From bc5e2e427110b01f4e5b03b5c599adb34b4fd1d9 Mon Sep 17 00:00:00 2001 From: Sander Dieleman Date: Sat, 22 Aug 2015 17:57:43 +0200 Subject: [PATCH] Hidden factors paper demo --- papers/Hidden factors.ipynb | 1348 +++++++++++++++++++++++++++++++++++ 1 file changed, 1348 insertions(+) create mode 100644 papers/Hidden factors.ipynb diff --git a/papers/Hidden factors.ipynb b/papers/Hidden factors.ipynb new file mode 100644 index 0000000..71c3581 --- /dev/null +++ b/papers/Hidden factors.ipynb @@ -0,0 +1,1348 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Discovering hidden factors of variation in deep networks\n", + "\n", + "**This is an example of how to implement the autoencoder architecture from [Cheung et al. (2014)](http://arxiv.org/abs/1412.6583) in Lasagne.**\n", + "\n", + "Some setup code follows, as well as code to load the MNIST dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using gpu device 0: GeForce GTX 980\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import theano\n", + "import theano.tensor as T\n", + "import lasagne as nn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset (15MB) can be downloaded with:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2015-08-22 17:45:09-- http://deeplearning.net/data/mnist/mnist.pkl.gz\n", + "Resolving deeplearning.net (deeplearning.net)... 132.204.26.28\n", + "Connecting to deeplearning.net (deeplearning.net)|132.204.26.28|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 16168813 (15M) [application/x-gzip]\n", + "Server file no newer than local file ‘mnist.pkl.gz’ -- not retrieving.\n", + "\n" + ] + } + ], + "source": [ + "!wget -N http://deeplearning.net/data/mnist/mnist.pkl.gz" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import gzip\n", + "import cPickle as pickle\n", + "import sys\n", + "\n", + "PY2 = sys.version_info[0] == 2 # check if we're running Python 2 or 3\n", + "# we need to know this because unpickling is slightly different in both cases\n", + "\n", + "if PY2:\n", + " def pickle_load(f, encoding):\n", + " return pickle.load(f)\n", + "else:\n", + " def pickle_load(f, encoding):\n", + " return pickle.load(f, encoding=encoding)\n", + "\n", + "def load_data():\n", + " \"\"\"Get data with labels, split into training, validation and test set.\"\"\"\n", + " with gzip.open('mnist.pkl.gz', 'rb') as f:\n", + " data = pickle_load(f, encoding='latin-1')\n", + " X_train, y_train = data[0]\n", + " X_valid, y_valid = data[1]\n", + " X_test, y_test = data[2]\n", + "\n", + " return dict(\n", + " X_train=theano.shared(nn.utils.floatX(X_train)),\n", + " y_train=T.cast(theano.shared(y_train), 'int32'),\n", + " X_valid=theano.shared(nn.utils.floatX(X_valid)),\n", + " y_valid=T.cast(theano.shared(y_valid), 'int32'),\n", + " X_test=theano.shared(nn.utils.floatX(X_test)),\n", + " y_test=T.cast(theano.shared(y_test), 'int32'),\n", + " num_examples_train=X_train.shape[0],\n", + " num_examples_valid=X_valid.shape[0],\n", + " num_examples_test=X_test.shape[0],\n", + " input_dim=X_train.shape[1],\n", + " output_dim=10,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**We'll define the model listed in the first column of Table 1 in the paper.**\n", + "\n", + "This model has an encoder with two ReLU layers. In the paper they both have 500 units but we'll make this configurable so we can speed up the experiment a bit if necessary.\n", + "\n", + "On top of the encoder are two representation layers: one is a 10-way softmax layer which represents the class of the input. This is the observed representation layer. Then there is also a latent representation layer which has two additional linear units (the authors use only two units for easy visualization).\n", + "\n", + "Both representation layers are concatenated and the decoder is stacked on top. This consists of three layers: two hidden ReLU layers with again 500 units, and finally a linear reconstruction layer with 784 outputs." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def build_model(input_dim, output_dim, batch_size,\n", + " num_hidden_units=500):\n", + " l_in = nn.layers.InputLayer((batch_size, input_dim))\n", + " \n", + " # encoder\n", + " l_encoder1 = nn.layers.DenseLayer(l_in, num_units=num_hidden_units)\n", + " l_encoder2 = nn.layers.DenseLayer(l_encoder1, num_units=num_hidden_units)\n", + " \n", + " # learned representation\n", + " l_observed = nn.layers.DenseLayer(l_encoder2, num_units=output_dim,\n", + " nonlinearity=T.nnet.softmax)\n", + " \n", + " l_latent = nn.layers.DenseLayer(l_encoder2, num_units=2,\n", + " nonlinearity=None) # linear\n", + " \n", + " l_representation = nn.layers.concat([l_observed, l_latent])\n", + " \n", + " # decoder\n", + " l_decoder1 = nn.layers.DenseLayer(l_representation, num_units=num_hidden_units)\n", + " l_decoder2 = nn.layers.DenseLayer(l_decoder1, num_units=num_hidden_units)\n", + " l_decoder_out = nn.layers.DenseLayer(l_decoder2, num_units=input_dim,\n", + " nonlinearity=None)\n", + " \n", + " return l_in, l_decoder_out, l_observed, l_latent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, **we'll define the cost function**. This consists of three parts: `alpha * U + beta * S + gamma * C` (formula 3 in the paper). `U` is the reconstruction cost, `S` is the supervised cost and `C` is the so-called \"XCov\" cost which disentangles the observed and latent variables of the encoder." