| { |
| "cells": [ |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements; and to You under the Apache License, Version 2.0. " |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# Classify images from MNIST using LeNet" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## Dataset\n", |
| "\n", |
| "Download the [dataset](http://deeplearning.net/data/mnist/mnist.pkl.gz) to your workspace (i.e. the notebook folder)." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 1, |
| "metadata": { |
| "collapsed": true |
| }, |
| "outputs": [], |
| "source": [ |
| "from __future__ import division\n", |
| "from builtins import zip\n", |
| "from builtins import str\n", |
| "from builtins import range\n", |
| "from past.utils import old_div\n", |
| "from future import standard_library\n", |
| "from __future__ import print_function\n", |
| "from tqdm import tnrange, tqdm_notebook\n", |
| "\n", |
| "standard_library.install_aliases()\n", |
| "import pickle, gzip\n", |
| "\n", |
| "# Load the dataset\n", |
| "f = gzip.open('mnist.pkl.gz', 'rb')\n", |
| "train_set, valid_set, _ = pickle.load(f, encoding='latin1')\n", |
| "f.close()" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 2, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "(50000, 784) (50000,)\n", |
| "(10000, 784) (10000,)\n" |
| ] |
| } |
| ], |
| "source": [ |
| "print(train_set[0].shape, train_set[1].shape)\n", |
| "print(valid_set[0].shape, valid_set[1].shape)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 3, |
| "metadata": { |
| "collapsed": true |
| }, |
| "outputs": [], |
| "source": [ |
| "import numpy as np\n", |
| "train_x = np.reshape(train_set[0], (50000, 1, 28, 28)).astype(np.float32, copy=False)\n", |
| "train_y = np.array(train_set[1]).astype(np.int32, copy=False)\n", |
| "valid_x = np.reshape(valid_set[0], (10000, 1, 28, 28))" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 4, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "data": { |
| "text/plain": [ |
| "<matplotlib.image.AxesImage at 0x7fdde5663438>" |
| ] |
| }, |
| "execution_count": 4, |
| "metadata": {}, |
| "output_type": "execute_result" |
| }, |
| { |
| "data": { |
| "image/png": 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by80srYKPXdPNeO3uDf+RdIlG3/F/QdLfFtFDmb4+JOmx7OfJonuTdLtGnwYOa/S9kask\nvU9St6TnJP23pPYm6u27kh6XtFujQessqLdlGn1Kv1vSruznkqKPXaKvQo4bn/ADguINPyAowg8E\nRfiBoAg/EBThB4Ii/EBQhB8IivADQf0/sEWOix6VKakAAAAASUVORK5CYII=\n", |
| "text/plain": [ |
| "<matplotlib.figure.Figure at 0x7fdde7991f60>" |
| ] |
| }, |
| "metadata": {}, |
| "output_type": "display_data" |
| } |
| ], |
| "source": [ |
| "%matplotlib inline\n", |
| "import matplotlib.pyplot as plt\n", |
| "plt.imshow(train_x[0][0])" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## Create the CNN model\n", |
| "\n", |
| "TODO: plot the net structure" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 5, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "('conv1', (32, 14, 14))\n", |
| "('relu1', (32, 14, 14))\n", |
| "('conv2', (32, 7, 7))\n", |
| "('relu2', (32, 7, 7))\n", |
| "('pool', (32, 4, 4))\n", |
| "('flat', (512,))\n", |
| "('dense', (10,))\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/plain": [ |
| "<singa.layer.Dense at 0x7fddc5ca3b00>" |
