keras_nocenter_loss.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import mnist\n",
    "from keras.layers import Input,Conv2D,Activation,Dense,Lambda,Flatten,Embedding,PReLU,BatchNormalization\n",
    "from keras.models import Model\n",
    "import keras.backend as K\n",
    "from keras.utils import to_categorical\n",
    "import numpy as np\n",
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 28, 28, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 26, 26, 32)        128       \n",
      "_________________________________________________________________\n",
      "p_re_lu_1 (PReLU)            (None, 26, 26, 32)        21632     \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 24, 24, 32)        9248      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 24, 24, 32)        128       \n",
      "_________________________________________________________________\n",
      "p_re_lu_2 (PReLU)            (None, 24, 24, 32)        18432     \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 20, 20, 64)        51264     \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 20, 20, 64)        256       \n",
      "_________________________________________________________________\n",
      "p_re_lu_3 (PReLU)            (None, 20, 20, 64)        25600     \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        102464    \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
      "_________________________________________________________________\n",
      "p_re_lu_4 (PReLU)            (None, 16, 16, 64)        16384     \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 10, 10, 128)       401536    \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 10, 10, 128)       512       \n",
      "_________________________________________________________________\n",
      "p_re_lu_5 (PReLU)            (None, 10, 10, 128)       12800     \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 4, 4, 128)         802944    \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 4, 4, 128)         512       \n",
      "_________________________________________________________________\n",
      "p_re_lu_6 (PReLU)            (None, 4, 4, 128)         2048      \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2)                 4098      \n",
      "_________________________________________________________________\n",
      "out1 (PReLU)                 (None, 2)                 2         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                30        \n",
      "=================================================================\n",
      "Total params: 1,470,594\n",
      "Trainable params: 1,469,698\n",
      "Non-trainable params: 896\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "inputs=Input((28,28,1))\n",
    "x=Conv2D(32,(3,3))(inputs)\n",
    "x=BatchNormalization()(x)\n",
    "x=PReLU()(x)\n",
    "x=Conv2D(32,(3,3))(x)\n",
    "x=BatchNormalization()(x)\n",
    "x=PReLU()(x)\n",
    "x=Conv2D(64,(5,5))(x)\n",
    "x=BatchNormalization()(x)\n",
    "x=PReLU()(x)\n",
    "x=Conv2D(64,(5,5))(x)\n",
    "x=BatchNormalization()(x)\n",
    "x=PReLU()(x)\n",
    "x=Conv2D(128,(7,7))(x)\n",
    "x=BatchNormalization()(x)\n",
    "x=PReLU()(x)\n",
    "x=Conv2D(128,(7,7))(x)\n",
    "x=BatchNormalization()(x)\n",
    "x=PReLU()(x)\n",
    "x=Flatten()(x)\n",
    "x=Dense(2)(x)\n",
    "out1=PReLU(name=\"out1\")(x)\n",
    "out2=Dense(10,activation=\"softmax\")(out1)\n",
    "\n",
    "model=Model(inputs,out2)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30000, 28, 28, 1)\n",
      "(30000,)\n",
      "(30000, 10)\n"
     ]
    }
   ],
   "source": [
    "path=\"mnist.npz\"\n",
    "f = np.load(path)\n",
    "x_train, y_train = f['x_train'][:30000], f['y_train'][:30000]\n",
    "x_test, y_test = f['x_test'], f['y_test']\n",
    "f.close()\n",
    "\n",
    "x_train=x_train.astype(\"float32\")\n",
    "x_test=x_test.astype(\"float32\")\n",
    "x_train/=255.\n",
    "x_test/=255.\n",
    "x_train=np.expand_dims(x_train,axis=-1)\n",
    "x_test=np.expand_dims(x_test,axis=-1)\n",
    "\n",
