Steganalysis/PhaseAwareNet_SRC/Caffe/PhaseAwareNet.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Training and testing PhaseAwareNet V Net with Miracle DataBase"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"caffe_root = '../../' # this file should be run from {caffe_root}/examples/PhaseAware \n",
"\n",
"import sys\n",
"import lmdb\n",
"\n",
"sys.path.insert(0, caffe_root + 'python')\n",
"import caffe\n",
"import os\n",
"\n",
"os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'\n",
"os.environ['CUDA_VISIBLE_DEVICES'] = '3'\n",
"\n",
"#caffe.set_device(1)\n",
"caffe.set_mode_gpu()\n",
"\n",
"import numpy as np\n",
"from pylab import *\n",
"%matplotlib inline\n",
"import tempfile\n",
"\n",
"sys.path.append(\"../pycaffe/layers\") # the datalayers we will use are in this directory.\n",
"sys.path.append(\"../pycaffe\") # the tools file is in this folder\n",
"\n",
"import tools #this contains some tools that we need\n",
"\n",
"from __future__ import division\n",
"\n",
"from caffe import layers as L\n",
"from caffe import params as P\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"define the PhaseAwareNet, VNet"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def VahidNet( train=True, num_conv0_kernels=4, batch_size = 40, learn_conv0=False, moving_average_fraction = 0.98):\n",
" \n",
" # set conv0 leraning parameters\n",
" if learn_conv0==False:\n",
" param_conv0 = [dict(lr_mult=0, decay_mult=0)] * 2\n",
" else:\n",
" param_conv0 = [dict(lr_mult=1, decay_mult=0), dict(lr_mult=0, decay_mult=0)]\n",
" \n",
" param_bn = [dict(lr_mult=1, decay_mult=0), dict(lr_mult=1, decay_mult=0),\n",
" dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)]\n",
" # define network model\n",
" n = caffe.NetSpec()\n",
" \n",
" \n",
" # Prepare the data source, using Mo's stego Aug data layer\n",
" subset = 'train' if train else 'test'\n",
" root = '/home/mchen/tmp/caffe/data/JStego/MiracleList/'\n",
" params = dict( batch_size = batch_size, im_shape = [512, 512], split = subset, root = root )\n",
"\n",
" n.data, n.label = L.Python( module = 'AugStegoDataLayer', layer = 'AugmentDataLayerSync', \n",
" ntop = 2, param_str = str( params ) )\n",
" \n",
" #n.data, n.label = L.Data(source=source, batch_size = batch_size, backend=1, ntop=2)\n",
" \n",
" n.conv0 = L.Convolution(n.data, kernel_size=5, stride=1, num_output=num_conv0_kernels, pad=2,\n",
" param = param_conv0, \n",
" weight_filler=dict(type='constant', value=0),\n",
" bias_filler=dict(type='constant', value=0))\n",
" \n",
" n.conv1 = L.Convolution(n.conv0, kernel_size=5, stride=1, num_output=8, pad=2,\n",
" param=[dict(lr_mult=1, decay_mult=0), dict(lr_mult=0, decay_mult=0)], \n",
" weight_filler=dict(type='gaussian', std=0.01),\n",
" bias_filler=dict(type='constant', value=0))\n",
" \n",
" n.abs1 = L.AbsVal(n.conv1)\n",
" \n",
" n.bn1 = L.BatchNorm(n.abs1, param=param_bn,\n",
" scale_filler=dict(type='constant', value=1),\n",
" bias_filler=dict(type='constant', value=0),\n",
" moving_average_fraction = moving_average_fraction,\n",
" eps=1e-4)\n",
" \n",
" n.tanh1 = L.TanH(n.bn1, in_place=True)\n",
" \n",
" n.conv2 = L.Convolution(n.bn1, kernel_size=5, stride=1, num_output=16, pad=2,\n",
" param=[dict(lr_mult=1, decay_mult=0), dict(lr_mult=0, decay_mult=0)], \n",
" weight_filler=dict(type='gaussian', std=0.01),\n",
" bias_filler=dict(type='constant', value=0))\n",
" \n",
" n.bn2 = L.BatchNorm(n.conv2, param=param_bn,\n",
" scale_filler=dict(type='constant', value=1),\n",
" bias_filler=dict(type='constant', value=0),\n",
" moving_average_fraction = moving_average_fraction,\n",
" eps=1e-4)\n",
" \n",
" n.tanh2 = L.TanH(n.bn2, in_place=True)\n",
" \n",
" n.sbp = L.SplitByPhase(n.tanh2)\n",
" \n",
" n.conv3 = L.Convolution(n.sbp, kernel_size=1, stride=1, num_output=128, pad=0,\n",
" param=[dict(lr_mult=1, decay_mult=0), dict(lr_mult=0, decay_mult=0)], \n",
" weight_filler=dict(type='gaussian', std=0.01),\n",
" bias_filler=dict(type='constant', value=0))\n",
" \n",
" n.bn3 = L.BatchNorm(n.conv3, param=param_bn,\n",
" scale_filler=dict(type='constant', value=1),\n",
" bias_filler=dict(type='constant', value=0),\n",
" moving_average_fraction = moving_average_fraction,\n",
" eps=1e-4)\n",
" \n",
" n.relu3 = L.ReLU(n.bn3, in_place=True)\n",
" \n",
" n.pool3 = L.Pooling(n.bn3, pool=P.Pooling.AVE, kernel_size=5, stride=2, pad=1)\n",
" \n",
" n.conv4 = L.Convolution(n.pool3, kernel_size=1, stride=1, num_output=256, pad=0,\n",
" param=[dict(lr_mult=1, decay_mult=0), dict(lr_mult=0, decay_mult=0)], \n",
" weight_filler=dict(type='gaussian', std=0.01),\n",
" bias_filler=dict(type='constant', value=0))\n",
" \n",
" n.bn4 = L.BatchNorm(n.conv4, param=param_bn,\n",