| ] |
| }, |
| "execution_count": 5, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "from singa import net as ffnet\n", |
| "from singa.layer import Conv2D, MaxPooling2D, Dropout, Activation, Flatten, Dense\n", |
| "from singa import optimizer, loss, metric\n", |
| "from singa import layer\n", |
| "layer.engine = 'singacpp'\n", |
| "net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy())\n", |
| "net.add(Conv2D('conv1', 32, 3, 2, input_sample_shape=(1,28,28)))\n", |
| "net.add(Activation('relu1'))\n", |
| "net.add(Conv2D('conv2', 32, 3, 2))\n", |
| "net.add(Activation('relu2'))\n", |
| "net.add(MaxPooling2D('pool', 3, 2))\n", |
| "net.add(Flatten('flat'))\n", |
| "net.add(Dense('dense', 10))" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## Initialize the parameters\n", |
| "\n", |
| "* weight matrix - guassian distribution\n", |
| "* bias - 0" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 6, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "conv1/weight (32, 9) 0.07648436725139618\n", |
| "conv1/bias (32,) 0.0\n", |
| "conv2/weight (32, 288) 0.08030246943235397\n", |
| "conv2/bias (32,) 0.0\n", |
| "dense/weight (512, 10) 0.07954108715057373\n", |
| "dense/bias (10,) 0.0\n" |
| ] |
| } |
| ], |
| "source": [ |
| "for pname, pval in zip(net.param_names(), net.param_values()):\n", |
| " if len(pval.shape) > 1:\n", |
| " pval.gaussian(0, 0.1)\n", |
| " else:\n", |
| " pval.set_value(0)\n", |
| " print(pname, pval.shape, pval.l1())" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## Set up the optimizer and tensors" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 7, |
| "metadata": { |
| "collapsed": true |
| }, |
| "outputs": [], |
| "source": [ |
| "from singa import tensor\n", |
| "#from singa.proto import core_pb2\n", |
| "from singa import device\n", |
| "from singa import utils\n", |
| "cpu = device.get_default_device()\n", |
| "\n", |
| "opt = optimizer.SGD(momentum=0.9, weight_decay=1e-4)\n", |
| "batch_size = 32\n", |
| "num_train_batch = old_div(train_x.shape[0], batch_size)\n", |
| "\n", |
| "tx = tensor.Tensor((batch_size, 1, 28, 28))\n", |
| "ty = tensor.Tensor((batch_size,), cpu , tensor.int32)\n", |
| "\n", |
| "# for progress bar\n", |
| "from tqdm import tnrange\n", |
| "idx = np.arange(train_x.shape[0], dtype=np.int32)" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## Conduct SGD\n", |
| "\n", |
| "1. process the training data multile time, each time is called on epoch; \n", |
| "2. for each epoch, read the data as mini-batches in random order\n", |
| "3. for each mini-batch, do BP and update the parameters " |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 8, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "data": { |
| "application/vnd.jupyter.widget-view+json": { |
| "model_id": "b9a8c3eff6744b90a2be87729cb60cd5" |
| } |
| }, |
| "metadata": {}, |
| "output_type": "display_data" |
| }, |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "\n", |
| "Epoch = 0, training loss = 0.291366, training accuracy = 0.907370\n" |
| ] |
| }, |
| { |
| "data": { |
| "application/vnd.jupyter.widget-view+json": { |
| "model_id": "4cca5a2fff6b47859adafae922ea20d9" |
| } |
| }, |
| "metadata": {}, |
