Initial commit

7279cc66 · Gokul · 062813ff · 7279cc66 · 7279cc66 · 7279cc66
Commit 7279cc66 authored 4 years ago by Gokul
--- a/Feature Extraction1.ipynb
+++ b/Feature Extraction1.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt; \n",
+    " \n",
+    "# Importing sklearn libraries\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
+    "import hypopt\n",
+    "# Importing hypopt library for grid search\n",
+    "from hypopt import GridSearch\n",
+    " \n",
+    "# Importing Keras libraries\n",
+    "from keras.utils import np_utils\n",
+    "from keras.models import Sequential\n",
+    "from keras.applications import VGG16\n",
+    "from keras.applications import imagenet_utils\n",
+    "from keras.callbacks import ModelCheckpoint\n",
+    "from keras.preprocessing.image import load_img\n",
+    "from keras.preprocessing.image import img_to_array\n",
+    "from keras.layers import Dense, Conv2D, MaxPooling2D\n",
+    "from keras.layers import Dropout, Flatten, GlobalAveragePooling2D\n",
+    " \n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_features(dataset, pre_model):\n",
+    " \n",
+    "    x_scratch = []\n",
+    " \n",
+    "    # loop over the images\n",
+    "    for imagePath in dataset:\n",
+    " \n",
+    "        # load the input image and image is resized to 224x224 pixels\n",
+    "        image = load_img(imagePath, target_size=(224, 224))\n",
+    "        image = img_to_array(image)\n",
+    " \n",
+    "        # preprocess the image by (1) expanding the dimensions and\n",
+    "        # (2) subtracting the mean RGB pixel intensity from the\n",
+    "        # ImageNet dataset\n",
+    "        image = np.expand_dims(image, axis=0)\n",
+    "        image = imagenet_utils.preprocess_input(image)\n",
+    " \n",
+    "        # add the image to the batch\n",
+    "        x_scratch.append(image)\n",
+    " \n",
+    "    x = np.vstack(x_scratch)\n",
+    "    features = pre_model.predict(x, batch_size=32)\n",
+    "    features_flatten = features.reshape((features.shape[0], 7 * 7 * 512))\n",
+    "    return x, features, features_flatten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[INFO] loading network...\n",
+      "Model: \"vgg16\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "input_1 (InputLayer)         (None, None, None, 3)     0         \n",
+      "_________________________________________________________________\n",
+      "block1_conv1 (Conv2D)        (None, None, None, 64)    1792      \n",
+      "_________________________________________________________________\n",
+      "block1_conv2 (Conv2D)        (None, None, None, 64)    36928     \n",
+      "_________________________________________________________________\n",
+      "block1_pool (MaxPooling2D)   (None, None, None, 64)    0         \n",
+      "_________________________________________________________________\n",
+      "block2_conv1 (Conv2D)        (None, None, None, 128)   73856     \n",
+      "_________________________________________________________________\n",
+      "block2_conv2 (Conv2D)        (None, None, None, 128)   147584    \n",
+      "_________________________________________________________________\n",
+      "block2_pool (MaxPooling2D)   (None, None, None, 128)   0         \n",
+      "_________________________________________________________________\n",
+      "block3_conv1 (Conv2D)        (None, None, None, 256)   295168    \n",
+      "_________________________________________________________________\n",
+      "block3_conv2 (Conv2D)        (None, None, None, 256)   590080    \n",
+      "_________________________________________________________________\n",
+      "block3_conv3 (Conv2D)        (None, None, None, 256)   590080    \n",
+      "_________________________________________________________________\n",
+      "block3_pool (MaxPooling2D)   (None, None, None, 256)   0         \n",
+      "_________________________________________________________________\n",
+      "block4_conv1 (Conv2D)        (None, None, None, 512)   1180160   \n",
+      "_________________________________________________________________\n",
+      "block4_conv2 (Conv2D)        (None, None, None, 512)   2359808   \n",
+      "_________________________________________________________________\n",