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def cost(x, y, x_recon, y_pred, z, alpha=1.0, beta=10.0, gamma=10.0):\n", + " \"\"\"\n", + " x, y: the input and the corresponding label\n", + " x_recon: the model reconstruction of the input\n", + " y_pred: the model prediction (observed variables)\n", + " z: the latent variables\n", + " \"\"\"\n", + " # reconstruction cost: mean squared error\n", + " U = T.mean((x - x_recon)**2)\n", + " \n", + " # supervised cost: categorical cross-entropy\n", + " S = T.mean(T.nnet.categorical_crossentropy(y_pred, y))\n", + " \n", + " # XCov cost: cross-covariance\n", + " y_pred_mean = T.mean(y_pred, axis=0, keepdims=True)\n", + " z_mean = T.mean(z, axis=0, keepdims=True)\n", + " y_pred_centered = y_pred - y_pred_mean # (n, i)\n", + " z_centered = z - z_mean # (n, j)\n", + " \n", + " outer_prod = (y_pred_centered.dimshuffle(0, 1, 'x') *\n", + " z_centered.dimshuffle(0, 'x', 1)) # (n, i, j)\n", + " C = 0.5 * T.sum(T.sqr(T.mean(outer_prod, axis=0)))\n", + " \n", + " # the total cost is a weighted sum\n", + " return alpha * U + beta * S + gamma * C" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's tie everything together: **load up the data, build the model, compile Theano functions.**" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data...\n", + "Building model and compiling functions...\n" + ] + } + ], + "source": [ + "num_epochs = 200\n", + "batch_size = 100\n", + "\n", + "print(\"Loading data...\")\n", + "dataset = load_data()\n", + "\n", + "print(\"Building model and compiling functions...\")\n", + "l_in, l_decoder_out, l_observed, l_latent = build_model(\n", + " input_dim=dataset['input_dim'],\n", + " output_dim=dataset['output_dim'],\n", + " batch_size=batch_size,\n", + ")\n", + "\n", + "x = l_in.input_var\n", + "y = T.ivector('y')\n", + "x_recon, y_pred, z = nn.layers.get_output([l_decoder_out, l_observed, l_latent])\n", + "# Note that we call get_output() once to get all the model outputs.\n", + "# This is to guarantee they are consistent and share computations where\n", + "# possible. When the network contains nondeterminism (such as dropout\n", + "# regularization), guaranteeing this consistency is crucially important.\n", + "loss = cost(x, y, x_recon, y_pred, z)\n", + "params = nn.layers.get_all_params(l_decoder_out)\n", + "\n", + "# # add some L2 regularization\n", + "# params_reg = nn.layers.get_all_params(l_decoder_out, regularizable=True)\n", + "# reg = sum(T.sum(p**2) for p in params_reg)\n", + "# loss += 0.01 * reg\n", + "\n", + "# The authors mention that they use adadelta, let's do the same\n", + "updates = nn.updates.adadelta(loss, params)\n", + "\n", + "# compile iteration functions\n", + "batch_index = T.iscalar('batch_index')\n", + "batch_slice = slice(batch_index * batch_size,\n", + " (batch_index + 1) * batch_size)\n", + "\n", + "pred = T.argmax(y_pred, axis=1)\n", + "accuracy = T.mean(T.eq(pred, y), dtype=theano.config.floatX)\n", + "\n", + "iter_train = theano.function(\n", + " [batch_index], loss,\n", + " updates=updates,\n", + " givens={\n", + " x: dataset['X_train'][batch_slice],\n", + " y: dataset['y_train'][batch_slice],\n", + " },\n", + ")\n", + "\n", + "iter_valid = theano.function(\n", + " [batch_index], [loss, accuracy],\n", + " givens={\n", + " x: dataset['X_valid'][batch_slice],\n", + " y: dataset['y_valid'][batch_slice],\n", + " },\n", + ")\n", + "\n", + "iter_test = theano.function(\n", + " [batch_index], [loss, accuracy],\n", + " givens={\n", + " x: dataset['X_test'][batch_slice],\n", + " y: dataset['y_test'][batch_slice],\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Now we're ready for training.**" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting training...\n", + "Epoch 1 of 200 took 1.834 s\n", + " training loss:\t\t2.671489\n", + " validation loss:\t\t1.361275\n", + " validation accuracy:\t\t96.14 %\n", + "Epoch 2 of 200 took 1.810 s\n", + " training loss:\t\t0.940993\n", + " validation loss:\t\t1.156180\n", + " validation accuracy:\t\t96.69 %\n", + "Epoch 3 of 200 took 1.809 s\n", + " training loss:\t\t0.588255\n", + " validation loss:\t\t1.091651\n", + " validation accuracy:\t\t97.12 %\n", + "Epoch 4 of 200 took 1.809 s\n", + " training loss:\t\t0.393767\n", + " validation loss:\t\t1.181311\n", + " validation accuracy:\t\t97.47 %\n", + "Epoch 5 of 200 took 1.809 s\n", + " training loss:\t\t0.278694\n", + " validation loss:\t\t1.029273\n", + " validation accuracy:\t\t97.73 %\n", + "Epoch 6 of 200 took 1.809 s\n", + " training loss:\t\t0.211380\n", + " validation loss:\t\t1.205028\n", + " validation accuracy:\t\t97.65 %\n", + "Epoch 7 of 200 took 1.808 s\n", + " training loss:\t\t0.181867\n", + " validation loss:\t\t1.338783\n", + " validation accuracy:\t\t97.41 %\n", + "Epoch 8 of 200 took 1.808 s\n", + " training loss:\t\t0.155826\n", + " validation loss:\t\t1.105785\n", + " validation accuracy:\t\t97.98 %\n", + "Epoch 9 of 200 took 1.809 s\n", + " training loss:\t\t0.132954\n", + " validation loss:\t\t1.057381\n", + " validation accuracy:\t\t98.07 %\n", + "Epoch 10 of 200 took 1.807 s\n", + " training loss:\t\t0.111767\n", + " validation loss:\t\t1.181179\n", + " validation accuracy:\t\t97.93 %\n", + "Epoch 11 of 200 took 1.808 s\n", + " training loss:\t\t0.101201\n", + " validation loss:\t\t1.211319\n", + " validation accuracy:\t\t97.89 %\n", + "Epoch 12 of 200 took 1.809 s\n", + " training loss:\t\t0.090519\n", + " validation loss:\t\t1.124524\n", + " validation accuracy:\t\t97.99 %\n", + "Epoch 13 of 200 took 1.818 s\n", + " training