| "output_type": "display_data" |
| }, |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "\n", |
| "Epoch = 1, training loss = 0.111163, training accuracy = 0.965089\n" |
| ] |
| } |
| ], |
| "source": [ |
| "for epoch in range(2):\n", |
| " np.random.shuffle(idx)\n", |
| " loss, acc = 0.0, 0.0\n", |
| " \n", |
| " bar = tnrange(num_train_batch, desc='Epoch %d' % epoch)\n", |
| " for b in bar:\n", |
| " x = train_x[idx[b * batch_size: (b + 1) * batch_size]]\n", |
| " y = train_y[idx[b * batch_size: (b + 1) * batch_size]]\n", |
| " tx.copy_from_numpy(x)\n", |
| " ty.copy_from_numpy(y)\n", |
| " grads, (l, a) = net.train(tx, ty)\n", |
| " loss += l\n", |
| " acc += a\n", |
| " for (s, p, g) in zip(net.param_names(), net.param_values(), grads):\n", |
| " opt.apply_with_lr(epoch, 0.01, g, p, str(s), b)\n", |
| " # update progress bar\n", |
| " bar.set_postfix(train_loss=l, train_accuracy=a)\n", |
| " print('Epoch = %d, training loss = %f, training accuracy = %f' % (epoch, old_div(loss, num_train_batch), old_div(acc, num_train_batch)))" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## Save model to disk" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 9, |
| "metadata": { |
| "collapsed": true |
| }, |
| "outputs": [], |
| "source": [ |
| "net.save('checkpoint')" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## Load model from disk" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 10, |
| "metadata": { |
| "scrolled": true |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "NOTE: If your model was saved using pickle, then set use_pickle=True for loading it\n" |
| ] |
| } |
| ], |
| "source": [ |
| "for pval in net.param_values():\n", |
| " pval.set_value(0)\n", |
| "net.load('checkpoint')" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## Do prediction" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 11, |
| "metadata": { |
| "collapsed": true |
| }, |
| "outputs": [], |
| "source": [ |
| "from PIL import Image\n", |
| "img = Image.open('static/digit.jpg').convert('L')\n", |
| "img = img.resize((28,28))\n", |
| "img = old_div(np.array(img, dtype=np.float32),255)\n", |
| "img = tensor.from_numpy(img)\n", |
| "img.reshape((1,1,28,28))\n", |
| "y=net.predict(img)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 12, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "data": { |
| "text/plain": [ |
| "[<matplotlib.lines.Line2D at 0x7fddc4b29240>]" |
| ] |
| }, |
| "execution_count": 12, |
| "metadata": {}, |
| "output_type": "execute_result" |
| }, |
| { |
| "data": { |
| "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", |
| "text/plain": [ |
| "<matplotlib.figure.Figure at 0x7fdde5663da0>" |
| ] |
| }, |
| "metadata": {}, |
| "output_type": "display_data" |
| } |
| ], |
| "source": [ |
| "prob=tensor.to_numpy(y)[0]\n", |
| "plt.plot(list(range(10)), prob)" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "## Debug\n", |
| "\n", |
| "Print l1 norm or parameter and layer feature\n", |
| "\n", |
| "1. parameter initialization\n", |
| "2. learning rate\n", |
| "3. weight decay\n" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 13, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "conv1/weight (32, 9) 7.971656799316406\n", |
| "conv1/bias (32,) 0.0\n", |