" scale_filler=dict(type='constant', value=1),\n",
" bias_filler=dict(type='constant', value=0),\n",
" moving_average_fraction = moving_average_fraction,\n",
" eps=1e-4)\n",
" \n",
" n.relu4 = L.ReLU(n.bn4, in_place=True)\n",
" \n",
" n.pool4 = L.Pooling(n.bn4, pool=P.Pooling.AVE, kernel_size=5, stride=2, pad=1)\n",
" \n",
" n.conv5 = L.Convolution(n.pool4, kernel_size=1, stride=1, num_output=512, pad=0,\n",
" param=[dict(lr_mult=1, decay_mult=0), dict(lr_mult=0, decay_mult=0)], \n",
" weight_filler=dict(type='gaussian', std=0.01),\n",
" bias_filler=dict(type='constant', value=0))\n",
" \n",
" n.bn5 = L.BatchNorm(n.conv5, param=param_bn,\n",
" scale_filler=dict(type='constant', value=1),\n",
" bias_filler=dict(type='constant', value=0),\n",
" moving_average_fraction = moving_average_fraction,\n",
" eps=1e-4)\n",
" \n",
" n.relu5 = L.ReLU(n.bn5, in_place=True)\n",
" \n",
" n.pool5 = L.Pooling(n.bn5, pool=P.Pooling.AVE, global_pooling=True)\n",
" \n",
" n.fc6 = L.InnerProduct(n.pool5, num_output=2, \n",
" param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],\n",
" weight_filler=dict(type='xavier'),\n",
" bias_filler=dict(type='constant', value=0.01))\n",
" \n",
" n.loss = L.SoftmaxWithLoss(n.fc6, n.label)\n",
" \n",
" n.acc = L.Accuracy(n.fc6, n.label)\n",
" \n",
" # write the net to a temporary file and return its filename\n",
" with tempfile.NamedTemporaryFile(delete=False) as f:\n",
" f.write(str(n.to_proto()))\n",
" return f.name\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Define the solver"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from caffe.proto import caffe_pb2\n",
"\n",
"def solver(train_net_path, test_net_path=None, base_lr=0.001, weight_decay = 0.01):\n",
" s = caffe_pb2.SolverParameter()\n",
"\n",
" # Specify locations of the train and (maybe) test networks.\n",
" s.train_net = train_net_path\n",
" if test_net_path is not None:\n",
" s.test_net.append(test_net_path)\n",
" s.test_interval = 1000000 # Test after every 1000 training iterations.\n",
" s.test_iter.append(1) # Test on 100 batches each time we test.\n",
"\n",
" # The number of iterations over which to average the gradient.\n",
" # Effectively boosts the training batch size by the given factor, without\n",
" # affecting memory utilization.\n",
" s.iter_size = 1\n",
" \n",
" s.max_iter = 1000000 # # of times to update the net (training iterations)\n",
" \n",
" # Solve using the stochastic gradient descent (SGD) algorithm.\n",
" # Other choices include 'Adam' and 'RMSProp'.\n",
" s.type = 'SGD'\n",
"\n",
" # Set the initial learning rate for SGD.\n",
" s.base_lr = base_lr\n",
"\n",
" # Set `lr_policy` to define how the learning rate changes during training.\n",
" # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\n",
" # every `stepsize` iterations.\n",
" s.lr_policy = 'step'\n",
" s.gamma = 0.75 \n",
" s.stepsize = 15000 #Every 20 epoch\n",
"\n",
" # Set other SGD hyperparameters. Setting a non-zero `momentum` takes a\n",
" # weighted average of the current gradient and previous gradients to make\n",
" # learning more stable. L2 weight decay regularizes learning, to help prevent\n",
" # the model from overfitting.\n",
" s.momentum = 0.9\n",
" s.weight_decay = weight_decay\n",
"\n",
" # Display the current training loss and accuracy every 1000 iterations.\n",
" s.display = 10\n",
"\n",
" # Snapshots are files used to store networks we've trained. Here, we'll\n",
" # snapshot every 10K iterations -- ten times during training.\n",
" s.snapshot = 6000\n",
" #s.snapshot_prefix = '/home/dde/Desktop/JpegTraningSnapshots/Vnet_BOSS_JUNI_04_QF75_KVM_lr1em3_wd5em4_120ep_NoSBP'\n",
" s.snapshot_prefix = '/home/mchen/tmp/caffe/examples/PhaseAwareNet/VahidNet'\n",
" \n",
" # Train on the GPU. Using the CPU to train large networks is very slow.\n",
" s.solver_mode = caffe_pb2.SolverParameter.GPU\n",
" \n",
" # Write the solver to a temporary file and return its filename.\n",
" with tempfile.NamedTemporaryFile(delete=False) as f:\n",
" f.write(str(s))\n",
" return f.name"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def run_solver(niter, solver, disp_interval=10):\n",
" \n",
" blobs = ('loss', 'acc')\n",
" loss, acc = ({name: np.zeros(niter) for name, _ in solvers}\n",
" for _ in blobs)\n",
" for it in range(niter):\n",
" for name, s in solvers:\n",
" s.step(1) # run a single SGD step in Caffe\n",
" loss[name][it], acc[name][it] = (s.net.blobs[b].data.copy() for b in blobs)\n",
" if it % disp_interval == 0 or it + 1 == niter:\n",
" if len(s.test_nets):\n",
" test_output = s.test_nets[0].forward()\n",
" test_acc = test_output['acc']\n",
" test_loss = test_output['loss']\n",
" else:\n",
" test_acc = 0\n",
" \n",
" test_loss = 0\n",