loss:\t\t0.087847\n", + " validation loss:\t\t1.138921\n", + " validation accuracy:\t\t98.04 %\n", + "Epoch 14 of 200 took 1.821 s\n", + " training loss:\t\t0.073586\n", + " validation loss:\t\t1.099415\n", + " validation accuracy:\t\t98.09 %\n", + "Epoch 15 of 200 took 1.821 s\n", + " training loss:\t\t0.069429\n", + " validation loss:\t\t1.075396\n", + " validation accuracy:\t\t98.25 %\n", + "Epoch 16 of 200 took 1.821 s\n", + " training loss:\t\t0.068662\n", + " validation loss:\t\t1.023246\n", + " validation accuracy:\t\t98.28 %\n", + "Epoch 17 of 200 took 1.821 s\n", + " training loss:\t\t0.061878\n", + " validation loss:\t\t0.944418\n", + " validation accuracy:\t\t98.27 %\n", + "Epoch 18 of 200 took 1.821 s\n", + " training loss:\t\t0.059150\n", + " validation loss:\t\t0.886993\n", + " validation accuracy:\t\t98.27 %\n", + "Epoch 19 of 200 took 1.821 s\n", + " training loss:\t\t0.056937\n", + " validation loss:\t\t0.857322\n", + " validation accuracy:\t\t98.31 %\n", + "Epoch 20 of 200 took 1.822 s\n", + " training loss:\t\t0.055922\n", + " validation loss:\t\t0.850304\n", + " validation accuracy:\t\t98.27 %\n", + "Epoch 21 of 200 took 1.821 s\n", + " training loss:\t\t0.055436\n", + " validation loss:\t\t0.833854\n", + " validation accuracy:\t\t98.28 %\n", + "Epoch 22 of 200 took 1.821 s\n", + " training loss:\t\t0.054958\n", + " validation loss:\t\t0.834664\n", + " validation accuracy:\t\t98.28 %\n", + "Epoch 23 of 200 took 1.821 s\n", + " training loss:\t\t0.054549\n", + " validation loss:\t\t0.835796\n", + " validation accuracy:\t\t98.28 %\n", + "Epoch 24 of 200 took 1.821 s\n", + " training loss:\t\t0.054217\n", + " validation loss:\t\t0.835802\n", + " validation accuracy:\t\t98.26 %\n", + "Epoch 25 of 200 took 1.821 s\n", + " training loss:\t\t0.053881\n", + " validation loss:\t\t0.834456\n", + " validation accuracy:\t\t98.26 %\n", + "Epoch 26 of 200 took 1.821 s\n", + " training loss:\t\t0.053560\n", + " validation loss:\t\t0.833901\n", + " validation accuracy:\t\t98.25 %\n", + "Epoch 27 of 200 took 1.822 s\n", + " training loss:\t\t0.053196\n", + " validation loss:\t\t0.832972\n", + " validation accuracy:\t\t98.27 %\n", + "Epoch 28 of 200 took 1.821 s\n", + " training loss:\t\t0.052761\n", + " validation loss:\t\t0.831908\n", + " validation accuracy:\t\t98.27 %\n", + "Epoch 29 of 200 took 1.821 s\n", + " training loss:\t\t0.052237\n", + " validation loss:\t\t0.831574\n", + " validation accuracy:\t\t98.26 %\n", + "Epoch 30 of 200 took 1.821 s\n", + " training loss:\t\t0.051667\n", + " validation loss:\t\t0.830366\n", + " validation accuracy:\t\t98.27 %\n", + "Epoch 31 of 200 took 1.821 s\n", + " training loss:\t\t0.051102\n", + " validation loss:\t\t0.829854\n", + " validation accuracy:\t\t98.27 %\n", + "Epoch 32 of 200 took 1.821 s\n", + " training loss:\t\t0.050603\n", + " validation loss:\t\t0.829118\n", + " validation accuracy:\t\t98.28 %\n", + "Epoch 33 of 200 took 1.822 s\n", + " training loss:\t\t0.050183\n", + " validation loss:\t\t0.827472\n", + " validation accuracy:\t\t98.28 %\n", + "Epoch 34 of 200 took 1.821 s\n", + " training loss:\t\t0.049826\n", + " validation loss:\t\t0.826339\n", + " validation accuracy:\t\t98.29 %\n", + "Epoch 35 of 200 took 1.822 s\n", + " training loss:\t\t0.049509\n", + " validation loss:\t\t0.825176\n", + " validation accuracy:\t\t98.31 %\n", + "Epoch 36 of 200 took 1.823 s\n", + " training loss:\t\t0.049210\n", + " validation loss:\t\t0.824300\n", + " validation accuracy:\t\t98.33 %\n", + "Epoch 37 of 200 took 1.823 s\n", + " training loss:\t\t0.048930\n", + " validation loss:\t\t0.823763\n", + " validation accuracy:\t\t98.32 %\n", + "Epoch 38 of 200 took 1.824 s\n", + " training loss:\t\t0.048663\n", + " validation loss:\t\t0.822455\n", + " validation accuracy:\t\t98.34 %\n", + "Epoch 39 of 200 took 1.823 s\n", + " training loss:\t\t0.048397\n", + " validation loss:\t\t0.821486\n", + " validation accuracy:\t\t98.35 %\n", + "Epoch 40 of 200 took 1.822 s\n", + " training loss:\t\t0.048133\n", + " validation loss:\t\t0.820341\n", + " validation accuracy:\t\t98.35 %\n", + "Epoch 41 of 200 took 1.823 s\n", + " training loss:\t\t0.047869\n", + " validation loss:\t\t0.819137\n", + " validation accuracy:\t\t98.35 %\n", + "Epoch 42 of 200 took 1.822 s\n", + " training loss:\t\t0.047604\n", + " validation loss:\t\t0.818364\n", + " validation accuracy:\t\t98.37 %\n", + "Epoch 43 of 200 took 1.822 s\n", + " training loss:\t\t0.047345\n", + " validation loss:\t\t0.816972\n", + " validation accuracy:\t\t98.38 %\n", + "Epoch 44 of 200 took 1.822 s\n", + " training loss:\t\t0.047086\n", + " validation loss:\t\t0.816157\n", + " validation accuracy:\t\t98.39 %\n", + "Epoch 45 of 200 took 1.823 s\n", + " training loss:\t\t0.046816\n", + " validation loss:\t\t0.816683\n", + " validation accuracy:\t\t98.40 %\n", + "Epoch 46 of 200 took 1.823 s\n", + " training loss:\t\t0.046624\n", + " validation loss:\t\t0.815241\n", + " validation accuracy:\t\t98.39 %\n", + "Epoch 47 of 200 took 1.822 s\n", + " training loss:\t\t0.046352\n", + " validation loss:\t\t0.813584\n", + " validation accuracy:\t\t98.36 %\n", + "Epoch 48 of 200 took 1.823 s\n", + " training loss:\t\t0.046093\n", + " validation loss:\t\t0.811693\n", + " validation accuracy:\t\t98.43 %\n", + "Epoch 49 of 200 took 1.822 s\n", + " training loss:\t\t0.045832\n", + " validation loss:\t\t0.812927\n", + " validation accuracy:\t\t98.45 %\n", + "Epoch 50 of 200 took 1.823 s\n", + " training