    "y_train_one_hot=to_categorical(y_train)\n",
    "y_test_one_hot=to_categorical(y_test)\n",
    "\n",
    "print(x_train.shape)\n",
    "print(y_train.shape)\n",
    "print(y_train_one_hot.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 30000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "30000/30000 [==============================] - 7s 225us/step - loss: 1.8695 - acc: 0.2726 - val_loss: 1.6434 - val_acc: 0.3969\n",
      "Epoch 2/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 1.5170 - acc: 0.4164 - val_loss: 1.3835 - val_acc: 0.4371\n",
      "Epoch 3/100\n",
      "30000/30000 [==============================] - 5s 153us/step - loss: 1.2795 - acc: 0.4600 - val_loss: 1.1941 - val_acc: 0.4756\n",
      "Epoch 4/100\n",
      "30000/30000 [==============================] - 5s 150us/step - loss: 1.1369 - acc: 0.4838 - val_loss: 1.1025 - val_acc: 0.4851\n",
      "Epoch 5/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 1.0401 - acc: 0.5002 - val_loss: 1.0033 - val_acc: 0.5075\n",
      "Epoch 6/100\n",
      "30000/30000 [==============================] - 5s 154us/step - loss: 0.9486 - acc: 0.5749 - val_loss: 0.9102 - val_acc: 0.6248\n",
      "Epoch 7/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.8424 - acc: 0.6532 - val_loss: 0.7999 - val_acc: 0.6592\n",
      "Epoch 8/100\n",
      "30000/30000 [==============================] - 5s 152us/step - loss: 0.7385 - acc: 0.6771 - val_loss: 0.7657 - val_acc: 0.6611\n",
      "Epoch 9/100\n",
      "30000/30000 [==============================] - 5s 150us/step - loss: 0.6591 - acc: 0.7337 - val_loss: 0.6345 - val_acc: 0.7648\n",
      "Epoch 10/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.5819 - acc: 0.8149 - val_loss: 0.6792 - val_acc: 0.7370\n",
      "Epoch 11/100\n",
      "30000/30000 [==============================] - 5s 152us/step - loss: 0.4996 - acc: 0.8419 - val_loss: 0.4742 - val_acc: 0.8401\n",
      "Epoch 12/100\n",
      "30000/30000 [==============================] - 5s 152us/step - loss: 0.4214 - acc: 0.8603 - val_loss: 0.3965 - val_acc: 0.8655\n",
      "Epoch 13/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.3596 - acc: 0.9203 - val_loss: 0.3604 - val_acc: 0.9150\n",
      "Epoch 14/100\n",
      "30000/30000 [==============================] - 5s 152us/step - loss: 0.3079 - acc: 0.9492 - val_loss: 0.4857 - val_acc: 0.8817\n",
      "Epoch 15/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.2575 - acc: 0.9547 - val_loss: 0.2658 - val_acc: 0.9494\n",
      "Epoch 16/100\n",
      "30000/30000 [==============================] - 5s 152us/step - loss: 0.2188 - acc: 0.9588 - val_loss: 0.2992 - val_acc: 0.9308\n",
      "Epoch 17/100\n",
      "30000/30000 [==============================] - 4s 149us/step - loss: 0.1901 - acc: 0.9634 - val_loss: 0.2870 - val_acc: 0.9338\n",
      "Epoch 18/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.1745 - acc: 0.9647 - val_loss: 0.2365 - val_acc: 0.9464\n",
      "Epoch 19/100\n",
      "30000/30000 [==============================] - 5s 152us/step - loss: 0.1561 - acc: 0.9679 - val_loss: 0.2064 - val_acc: 0.9564\n",
      "Epoch 20/100\n",
      "30000/30000 [==============================] - 5s 152us/step - loss: 0.1397 - acc: 0.9715 - val_loss: 0.1989 - val_acc: 0.9553\n",
      "Epoch 21/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.1279 - acc: 0.9741 - val_loss: 0.1973 - val_acc: 0.9580\n",
      "Epoch 22/100\n",
      "30000/30000 [==============================] - 5s 155us/step - loss: 0.1168 - acc: 0.9764 - val_loss: 0.2733 - val_acc: 0.9339\n",
      "Epoch 23/100\n",
      "30000/30000 [==============================] - 5s 153us/step - loss: 0.1093 - acc: 0.9777 - val_loss: 0.1687 - val_acc: 0.9624\n",
      "Epoch 24/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.1019 - acc: 0.9784 - val_loss: 0.1559 - val_acc: 0.9661\n",
      "Epoch 25/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.0914 - acc: 0.9806 - val_loss: 0.1493 - val_acc: 0.9674\n",
      "Epoch 26/100\n",
      "30000/30000 [==============================] - 4s 150us/step - loss: 0.0858 - acc: 0.9825 - val_loss: 0.1398 - val_acc: 0.9678\n",