" #print test_acc\n",
" n = 'stego'\n",
" loss_disp = '%s: train_loss=%.3f, train_acc=%2d%%, test_loss=%.3f, test_acc=%2d%%' % (n, loss[n][it], np.round(100*acc[n][it]), test_loss, np.round(100*test_acc))\n",
" #loss_disp = '; '.join('%s: loss=%.3f, train_acc=%2d%%, test_acc=%2d%%' %\n",
" # (n, loss[n][it], np.round(100*acc[n][it]), test_acc)\n",
" # for n, _ in solvers)\n",
" print '%3d) %s' % (it, loss_disp) \n",
" # Save the learned weights from both nets.\n",
" weight_dir = tempfile.mkdtemp()\n",
" weights = {}\n",
" for name, s in solvers:\n",
" filename = 'weights.%s.caffemodel' % name\n",
" weights[name] = os.path.join(weight_dir, filename)\n",
" s.net.save(weights[name])\n",
" return loss, acc, weights"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def get_vahidnet_solver(model=None, base_lr=1e-3, weight_decay=0.01,\n",
" num_conv0_kernels=4, learn_conv0=False, moving_average_fraction=0.95):\n",
" KV = np.array([[-1,2,-2,2,-1],[2,-6,8,-6,2],[-2,8,-12,8,-2],[2,-6,8,-6,2],[-1,2,-2,2,-1]], dtype=np.float32)/12\n",
" KM = np.array([[0,0,5.2,0,0],[0,23.4,36.4,23.4,0],[5.2,36.4,-261,36.4,5.2],[0,23.4,36.4,23.4,0],[0,0,5.2,0,0]], dtype=np.float32)/261\n",
" GH = np.array([[0.0562,-0.1354,0,0.1354,-0.0562],[0.0818,-0.1970,0,0.1970,-0.0818],[0.0926,-0.2233,0,0.2233,-0.0926],[0.0818,-0.1970,0,0.1970,-0.0818],[0.0562,-0.1354,0,0.1354,-0.0562]], dtype=np.float32)\n",
" GV = np.fliplr(GH).T.copy()\n",
" \n",
" assert(num_conv0_kernels>0 and num_conv0_kernels<5)\n",
" \n",
" trn_net = VahidNet(True, num_conv0_kernels=num_conv0_kernels,\n",
" learn_conv0=learn_conv0, moving_average_fraction = moving_average_fraction)\n",
" \n",
" val_net = VahidNet(False, num_conv0_kernels=num_conv0_kernels,\n",
" learn_conv0=learn_conv0, moving_average_fraction = moving_average_fraction)\n",
" \n",
" solver_path = solver(trn_net, val_net, base_lr=base_lr, weight_decay=weight_decay)\n",
" \n",
" shisnet_solver = caffe.get_solver(solver_path)\n",
" \n",
" if model != None:\n",
" shisnet_solver.net.copy_from(model)\n",
" else:\n",
" if learn_conv0:\n",
" for k in xrange(num_conv0_kernels):\n",
" norm_const = np.delete(shisnet_solver.net.params['conv0'][0].data[k,0,:,:].flatten(), 12).sum()\n",
" shisnet_solver.net.params['conv0'][0].data[k,0,:,:] /= norm_const\n",
" shisnet_solver.net.params['conv0'][0].data[k,0,2,2] = -1\n",
" else:\n",
" if num_conv0_kernels>0:\n",
" shisnet_solver.net.params['conv0'][0].data[0] = KV\n",
" if num_conv0_kernels>1:\n",
" shisnet_solver.net.params['conv0'][0].data[1] = KM\n",
" if num_conv0_kernels>2:\n",
" shisnet_solver.net.params['conv0'][0].data[2] = GH\n",
" if num_conv0_kernels>3:\n",
" shisnet_solver.net.params['conv0'][0].data[3] = GV\n",
" \n",
" return shisnet_solver"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Train the Vahid Net"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BatchLoader initialized with 30000 images\n",
"AugmentStegoDataLayerSync initialized for split: train, with bs: 40, im_shape: [512, 512].\n",
"BatchLoader initialized with 10000 images\n",
"AugmentStegoDataLayerSync initialized for split: test, with bs: 40, im_shape: [512, 512].\n",
"Running solvers for 150000 iterations...\n",
" 0) stego: train_loss=0.707, train_acc=50%, test_loss=0.830, test_acc=50%\n",
" 10) stego: train_loss=0.694, train_acc=50%, test_loss=0.709, test_acc=50%\n",
" 20) stego: train_loss=0.693, train_acc=52%, test_loss=0.706, test_acc=50%\n",
" 30) stego: train_loss=0.688, train_acc=50%, test_loss=0.695, test_acc=55%\n",
" 40) stego: train_loss=0.685, train_acc=52%, test_loss=0.685, test_acc=52%\n",
" 50) stego: train_loss=0.680, train_acc=65%, test_loss=0.688, test_acc=52%\n",
" 60) stego: train_loss=0.676, train_acc=55%, test_loss=0.693, test_acc=50%\n",
" 70) stego: train_loss=0.685, train_acc=52%, test_loss=0.788, test_acc=50%\n",
" 80) stego: train_loss=0.657, train_acc=55%, test_loss=0.759, test_acc=55%\n",
" 90) stego: train_loss=0.670, train_acc=57%, test_loss=0.846, test_acc=52%\n",
"100) stego: train_loss=0.633, train_acc=62%, test_loss=0.836, test_acc=52%\n",
"110) stego: train_loss=0.631, train_acc=68%, test_loss=0.624, test_acc=73%\n",
"120) stego: train_loss=0.615, train_acc=68%, test_loss=0.702, test_acc=62%\n",
"130) stego: train_loss=0.652, train_acc=52%, test_loss=0.658, test_acc=62%\n",
"140) stego: train_loss=0.651, train_acc=73%, test_loss=0.628, test_acc=65%\n",
"150) stego: train_loss=0.692, train_acc=57%, test_loss=0.833, test_acc=52%\n",
"160) stego: train_loss=0.642, train_acc=55%, test_loss=0.675, test_acc=55%\n",
"170) stego: train_loss=0.605, train_acc=70%, test_loss=0.641, test_acc=55%\n",
"180) stego: train_loss=0.606, train_acc=75%, test_loss=0.754, test_acc=57%\n",