loss:\t\t0.045672\n", + " validation loss:\t\t0.812513\n", + " validation accuracy:\t\t98.41 %\n", + "Epoch 51 of 200 took 1.822 s\n", + " training loss:\t\t0.045488\n", + " validation loss:\t\t0.809735\n", + " validation accuracy:\t\t98.43 %\n", + "Epoch 52 of 200 took 1.822 s\n", + " training loss:\t\t0.045230\n", + " validation loss:\t\t0.808811\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 53 of 200 took 1.823 s\n", + " training loss:\t\t0.045093\n", + " validation loss:\t\t0.808888\n", + " validation accuracy:\t\t98.45 %\n", + "Epoch 54 of 200 took 1.822 s\n", + " training loss:\t\t0.044886\n", + " validation loss:\t\t0.807275\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 55 of 200 took 1.823 s\n", + " training loss:\t\t0.044714\n", + " validation loss:\t\t0.807393\n", + " validation accuracy:\t\t98.48 %\n", + "Epoch 56 of 200 took 1.823 s\n", + " training loss:\t\t0.044539\n", + " validation loss:\t\t0.809609\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 57 of 200 took 1.823 s\n", + " training loss:\t\t0.044468\n", + " validation loss:\t\t0.805800\n", + " validation accuracy:\t\t98.46 %\n", + "Epoch 58 of 200 took 1.822 s\n", + " training loss:\t\t0.044285\n", + " validation loss:\t\t0.807691\n", + " validation accuracy:\t\t98.48 %\n", + "Epoch 59 of 200 took 1.823 s\n", + " training loss:\t\t0.044199\n", + " validation loss:\t\t0.803543\n", + " validation accuracy:\t\t98.48 %\n", + "Epoch 60 of 200 took 1.822 s\n", + " training loss:\t\t0.044012\n", + " validation loss:\t\t0.802549\n", + " validation accuracy:\t\t98.44 %\n", + "Epoch 61 of 200 took 1.822 s\n", + " training loss:\t\t0.043917\n", + " validation loss:\t\t0.801437\n", + " validation accuracy:\t\t98.48 %\n", + "Epoch 62 of 200 took 1.822 s\n", + " training loss:\t\t0.043745\n", + " validation loss:\t\t0.803705\n", + " validation accuracy:\t\t98.45 %\n", + "Epoch 63 of 200 took 1.823 s\n", + " training loss:\t\t0.043631\n", + " validation loss:\t\t0.802993\n", + " validation accuracy:\t\t98.50 %\n", + "Epoch 64 of 200 took 1.823 s\n", + " training loss:\t\t0.043474\n", + " validation loss:\t\t0.802337\n", + " validation accuracy:\t\t98.49 %\n", + "Epoch 65 of 200 took 1.823 s\n", + " training loss:\t\t0.043355\n", + " validation loss:\t\t0.801164\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 66 of 200 took 1.821 s\n", + " training loss:\t\t0.043282\n", + " validation loss:\t\t0.802983\n", + " validation accuracy:\t\t98.45 %\n", + "Epoch 67 of 200 took 1.823 s\n", + " training loss:\t\t0.043141\n", + " validation loss:\t\t0.802619\n", + " validation accuracy:\t\t98.46 %\n", + "Epoch 68 of 200 took 1.822 s\n", + " training loss:\t\t0.043034\n", + " validation loss:\t\t0.799617\n", + " validation accuracy:\t\t98.48 %\n", + "Epoch 69 of 200 took 1.823 s\n", + " training loss:\t\t0.042961\n", + " validation loss:\t\t0.800365\n", + " validation accuracy:\t\t98.43 %\n", + "Epoch 70 of 200 took 1.822 s\n", + " training loss:\t\t0.042838\n", + " validation loss:\t\t0.801436\n", + " validation accuracy:\t\t98.42 %\n", + "Epoch 71 of 200 took 1.822 s\n", + " training loss:\t\t0.042733\n", + " validation loss:\t\t0.801578\n", + " validation accuracy:\t\t98.43 %\n", + "Epoch 72 of 200 took 1.823 s\n", + " training loss:\t\t0.042627\n", + " validation loss:\t\t0.800179\n", + " validation accuracy:\t\t98.45 %\n", + "Epoch 73 of 200 took 1.822 s\n", + " training loss:\t\t0.042475\n", + " validation loss:\t\t0.799656\n", + " validation accuracy:\t\t98.42 %\n", + "Epoch 74 of 200 took 1.823 s\n", + " training loss:\t\t0.042460\n", + " validation loss:\t\t0.800980\n", + " validation accuracy:\t\t98.41 %\n", + "Epoch 75 of 200 took 1.823 s\n", + " training loss:\t\t0.042307\n", + " validation loss:\t\t0.800543\n", + " validation accuracy:\t\t98.45 %\n", + "Epoch 76 of 200 took 1.823 s\n", + " training loss:\t\t0.042245\n", + " validation loss:\t\t0.797565\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 77 of 200 took 1.822 s\n", + " training loss:\t\t0.042142\n", + " validation loss:\t\t0.802954\n", + " validation accuracy:\t\t98.40 %\n", + "Epoch 78 of 200 took 1.821 s\n", + " training loss:\t\t0.042028\n", + " validation loss:\t\t0.800872\n", + " validation accuracy:\t\t98.45 %\n", + "Epoch 79 of 200 took 1.822 s\n", + " training loss:\t\t0.041955\n", + " validation loss:\t\t0.801603\n", + " validation accuracy:\t\t98.42 %\n", + "Epoch 80 of 200 took 1.822 s\n", + " training loss:\t\t0.041882\n", + " validation loss:\t\t0.799396\n", + " validation accuracy:\t\t98.42 %\n", + "Epoch 81 of 200 took 1.822 s\n", + " training loss:\t\t0.041786\n", + " validation loss:\t\t0.799887\n", + " validation accuracy:\t\t98.44 %\n", + "Epoch 82 of 200 took 1.822 s\n", + " training loss:\t\t0.041701\n", + " validation loss:\t\t0.799053\n", + " validation accuracy:\t\t98.43 %\n", + "Epoch 83 of 200 took 1.823 s\n", + " training loss:\t\t0.041654\n", + " validation loss:\t\t0.798195\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 84 of 200 took 1.823 s\n", + " training loss:\t\t0.041562\n", + " validation loss:\t\t0.798761\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 85 of 200 took 1.823 s\n", + " training loss:\t\t0.041442\n", + " validation loss:\t\t0.802000\n", + " validation accuracy:\t\t98.46 %\n", + "Epoch 86 of 200 took 1.822 s\n", + " training loss:\t\t0.041399\n", + " validation loss:\t\t0.797419\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 87 of 200 