| "conv2/weight (32, 288) 8.005664825439453\n", |
| "conv2/bias (32,) 0.0\n", |
| "dense/weight (512, 10) 7.921195983886719\n", |
| "dense/bias (10,) 0.0\n", |
| "\n", |
| "\n", |
| "Epoch 0\n", |
| "-->conv1: 4.059905\n", |
| "conv1-->relu1: 2.407956\n", |
| "relu1-->conv2: 620.319519\n", |
| "conv2-->relu2: 302.810760\n", |
| "relu2-->pool: 965.994873\n", |
| "pool-->flat: 965.994873\n", |
| "flat-->dense: 276680.062500\n", |
| "-->dense: 0.270273\n", |
| "dense-->flat: 0.270273\n", |
| "flat-->pool: 0.074453\n", |
| "pool-->relu2: 0.046011\n", |
| "relu2-->conv2: 8.166893\n", |
| "conv2-->relu1: 2.553801\n", |
| "relu1-->conv1: 299.891296\n", |
| "\n", |
| " loss = 68.231674, params\n", |
| "conv1/weight 9.855006217956543\n", |
| "conv1/bias 9.832422256469727\n", |
| "conv2/weight 8.0507173538208\n", |
| "conv2/bias 0.1285100281238556\n", |
| "dense/weight 8.218809127807617\n", |
| "dense/bias 0.0013595324708148837\n", |
| "\n", |
| "\n", |
| "Epoch 1\n", |
| "-->conv1: 17.634811\n", |
| "conv1-->relu1: 3.629616\n", |
| "relu1-->conv2: 1683.136475\n", |
| "conv2-->relu2: 389.035248\n", |
| "relu2-->pool: 934.496582\n", |
| "pool-->flat: 934.496582\n", |
| "flat-->dense: 1198527.500000\n", |
| "-->dense: 0.322575\n", |
| "dense-->flat: 0.322575\n", |
| "flat-->pool: 0.094647\n", |
| "pool-->relu2: 0.039437\n", |
| "relu2-->conv2: 8.781067\n", |
| "conv2-->relu1: 2.430810\n", |
| "relu1-->conv1: 502.457764\n", |
| "\n", |
| " loss = 79.148743, params\n", |
| "conv1/weight 14.95351791381836\n", |
| "conv1/bias 28.66775131225586\n", |
| "conv2/weight 8.543733596801758\n", |
| "conv2/bias 0.320060670375824\n", |
| "dense/weight 9.1849365234375\n", |
| "dense/bias 0.0028705699369311333\n", |
| "\n", |
| "\n", |
| "Epoch 2\n", |
| "-->conv1: 44.400776\n", |
| "conv1-->relu1: 8.777474\n", |
| "relu1-->conv2: 12810.666016\n", |
| "conv2-->relu2: 1705.242798\n", |
| "relu2-->pool: 2268.597656\n", |
| "pool-->flat: 2268.597656\n", |
| "flat-->dense: 4815859.500000\n", |
| "-->dense: 0.347976\n", |
| "dense-->flat: 0.347976\n", |
| "flat-->pool: 0.109863\n", |
| "pool-->relu2: 0.024067\n", |
| "relu2-->conv2: 7.023767\n", |
| "conv2-->relu1: 1.075155\n", |
| "relu1-->conv1: 418.399963\n", |
| "\n", |
| " loss = 76.419479, params\n", |
| "conv1/weight 29.47024154663086\n", |
| "conv1/bias 52.10609436035156\n", |
| "conv2/weight 10.518251419067383\n", |
| "conv2/bias 0.5825839042663574\n", |
| "dense/weight 12.961698532104492\n", |
| "dense/bias 0.004623417742550373\n", |
| "\n", |
| "\n", |
| "Epoch 3\n", |
| "-->conv1: 82.561668\n", |
| "conv1-->relu1: 2.029232\n", |
| "relu1-->conv2: 19918.304688\n", |
| "conv2-->relu2: 2221.255859\n", |
| "relu2-->pool: 3186.028076\n", |
| "pool-->flat: 3186.028076\n", |
| "flat-->dense: 8799418.000000\n", |
| "-->dense: 0.585852\n", |
| "dense-->flat: 0.585852\n", |
| "flat-->pool: 0.186150\n", |
| "pool-->relu2: 0.025595\n", |
| "relu2-->conv2: 10.546330\n", |
| "conv2-->relu1: 1.008846\n", |
| "relu1-->conv1: 2478.874512\n", |
| "\n", |
| " loss = 87.336548, params\n", |
| "conv1/weight 45.3760871887207\n", |
| "conv1/bias 122.97279357910156\n", |
| "conv2/weight 12.587639808654785\n", |
| "conv2/bias 0.9543725252151489\n", |
