB+Tk\n/doZRZELorsNethndBgs+6rVdDrtqnE7Y8DHjx8jpezbDZ6fn7M3cf2A75yccLX4K5RSHB0dMh6P\nefToEUfHhwhhqQpnKpkITZpEULaYyrCtSyoMtW1ZFVmv94OueqE0Urpdc4guewaKX8eFECRRgDUd\nWNWEriVfa9FKs95kiNCihUEJ0LQEwqJDTSAt+6MpV5dLTtcvmF+s2Z+OWC3npIdjEE7bbVonsUE6\nzX2cRK73Li6x9xWgxlfHtXLn21iENZiypu16t/6iD2N2LXWGFRMPvg6pe55lEIYh2+2aGle9nCRp\nb04Y6qi/d4faSGNMD7j5NcE7EvskNE4mRHHIKAppm4oXtiLP50TxPovFAmst03HKdrtlPnfU4WQ8\nIk1dn3YhLMY2WNtiTNvtqxYpcWuCcsla29ZYKwD303fXKEu3lxfdeQB3/2VZ1tPZF4sFSqk+ub5Z\npRuaDWVZxrqrUkdpQtsBXjJwzvMo1+LSn6OXL18675CiIN9mTGd3egNOb27Wd1Gpi36P9ZRxb+4W\nWLGLBZSi6lzFh4C6l31JKXtTQP9+HmTwPbhH450pa9/6kx0AUxdu7fYad0RL29YYo6/JCDyTAUCY\nqr/uLnaxfZVahQG1aVGBpi6d474xLXXT0EQxOh7RbLcIJKGyZFXOy9NLTJAyv7qivneHJA7ZLpcs\nX55RbmviIEZa7VzLZcvR7TvYumKxWhKMpv09frNSeaOL5C/0sOyYeMPkUQ7+f7Nai6XrDSwGctZd\nnHgzYRqCbj2rsBuvSiaHle2bAIZ/3O9tfo1v2pam3bVBheuVdQ/meXNF//hmk12jSvt9w8ki6H8f\nHtfNivXN6//TVDlflay+agz/fnOvGybIN6nnN4Gfm5XfV1XaX3X8P2sF+2Zi/VmP36wOD4tVHjQY\nJvnuSQYrLFiL6u5J5fPh/sYc/C7ojMk+25jt5vFK+ZPP5M9VYj2dTplOp5RlyXiS0jQVWivW63Uf\nhIdhSJ7nHB4estlsnPayc41cr9cURcHp6Sl/9f0f8o1vfINttsaYhgcP7rJarZjP57z22n0WiwVH\nx3vM51e0reHq6or9o0P+7b/9t/yrf/WvODs7YzabcXzsjNNcBXnUb5onJyfs7e3x7rvv8t3vfhet\ndd+3L4oi3n77bZRSjMfjfgEoS4e++sDCfy+PEtd1izFgjWA8HrG3t+fOw8b0C4YPArwxSt3UuFaK\njhI+HqeYpqWq2x5ZNsb07QZGoxFtvu7R9aZx/Vb95jykfKdpuqNcdv1hfZLpF02lgv41WXaJEF4v\nERBFuq9oiI7i4SoLmtE0hpULzKNQs14uqfI1om0wpsEqQZY5d8irc4mOYtrKbby2bVAShA4wUvWI\n5/7+PuAq49vtlqaba2+//Tbf+ta3rrmEX11dEIUuYHAO8rZnJHgjjTRNEUJwdXXF8fEx5WbbV1+q\nqupMcVaEQczdu3cJw5Dnz5/z9OnTXg7g+1uvVpv+3thuHDhwcHDAZrPpk35rrWthEXbaxbzg+fPn\nHB8fuyDz5z4jf/ZxcXnJOIn7SojtNs44jtl2LTTatu03xiG11INSvk1MEIaucv/ihUusje0pnLZt\n0V2gGipNVRe01mKw1NYFy/EohcAlsVZqWtyi6+bDxCVDQvVIvut3bImiHa3NGtMxKwxKu3kwSR01\nX8Qh1pS9hizQzu08DGOEUFxcviRoJFVWUVWCtm04Oz/lJMsYj2bEyYhttuzPg0dx5/M5o9GI0d7h\ngArWuVsOqHd+nvt1xG9OAPP5HGMMBwcHvfdAGIY92ycMw/57ewRcCNFLKrIsYzrZY7VasVqtWCwW\n/cZ3cDDjrGsd5wPioiicfns2g6rmz//8f3Fy+7BLPDbdmiGIhMJWDVVZMR3NSPcOefnxR+RNRWMM\nwope4iHEj1LxhsHGEPhywZ2lsU1/LnPToKUgjkJkmGDrgrKumSQhZZFTZBukgDTew1iDaGvK7RYt\nXLIUCEVZN2g87Q5QIKxAWEiimNYIUO7//tz6NXs8cfdYJCV1U5Nttn9txeIXZYiuTZG1AloIpEYL\njS1buJFc98Cl1kTJhKLI2C6WhIFCS8E4TJHda4aUcK/HNkYghO6qMwFpx5rwLTizeuOMOZMR5cZy\n751fdqBs5ujneVFSVV0LH9XRSrUhThKK1oHlvjLtqj8tqtsXy6oiCDQqigmFJNKO3l1VWb9HKy0Q\nAhLhzdwsKnASo6ooaYx17dewFJ1sy/fRpQnRVhAg+3/5asModEB9EEga5YDrJI4JkEgrMMZVo09P\nT3nx/AknX/0qP3j5jMlkghQOFEiTiOlkRFlkbDcrt66qwVwB2qoiW8yZTqdEMmK5XFKst93jFtEa\nqioHYwi79p5FXaHzDC0Vabd3WyyqrrBlTrFes9lu2U9fo8qKvuDgZSXu/hFQPEFLjQzHrLKKsjVY\nGZCWubu2ScxoNCLSkkWVUdU1cSuYz+fs7e1x597tvthRm4o0gThKuLiYg5dsCY01LYfSYNdXbATQ\nCjZlQVYW7B0eotlw/vIRz/cCnlfO7DKoDdHdI6TVSBWRTA/Ye3jo7om/+i7VPCee7r9Svyql80/h\nczKXjen0qF2C5RNgBom0l6v1VbxhUolw17OTRvqC149+zo4JCLsuMcMxTKiHP30xZ1gd7rtC+BgB\netDHx9I+JnVSOwnWMRf8+6gbAMAw0TTGIMyube7N4xxWWYf7zDUA4iccPwLM/Jgx3OP8T7/X+xhh\nWNm+OYYMllcdx/Dv/b3w1xzzT5t0vyqpvZnYejDD53ie9XINEMC1S1T8v9y92bMk2X3f9zlbLrXe\ne+su3T09+wwwGBIaLkMTwVDApigG9WAFQy/WG603hx/8f/CPUVgmIyxLEQyHLBl4kUSaFGBgMMRw\npqeXuy+15XrO8cPJk5W3MaAByiLRPogbPehbXZWZdZbf8l0ERoImoPT69/GhCP+zXvewyNDrJ/wc\n9KxXKrFeLpe9QmiWR2GsJnhcWstoNOLJkyd8+9vfZrPZ9F6zEaIbvxTvPW+++SZHR0f4C4v3tod8\nBgukLbI7eLIso9iWSBmgEL/zO7/DarViMplwfn7ebx5N0zCdToOi9HjcL/6yLHnjjTd6npj3nouL\nC5qmYba/1yejw8pZmiq+//3vc/LgiO12pz4d1fIgeHpHjpa1Hs+uQ+pcZ9Ukgrpq1flEW23wXgwE\nU9p7UJ3hRhirxIFz0MFUbJjgMdgeIgWs414gHyElUuoezhO7C7CzApMyCArF93GEA8mYhLqrrldV\nxXazoqlLTAe/kgiQIUmuG89el1x4HzjZTUetjJupMSEI010i75zDI3obiNls1lfF4/tETmAoOgjw\nnb0B96E4WZbx4sUL9vYn/fOLXfKyLEmTvOe2xeQmdrLTNKWuA3/cmLS/XudDF6Qsy54KEBMWa3cQ\nxQjfP727w3bfz6swmrrl4uIKieBgbz8Ir3Vdj9S7vmMVUR3xeSIERRk8qCMcfH9/n+ubGxAi8KOF\n7LlWRimUD5VMIbrCRbFFaZCZQUhJludhLq1WtK7Cek3VWmZ7+2hl0EYxzidI0eC9C4KB83k3111o\nTmoZFFBt8FPHGIzuDmuncEKhVGct4gO1wJgUCMm9rSxCetrGYp3jsx9/zjsfbUiTDclidI9vD+y4\nhlqTduicYYcvWpJIKXsaQVEUHBwc8Pz5834uXV1d9b7OsQsVk9H4fsMgKHarYvcpomRi8SdyaIUQ\nfPjhh5y/eNJ10YK4zXQ65eZ2ybNnz9je3jAe510l2DEaZ9i68wttAvogyzLGe1Nmi33uvrcmn89p\n/H3YqdSd4myoKvbXF/eU4X+HL8SSj8bM50ERutisaKyjbgvSNIGqJEt0n8Rs1ivGsxkKx/LmCi0d\nwrfkaYpvWypbUWjLJDehwNLBQ10bBFU26yVSpRydHPdr1+ggoBc57pvtltSBQmBdw80rYrc1HCGA\n2QUsQxhf3K/i3juEhDrnGE2mCLVTfY1BXyzKBvip7KlEdCrisdvaRiqIc9zd3fUCkmma4pK0/3zv\nfV+YE1Ki02AFBDtYYOwWxz0b6K8jzCXZxxGxi7sL+MM5HT5rZ8GknWNT7/iaEW4ZC14vI72irkFM\n2FprQXVevJ5Q4JGyK8ou+47yEEbuXECbabODhPdq5X4HP3UDGLh3nqoJXeII8R4Wpqy1iKYhHeU9\naktAN+9f8uXunpHFsykKtlWJE9C4nfWQBFKT4ZXCdnoETdvStAWqi0/Wd0tGnWCaEsEONHbgo2BV\nvDchBM76ruhRIVTCbDqjbVy4UOmwraOqa9JRjlSatnWs79Ys5pLZdI5tLJMkY319y8PZAb7zz56N\nFxwdHPfFycRoEv2T/u1xDoeEfsfX/EUf3jn8oPMe48mkg1APi93OueDRbTv4tIfadvZEHQw7rg3Y\nJX/DLuqw4EYnGNWrj3evi3vIy+8xjIfiT0T2GWMomxADX1xc4JzrBdHG43GPcohd66qqemrDkNMd\n12WIb12/Foavudc17Ub87oeonZ8l4Rwm6MP7fXl8VZf15Wca18Iwvh9ea4wzY8L88mfH/SjGYfH7\nHIrXDa/nq7q8f919vnwtw58hzDtC8ndoCdXvw/1nAokH68BIQS4VuQPT7b8B2RboWX2Rq9tDv+Lh\n9gWX4U9E+/2s45VKrPOO/wCh07Jc3rK/HxSzP/nkE8bjcR+IZ1nWd3zfe++9/tAxxvBbv/Vb/G//\n8l/z5ptvMplM2G7XbLdbHj46YX9/TZqlpKnsF5I2GtGJ5Nze3gZF6S5Qi5+T5FmfWEIQUYhKlS9e\nvGBvb69POo+OjoCdWFUMMKK65umL57z22mscHh1wevqU6voyXLtOaOqWo5MTRqMxZVlQNTXGZpgk\nbDRVVaBUF0QK39tjGGNot0FpNNEanaS0ddN/fuRhRdXSAL3r+CtKEegKYVJGjnJUQffeI5Xpg+oI\nA0uShCwdkaYZm3UBXgakkPNBHVwpjNEBRt4l2EpEQbAAMfOuZXl3w3a1JDfdYm8bnHUoY8KhbluK\n9S1JkiJ8Q1WH7m5ckFL4Xg3VjBP2ZyGgNlm4/s8++6xf6HHTnUxHg4CwCZ0OQodubz/YaG23W6bT\naRAxauoerSBEEEq7ubnpN+ftdkvTNHzwwQe8ePGiT5S++OILlnfrrgseikBSSuaT+T3RlslkgpRB\n0bhqwjXuz/fCZ43GPHv6FOteHSj4eDylaVpca7lsr1kswn3ko+ABDfQoici1dC4IVNSd8jZSkOU5\n2zLYtUxmU4rNltb5PkmsttsAYy4LZrmmtS1l45ClQCpH5T136xW5CzBdJ0Nia0yCc4AKBRVnHY0t\nB8mCpe6E8mKhidZiTFB4b9qKbVGRdJ6IRRls2/I8pyrrntohpKKsakTjoAkH2WpZcqdbnj09JTX7\nSL9iPM364DcG+fHw22w2QZ+gdaRp1lVwdwdj9IJNkoSzs7PeI3e1WjEej0Nxqvt9rM7GA/meeNOg\ngh/5n1ppyuKOx48fc3FxxarjrTnnePz4MX/1o+/z+MEx12VI9M8vLzk/P2dTlqQidArivpWknagM\nFtHRNvaOFhw/eMjTs6swB+oG0bp+7zRao3WX1HQwMMRXw87iXhWT21W3fuu28yROEkyWA5b9g/2w\n/tZLqrrBeagai/QltrWkBpp2w2wyodg0mMTQ2IpUpX2C2HqHdVCWW6azrE8cym1B0RX05vv7CKXI\nxiPq2yXOOz7/4gv2Dxf/Rdfff4kxDAilVvcCsHi+xCJh0I6oKTuV63iWRArCUAgtFg8V94V2/uqv\n/gohBG+++WbgEGciiMCJHb2paRqUD2dtdIS4vLwEYDwe3yu6x7UVg7l4TzGwjAlxkiSUHTopz/M+\nAY0FcCXNPeh7TBaiVV2kVUTOqVIK1wXCxpi+uA8hjgh6HPOeLhZRZIIehQAAIABJREFUEWEeF712\ny2wWVPmzLAsWkOwK5k3TsFwGO8Y8z7He9s82XocQAucdVd1S2YbSNmF92nDvZVXSlhWvHR+Sd7GW\njAG47RAg3lHWVY94E10xtKwrnPfMOu2b+GyVlGR6hBCKm+VdoLl4h2gaVusNi8WC69tbzr58RjbK\ngzVo02KbtkcnbjYb7u7uek0TYxR3dytMmjPf32O2d8Dd3RKZJRhXU7ct9RqMSJikCjNT5PmYur7l\n8vQObVOyB494cPwmr588ZLJ/jNEJrnFUVYNShjQ1HMz3aH2NISDznHdd7C36/7m/kVnP3914ea98\nueMa1+UwKYrJ58td2pjcDRWehxDlmDyF9RWbQu7edbwMAY+fPUxw4z4Rr6HuusoRKRVj8uE1xM/Y\ndvFBbHwN1/mwEEVXzImfBTu0hxT3O9MvJ5k/a2FlmGz+TRLxYTc/ntMvvz6Ovqjm7gvSxecck9fh\nsx7e+/9b9/pnHS+//uU5NGwWxP315YKFFALlAjpHK0UiQjNFuYBMRIa1+PIjfTnB/2mjLxT+/7Vj\nXXRc5WjxsFqJ7qAuef3119Fa8/HHH/cCIUOBhZc7sd/73vf4vd/7PZqmYrFYkOcpFxcX/If/8B/4\nJ//k9ymrFZPpjKoKVeb1eo3F8+LFC775zW/2vKnZbMYPf/hDfvkbH7LdbjurL8PR0VEPQYmTNB5g\n63WA/SZ51m86w8k85JYOA4S+89J15Kxt8SJW08MGVRSb3UR0bah0dzYlpU7xaUvabY7LOsBLhRC7\n5ICfVP32riP/A2KweIfdBMRuAcYDu21bjL7P94jnTHwuUTnd+lDh1t11CGGQ3oG3AcrnHVqnuI5f\nirdUZU2She52+NwowhAg870oQyeEEmGI8Tp192xjdyBNU87Ozqjrmuks75Nh0QmzJSbtOdYx0c2y\nrN+Et9vtPaVX7z1JkvZQ+xhQ/eAHP+Bb3/oWVVVxd3eHkqZ/plprqrLoO/XxOe8ODkmeGyadN3FM\nEsbjMcuOn/oqjKaxjEyCTjTeOU5Pz3nR2qDouhcoEzGZjtBBay2bMvDeszxDp0m/VsqypCy2QTFb\n7YROkjTFeYuwuvO6bhmNxqSjDLQMPHw809kUNRrx7OYSRChSjSaK1WqDQjI+mXJ7u+og3Mm9DkmE\nOecqrAGtJEpCua1AC9qmpm1t190OB0dAQ1RsVmtEEmCKUkiU1NRlwZ9/+kMui4R//N/OefzIMJ6O\ne5pBDMrH43H4fBcOme12y2gUnA6aete5igdnVVW9HU2cT6NRmOfHx8f9+8eilBCiF9SLVirxMItU\nh81mw3y2zw9+8AO++93v9sWs9957r3dDODs7Y313w+3tLWXd9AmEdA1Ij5CB2+qcQClQWlO3kGQp\nJ288Zrq3z+2nf8V8PGG7LhihsB3qUirVdQk94BBe9h3suNcPAzsABmIt2XhM8eIF2gs21YaisWTC\nsyxqmrruOLOwUAlOSrJkSVNtSZTmzTeOcc6xODhhOtYIWtzaUbUVtrP4kEIwm05w1nN3eU0+GWOU\nZj6dMZqE7snZxTnrYsvq/ILpdMob77/D8xfnf3uL8f+jMQySlAwF6XhuRS/6qCey3pZs1iHJXOwd\n9IizKEAWz7oh+gC/43HHeXhwcNC/d4RpRp2LWDRWfldgWq3CGt5ut9zd3SGMvnc2xAR/u93eSyzi\n2Ry7ZHEPGBaTe36qG5yd3b4l1c5mKCYn8fOWyyVSqF6ZOxSaSrwXlGXNdltiTIbu1pzvXAdcj2QL\nxbUIi49rtWxd32iICUbS0WbacktRhKQ8os/m8zlKadQ8p7iqmezN+7Pce880z/Dbqkd2ZXpE27RU\nbYmrdxotTdNguzgmyzKW2zVeS4TUqDTp54WzFotDeRMcSOqGZh1UvCWeTOe4xjJOM8q6oi0rNKBN\nQmmhbi3bMgh5IhUHh0eh0LG6Yz6fh8R6ccDt3YrGt0gtcK1AJ4bjxRFtbTFZwsHjQx4/esTDxw+Z\nzWa88/qb3FxckilDrgxVVZPoHJlq9uYK7x3Ol7z26AHPnn0RrP2koml2nTaEwAtBI1yIY16FMYhV\nh8nNsLAa12NMVqX7SW7xy13TWKgdJnZxnryckMZCT/y7l7nCsaAcu63DxDAW4mG3xmKjo+f1WwfO\nYZTGaEPZWnAeJXYJXI82GKCdvurvnQvvNdwnflpy/bOMl//NX9exHv6blzu+w3//VUn+y+iBYWL9\nVe8Zn3VMcn+WpP9nuddh4XX4bIe/j9/vcM8fXrvv8hKlZHBmERKJ72lYHs9XOskLvvrv4Sfg/sNr\n/lnHX5+q/4KNxcGC/f19nHM9Hypu/FVV8eLFC/7wD/+QyWSCMYYnT570i2qzCZDq7TaI+Xz88