"190) stego: train_loss=0.530, train_acc=80%, test_loss=0.644, test_acc=60%\n",
"200) stego: train_loss=0.537, train_acc=68%, test_loss=0.862, test_acc=57%\n",
"210) stego: train_loss=0.638, train_acc=65%, test_loss=1.137, test_acc=57%\n",
"220) stego: train_loss=0.646, train_acc=60%, test_loss=1.180, test_acc=50%\n",
"230) stego: train_loss=0.646, train_acc=65%, test_loss=1.128, test_acc=50%\n",
"240) stego: train_loss=0.659, train_acc=62%, test_loss=0.575, test_acc=75%\n",
"250) stego: train_loss=0.610, train_acc=65%, test_loss=0.842, test_acc=60%\n",
"260) stego: train_loss=0.553, train_acc=70%, test_loss=0.589, test_acc=65%\n",
"270) stego: train_loss=0.560, train_acc=73%, test_loss=0.643, test_acc=70%\n",
"280) stego: train_loss=0.497, train_acc=73%, test_loss=0.622, test_acc=60%\n",
"290) stego: train_loss=0.631, train_acc=62%, test_loss=0.594, test_acc=65%\n",
"300) stego: train_loss=0.485, train_acc=70%, test_loss=1.184, test_acc=52%\n",
"310) stego: train_loss=0.571, train_acc=65%, test_loss=0.652, test_acc=62%\n",
"320) stego: train_loss=0.517, train_acc=77%, test_loss=0.575, test_acc=68%\n",
"330) stego: train_loss=0.577, train_acc=75%, test_loss=0.617, test_acc=68%\n",
"340) stego: train_loss=0.509, train_acc=80%, test_loss=0.572, test_acc=77%\n",
"350) stego: train_loss=0.602, train_acc=62%, test_loss=0.524, test_acc=68%\n",
"360) stego: train_loss=0.458, train_acc=85%, test_loss=0.687, test_acc=60%\n",
"370) stego: train_loss=0.605, train_acc=68%, test_loss=0.588, test_acc=73%\n",
"380) stego: train_loss=0.521, train_acc=75%, test_loss=1.332, test_acc=57%\n",
"390) stego: train_loss=0.532, train_acc=70%, test_loss=0.605, test_acc=80%\n",
"400) stego: train_loss=0.483, train_acc=77%, test_loss=0.582, test_acc=73%\n",
"410) stego: train_loss=0.561, train_acc=73%, test_loss=1.012, test_acc=65%\n",
"420) stego: train_loss=0.568, train_acc=70%, test_loss=0.579, test_acc=73%\n",
"430) stego: train_loss=0.538, train_acc=77%, test_loss=0.732, test_acc=60%\n",
"440) stego: train_loss=0.564, train_acc=75%, test_loss=0.601, test_acc=62%\n",
"450) stego: train_loss=0.630, train_acc=68%, test_loss=0.570, test_acc=70%\n",
"460) stego: train_loss=0.603, train_acc=77%, test_loss=0.509, test_acc=73%\n",
"470) stego: train_loss=0.479, train_acc=80%, test_loss=0.490, test_acc=75%\n",
"480) stego: train_loss=0.545, train_acc=77%, test_loss=1.395, test_acc=50%\n",
"490) stego: train_loss=0.503, train_acc=80%, test_loss=0.579, test_acc=73%\n",
"500) stego: train_loss=0.492, train_acc=75%, test_loss=0.520, test_acc=80%\n",
"510) stego: train_loss=0.433, train_acc=82%, test_loss=0.609, test_acc=65%\n",
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"148720) stego: train_loss=0.114, train_acc=93%, test_loss=0.141, test_acc=90%\n",
"148730) stego: train_loss=0.151, train_acc=98%, test_loss=0.155, test_acc=93%\n",
"148740) stego: train_loss=0.110, train_acc=98%, test_loss=0.097, test_acc=95%\n",
"148750) stego: train_loss=0.168, train_acc=93%, test_loss=0.182, test_acc=93%\n",
"148760) stego: train_loss=0.089, train_acc=95%, test_loss=0.286, test_acc=85%\n",
"148770) stego: train_loss=0.095, train_acc=95%, test_loss=0.301, test_acc=90%\n",
"148780) stego: train_loss=0.055, train_acc=100%, test_loss=0.255, test_acc=90%\n",
"148790) stego: train_loss=0.062, train_acc=98%, test_loss=0.221, test_acc=93%\n",
"148800) stego: train_loss=0.093, train_acc=95%, test_loss=0.091, test_acc=95%\n",
"148810) stego: train_loss=0.229, train_acc=95%, test_loss=0.198, test_acc=90%\n",
"148820) stego: train_loss=0.096, train_acc=95%, test_loss=0.107, test_acc=95%\n",
"148830) stego: train_loss=0.080, train_acc=95%, test_loss=0.217, test_acc=93%\n",
"148840) stego: train_loss=0.038, train_acc=100%, test_loss=0.304, test_acc=90%\n",
"148850) stego: train_loss=0.090, train_acc=98%, test_loss=0.196, test_acc=93%\n",
"148860) stego: train_loss=0.198, train_acc=88%, test_loss=0.200, test_acc=93%\n",
"148870) stego: train_loss=0.085, train_acc=95%, test_loss=0.360, test_acc=93%\n",
"148880) stego: train_loss=0.152, train_acc=88%, test_loss=0.075, test_acc=98%\n",
"148890) stego: train_loss=0.275, train_acc=90%, test_loss=0.152, test_acc=95%\n",
"148900) stego: train_loss=0.098, train_acc=95%, test_loss=0.103, test_acc=95%\n",
"148910) stego: train_loss=0.054, train_acc=100%, test_loss=0.191, test_acc=90%\n",
"148920) stego: train_loss=0.118, train_acc=98%, test_loss=0.112, test_acc=98%\n",
"148930) stego: train_loss=0.092, train_acc=95%, test_loss=0.192, test_acc=90%\n",
"148940) stego: train_loss=0.187, train_acc=93%, test_loss=0.257, test_acc=95%\n",
"148950) stego: train_loss=0.063, train_acc=100%, test_loss=0.132, test_acc=93%\n",
