vggnet_with_keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import numpy as np\n",
    "import warnings\n",
    "\n",
    "from keras.models import Model\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Input\n",
    "from keras.layers import Conv2D\n",
    "from keras.layers import MaxPooling2D\n",
    "from keras.layers import GlobalMaxPooling2D\n",
    "from keras.layers import GlobalAveragePooling2D\n",
    "from keras.preprocessing import image\n",
    "from keras.utils import layer_utils\n",
    "from keras.utils.data_utils import get_file\n",
    "from keras import backend as K\n",
    "from keras.applications.imagenet_utils import decode_predictions\n",
    "from keras.applications.imagenet_utils import preprocess_input\n",
    "from keras.applications.imagenet_utils import _obtain_input_shape # this will work for older versions of keras. 2.2.0 or before\n",
    "from keras.engine.topology import get_source_inputs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def VGG16( weights='imagenet',\n",
    "          input_tensor=None, input_shape=None,\n",
    "          pooling=None,\n",
    "          classes=1000):\n",
    "   \n",
    "    img_rows, img_cols = 224, 224\n",
    "    img_channels = 3\n",
    "\n",
    "    img_dim = (img_rows, img_cols, img_channels)\n",
    "   \n",
    "    img_input = Input(shape=img_dim)\n",
    "    \n",
    "    # Block 1\n",
    "    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)\n",
    "    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)\n",
    "\n",
    "    # Block 2\n",
    "    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)\n",
    "    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)\n",
    "\n",
    "    # Block 3\n",
    "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)\n",
    "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)\n",
    "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)\n",
    "\n",
    "    # Block 4\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)\n",
    "\n",
    "    # Block 5\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)\n",
    "\n",
    "    \n",
    "    # Classification block\n",
    "    x = Flatten(name='flatten')(x)\n",
    "    x = Dense(4096, activation='relu', name='fc1')(x)\n",
    "    x = Dense(4096, activation='relu', name='fc2')(x)\n",
    "    x = Dense(classes, activation='softmax', name='predictions')(x)\n",
    "    \n",
    "    \n",
    "    if input_tensor is not None:\n",
    "        inputs = get_source_inputs(input_tensor)\n",
    "    else:\n",
    "        inputs = img_input\n",
    "    # Create model.\n",
    "    model = Model(inputs, x, name='vgg16')\n",
    "\n",
    "    # load weights\n",
    "    if weights == 'imagenet':\n",
    "       \n",
    "        weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels.h5',\n",
    "                                    WEIGHTS_PATH,\n",
    "                                    cache_subdir='models')\n",
    "        \n",
    "        model.load_weights(weights_path)\n",
    "    return model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input image shape: (1, 224, 224, 3)\n",
      "Predicted: [[('n02107142', 'Doberman', 0.3359479), ('n02105412', 'kelpie', 0.21615942), ('n02106550', 'Rottweiler', 0.1769872), ('n02089078', 'black-and-tan_coonhound', 0.12776804), ('n02107312', 'miniature_pinscher', 0.03730356)]]\n",
      "top matching class is : [[('n02107142', 'Doberman', 0.3359479)]]\n"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    model = VGG16( weights='imagenet')\n",
    "\n",
    "    img_path = 'dog.jpg'\n",
    "    img = image.load_img(img_path, target_size=(224, 224))  # target size will resize the image into 224,224\n",
    "    x = image.img_to_array(img)\n",
    "    x = np.expand_dims(x, axis=0)\n",
    "    x = preprocess_input(x)\n",
    "    print('Input image shape:', x.shape)\n",
    "\n",
    "    preds = model.predict(x)\n",
    "    print('Predicted:', decode_predictions(preds))\n",
    "    \n",
    "    pred_class = decode_predictions(preds, top=1)\n",
    "    print(\"top matching class is :\",pred_class)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "decode_predictions(pred, model = c(\"Xception\", \"VGG16\", \"VGG19\", \"ResNet50\",\"InceptionV3\"), top = 5)\n",
    "\n",
    "pred:the output of predictions from the specified model\n",
    "\n",
    "model:the model you wish to preprocess to\n",
    "\n",
    "top:integer, how many top-guesses to return."
   ]
  }
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