| "dense/weight 21.757226943969727\n", |
| "dense/bias 0.007790021598339081\n", |
| "\n", |
| "\n", |
| "Epoch 4\n", |
| "-->conv1: 181.790573\n", |
| "conv1-->relu1: 0.000030\n", |
| "relu1-->conv2: 1.017071\n", |
| "conv2-->relu2: 0.120676\n", |
| "relu2-->pool: 0.305297\n", |
| "pool-->flat: 0.305297\n", |
| "flat-->dense: 2401.528809\n", |
| "-->dense: 0.703576\n", |
| "dense-->flat: 0.703576\n", |
| "flat-->pool: 0.229087\n", |
| "pool-->relu2: 0.029813\n", |
| "relu2-->conv2: 13.549859\n", |
| "conv2-->relu1: 0.006924\n", |
| "relu1-->conv1: 4.986567\n", |
| "\n", |
| " loss = 68.686081, params\n", |
| "conv1/weight 59.74170684814453\n", |
| "conv1/bias 191.91458129882812\n", |
| "conv2/weight 14.60444450378418\n", |
| "conv2/bias 1.4008562564849854\n", |
| "dense/weight 30.463171005249023\n", |
| "dense/bias 0.010233680717647076\n", |
| "\n", |
| "\n", |
| "Epoch 5\n", |
| "-->conv1: 260.486359\n", |
| "conv1-->relu1: 0.000000\n", |
| "relu1-->conv2: 1.400860\n", |
| "conv2-->relu2: 0.062520\n", |
| "relu2-->pool: 0.062520\n", |
| "pool-->flat: 0.062520\n", |
| "flat-->dense: 296.831329\n", |
| "-->dense: 2.126958\n", |
| "dense-->flat: 2.126958\n", |
| "flat-->pool: 0.694517\n", |
| "pool-->relu2: 0.014730\n", |
| "relu2-->conv2: 6.709585\n", |
| "conv2-->relu1: 0.000000\n", |
| "relu1-->conv1: 0.000000\n", |
| "\n", |
| " loss = 65.246483, params\n", |
| "conv1/weight 72.80561828613281\n", |
| "conv1/bias 253.96200561523438\n", |
| "conv2/weight 16.481393814086914\n", |
| "conv2/bias 1.8957630395889282\n", |
| "dense/weight 38.40126419067383\n", |
| "dense/bias 0.014084763824939728\n", |
| "\n", |
| "\n", |
| "Epoch 6\n", |
| "-->conv1: 347.085114\n", |
| "conv1-->relu1: 0.000000\n", |
| "relu1-->conv2: 1.895745\n", |
| "conv2-->relu2: 0.013967\n", |
| "relu2-->pool: 0.013967\n", |
| "pool-->flat: 0.013967\n", |
| "flat-->dense: 33.670372\n", |
| "-->dense: 1.395184\n", |
| "dense-->flat: 1.395184\n", |
| "flat-->pool: 0.455570\n", |
| "pool-->relu2: 0.009508\n", |
| "relu2-->conv2: 3.835509\n", |
| "conv2-->relu1: 0.000000\n", |
| "relu1-->conv1: 0.000000\n", |
| "\n", |
| " loss = 38.581238, params\n", |
| "conv1/weight 84.56834411621094\n", |
| "conv1/bias 309.804443359375\n", |
| "conv2/weight 18.20405387878418\n", |
| "conv2/bias 2.4867684841156006\n", |
| "dense/weight 45.590518951416016\n", |
| "dense/bias 0.016868162900209427\n", |
| "\n", |
| "\n", |
| "Epoch 7\n", |
| "-->conv1: 416.296112\n", |
| "conv1-->relu1: 0.000000\n", |
| "relu1-->conv2: 2.486759\n", |
| "conv2-->relu2: 0.000000\n", |
| "relu2-->pool: 0.000000\n", |
| "pool-->flat: 0.000000\n", |
| "flat-->dense: 0.016868\n", |
| "-->dense: 1.538023\n", |
| "dense-->flat: 1.538023\n", |
| "flat-->pool: 0.502211\n", |
| "pool-->relu2: 0.000000\n", |
| "relu2-->conv2: 0.000000\n", |
| "conv2-->relu1: 0.000000\n", |
| "relu1-->conv1: 0.000000\n", |
| "\n", |
| " loss = 2.308411, params\n", |
| "conv1/weight 95.16199493408203\n", |
| "conv1/bias 360.06231689453125\n", |
| "conv2/weight 19.77170753479004\n", |
| "conv2/bias 3.043811798095703\n", |
| "dense/weight 52.087501525878906\n", |
| "dense/bias 0.01910635642707348\n", |
| "\n", |
| "\n", |
| "Epoch 8\n", |
| "-->conv1: 469.519379\n", |
| "conv1-->relu1: 0.000000\n", |