"148960) stego: train_loss=0.051, train_acc=98%, test_loss=0.228, test_acc=90%\n",
"148970) stego: train_loss=0.131, train_acc=95%, test_loss=0.190, test_acc=93%\n",
"148980) stego: train_loss=0.138, train_acc=95%, test_loss=0.139, test_acc=93%\n",
"148990) stego: train_loss=0.052, train_acc=100%, test_loss=0.147, test_acc=93%\n",
"149000) stego: train_loss=0.114, train_acc=95%, test_loss=0.187, test_acc=90%\n",
"149010) stego: train_loss=0.098, train_acc=95%, test_loss=0.332, test_acc=90%\n",
"149020) stego: train_loss=0.120, train_acc=95%, test_loss=0.475, test_acc=90%\n",
"149030) stego: train_loss=0.193, train_acc=88%, test_loss=0.270, test_acc=85%\n",
"149040) stego: train_loss=0.228, train_acc=88%, test_loss=0.229, test_acc=93%\n",
"149050) stego: train_loss=0.384, train_acc=90%, test_loss=0.092, test_acc=95%\n",
"149060) stego: train_loss=0.068, train_acc=98%, test_loss=0.090, test_acc=95%\n",
"149070) stego: train_loss=0.170, train_acc=93%, test_loss=0.263, test_acc=93%\n",
"149080) stego: train_loss=0.104, train_acc=95%, test_loss=0.180, test_acc=95%\n",
"149090) stego: train_loss=0.111, train_acc=95%, test_loss=0.127, test_acc=98%\n",
"149100) stego: train_loss=0.144, train_acc=93%, test_loss=0.131, test_acc=95%\n",
"149110) stego: train_loss=0.038, train_acc=100%, test_loss=0.066, test_acc=98%\n",
"149120) stego: train_loss=0.109, train_acc=98%, test_loss=0.054, test_acc=100%\n",
"149130) stego: train_loss=0.154, train_acc=93%, test_loss=0.126, test_acc=93%\n",
"149140) stego: train_loss=0.116, train_acc=98%, test_loss=0.198, test_acc=98%\n",
"149150) stego: train_loss=0.117, train_acc=93%, test_loss=0.120, test_acc=93%\n",
"149160) stego: train_loss=0.085, train_acc=98%, test_loss=0.510, test_acc=88%\n",
"149170) stego: train_loss=0.049, train_acc=98%, test_loss=0.252, test_acc=90%\n",
"149180) stego: train_loss=0.125, train_acc=95%, test_loss=0.126, test_acc=95%\n",
"149190) stego: train_loss=0.022, train_acc=100%, test_loss=0.125, test_acc=90%\n",
"149200) stego: train_loss=0.107, train_acc=95%, test_loss=0.264, test_acc=95%\n",
"149210) stego: train_loss=0.366, train_acc=88%, test_loss=0.145, test_acc=95%\n",
"149220) stego: train_loss=0.134, train_acc=93%, test_loss=0.254, test_acc=95%\n",
"149230) stego: train_loss=0.049, train_acc=100%, test_loss=0.041, test_acc=100%\n",
"149240) stego: train_loss=0.074, train_acc=98%, test_loss=0.189, test_acc=90%\n",
"149250) stego: train_loss=0.343, train_acc=93%, test_loss=0.086, test_acc=100%\n",
"149260) stego: train_loss=0.185, train_acc=95%, test_loss=0.117, test_acc=95%\n",
"149270) stego: train_loss=0.111, train_acc=93%, test_loss=0.088, test_acc=95%\n",
"149280) stego: train_loss=0.086, train_acc=98%, test_loss=0.134, test_acc=98%\n",
"149290) stego: train_loss=0.183, train_acc=93%, test_loss=0.083, test_acc=98%\n",
"149300) stego: train_loss=0.365, train_acc=85%, test_loss=0.110, test_acc=95%\n",
"149310) stego: train_loss=0.036, train_acc=100%, test_loss=0.211, test_acc=93%\n",
"149320) stego: train_loss=0.253, train_acc=90%, test_loss=0.044, test_acc=100%\n",
"149330) stego: train_loss=0.070, train_acc=98%, test_loss=0.119, test_acc=98%\n",
"149340) stego: train_loss=0.058, train_acc=98%, test_loss=0.314, test_acc=95%\n",
"149350) stego: train_loss=0.053, train_acc=100%, test_loss=0.072, test_acc=98%\n",
"149360) stego: train_loss=0.141, train_acc=93%, test_loss=0.123, test_acc=98%\n",
"149370) stego: train_loss=0.113, train_acc=95%, test_loss=0.157, test_acc=98%\n",
"149380) stego: train_loss=0.134, train_acc=95%, test_loss=0.064, test_acc=100%\n",
"149390) stego: train_loss=0.055, train_acc=98%, test_loss=0.091, test_acc=98%\n",
"149400) stego: train_loss=0.043, train_acc=100%, test_loss=0.273, test_acc=93%\n",
"149410) stego: train_loss=0.055, train_acc=100%, test_loss=0.438, test_acc=85%\n",
"149420) stego: train_loss=0.110, train_acc=98%, test_loss=0.134, test_acc=93%\n",
"149430) stego: train_loss=0.137, train_acc=98%, test_loss=0.300, test_acc=93%\n",
"149440) stego: train_loss=0.062, train_acc=98%, test_loss=0.245, test_acc=93%\n",
"149450) stego: train_loss=0.138, train_acc=98%, test_loss=0.079, test_acc=98%\n",
"149460) stego: train_loss=0.066, train_acc=100%, test_loss=0.283, test_acc=90%\n",
"149470) stego: train_loss=0.068, train_acc=100%, test_loss=0.311, test_acc=93%\n",
"149480) stego: train_loss=0.090, train_acc=98%, test_loss=0.263, test_acc=93%\n",
"149490) stego: train_loss=0.167, train_acc=95%, test_loss=0.280, test_acc=95%\n",
"149500) stego: train_loss=0.081, train_acc=98%, test_loss=0.727, test_acc=85%\n",
"149510) stego: train_loss=0.197, train_acc=90%, test_loss=0.112, test_acc=93%\n",
"149520) stego: train_loss=0.034, train_acc=100%, test_loss=0.186, test_acc=90%\n",
