data_augumentation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 24s 2us/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f425986a780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Standardize images across the dataset, mean=0, stdev=1\n",
    "from keras.datasets import mnist\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from matplotlib import pyplot\n",
    "from keras import backend as K\n",
    "K.set_image_dim_ordering('th')\n",
    "# load data\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "# reshape to be [samples][pixels][width][height]\n",
    "X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)\n",
    "X_test = X_test.reshape(X_test.shape[0], 1, 28, 28)\n",
    "# convert from int to float\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "# define data preparation\n",
    "datagen = ImageDataGenerator(featurewise_center=True, featurewise_std_normalization=True)\n",
    "# fit parameters from data\n",
    "datagen.fit(X_train)\n",
    "# configure batch size and retrieve one batch of images\n",
    "for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=9):\n",
    "# create a grid of 3x3 images\n",
    "    for i in range(0, 9):\n",
    "        pyplot.subplot(330 + 1 + i)\n",
    "        pyplot.imshow(X_batch[i].reshape(28, 28), cmap=pyplot.get_cmap('gray'))\n",
    "    # show the plot\n",
    "    pyplot.show()\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gaddafi/deepLearn/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n",
      "/home/gaddafi/deepLearn/lib/python3.6/site-packages/keras/preprocessing/image.py:492: UserWarning: This ImageDataGenerator specifies `zca_whitening`, which overrides setting of `featurewise_center`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n"
     ]
    },
    {
     "ename": "MemoryError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-603e699e0e71>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mdatagen\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImageDataGenerator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzca_whitening\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;31m# fit parameters from data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mdatagen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;31m# configure batch size and retrieve one batch of images\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mX_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_batch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdatagen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/deepLearn/lib/python3.6/site-packages/keras/preprocessing/image.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, augment, rounds, seed)\u001b[0m\n\u001b[1;32m    750\u001b[0m             \u001b[0mflat_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    751\u001b[0m             \u001b[0mnum_examples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mflat_x\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 752\u001b[0;31m             \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msvd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflat_x\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_examples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    753\u001b[0m             s_expand = np.hstack((s, np.zeros(vt.shape[0] - num_examples,\n\u001b[1;32m    754\u001b[0m                                               dtype=flat_x.dtype)))\n",
      "\u001b[0;32m~/deepLearn/lib/python3.6/site-packages/scipy/linalg/decomp_svd.py\u001b[0m in \u001b[0;36msvd\u001b[0;34m(a, full_matrices, compute_uv, overwrite_a, check_finite, lapack_driver)\u001b[0m\n\u001b[1;32m    127\u001b[0m     \u001b[0;31m# perform decomposition\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m     u, s, v, info = gesXd(a1, compute_uv=compute_uv, lwork=lwork,\n\u001b[0;32m--> 129\u001b[0;31m                           full_matrices=full_matrices, overwrite_a=overwrite_a)\n\u001b[0m\u001b[1;32m    130\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minfo\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mMemoryError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# ZCA whitening\n",
    "from keras.datasets import mnist\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from matplotlib import pyplot\n",
    "from keras import backend as K\n",
    "K.set_image_dim_ordering('th')\n",
    "# load data\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "# reshape to be [samples][pixels][width][height]\n",
    "X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)\n",
    "X_test = X_test.reshape(X_test.shape[0], 1, 28, 28)\n",
    "# convert from int to float\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "# define data preparation\n",
    "datagen = ImageDataGenerator(zca_whitening=True)\n",
    "# fit parameters from data\n",
    "datagen.fit(X_train)\n",
    "# configure batch size and retrieve one batch of images\n",
    "for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=9):\n",
    "# create a grid of 3x3 images\n",
    "    for i in range(0, 9):\n",
    "        pyplot.subplot(330 + 1 + i)\n",
    "        pyplot.imshow(X_batch[i].reshape(28, 28), cmap=pyplot.get_cmap('gray'))\n",
    "    # show the plot\n",
    "    pyplot.show()\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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