| "relu1-->conv2: 3.043824\n", |
| "conv2-->relu2: 0.000000\n", |
| "relu2-->pool: 0.000000\n", |
| "pool-->flat: 0.000000\n", |
| "flat-->dense: 0.019106\n", |
| "-->dense: 1.641877\n", |
| "dense-->flat: 1.641877\n", |
| "flat-->pool: 0.536123\n", |
| "pool-->relu2: 0.000000\n", |
| "relu2-->conv2: 0.000000\n", |
| "conv2-->relu1: 0.000000\n", |
| "relu1-->conv1: 0.000000\n", |
| "\n", |
| " loss = 2.301430, params\n", |
| "conv1/weight 104.70797729492188\n", |
| "conv1/bias 405.2940368652344\n", |
| "conv2/weight 21.199617385864258\n", |
| "conv2/bias 3.545147657394409\n", |
| "dense/weight 57.95866775512695\n", |
| "dense/bias 0.02116413414478302\n", |
| "\n", |
| "\n", |
| "Epoch 9\n", |
| "-->conv1: 525.609802\n", |
| "conv1-->relu1: 0.000000\n", |
| "relu1-->conv2: 3.545132\n", |
| "conv2-->relu2: 0.000000\n", |
| "relu2-->pool: 0.000000\n", |
| "pool-->flat: 0.000000\n", |
| "flat-->dense: 0.021164\n", |
| "-->dense: 2.182356\n", |
| "dense-->flat: 2.182356\n", |
| "flat-->pool: 0.712606\n", |
| "pool-->relu2: 0.000000\n", |
| "relu2-->conv2: 0.000000\n", |
| "conv2-->relu1: 0.000000\n", |
| "relu1-->conv1: 0.000000\n", |
| "\n", |
| " loss = 2.316046, params\n", |
| "conv1/weight 113.30311584472656\n", |
| "conv1/bias 446.00213623046875\n", |
| "conv2/weight 22.49642562866211\n", |
| "conv2/bias 3.9963467121124268\n", |
| "dense/weight 63.25651931762695\n", |
| "dense/bias 0.02255747839808464\n" |
| ] |
| } |
| ], |
| "source": [ |
| "np.random.shuffle(idx)\n", |
| "ffnet.verbose=True\n", |
| "for pname, pval in zip(net.param_names(), net.param_values()):\n", |
| " if len(pval.shape) > 1:\n", |
| " pval.gaussian(0, 10)\n", |
| " else:\n", |
| " pval.set_value(0)\n", |
| " print(pname, pval.shape, pval.l1())\n", |
| "for b in range(10):\n", |
| " print(\"\\n\\nEpoch %d\" % b)\n", |
| " x = train_x[idx[b * batch_size: (b + 1) * batch_size]]\n", |
| " y = train_y[idx[b * batch_size: (b + 1) * batch_size]]\n", |
| " tx.copy_from_numpy(x)\n", |
| " ty.copy_from_numpy(y)\n", |
| " grads, (l, a) = net.train(tx, ty)\n", |
| " print('\\n loss = %f, params' % l)\n", |
| " for (s, p, g) in zip(net.param_names(), net.param_values(), grads):\n", |
| " opt.apply_with_lr(epoch, 0.01, g, p, str(s), b)\n", |
| " print(s, p.l1())" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 14, |
| "metadata": { |
| "collapsed": true |
| }, |
| "outputs": [], |
| "source": [ |
| "def vis_square(data):\n", |
| " \"\"\"Take an array of shape (n, height, width) or (n, height, width, 3)\n", |
| " and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\"\"\"\n", |
| " \n", |
| " # normalize data for display\n", |
| " data = old_div((data - data.min()), (data.max() - data.min()))\n", |
| " \n", |
| " # force the number of filters to be square\n", |
| " n = int(np.ceil(np.sqrt(data.shape[0])))\n", |
| " padding = (((0, n ** 2 - data.shape[0]),\n", |
| " (0, 1), (0, 1)) # add some space between filters\n", |
| " + ((0, 0),) * (data.ndim - 3)) # don't pad the last dimension (if there is one)\n", |
| " data = np.pad(data, padding, mode='constant', constant_values=1) # pad with ones (white)\n", |
| " \n", |
| " # tile the filters into an image\n", |
| " data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n", |