"149530) stego: train_loss=0.047, train_acc=100%, test_loss=0.328, test_acc=85%\n",
"149540) stego: train_loss=0.092, train_acc=95%, test_loss=0.262, test_acc=90%\n",
"149550) stego: train_loss=0.104, train_acc=95%, test_loss=0.129, test_acc=98%\n",
"149560) stego: train_loss=0.050, train_acc=100%, test_loss=0.062, test_acc=100%\n",
"149570) stego: train_loss=0.112, train_acc=95%, test_loss=0.297, test_acc=90%\n",
"149580) stego: train_loss=0.050, train_acc=100%, test_loss=0.101, test_acc=95%\n",
"149590) stego: train_loss=0.074, train_acc=98%, test_loss=0.125, test_acc=93%\n",
"149600) stego: train_loss=0.074, train_acc=100%, test_loss=0.259, test_acc=88%\n",
"149610) stego: train_loss=0.219, train_acc=90%, test_loss=0.206, test_acc=90%\n",
"149620) stego: train_loss=0.060, train_acc=100%, test_loss=0.183, test_acc=93%\n",
"149630) stego: train_loss=0.199, train_acc=93%, test_loss=0.078, test_acc=95%\n",
"149640) stego: train_loss=0.115, train_acc=93%, test_loss=0.313, test_acc=88%\n",
"149650) stego: train_loss=0.028, train_acc=100%, test_loss=0.124, test_acc=95%\n",
"149660) stego: train_loss=0.184, train_acc=95%, test_loss=0.030, test_acc=100%\n",
"149670) stego: train_loss=0.160, train_acc=95%, test_loss=0.344, test_acc=90%\n",
"149680) stego: train_loss=0.172, train_acc=95%, test_loss=0.067, test_acc=100%\n",
"149690) stego: train_loss=0.173, train_acc=95%, test_loss=0.307, test_acc=93%\n",
"149700) stego: train_loss=0.054, train_acc=100%, test_loss=0.215, test_acc=95%\n",
"149710) stego: train_loss=0.092, train_acc=98%, test_loss=0.320, test_acc=95%\n",
"149720) stego: train_loss=0.171, train_acc=90%, test_loss=0.087, test_acc=98%\n",
"149730) stego: train_loss=0.169, train_acc=95%, test_loss=0.256, test_acc=93%\n",
"149740) stego: train_loss=0.082, train_acc=98%, test_loss=0.262, test_acc=95%\n",
"149750) stego: train_loss=0.028, train_acc=100%, test_loss=0.057, test_acc=100%\n",
"149760) stego: train_loss=0.048, train_acc=100%, test_loss=0.207, test_acc=93%\n",
"149770) stego: train_loss=0.074, train_acc=98%, test_loss=0.184, test_acc=90%\n",
"149780) stego: train_loss=0.216, train_acc=90%, test_loss=0.286, test_acc=95%\n",
"149790) stego: train_loss=0.130, train_acc=93%, test_loss=0.250, test_acc=93%\n",
"149800) stego: train_loss=0.074, train_acc=98%, test_loss=0.101, test_acc=95%\n",
"149810) stego: train_loss=0.115, train_acc=95%, test_loss=0.140, test_acc=95%\n",
"149820) stego: train_loss=0.031, train_acc=100%, test_loss=0.106, test_acc=98%\n",
"149830) stego: train_loss=0.114, train_acc=98%, test_loss=0.237, test_acc=90%\n",
"149840) stego: train_loss=0.116, train_acc=98%, test_loss=0.312, test_acc=88%\n",
"149850) stego: train_loss=0.037, train_acc=100%, test_loss=0.432, test_acc=88%\n",
"149860) stego: train_loss=0.105, train_acc=98%, test_loss=0.164, test_acc=95%\n",
"149870) stego: train_loss=0.083, train_acc=98%, test_loss=0.215, test_acc=85%\n",
"149880) stego: train_loss=0.112, train_acc=98%, test_loss=0.439, test_acc=93%\n",
"149890) stego: train_loss=0.246, train_acc=93%, test_loss=0.082, test_acc=95%\n",
"149900) stego: train_loss=0.132, train_acc=93%, test_loss=0.298, test_acc=88%\n",
"149910) stego: train_loss=0.060, train_acc=98%, test_loss=0.156, test_acc=93%\n",
"149920) stego: train_loss=0.029, train_acc=100%, test_loss=0.078, test_acc=100%\n",
"149930) stego: train_loss=0.114, train_acc=95%, test_loss=0.071, test_acc=100%\n",
"149940) stego: train_loss=0.052, train_acc=98%, test_loss=0.111, test_acc=95%\n",
"149950) stego: train_loss=0.061, train_acc=100%, test_loss=0.218, test_acc=93%\n",
"149960) stego: train_loss=0.068, train_acc=98%, test_loss=0.080, test_acc=98%\n",
"149970) stego: train_loss=0.028, train_acc=100%, test_loss=0.081, test_acc=98%\n",
"149980) stego: train_loss=0.118, train_acc=95%, test_loss=0.383, test_acc=85%\n",
"149990) stego: train_loss=0.051, train_acc=98%, test_loss=0.165, test_acc=90%\n",
"149999) stego: train_loss=0.056, train_acc=100%, test_loss=0.142, test_acc=93%\n",
"Done.\n"
]
}
],
"source": [
"niter = 150000 # 200 epochs\n",
"\n",
"\n",
"#model = 'JPGSteganalysis_KM_NVC_40000iter_wd5e3.caffemodel'\n",
"model = None\n",
"\n",
"base_lr = 0.001\n",
"weight_decay = 1e-2\n",
"\n",
"num_conv0_kernels = 4\n",
"learn_conv0 = False\n",
"moving_average_fraction = 0\n",
"\n",
"vahid_net_solver = get_vahidnet_solver( model=model, base_lr=base_lr, weight_decay=weight_decay,\n",
" num_conv0_kernels=num_conv0_kernels, learn_conv0=learn_conv0, \n",
" moving_average_fraction=moving_average_fraction)\n",
"\n",
"print 'Running solvers for %d iterations...' % niter\n",
"solvers = [('stego', vahid_net_solver)] \n",