took 1.823 s\n", + " training loss:\t\t0.041380\n", + " validation loss:\t\t0.801245\n", + " validation accuracy:\t\t98.44 %\n", + "Epoch 88 of 200 took 1.823 s\n", + " training loss:\t\t0.041262\n", + " validation loss:\t\t0.797175\n", + " validation accuracy:\t\t98.46 %\n", + "Epoch 89 of 200 took 1.822 s\n", + " training loss:\t\t0.041114\n", + " validation loss:\t\t0.801705\n", + " validation accuracy:\t\t98.45 %\n", + "Epoch 90 of 200 took 1.823 s\n", + " training loss:\t\t0.041117\n", + " validation loss:\t\t0.798793\n", + " validation accuracy:\t\t98.48 %\n", + "Epoch 91 of 200 took 1.827 s\n", + " training loss:\t\t0.041054\n", + " validation loss:\t\t0.799963\n", + " validation accuracy:\t\t98.44 %\n", + "Epoch 92 of 200 took 1.823 s\n", + " training loss:\t\t0.040905\n", + " validation loss:\t\t0.798990\n", + " validation accuracy:\t\t98.44 %\n", + "Epoch 93 of 200 took 1.823 s\n", + " training loss:\t\t0.040908\n", + " validation loss:\t\t0.801326\n", + " validation accuracy:\t\t98.46 %\n", + "Epoch 94 of 200 took 1.823 s\n", + " training loss:\t\t0.040797\n", + " validation loss:\t\t0.801674\n", + " validation accuracy:\t\t98.51 %\n", + "Epoch 95 of 200 took 1.823 s\n", + " training loss:\t\t0.040837\n", + " validation loss:\t\t0.797612\n", + " validation accuracy:\t\t98.49 %\n", + "Epoch 96 of 200 took 1.823 s\n", + " training loss:\t\t0.040680\n", + " validation loss:\t\t0.799269\n", + " validation accuracy:\t\t98.48 %\n", + "Epoch 97 of 200 took 1.822 s\n", + " training loss:\t\t0.040642\n", + " validation loss:\t\t0.800045\n", + " validation accuracy:\t\t98.49 %\n", + "Epoch 98 of 200 took 1.822 s\n", + " training loss:\t\t0.040588\n", + " validation loss:\t\t0.799023\n", + " validation accuracy:\t\t98.51 %\n", + "Epoch 99 of 200 took 1.823 s\n", + " training loss:\t\t0.040527\n", + " validation loss:\t\t0.796965\n", + " validation accuracy:\t\t98.49 %\n", + "Epoch 100 of 200 took 1.824 s\n", + " training loss:\t\t0.040489\n", + " validation loss:\t\t0.796818\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 101 of 200 took 1.823 s\n", + " training loss:\t\t0.040382\n", + " validation loss:\t\t0.798143\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 102 of 200 took 1.823 s\n", + " training loss:\t\t0.040370\n", + " validation loss:\t\t0.800556\n", + " validation accuracy:\t\t98.49 %\n", + "Epoch 103 of 200 took 1.823 s\n", + " training loss:\t\t0.040258\n", + " validation loss:\t\t0.797174\n", + " validation accuracy:\t\t98.48 %\n", + "Epoch 104 of 200 took 1.822 s\n", + " training loss:\t\t0.040262\n", + " validation loss:\t\t0.800824\n", + " validation accuracy:\t\t98.48 %\n", + "Epoch 105 of 200 took 1.822 s\n", + " training loss:\t\t0.040177\n", + " validation loss:\t\t0.797597\n", + " validation accuracy:\t\t98.50 %\n", + "Epoch 106 of 200 took 1.822 s\n", + " training loss:\t\t0.040149\n", + " validation loss:\t\t0.797423\n", + " validation accuracy:\t\t98.49 %\n", + "Epoch 107 of 200 took 1.822 s\n", + " training loss:\t\t0.040073\n", + " validation loss:\t\t0.797572\n", + " validation accuracy:\t\t98.49 %\n", + "Epoch 108 of 200 took 1.822 s\n", + " training loss:\t\t0.039984\n", + " validation loss:\t\t0.799811\n", + " validation accuracy:\t\t98.49 %\n", + "Epoch 109 of 200 took 1.823 s\n", + " training loss:\t\t0.039956\n", + " validation loss:\t\t0.799266\n", + " validation accuracy:\t\t98.49 %\n", + "Epoch 110 of 200 took 1.823 s\n", + " training loss:\t\t0.039931\n", + " validation loss:\t\t0.797740\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 111 of 200 took 1.823 s\n", + " training loss:\t\t0.039801\n", + " validation loss:\t\t0.801227\n", + " validation accuracy:\t\t98.45 %\n", + "Epoch 112 of 200 took 1.821 s\n", + " training loss:\t\t0.039806\n", + " validation loss:\t\t0.798329\n", + " validation accuracy:\t\t98.48 %\n", + "Epoch 113 of 200 took 1.823 s\n", + " training loss:\t\t0.039768\n", + " validation loss:\t\t0.796590\n", + " validation accuracy:\t\t98.48 %\n", + "Epoch 114 of 200 took 1.822 s\n", + " training loss:\t\t0.039712\n", + " validation loss:\t\t0.797877\n", + " validation accuracy:\t\t98.48 %\n", + "Epoch 115 of 200 took 1.822 s\n", + " training loss:\t\t0.039629\n", + " validation loss:\t\t0.795346\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 116 of 200 took 1.822 s\n", + " training loss:\t\t0.039594\n", + " validation loss:\t\t0.794075\n", + " validation accuracy:\t\t98.46 %\n", + "Epoch 117 of 200 took 1.822 s\n", + " training loss:\t\t0.039566\n", + " validation loss:\t\t0.798663\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 118 of 200 took 1.822 s\n", + " training loss:\t\t0.039524\n", + " validation loss:\t\t0.796649\n", + " validation accuracy:\t\t98.46 %\n", + "Epoch 119 of 200 took 1.823 s\n", + " training loss:\t\t0.039479\n", + " validation loss:\t\t0.797728\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 120 of 200 took 1.822 s\n", + " training loss:\t\t0.039432\n", + " validation loss:\t\t0.799023\n", + " validation accuracy:\t\t98.45 %\n", + "Epoch 121 of 200 took 1.822 s\n", + " training loss:\t\t0.039399\n", + " validation loss:\t\t0.796112\n", + " validation accuracy:\t\t98.48 %\n", + "Epoch 122 of 200 took 1.823 s\n", + " training loss:\t\t0.039344\n", + " validation loss:\t\t0.796712\n", + " validation accuracy:\t\t98.46 %\n", + "Epoch 123 of 200 took 1.824 s\n", + " training loss:\t\t0.039302\n", + " validation loss:\t\t0.797688\n", + " validation