+      "block4_conv3 (Conv2D)        (None, None, None, 512)   2359808   \n",
+      "_________________________________________________________________\n",
+      "block4_pool (MaxPooling2D)   (None, None, None, 512)   0         \n",
+      "_________________________________________________________________\n",
+      "block5_conv1 (Conv2D)        (None, None, None, 512)   2359808   \n",
+      "_________________________________________________________________\n",
+      "block5_conv2 (Conv2D)        (None, None, None, 512)   2359808   \n",
+      "_________________________________________________________________\n",
+      "block5_conv3 (Conv2D)        (None, None, None, 512)   2359808   \n",
+      "_________________________________________________________________\n",
+      "block5_pool (MaxPooling2D)   (None, None, None, 512)   0         \n",
+      "=================================================================\n",
+      "Total params: 14,714,688\n",
+      "Trainable params: 14,714,688\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load the VGG16 network\n",
+    "print(\"[INFO] loading network...\")\n",
+    " \n",
+    "# chop the top dense layers, include_top=False\n",
+    "model = VGG16(weights=\"imagenet\", include_top=False)\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import h5py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<HDF5 file \"pgm_pro.h5\" (mode r)>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f = h5py.File('pgm_pro.h5', 'r')\n",
+    "f"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['x']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(f.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(262144, 96, 96, 3)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d=f['x']\n",
+    "d.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_scratch = []\n",
+    "for image in d:\n",
+    "    image = np.expand_dims(image, axis=0)\n",
+    "    image = imagenet_utils.preprocess_input(image)\n",
+    " \n",
+    "    # add the image to the batch\n",
+    "    x_scratch.append(image)\n",
+    " \n",
+    "x = np.vstack(x_scratch)\n",
+    "features = pre_model.predict(x, batch_size=32)\n",
+    "features_flatten = features.reshape((features.shape[0], 7 * 7 * 512))\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(x.shape,features.shape,features_flatten.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
+%% Cell type:code id: tags:
+
+``` python
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt;
+
+# Importing sklearn libraries
+from sklearn.model_selection import GridSearchCV
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import confusion_matrix, accuracy_score
+import hypopt
+# Importing hypopt library for grid search
+from hypopt import GridSearch
+
+# Importing Keras libraries
+from keras.utils import np_utils
+from keras.models import Sequential
+from keras.applications import VGG16
+from keras.applications import imagenet_utils
+from keras.callbacks import ModelCheckpoint
+from keras.preprocessing.image import load_img
+from keras.preprocessing.image import img_to_array
+from keras.layers import Dense, Conv2D, MaxPooling2D
+from keras.layers import Dropout, Flatten, GlobalAveragePooling2D
+
+import warnings
+warnings.filterwarnings('ignore')
+```
+
+%% Output
+
+    Using TensorFlow backend.
+
+%% Cell type:code id: tags:
+
+``` python
+def create_features(dataset, pre_model):
+
+    x_scratch = []
+
+    # loop over the images
+    for imagePath in dataset:
+
+        # load the input image and image is resized to 224x224 pixels
+        image = load_img(imagePath, target_size=(224, 224))
+        image = img_to_array(image)
+
+        # preprocess the image by (1) expanding the dimensions and
+        # (2) subtracting the mean RGB pixel intensity from the
+        # ImageNet dataset
+        image = np.expand_dims(image, axis=0)
+        image = imagenet_utils.preprocess_input(image)
+
+        # add the image to the batch
+        x_scratch.append(image)
+
+    x = np.vstack(x_scratch)
+    features = pre_model.predict(x, batch_size=32)
+    features_flatten = features.reshape((features.shape[0], 7 * 7 * 512))
+    return x, features, features_flatten
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# load the VGG16 network
+print("[INFO] loading network...")