"loss, acc, weights = run_solver(niter, solvers)\n",
"print 'Done.'\n",
"\n",
"train_loss = loss['stego']\n",
"train_acc = acc['stego']\n",
"stego_weights = weights['stego']\n",
"\n",
"# Delete solvers to save memory.\n",
"del vahid_net_solver, solvers"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# !cp {\"%s\" % stego_weights} JPGSteganalysis_Vnet_BOSS_UED_02_QF95_KVMGHV_lr1em3g085ep10k_wd1em2_300k_bs40_MAF095_S_OrigCS.caffemodel\n",
"!cp {\"%s\" % stego_weights} VahidNet_JUNI_04_Miracle_Aug.caffemodel"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"plt.plot(train_loss)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"plt.plot(train_acc)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# update_progress() : Displays or updates a console progress bar\n",
"## Accepts a float between 0 and 1. Any int will be converted to a float.\n",
"## A value under 0 represents a 'halt'.\n",
"## A value at 1 or bigger represents 100%\n",
"def update_progress(progress):\n",
" barLength = 10 # Modify this to change the length of the progress bar\n",
" status = \"\"\n",
" if isinstance(progress, int):\n",
" progress = float(progress)\n",
" if not isinstance(progress, float):\n",
" progress = 0\n",
" status = \"error: progress var must be float\\r\\n\"\n",
" if progress < 0:\n",
" progress = 0\n",
" status = \"Halt...\\r\\n\"\n",
" if progress >= 1:\n",
" progress = 1\n",
" status = \"Done...\\r\\n\"\n",
" block = int(round(barLength*progress))\n",
" text = \"\\rPercent: [{0}] {1}% {2}\".format( \"#\"*block + \"-\"*(barLength-block), progress*100, status)\n",
" sys.stdout.write(text)\n",
" sys.stdout.flush()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def compute_refinement_moments (net, collection_iters = 2000):\n",
" bn1 = {}\n",
" bn1['mean'] = np.zeros((collection_iters, net.params['bn1'][2].data.shape[1]))\n",
" bn1['var'] = np.zeros((collection_iters, net.params['bn1'][2].data.shape[1]))\n",
" bn1['ma_mean'] = np.squeeze(net.params['bn1'][2].data).copy()\n",
" bn1['ma_var'] = np.squeeze(net.params['bn1'][3].data).copy()\n",
"\n",
" bn2 = {}\n",
" bn2['mean'] = np.zeros((collection_iters, net.params['bn2'][2].data.shape[1]))\n",
" bn2['var'] = np.zeros((collection_iters, net.params['bn2'][2].data.shape[1]))\n",
" bn2['ma_mean'] = np.squeeze(net.params['bn2'][2].data).copy()\n",
" bn2['ma_var'] = np.squeeze(net.params['bn2'][3].data).copy()\n",
"\n",
" bn3 = {}\n",
" bn3['mean'] = np.zeros((collection_iters, net.params['bn3'][2].data.shape[1]))\n",
" bn3['var'] = np.zeros((collection_iters, net.params['bn3'][2].data.shape[1]))\n",
" bn3['ma_mean'] = np.squeeze(net.params['bn3'][2].data).copy()\n",
" bn3['ma_var'] = np.squeeze(net.params['bn3'][3].data).copy()\n",
"\n",
" bn4 = {}\n",
" bn4['mean'] = np.zeros((collection_iters, net.params['bn4'][2].data.shape[1]))\n",
" bn4['var'] = np.zeros((collection_iters, net.params['bn4'][2].data.shape[1]))\n",
" bn4['ma_mean'] = np.squeeze(net.params['bn4'][2].data).copy()\n",
" bn4['ma_var'] = np.squeeze(net.params['bn4'][3].data).copy()\n",
"\n",
" bn5 = {}\n",
" bn5['mean'] = np.zeros((collection_iters, net.params['bn5'][2].data.shape[1]))\n",
" bn5['var'] = np.zeros((collection_iters, net.params['bn5'][2].data.shape[1]))\n",
" bn5['ma_mean'] = np.squeeze(net.params['bn5'][2].data).copy()\n",
" bn5['ma_var'] = np.squeeze(net.params['bn5'][3].data).copy()\n",
"\n",
" for it in xrange(collection_iters):\n",
" net.forward()\n",
"\n",
" bn1['mean'][it, :] = np.squeeze(net.params['bn1'][2].data).copy()\n",
" bn1['var'][it, :] = np.squeeze(net.params['bn1'][3].data).copy()\n",
"\n",
" bn2['mean'][it, :] = np.squeeze(net.params['bn2'][2].data).copy()\n",
" bn2['var'][it, :] = np.squeeze(net.params['bn2'][3].data).copy()\n",
"\n",
" bn3['mean'][it, :] = np.squeeze(net.params['bn3'][2].data).copy()\n",
" bn3['var'][it, :] = np.squeeze(net.params['bn3'][3].data).copy()\n",
"\n",
" bn4['mean'][it, :] = np.squeeze(net.params['bn4'][2].data).copy()\n",
" bn4['var'][it, :] = np.squeeze(net.params['bn4'][3].data).copy()\n",
"\n",
" bn5['mean'][it, :] = np.squeeze(net.params['bn5'][2].data).copy()\n",
" bn5['var'][it, :] = np.squeeze(net.params['bn5'][3].data).copy()\n",
"\n",
" update_progress((it+1)/collection_iters)\n",
"\n",
" bn1['rf_mean'] = np.median(bn1['mean'], axis=0)\n",
" bn1['rf_var'] = np.median(bn1['var'], axis=0)\n",
"\n",
" bn2['rf_mean'] = np.median(bn2['mean'], axis=0)\n",
" bn2['rf_var'] = np.median(bn2['var'], axis=0)\n",
"\n",
" bn3['rf_mean'] = np.median(bn3['mean'], axis=0)\n",
" bn3['rf_var'] = np.median(bn3['var'], axis=0)\n",
"\n",
" bn4['rf_mean'] = np.median(bn4['mean'], axis=0)\n",
" bn4['rf_var'] = np.median(bn4['var'], axis=0)\n",
"\n",