accuracy:\t\t98.44 %\n", + "Epoch 124 of 200 took 1.823 s\n", + " training loss:\t\t0.039189\n", + " validation loss:\t\t0.797736\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 125 of 200 took 1.823 s\n", + " training loss:\t\t0.039226\n", + " validation loss:\t\t0.797844\n", + " validation accuracy:\t\t98.46 %\n", + "Epoch 126 of 200 took 1.823 s\n", + " training loss:\t\t0.039162\n", + " validation loss:\t\t0.796154\n", + " validation accuracy:\t\t98.45 %\n", + "Epoch 127 of 200 took 1.823 s\n", + " training loss:\t\t0.039167\n", + " validation loss:\t\t0.797422\n", + " validation accuracy:\t\t98.46 %\n", + "Epoch 128 of 200 took 1.823 s\n", + " training loss:\t\t0.039077\n", + " validation loss:\t\t0.796741\n", + " validation accuracy:\t\t98.44 %\n", + "Epoch 129 of 200 took 1.823 s\n", + " training loss:\t\t0.039081\n", + " validation loss:\t\t0.795700\n", + " validation accuracy:\t\t98.48 %\n", + "Epoch 130 of 200 took 1.822 s\n", + " training loss:\t\t0.038985\n", + " validation loss:\t\t0.795812\n", + " validation accuracy:\t\t98.48 %\n", + "Epoch 131 of 200 took 1.822 s\n", + " training loss:\t\t0.038954\n", + " validation loss:\t\t0.796567\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 132 of 200 took 1.823 s\n", + " training loss:\t\t0.038954\n", + " validation loss:\t\t0.795581\n", + " validation accuracy:\t\t98.45 %\n", + "Epoch 133 of 200 took 1.822 s\n", + " training loss:\t\t0.038882\n", + " validation loss:\t\t0.794117\n", + " validation accuracy:\t\t98.48 %\n", + "Epoch 134 of 200 took 1.823 s\n", + " training loss:\t\t0.038818\n", + " validation loss:\t\t0.795517\n", + " validation accuracy:\t\t98.46 %\n", + "Epoch 135 of 200 took 1.823 s\n", + " training loss:\t\t0.038834\n", + " validation loss:\t\t0.794132\n", + " validation accuracy:\t\t98.46 %\n", + "Epoch 136 of 200 took 1.822 s\n", + " training loss:\t\t0.038802\n", + " validation loss:\t\t0.793741\n", + " validation accuracy:\t\t98.46 %\n", + "Epoch 137 of 200 took 1.823 s\n", + " training loss:\t\t0.038756\n", + " validation loss:\t\t0.791921\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 138 of 200 took 1.823 s\n", + " training loss:\t\t0.038715\n", + " validation loss:\t\t0.793423\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 139 of 200 took 1.823 s\n", + " training loss:\t\t0.038719\n", + " validation loss:\t\t0.792457\n", + " validation accuracy:\t\t98.45 %\n", + "Epoch 140 of 200 took 1.823 s\n", + " training loss:\t\t0.038624\n", + " validation loss:\t\t0.792965\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 141 of 200 took 1.823 s\n", + " training loss:\t\t0.038631\n", + " validation loss:\t\t0.793420\n", + " validation accuracy:\t\t98.45 %\n", + "Epoch 142 of 200 took 1.823 s\n", + " training loss:\t\t0.038587\n", + " validation loss:\t\t0.789128\n", + " validation accuracy:\t\t98.45 %\n", + "Epoch 143 of 200 took 1.823 s\n", + " training loss:\t\t0.038523\n", + " validation loss:\t\t0.793483\n", + " validation accuracy:\t\t98.46 %\n", + "Epoch 144 of 200 took 1.823 s\n", + " training loss:\t\t0.038550\n", + " validation loss:\t\t0.793610\n", + " validation accuracy:\t\t98.46 %\n", + "Epoch 145 of 200 took 1.823 s\n", + " training loss:\t\t0.038479\n", + " validation loss:\t\t0.792183\n", + " validation accuracy:\t\t98.45 %\n", + "Epoch 146 of 200 took 1.823 s\n", + " training loss:\t\t0.038426\n", + " validation loss:\t\t0.794518\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 147 of 200 took 1.823 s\n", + " training loss:\t\t0.038396\n", + " validation loss:\t\t0.789904\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 148 of 200 took 1.824 s\n", + " training loss:\t\t0.038396\n", + " validation loss:\t\t0.790093\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 149 of 200 took 1.823 s\n", + " training loss:\t\t0.038310\n", + " validation loss:\t\t0.792157\n", + " validation accuracy:\t\t98.49 %\n", + "Epoch 150 of 200 took 1.822 s\n", + " training loss:\t\t0.038274\n", + " validation loss:\t\t0.790261\n", + " validation accuracy:\t\t98.48 %\n", + "Epoch 151 of 200 took 1.822 s\n", + " training loss:\t\t0.038283\n", + " validation loss:\t\t0.789809\n", + " validation accuracy:\t\t98.49 %\n", + "Epoch 152 of 200 took 1.822 s\n", + " training loss:\t\t0.038255\n", + " validation loss:\t\t0.791548\n", + " validation accuracy:\t\t98.49 %\n", + "Epoch 153 of 200 took 1.823 s\n", + " training loss:\t\t0.038192\n", + " validation loss:\t\t0.792163\n", + " validation accuracy:\t\t98.48 %\n", + "Epoch 154 of 200 took 1.822 s\n", + " training loss:\t\t0.038175\n", + " validation loss:\t\t0.791461\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 155 of 200 took 1.822 s\n", + " training loss:\t\t0.038183\n", + " validation loss:\t\t0.790093\n", + " validation accuracy:\t\t98.51 %\n", + "Epoch 156 of 200 took 1.823 s\n", + " training loss:\t\t0.038110\n", + " validation loss:\t\t0.792134\n", + " validation accuracy:\t\t98.47 %\n", + "Epoch 157 of 200 took 1.822 s\n", + " training loss:\t\t0.038171\n", + " validation loss:\t\t0.792019\n", + " validation accuracy:\t\t98.51 %\n", + "Epoch 158 of 200 took 1.822 s\n", + " training loss:\t\t0.038035\n", + " validation loss:\t\t0.791494\n", + " validation accuracy:\t\t98.50 %\n", + "Epoch 159 of 200 took 1.823 s\n", + " training loss:\t\t0.038044\n", + " validation loss:\t\t0.790218\n", + " validation accuracy:\t\t98.50 %\n", + "Epoch 160 of 200 took 1.822 s\n", + " training