+
+# chop the top dense layers, include_top=False
+model = VGG16(weights="imagenet", include_top=False)
+model.summary()
+```
+
+%% Output
+
+    [INFO] loading network...
+    Model: "vgg16"
+    _________________________________________________________________
+    Layer (type)                 Output Shape              Param #
+    =================================================================
+    input_1 (InputLayer)         (None, None, None, 3)     0
+    _________________________________________________________________
+    block1_conv1 (Conv2D)        (None, None, None, 64)    1792
+    _________________________________________________________________
+    block1_conv2 (Conv2D)        (None, None, None, 64)    36928
+    _________________________________________________________________
+    block1_pool (MaxPooling2D)   (None, None, None, 64)    0
+    _________________________________________________________________
+    block2_conv1 (Conv2D)        (None, None, None, 128)   73856
+    _________________________________________________________________
+    block2_conv2 (Conv2D)        (None, None, None, 128)   147584
+    _________________________________________________________________
+    block2_pool (MaxPooling2D)   (None, None, None, 128)   0
+    _________________________________________________________________
+    block3_conv1 (Conv2D)        (None, None, None, 256)   295168
+    _________________________________________________________________
+    block3_conv2 (Conv2D)        (None, None, None, 256)   590080
+    _________________________________________________________________
+    block3_conv3 (Conv2D)        (None, None, None, 256)   590080
+    _________________________________________________________________
+    block3_pool (MaxPooling2D)   (None, None, None, 256)   0
+    _________________________________________________________________
+    block4_conv1 (Conv2D)        (None, None, None, 512)   1180160
+    _________________________________________________________________
+    block4_conv2 (Conv2D)        (None, None, None, 512)   2359808
+    _________________________________________________________________
+    block4_conv3 (Conv2D)        (None, None, None, 512)   2359808
+    _________________________________________________________________
+    block4_pool (MaxPooling2D)   (None, None, None, 512)   0
+    _________________________________________________________________
+    block5_conv1 (Conv2D)        (None, None, None, 512)   2359808
+    _________________________________________________________________
+    block5_conv2 (Conv2D)        (None, None, None, 512)   2359808
+    _________________________________________________________________
+    block5_conv3 (Conv2D)        (None, None, None, 512)   2359808
+    _________________________________________________________________
+    block5_pool (MaxPooling2D)   (None, None, None, 512)   0
+    =================================================================
+    Total params: 14,714,688
+    Trainable params: 14,714,688
+    Non-trainable params: 0
+    _________________________________________________________________
+
+%% Cell type:code id: tags:
+
+``` python
+import h5py
+```
+
+%% Cell type:code id: tags:
+
+``` python
+f = h5py.File('pgm_pro.h5', 'r')
+f
+```
+
+%% Output
+
+    <HDF5 file "pgm_pro.h5" (mode r)>
+
+%% Cell type:code id: tags:
+
+``` python
+list(f.keys())
+```
+
+%% Output
+
+    ['x']
+
+%% Cell type:code id: tags:
+
+``` python
+d=f['x']
+d.shape
+```
+
+%% Output
+
+    (262144, 96, 96, 3)
+
+%% Cell type:code id: tags:
+
+``` python
+x_scratch = []
+for image in d:
+    image = np.expand_dims(image, axis=0)
+    image = imagenet_utils.preprocess_input(image)
+
+    # add the image to the batch
+    x_scratch.append(image)
+
+x = np.vstack(x_scratch)
+features = pre_model.predict(x, batch_size=32)
+features_flatten = features.reshape((features.shape[0], 7 * 7 * 512))
+
+```
+
+%% Cell type:code id: tags:
+
+``` python
+print(x.shape,features.shape,features_flatten.shape)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```
--- a/PGM_ANN(Final Updated).ipynb
+++ b/PGM_ANN(Final Updated).ipynb
--- a/image_03.png
+++ b/image_03.png
--- a/image_04.png
+++ b/image_04.png
--- a/image_05.png
+++ b/image_05.png