      "Epoch 27/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.0813 - acc: 0.9830 - val_loss: 0.1343 - val_acc: 0.9702\n",
      "Epoch 28/100\n",
      "30000/30000 [==============================] - 4s 150us/step - loss: 0.0761 - acc: 0.9844 - val_loss: 0.1808 - val_acc: 0.9582\n",
      "Epoch 29/100\n",
      "30000/30000 [==============================] - 5s 152us/step - loss: 0.0708 - acc: 0.9861 - val_loss: 0.1446 - val_acc: 0.9689\n",
      "Epoch 30/100\n",
      "30000/30000 [==============================] - 5s 150us/step - loss: 0.0660 - acc: 0.9870 - val_loss: 0.1322 - val_acc: 0.9704\n",
      "Epoch 31/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.0607 - acc: 0.9892 - val_loss: 0.1380 - val_acc: 0.9706\n",
      "Epoch 32/100\n",
      "30000/30000 [==============================] - 5s 150us/step - loss: 0.0568 - acc: 0.9902 - val_loss: 0.1247 - val_acc: 0.9722\n",
      "Epoch 33/100\n",
      "30000/30000 [==============================] - 5s 152us/step - loss: 0.0540 - acc: 0.9903 - val_loss: 0.1623 - val_acc: 0.9637\n",
      "Epoch 34/100\n",
      "30000/30000 [==============================] - 4s 150us/step - loss: 0.0521 - acc: 0.9899 - val_loss: 0.1293 - val_acc: 0.9714\n",
      "Epoch 35/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.0519 - acc: 0.9906 - val_loss: 0.1321 - val_acc: 0.9712\n",
      "Epoch 36/100\n",
      "30000/30000 [==============================] - 5s 152us/step - loss: 0.0462 - acc: 0.9925 - val_loss: 0.1177 - val_acc: 0.9747\n",
      "Epoch 37/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.0432 - acc: 0.9926 - val_loss: 0.1376 - val_acc: 0.9687\n",
      "Epoch 38/100\n",
      "30000/30000 [==============================] - 4s 150us/step - loss: 0.0414 - acc: 0.9940 - val_loss: 0.1183 - val_acc: 0.9733\n",
      "Epoch 39/100\n",
      "30000/30000 [==============================] - 4s 149us/step - loss: 0.0425 - acc: 0.9937 - val_loss: 0.1248 - val_acc: 0.9725\n",
      "Epoch 40/100\n",
      "30000/30000 [==============================] - 4s 150us/step - loss: 0.0384 - acc: 0.9944 - val_loss: 0.1591 - val_acc: 0.9656\n",
      "Epoch 41/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.0374 - acc: 0.9942 - val_loss: 0.1141 - val_acc: 0.9743\n",
      "Epoch 42/100\n",
      "30000/30000 [==============================] - 4s 149us/step - loss: 0.0347 - acc: 0.9957 - val_loss: 0.1177 - val_acc: 0.9734\n",
      "Epoch 43/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.0339 - acc: 0.9957 - val_loss: 0.1145 - val_acc: 0.9744\n",
      "Epoch 44/100\n",
      "30000/30000 [==============================] - 5s 150us/step - loss: 0.0312 - acc: 0.9966 - val_loss: 0.1208 - val_acc: 0.9742\n",
      "Epoch 45/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.0303 - acc: 0.9966 - val_loss: 0.1184 - val_acc: 0.9746\n",
      "Epoch 46/100\n",
      "30000/30000 [==============================] - 5s 152us/step - loss: 0.0289 - acc: 0.9975 - val_loss: 0.1144 - val_acc: 0.9752\n",
      "Epoch 47/100\n",
      "30000/30000 [==============================] - 5s 153us/step - loss: 0.0272 - acc: 0.9974 - val_loss: 0.1252 - val_acc: 0.9733\n",
      "Epoch 48/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.0260 - acc: 0.9979 - val_loss: 0.1177 - val_acc: 0.9739\n",
      "Epoch 49/100\n",
      "30000/30000 [==============================] - 5s 153us/step - loss: 0.0252 - acc: 0.9982 - val_loss: 0.1122 - val_acc: 0.9755\n",
      "Epoch 50/100\n",
      "30000/30000 [==============================] - 5s 152us/step - loss: 0.0254 - acc: 0.9977 - val_loss: 0.1229 - val_acc: 0.9718\n",
      "Epoch 51/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.0247 - acc: 0.9975 - val_loss: 0.1186 - val_acc: 0.9752\n",
      "Epoch 52/100\n",
      "30000/30000 [==============================] - 5s 152us/step - loss: 0.0229 - acc: 0.9982 - val_loss: 0.1193 - val_acc: 0.9741\n",
      "Epoch 53/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.0227 - acc: 0.9980 - val_loss: 0.1136 - val_acc: 0.9765\n",