| " data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n", |
| " \n", |
| " plt.imshow(data); plt.axis('off')" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 15, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "NOTE: If your model was saved using pickle, then set use_pickle=True for loading it\n" |
| ] |
| } |
| ], |
| "source": [ |
| "np.random.shuffle(idx)\n", |
| "ffnet.verbose=False\n", |
| "net.load('checkpoint')\n", |
| "b=1\n", |
| "x = train_x[idx[b * batch_size: (b + 1) * batch_size]] \n", |
| "tx.copy_from_numpy(x)\n", |
| "\n", |
| "r = net.forward(False, tx, ['relu1', 'relu2']) " |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 16, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "data": { |
| "image/png": 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BWHhTjy5pw/fs0ipN/oae2M4d679OCEklVHxCDGTeuPq54ydnHjRwGe9SJrddFbSdEnFW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\nYII=\n", |
| "text/plain": [ |
| "<matplotlib.figure.Figure at 0x7fddc4be1e48>" |
| ] |
| }, |
| "metadata": {}, |
| "output_type": "display_data" |
| } |
| ], |
| "source": [ |
| "r1 = tensor.to_numpy(r['relu1'])[0]\n", |
| "vis_square(r1)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 17, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "data": { |
| "image/png": 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|
| "text/plain": [ |
| "<matplotlib.figure.Figure at 0x7fdde5696208>" |
| ] |
| }, |
| "metadata": {}, |
| "output_type": "display_data" |
| } |
| ], |
| "source": [ |
| "r2 = tensor.to_numpy(r['relu2'])[0]\n", |
| "vis_square(r2)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 18, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "(32, 288)\n" |
| ] |
| } |
| ], |
| "source": [ |
| "p=net.param_values()[2]\n", |
| "print(p.shape)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 19, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "data": { |
| "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", |
| "text/plain": [ |
| "<matplotlib.figure.Figure at 0x7fddc4a323c8>" |
| ] |
| }, |
| "metadata": {}, |
| "output_type": "display_data" |
| } |
| ], |
| "source": [ |
| "vis_square(tensor.to_numpy(p)[0].reshape(32, 3,3))" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": null, |
| "metadata": { |
| "collapsed": true |
| }, |
| "outputs": [], |
| "source": [] |
| } |
| ], |
| "metadata": { |
| "anaconda-cloud": {}, |
| "kernelspec": { |
| "display_name": "py3", |
| "language": "python", |
| "name": "py3" |
| }, |
| "language_info": { |
| "codemirror_mode": { |
| "name": "ipython", |
| "version": 3 |
| }, |
| "file_extension": ".py", |
| "mimetype": "text/x-python", |
| "name": "python", |
| "nbconvert_exporter": "python", |
| "pygments_lexer": "ipython3", |
| "version": "3.5.3" |
| }, |
| "widgets": { |
| "state": { |
| "0678ea185e8c48a9ab20bdb956e6b18a": { |
| "views": [ |
| { |
| "cell_index": 13 |
| } |
| ] |
| }, |
| "1373fb7b9e754b639a3f27ebf0372d70": { |
| "views": [ |
| { |
| "cell_index": 13 |
| } |
| ] |
| }, |
| "49561f2ab00b457f82357766d967ca8b": { |
| "views": [ |
| { |
| "cell_index": 13 |
| } |
| ] |
| } |
| }, |
| "version": "1.2.0" |
| } |
| }, |
| "nbformat": 4, |
| "nbformat_minor": 2 |
| } |
