Keras - Sequential model to Functional example

README.md

Example of transfer learning taken from https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3

vgg16.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    }
   ],
   "source": [
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#code from https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Flatten, Dense, Dropout\n",
    "from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D\n",
    "from keras.optimizers import SGD\n",
    "import cv2, numpy as np\n",
    "\n",
    "def VGG_16(weights_path=None):\n",
    "    model = Sequential()\n",
    "    model.add(ZeroPadding2D((1,1),input_shape=(3,224,224)))\n",
    "    model.add(Convolution2D(64, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(64, 3, 3, activation='relu'))\n",
    "    model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    "\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(128, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(128, 3, 3, activation='relu'))\n",
    "    model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    "\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(256, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(256, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(256, 3, 3, activation='relu'))\n",
    "    model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    "\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(512, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(512, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(512, 3, 3, activation='relu'))\n",
    "    model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    "\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(512, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(512, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(512, 3, 3, activation='relu'))\n",
    "    model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    "\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(4096, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(4096, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(1000, activation='softmax'))\n",
    "\n",
    "    if weights_path:\n",
    "        model.load_weights(weights_path)\n",
    "\n",
    "    return model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "md = VGG_16('vgg16_weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "md.compile(optimizer=sgd, loss='categorical_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<keras.layers.convolutional.ZeroPadding2D at 0x7f1113ac0790>,\n",
       " <keras.layers.convolutional.Convolution2D at 0x7f1113ace6d0>,\n",
       " <keras.layers.convolutional.ZeroPadding2D at 0x7f1113ad2050>,\n",
       " <keras.layers.convolutional.Convolution2D at 0x7f1113ad2d90>,\n",
       " <keras.layers.pooling.MaxPooling2D at 0x7f1113a79050>,\n",
       " <keras.layers.convolutional.ZeroPadding2D at 0x7f1113a79210>,\n",
       " <keras.layers.convolutional.Convolution2D at 0x7f1113b392d0>,\n",
       " <keras.layers.convolutional.ZeroPadding2D at 0x7f1113a87bd0>,\n",
       " <keras.layers.convolutional.Convolution2D at 0x7f1113a14910>,\n",
       " <keras.layers.pooling.MaxPooling2D at 0x7f1113a239d0>,\n",
       " <keras.layers.convolutional.ZeroPadding2D at 0x7f1113a23b90>,\n",
       " <keras.layers.convolutional.Convolution2D at 0x7f1113a318d0>,\n",
       " <keras.layers.convolutional.ZeroPadding2D at 0x7f1113a23450>,\n",
       " <keras.layers.convolutional.Convolution2D at 0x7f1113a4c850>,\n",
       " <keras.layers.convolutional.ZeroPadding2D at 0x7f11139dc2d0>,\n",
       " <keras.layers.convolutional.Convolution2D at 0x7f1113a4c450>,\n",
       " <keras.layers.pooling.MaxPooling2D at 0x7f11139f61d0>,\n",
       " <keras.layers.convolutional.ZeroPadding2D at 0x7f11139f6390>,\n",
       " <keras.layers.convolutional.Convolution2D at 0x7f1113a071d0>,\n",
       " <keras.layers.convolutional.ZeroPadding2D at 0x7f1113994290>,\n",
       " <keras.layers.convolutional.Convolution2D at 0x7f1113994f90>,\n",
       " <keras.layers.convolutional.ZeroPadding2D at 0x7f1113a07810>,\n",
       " <keras.layers.convolutional.Convolution2D at 0x7f11139aff10>,\n",
       " <keras.layers.pooling.MaxPooling2D at 0x7f11139c1990>,\n",
       " <keras.layers.convolutional.ZeroPadding2D at 0x7f11139c1b50>,\n",
       " <keras.layers.convolutional.Convolution2D at 0x7f11139cd510>,\n",
       " <keras.layers.convolutional.ZeroPadding2D at 0x7f111395ca50>,\n",
       " <keras.layers.convolutional.Convolution2D at 0x7f111396a890>,\n",
       " <keras.layers.convolutional.ZeroPadding2D at 0x7f111397a950>,\n",
       " <keras.layers.convolutional.Convolution2D at 0x7f1113988410>,\n",
       " <keras.layers.pooling.MaxPooling2D at 0x7f1117231ad0>,\n",
       " <keras.layers.core.Flatten at 0x7f1113e9cd50>,\n",
       " <keras.layers.core.Dense at 0x7f11139cd890>,\n",
       " <keras.layers.core.Dropout at 0x7f1113c85b90>,\n",
       " <keras.layers.core.Dense at 0x7f1113d12910>,\n",
       " <keras.layers.core.Dropout at 0x7f1113c7ec50>,\n",
       " <keras.layers.core.Dense at 0x7f1113c5ca10>]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "l=md.layers[-10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.01030169, -0.00784571, -0.00398386],\n",
       "       [-0.00622659, -0.00439948, -0.00803796],\n",
       "       [-0.0081472 ,  0.0021277 , -0.00112611]], dtype=float32)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.get_weights()[0][0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "func = md.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'keras.engine.training.Model'> <class 'keras.models.Sequential'>\n"
     ]
    }
   ],
   "source": [
    "print type(func), type(md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.01030169, -0.00784571, -0.00398386],\n",
       "       [-0.00622659, -0.00439948, -0.00803796],\n",
       "       [-0.0081472 ,  0.0021277 , -0.00112611]], dtype=float32)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "func.layers[-10].get_weights()[0][0][0]"
   ]
  }
 ],
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