" bn5['rf_mean'] = np.median(bn5['mean'], axis=0)\n",
" bn5['rf_var'] = np.median(bn5['var'], axis=0)\n",
" return bn1, bn2, bn3, bn4, bn5"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def embed_refinement_moments(net, bn1, bn2, bn3, bn4, bn5):\n",
" net.params['bn1'][2].data[0,:,0,0] = bn1['rf_mean'].copy()\n",
" net.params['bn1'][3].data[0,:,0,0] = bn1['rf_var'].copy()\n",
"\n",
" net.params['bn2'][2].data[0,:,0,0] = bn2['rf_mean'].copy()\n",
" net.params['bn2'][3].data[0,:,0,0] = bn2['rf_var'].copy()\n",
"\n",
" net.params['bn3'][2].data[0,:,0,0] = bn3['rf_mean'].copy()\n",
" net.params['bn3'][3].data[0,:,0,0] = bn3['rf_var'].copy()\n",
"\n",
" net.params['bn4'][2].data[0,:,0,0] = bn4['rf_mean'].copy()\n",
" net.params['bn4'][3].data[0,:,0,0] = bn4['rf_var'].copy()\n",
"\n",
" net.params['bn5'][2].data[0,:,0,0] = bn5['rf_mean'].copy()\n",
" net.params['bn5'][3].data[0,:,0,0] = bn5['rf_var'].copy() "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def eval_stego_net( weights, test_iters=None, refine_bn=True):\n",
" \n",
" if refine_bn:\n",
" net = VahidNet( True, batch_size = 64, moving_average_fraction = 0)\n",
" net = caffe.Net(net, weights, caffe.TRAIN)\n",
" bn1, bn2, bn3, bn4, bn5 = compute_refinement_moments(net, collection_iters = 3000)\n",
" del net\n",
" \n",
" net = VahidNet(False)\n",
" \n",
" test_net = caffe.Net(net, weights, caffe.TEST)\n",
" \n",
" if refine_bn:\n",
" embed_refinement_moments(test_net, bn1, bn2, bn3, bn4, bn5)\n",
" \n",
" batch_size = test_net.blobs['data'].shape[0]\n",
" num_entries = 8000\n",
" if test_iters==None:\n",
" test_iters = int(np.floor(num_entries/batch_size))\n",
" \n",
" print 'Number of test samples: ', num_entries\n",
" print 'Testing for %d iterations using batch size %d...' % (test_iters, batch_size)\n",
" \n",
" accuracy = 0\n",
" for it in xrange(test_iters):\n",
" accuracy += test_net.forward()['acc']\n",
" update_progress((it+1)/test_iters)\n",
" accuracy /= test_iters\n",
" del test_net\n",
" \n",
" return accuracy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BatchLoader initialized with 30000 images\n",
"AugmentStegoDataLayerSync initialized for split: train, with bs: 64, im_shape: [512, 512].\n",
"Percent: [##########] 100% Done...7% \n",
"BatchLoader initialized with 10000 images\n",
"AugmentStegoDataLayerSync initialized for split: test, with bs: 40, im_shape: [512, 512].\n",
"Number of test samples: 8000\n",
"Testing for 200 iterations using batch size 40...\n",
"Percent: [##########] 96.0% "
]
}
],
"source": [
"weights = 'VahidNet_JUNI_04_Miracle_Aug.caffemodel'\n",
"\n",
"accuracy = eval_stego_net( weights, refine_bn=True)\n",
"\n",
"print 'Accuracy: ', accuracy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Keep training\n",
"\n",
"niter = 12000 # number of iterations to train 40000\n",
"\n",
"model = 'VahidNet_JUNI_04_AUG.caffemodel'\n",
"\n",
"base_lr = 0.001\n",
"\n",
"# Bring down the base_lr to be last one\n",
"for i in range(72000//6000 + 1):\n",
" base_lr = base_lr * 0.75\n",
"\n",
"num_conv0_kernels = 4\n",
"learn_conv0 = False\n",
"moving_average_fraction = 0\n",
"\n",
"vahid_net_solver, num_trn_entries, num_val_entries = get_vahidnet_solver(\n",
" trn_source=trn_source, val_source=val_source, model=model, \n",
" base_lr=base_lr, weight_decay=weight_decay,\n",
" num_conv0_kernels=num_conv0_kernels, learn_conv0=learn_conv0, \n",
" moving_average_fraction=moving_average_fraction)\n",
"\n",
"print 'Running solvers for %d iterations...' % niter\n",
"solvers = [('stego', vahid_net_solver)] \n",
"loss, acc, weights = run_solver(niter, solvers)\n",
"print 'Done.'\n",
"\n",
"train_loss = loss['stego']\n",
"train_acc = acc['stego']\n",
"stego_weights = weights['stego']\n",
"\n",
"# Delete solvers to save memory.\n",
"del vahid_net_solver, solvers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"!cp {\"%s\" % stego_weights} VahidNet_JUNI_04_AUG_more.caffemodel"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"TRN_Source = '/home/mchen/tmp/caffe/examples/StegoLibrary/JUNI_04_AUG/JUNI_0.4_trn_lmdb'\n",
"TST_Source = '/home/mchen/tmp/caffe/examples/StegoLibrary/JUNI_04_AUG/JUNI_0.4_val_lmdb'\n",
"\n",
"weights = 'VahidNet_JUNI_04_AUG_more.caffemodel'\n",
"\n",
"accuracy = eval_stego_net( TRN_Source, TST_Source, weights, refine_bn=True)\n",
"\n",
"print 'Accuracy: ', accuracy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"description": "Define, train, and test the classic LeNet with the Python interface.",
"example_name": "Learning LeNet",
"include_in_docs": true,
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
},
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"nbformat": 4,
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