ces\nVivOz89pmoabmxuOj497QbLI2XzZA+/b3/52sErpeNtnZ2fM5/M+kfQ+WNL88Ic/ZDQasVwu+2uO\n1/D8+fNwMA1EWiK0NRYC/viP/5g8z3tYW0iwayaTGdfX1zx//rx/fazMxS5OtGwRIijmGZ321mLT\n6byv9Eal0BgExI5+VPsUIvDQhNxVC2NxINoSDLsKcROLSWrclK21Pb88blBJEnim0aM7Bv4QKupH\nR0fM5/PepsxaS1VsaNoKcH1QYozBuoZqu+H64jxYbnTBSejwBmGayNUty5Lz8/M+sIoB2OPHwfLm\ntdde6zfnqEQthGA8nrC3t0eahuQ6ftfr9ZqmaXj69Cnn5+d9UBbtVZLE9HDzxSJws6JlRJ7n7O/v\n94ljKIwUvS1KrM7FrmIIkup+HkdBvPPzc6bTKUWx/ttflH/DYYzp1eCllL0veNM2nJ6ecn193R/g\nUf1XadXTLIa88wgL1B1UsiyLsEY79EHbBm/2Hm4pwApPY1sa24ZOp5QIAWlqeuQLgFa6g2xLtJEo\nLUA4rGvwWEyi0CYo2YbAu6LpguyYpJZFSZalPbrFe993pKL/rbMCG+QMEELiveDZsxf8q3/1J3zx\n+dPepm2xCHvgMHA4ODhgsVj00Nf4uyiyFxPkCA+P0HAhAtdXStnvi3EvGHYbYkK0Wq163mtd15yd\nneGd5+bmhrYNtoLWhj1hfz8IRsWgqhfm6xSIIQiyaa375x2RLkII0smI2f4e88MDlNGYxKCEJFGa\nVO4g8bEAFQ/hlw+/rzoMlVLILum+vr6mbrpiIALnYVNUrDYFtfVUdUOSjRDaULeWpirxvibNNU1d\ngG/Z25ugjWRvf87B/j6j0YhRmjEfBw2KqqpwBBugzCT37vXq6qp3lzg4XLC/OAAhWG1fnbX8VWPY\nqY5BUIQrx/kVdI46iAH3A8F41sT9OQbRUVk8Btlx74zCSzFJjIF/RCDF94xF0aH14pAGEYvwMbgf\ndnWH51mca/HP+O+GUMkYKGqtOyrKrrsXA/34fIbK3GG/29Ev4rOJHbW4fuLcLztqTIwdvPcBTt3s\n7COHAfZQP2XYrQk88SCmJ3UojGSjnCQLKAyTJvfOde89rW2DK0ldUXe2fv3e2SXWrQt9W+dDB8k6\n1/13+DvroG0cbd2AdUjv+/XZNk3oTutO+b1qaOum7+xHr/TpdBoogl2ckWWjvuDturPVJBqtFKlJ\nGGVj5qMZ77z+Nh998CHfeO8Dvv7uh3ztrQ8QTpOlUxIzIs2m7M+OydIxeIX1gnwyQeuE2WwWYh7v\nUSKE6VKI/ueVE/ZXCV5leJNjTc5Wau48rN2WpV3TGMtts2RDwcpuuCyuyTpRPqcEJJpKONa2ZuMa\nVm1FowXLpmTZlNTSUzc16+0meJo7G+ZMZ1Ubz/s4z2McWtc1UoFRIFzD2ChkXSGqkhTPWEm0txjp\nMQpkV6wtqy1SBccGj0UqkFoglSHPx0ymc/LRhNF4St1YMpMwG09IVLgWqRUNDpklfQNJa01mElJt\n0F5AE2zk5OD7H9Ighh3e4c/LiSXwE3tOLAzCfYGxrzrr4pqMBb74mXCf0x6LBMMi4fC64n41ROfE\neD3mC8OfYdIL/EQBBXaNumE3PToP2LbFdjpO+Psd6mFRJ+YUwS1AoJ1j5ODYKR45w0NneeA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YIdx3wymYTikAgRTejSePIsIxtr6s2aqmzQOqFpWowJAf18Ou47WXVZojoF4TzPEXjy1JClKZv1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e6KQR6L0AIsv5nDIvKFxNrg2FNrQxoLWi89ABKnjJ577SSVcUfrOQjugoD0uFb67G+lt2pSIj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rwNlBLGS5Z/t86Rg2SuK6Vl+KHETA5f5GHFS5wUSwa1KvdWmGh3zXn52j0kOqs7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\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TPMPFcs1rbzQ8fdrQDdf0QyQmjcaSk8J7iRetyRhlWKwMq3rFYt6wWjY8fVxD7MjZTeik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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb7478a5b10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"batches = get_batches('train', batch_size=batch_size)\n",
"val_batches = get_batches('valid', batch_size=batch_size)\n",
"imgs,labels = next(batches)\n",
"\n",
"# This shows the 'ground truth'\n",
"plots(imgs, titles=labels)"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"The VGG model returns 1,000 probabilities for each image, representing the probability that the model assigns to each possible imagenet category for each image. By finding the index with the largest probability (with *np.argmax()*) we can find the predicted label."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false,
"hidden": true
},
"outputs": [],
"source": [
"def pred_batch(imgs):\n",
" preds = model.predict(imgs)\n",
" idxs = np.argmax(preds, axis=1)\n",
"\n",
" print('Shape: {}'.format(preds.shape))\n",
" print('First 5 classes: {}'.format(classes[:5]))\n",
" print('First 5 probabilities: {}\\n'.format(preds[0, :5]))\n",
" print('Predictions prob/class: ')\n",
" \n",
" for i in range(len(idxs)):\n",
" idx = idxs[i]\n",
" print (' {:.4f}/{}'.format(preds[i, idx], classes[idx]))"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false,
"hidden": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape: (4, 1000)\n",
"First 5 classes: [u'tench', u'goldfish', u'great_white_shark', u'tiger_shark', u'hammerhead']\n",
"First 5 probabilities: [ 7.6208e-07 6.8530e-07 1.0787e-06 3.1267e-06 9.2951e-06]\n",
"\n",
"Predictions prob/class: \n",
" 0.6808/schipperke\n",
" 0.7532/beagle\n",
" 0.3590/tabby\n",
" 0.5831/schipperke\n"
]
}
],
"source": [
"pred_batch(imgs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
},
"nav_menu": {},
"nbpresent": {
"slides": {
"28b43202-5690-4169-9aca-6b9dabfeb3ec": {
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"regions": {
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