loss:\t\t0.038025\n", + " validation loss:\t\t0.789077\n", + " validation accuracy:\t\t98.49 %\n", + "Epoch 161 of 200 took 1.823 s\n", + " training loss:\t\t0.038014\n", + " validation loss:\t\t0.788483\n", + " validation accuracy:\t\t98.50 %\n", + "Epoch 162 of 200 took 1.823 s\n", + " training loss:\t\t0.037947\n", + " validation loss:\t\t0.792763\n", + " validation accuracy:\t\t98.51 %\n", + "Epoch 163 of 200 took 1.823 s\n", + " training loss:\t\t0.037908\n", + " validation loss:\t\t0.789718\n", + " validation accuracy:\t\t98.50 %\n", + "Epoch 164 of 200 took 1.823 s\n", + " training loss:\t\t0.037912\n", + " validation loss:\t\t0.789089\n", + " validation accuracy:\t\t98.52 %\n", + "Epoch 165 of 200 took 1.822 s\n", + " training loss:\t\t0.037814\n", + " validation loss:\t\t0.794081\n", + " validation accuracy:\t\t98.49 %\n", + "Epoch 166 of 200 took 1.823 s\n", + " training loss:\t\t0.037838\n", + " validation loss:\t\t0.790758\n", + " validation accuracy:\t\t98.52 %\n", + "Epoch 167 of 200 took 1.823 s\n", + " training loss:\t\t0.037820\n", + " validation loss:\t\t0.792051\n", + " validation accuracy:\t\t98.51 %\n", + "Epoch 168 of 200 took 1.822 s\n", + " training loss:\t\t0.037763\n", + " validation loss:\t\t0.790883\n", + " validation accuracy:\t\t98.53 %\n", + "Epoch 169 of 200 took 1.823 s\n", + " training loss:\t\t0.037754\n", + " validation loss:\t\t0.787345\n", + " validation accuracy:\t\t98.53 %\n", + "Epoch 170 of 200 took 1.822 s\n", + " training loss:\t\t0.037745\n", + " validation loss:\t\t0.789964\n", + " validation accuracy:\t\t98.52 %\n", + "Epoch 171 of 200 took 1.823 s\n", + " training loss:\t\t0.037684\n", + " validation loss:\t\t0.792878\n", + " validation accuracy:\t\t98.54 %\n", + "Epoch 172 of 200 took 1.823 s\n", + " training loss:\t\t0.037675\n", + " validation loss:\t\t0.792807\n", + " validation accuracy:\t\t98.54 %\n", + "Epoch 173 of 200 took 1.822 s\n", + " training loss:\t\t0.037659\n", + " validation loss:\t\t0.787230\n", + " validation accuracy:\t\t98.51 %\n", + "Epoch 174 of 200 took 1.823 s\n", + " training loss:\t\t0.037646\n", + " validation loss:\t\t0.790475\n", + " validation accuracy:\t\t98.52 %\n", + "Epoch 175 of 200 took 1.822 s\n", + " training loss:\t\t0.037594\n", + " validation loss:\t\t0.789190\n", + " validation accuracy:\t\t98.52 %\n", + "Epoch 176 of 200 took 1.822 s\n", + " training loss:\t\t0.037592\n", + " validation loss:\t\t0.787848\n", + " validation accuracy:\t\t98.51 %\n", + "Epoch 177 of 200 took 1.823 s\n", + " training loss:\t\t0.037485\n", + " validation loss:\t\t0.791303\n", + " validation accuracy:\t\t98.51 %\n", + "Epoch 178 of 200 took 1.822 s\n", + " training loss:\t\t0.037550\n", + " validation loss:\t\t0.790669\n", + " validation accuracy:\t\t98.53 %\n", + "Epoch 179 of 200 took 1.822 s\n", + " training loss:\t\t0.037482\n", + " validation loss:\t\t0.789096\n", + " validation accuracy:\t\t98.52 %\n", + "Epoch 180 of 200 took 1.823 s\n", + " training loss:\t\t0.037491\n", + " validation loss:\t\t0.789276\n", + " validation accuracy:\t\t98.50 %\n", + "Epoch 181 of 200 took 1.823 s\n", + " training loss:\t\t0.037458\n", + " validation loss:\t\t0.789388\n", + " validation accuracy:\t\t98.52 %\n", + "Epoch 182 of 200 took 1.823 s\n", + " training loss:\t\t0.037417\n", + " validation loss:\t\t0.790275\n", + " validation accuracy:\t\t98.50 %\n", + "Epoch 183 of 200 took 1.823 s\n", + " training loss:\t\t0.037440\n", + " validation loss:\t\t0.787772\n", + " validation accuracy:\t\t98.51 %\n", + "Epoch 184 of 200 took 1.822 s\n", + " training loss:\t\t0.037369\n", + " validation loss:\t\t0.788907\n", + " validation accuracy:\t\t98.53 %\n", + "Epoch 185 of 200 took 1.822 s\n", + " training loss:\t\t0.037361\n", + " validation loss:\t\t0.789464\n", + " validation accuracy:\t\t98.53 %\n", + "Epoch 186 of 200 took 1.822 s\n", + " training loss:\t\t0.037324\n", + " validation loss:\t\t0.787420\n", + " validation accuracy:\t\t98.52 %\n", + "Epoch 187 of 200 took 1.822 s\n", + " training loss:\t\t0.037341\n", + " validation loss:\t\t0.790022\n", + " validation accuracy:\t\t98.49 %\n", + "Epoch 188 of 200 took 1.823 s\n", + " training loss:\t\t0.037282\n", + " validation loss:\t\t0.791168\n", + " validation accuracy:\t\t98.50 %\n", + "Epoch 189 of 200 took 1.822 s\n", + " training loss:\t\t0.037257\n", + " validation loss:\t\t0.790791\n", + " validation accuracy:\t\t98.53 %\n", + "Epoch 190 of 200 took 1.822 s\n", + " training loss:\t\t0.037264\n", + " validation loss:\t\t0.789268\n", + " validation accuracy:\t\t98.51 %\n", + "Epoch 191 of 200 took 1.822 s\n", + " training loss:\t\t0.037193\n", + " validation loss:\t\t0.789992\n", + " validation accuracy:\t\t98.52 %\n", + "Epoch 192 of 200 took 1.823 s\n", + " training loss:\t\t0.037195\n", + " validation loss:\t\t0.790796\n", + " validation accuracy:\t\t98.53 %\n", + "Epoch 193 of 200 took 1.823 s\n", + " training loss:\t\t0.037177\n", + " validation loss:\t\t0.791499\n", + " validation accuracy:\t\t98.52 %\n", + "Epoch 194 of 200 took 1.823 s\n", + " training loss:\t\t0.037157\n", + " validation loss:\t\t0.792934\n", + " validation accuracy:\t\t98.52 %\n", + "Epoch 195 of 200 took 1.824 s\n", + " training loss:\t\t0.037108\n", + " validation loss:\t\t0.792228\n", + " validation accuracy:\t\t98.50 %\n", + "Epoch 196 of 200 took 1.823 s\n", + " training loss:\t\t0.037137\n", + " validation loss:\t\t0.790318\n", + " validation accuracy:\t\t98.51 %\n", + "Epoch 197 of 200 took 1.822 s\n", + " training loss:\t\t0.037081\n", + " validation loss:\t\t0.790916\n", + " validation accuracy:\t\t98.53 %\n", + "Epoch 198 of 200 took 1.822 s\n", + " training loss:\t\t0.037013\n", + " validation loss:\t\t0.792986\n", + " validation accuracy:\t\t98.51 %\n", + "Epoch 199 of 200 took 1.822 s\n", + " training loss:\t\t0.037044\n", + " validation loss:\t\t0.791534\n", + " validation accuracy:\t\t98.52 %\n", + "Epoch 200 of 200 took 1.823 s\n", + " training loss:\t\t0.036967\n", + " validation loss:\t\t0.791498\n", + " validation accuracy:\t\t98.52 %\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "num_batches_train = dataset['num_examples_train'] // batch_size\n", + "num_batches_valid = dataset['num_examples_valid'] // batch_size\n", + "\n", + "print(\"Starting training...