      "Epoch 54/100\n",
      "30000/30000 [==============================] - 5s 152us/step - loss: 0.0218 - acc: 0.9983 - val_loss: 0.1296 - val_acc: 0.9731\n",
      "Epoch 55/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.0215 - acc: 0.9982 - val_loss: 0.1233 - val_acc: 0.9736\n",
      "Epoch 56/100\n",
      "30000/30000 [==============================] - 5s 150us/step - loss: 0.0198 - acc: 0.9988 - val_loss: 0.1195 - val_acc: 0.9752\n",
      "Epoch 57/100\n",
      "30000/30000 [==============================] - 5s 154us/step - loss: 0.0202 - acc: 0.9983 - val_loss: 0.1160 - val_acc: 0.9755\n",
      "Epoch 58/100\n",
      "30000/30000 [==============================] - 5s 153us/step - loss: 0.0201 - acc: 0.9982 - val_loss: 0.1342 - val_acc: 0.9719\n",
      "Epoch 59/100\n",
      "30000/30000 [==============================] - 5s 152us/step - loss: 0.0195 - acc: 0.9985 - val_loss: 0.1584 - val_acc: 0.9652\n",
      "Epoch 60/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.0192 - acc: 0.9987 - val_loss: 0.1309 - val_acc: 0.9743\n",
      "Epoch 61/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.0180 - acc: 0.9989 - val_loss: 0.1166 - val_acc: 0.9751\n",
      "Epoch 62/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.0173 - acc: 0.9989 - val_loss: 0.1152 - val_acc: 0.9753\n",
      "Epoch 63/100\n",
      "30000/30000 [==============================] - 5s 150us/step - loss: 0.0173 - acc: 0.9989 - val_loss: 0.1175 - val_acc: 0.9755\n",
      "Epoch 64/100\n",
      "30000/30000 [==============================] - 5s 154us/step - loss: 0.0201 - acc: 0.9977 - val_loss: 0.1254 - val_acc: 0.9737\n",
      "Epoch 65/100\n",
      "30000/30000 [==============================] - 5s 151us/step - loss: 0.0168 - acc: 0.9987 - val_loss: 0.1149 - val_acc: 0.9756\n",
      "Epoch 66/100\n",
      "30000/30000 [==============================] - 5s 153us/step - loss: 0.0159 - acc: 0.9991 - val_loss: 0.1319 - val_acc: 0.9722\n",
      "Epoch 67/100\n",
      "30000/30000 [==============================] - 5s 154us/step - loss: 0.0156 - acc: 0.9992 - val_loss: 0.1143 - val_acc: 0.9763\n",
      "Epoch 68/100\n",
      "30000/30000 [==============================] - 4s 150us/step - loss: 0.0144 - acc: 0.9994 - val_loss: 0.1229 - val_acc: 0.9752\n",
      "Epoch 69/100\n",
      "30000/30000 [==============================] - 5s 152us/step - loss: 0.0144 - acc: 0.9993 - val_loss: 0.1149 - val_acc: 0.9761\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1f063e8dac8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer=\"sgd\",\n",
    "                          loss=\"categorical_crossentropy\",\n",
    "                          metrics=[\"acc\"])\n",
    "model.fit(x=x_train,y=y_train_one_hot,\n",
    "                     batch_size=512,epochs=100,verbose=1,validation_data=(x_test,y_test_one_hot),\n",
    "                     callbacks=[EarlyStopping(monitor=\"val_loss\",patience=20)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 2)\n",
      "(10, 2)\n"
     ]
    }
   ],
   "source": [
    "func=K.function([model.input],[model.get_layer('out1').output])\n",
    "test_features= func([x_test])[0]\n",
    "print(test_features.shape)\n",
    "\n",
    "test_centers=np.dot(np.transpose(y_test_one_hot),test_features)\n",
    "test_centers_count=np.sum(np.transpose(y_test_one_hot),axis=1,keepdims=True)\n",
    "test_centers/=test_centers_count\n",
    "print(test_centers.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KoE/lkRrhz0HsN1K7I2Q3mLLQkrdHb2j+gJT3zkyQz6ghsMgtcOfwQWhQwfWb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KGgN00IdIbCVnferhp+OPElw6lAhwe1r7wjjGN9cYUnsyqHc1cdv8QnPcBZLgLtHnEVy7o6QG4E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\naIGTucFTiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f2aec35c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.scatter(test_features[:,0],test_features[:,1],c=y_test,edgecolor=\"none\",s=5)\n",
    "plt.scatter(test_centers[:,0],test_centers[:,1],c=\"black\",marker=\"*\",edgecolor=\"none\",s=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}