\")\n", + "now = time.time()\n", + "\n", + "try:\n", + " for epoch in range(num_epochs):\n", + " batch_train_losses = []\n", + " for b in range(num_batches_train):\n", + " batch_train_loss = iter_train(b)\n", + " batch_train_losses.append(batch_train_loss)\n", + "\n", + " avg_train_loss = np.mean(batch_train_losses)\n", + "\n", + " batch_valid_losses = []\n", + " batch_valid_accuracies = []\n", + " for b in range(num_batches_valid):\n", + " batch_valid_loss, batch_valid_accuracy = iter_valid(b)\n", + " batch_valid_losses.append(batch_valid_loss)\n", + " batch_valid_accuracies.append(batch_valid_accuracy)\n", + "\n", + " avg_valid_loss = np.mean(batch_valid_losses)\n", + " avg_valid_accuracy = np.mean(batch_valid_accuracies)\n", + "\n", + " print(\"Epoch %d of %d took %.3f s\" % (epoch + 1, num_epochs, time.time() - now))\n", + " now = time.time()\n", + " print(\" training loss:\\t\\t%.6f\" % avg_train_loss)\n", + " print(\" validation loss:\\t\\t%.6f\" % avg_valid_loss)\n", + " print(\" validation accuracy:\\t\\t%.2f %%\" % (avg_valid_accuracy * 100))\n", + "except KeyboardInterrupt:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's create a function that computes the latent representation variables z so we can visualize them on the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "z = nn.layers.get_output(l_latent)\n", + "\n", + "compute_z = theano.function(\n", + " [], z,\n", + " givens={\n", + " x: dataset['X_test'],\n", + " },\n", + ")\n", + "\n", + "z_vals = compute_z()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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YUCjE2Pbyl5qQHg0+5fJitrwI4yw+s/3zfJ+lpQrzeY9SKaBYrJCmKbOZT7m89DmBs+1F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\nxQUMI8P58w612jiu6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure()\n", + "plt.scatter(z_vals[:, 0], z_vals[:, 1], alpha=0.1)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This corresponds to figure 3a in the paper. Our latent variables have a smaller magnitude overall, but exhibit a roughly Gaussian distribution just like in the paper.\n", + "\n", + "Now let's generate some reconstructions. Our aim is to reproduce figures 3b and 3c. We'll need to compile a function that allows us to set the desired values for the latent variables `y` and `z`.\n", + "\n", + "Lasagne makes it possible to map any layer in the network to a custom expression by supplying a dictionary to `nn.layers.get_output()`. We'll make use of this to 'clamp' the observed and latent representation variables." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "y_clamped = T.ivector('y_clamped')\n", + "z_clamped = T.matrix('z_clamped')\n", + "\n", + "x_recon = nn.layers.get_output(l_decoder_out, {\n", + " l_observed: nn.utils.one_hot(y_clamped, m=10),\n", + " l_latent: z_clamped,\n", + " })\n", + "\n", + "reconstruct = theano.function([y_clamped, z_clamped], x_recon)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "z_max = 0.3\n", + "ys = np.repeat(np.arange(10), 9).astype('int32')\n", + "zs = np.tile(np.linspace(-z_max, z_max, 9), 10).astype(theano.config.floatX)\n", + "z1s = np.vstack([zs, np.zeros_like(zs)]).T\n", + "z2s = np.vstack([np.zeros_like(zs), zs]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "reconstructions_z1 = reconstruct(ys, z1s)\n", + "reconstructions_z2 = reconstruct(ys, z2s)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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CMZzN7yqvpCFxCgCvoyjKVQgUsHq9bnuHNKpFBe3md9CSa1OrSmvmChCjWMw6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\n5PN545KlUslcvJlMxgLm+EMRu1gsvtIXWgtTEbm513g8DgCWWEdClEwmE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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "im1 = reconstructions_z1.reshape(10, 9, 28, 28).transpose(1, 2, 0, 3).reshape(9 * 28, 10 * 28)\n", + "plt.imshow(im1, cmap=plt.cm.gray)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Cws4iuw8JOckGxe1By3uEwd0It0oIplug1dVVjUajBbjpltkn1t0JrDGL6IjB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SqqUhWdthqmHBwcHFgXCtZhAmDpL777hEdT+kzJZBIrKyt4/fo1qtUqGo0GfvjhB6vFPDs7i+fP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\nYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "im2 = reconstructions_z2.reshape(10, 9, 28, 28).transpose(1, 2, 0, 3).reshape(9 * 28, 10 * 28)\n", + "plt.imshow(im2, cmap=plt.cm.gray)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The reconstructions show some interesting structure as `z1` and `z2` are varied. Pretty cool!" + ] + } + ], + "metadata": { + "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" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}