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sanjushekhawat submission: Predicting Pneumonia using CNN #171

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1 change: 0 additions & 1 deletion ...submission:Comparing performance of three different clustering algorithms on IRIS Dataset
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1 change: 0 additions & 1 deletion ...m/Project/Tushar submission: Comparing SVM with 3 layer Neural Network on Titanic Dataset
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153 changes: 153 additions & 0 deletions [email protected]/sanju_cnn.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import tensorflow\n",
"import tensorflow.keras\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Convolution2D\n",
"from tensorflow.keras.layers import MaxPooling2D\n",
"from tensorflow.keras.layers import Flatten\n",
"from tensorflow.keras.layers import Dense\n",
"from tensorflow.keras.layers import Reshape\n",
"\n",
"classifier = Sequential()\n",
"\n",
"classifier.add(Convolution2D(32,(3,3),input_shape=(64,64,3),activation='relu'))\n",
"classifier.add(MaxPooling2D(pool_size=(2,2)))\n",
"\n",
"classifier.add(Convolution2D(32,(3,3),input_shape=(64,64,3),activation='relu'))\n",
"classifier.add(MaxPooling2D(pool_size=(2,2)))\n",
"\n",
"classifier.add(Convolution2D(32,(3,3),input_shape=(64,64,3),activation='relu'))\n",
"classifier.add(MaxPooling2D(pool_size=(2,2)))\n",
"\n",
"\n",
"classifier.add(Convolution2D(32,(3,3),input_shape=(64,64,3),activation='relu'))\n",
"classifier.add(MaxPooling2D(pool_size=(2,2)))\n",
"\n",
"\n",
"classifier.add(Flatten())\n",
"classifier.add(Dense(units=128,activation='relu'))\n",
"classifier.add(Dense(units=1,activation='sigmoid'))\n",
"classifier.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\n",
"\n",
"print(classifier.summary())\n",
"\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"\n",
"train_datagen=ImageDataGenerator(rescale=1/1.255,shear_range=0.2,zoom_range=0.2,horizontal_flip=True)\n",
"test_datagen = ImageDataGenerator(rescale=1./255)\n",
"training_set = train_datagen.flow_from_directory(\n",
" 'C:/Users/rishi/OneDrive/Desktop/chest_xray/chest_xray/train',\n",
" target_size=(64, 64),\n",
" batch_size=32,\n",
" class_mode='binary')\n",
"\n",
"test_set = train_datagen.flow_from_directory(\n",
" 'C:/Users/rishi/OneDrive/Desktop/chest_xray/chest_xray/test',\n",
" target_size=(64, 64),\n",
" batch_size=32,\n",
" class_mode='binary')\n",
"\n",
"val_set = train_datagen.flow_from_directory(\n",
" 'C:/Users/rishi/OneDrive/Desktop/chest_xray/chest_xray/val',\n",
" target_size=(64, 64),\n",
" batch_size=32,\n",
" class_mode='binary')\n",
"\n",
"cl=classifier.fit_generator(\n",
" training_set,\n",
" steps_per_epoch=163,\n",
" epochs=10,\n",
" validation_data=test_set,\n",
" validation_steps=624/32)\n",
" \n",
"\n",
"from sklearn.metrics import classification_report, confusion_matrix\n",
"\n",
"\n",
"\n",
"\n",
"labels = test_set.class_indices\n",
"\n",
"labels = {v: k for k, v in labels.items()}\n",
"\n",
"classes = list(labels.values())\n",
"\n",
"y_pred = classifier.predict(test_set)\n",
"\n",
"y_pred =(y_pred>0.5)\n",
"\n",
"print(confusion_matrix(test_set.classes, y_pred))\n",
"\n",
"print(classification_report(test_set.classes, y_pred, target_names=classes))\n",
"print(labels)\n",
"\n",
"\n",
"test_accuracy=classifier.evaluate_generator(test_set,624)\n",
"print(\"Test accuracy:\",test_accuracy[1]*100,'%')\n",
"\n",
"\n",
"from sklearn.metrics import accuracy_score\n",
"print(accuracy_score(test_set.classes, y_pred))\n",
"\n",
"from sklearn.metrics import recall_score\n",
"recall_score(test_set.classes, y_pred)\n",
"\n",
"from sklearn.metrics import precision_score\n",
"precision_score(test_set.classes, y_pred)\n",
"\n",
"from sklearn.metrics import f1_score\n",
"f1_score(test_set.classes, y_pred)\n",
"\n",
"\n",
"\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.plot(cl.history['acc'])\n",
"plt.plot(cl.history['val_acc'])\n",
"plt.title('Model Accuracy')\n",
"plt.ylabel('Accuracy')\n",
"plt.xlabel('Epoch')\n",
"plt.legend(['training_set', 'test_set'], loc='upper left')\n",
"plt.show()\n",
"\n",
"#Loss\n",
"plt.plot(cl.history['loss'])\n",
"plt.plot(cl.history['val_loss'])\n",
"plt.title('Loss')\n",
"plt.ylabel('Loss')\n",
"plt.xlabel('Epoch')\n",
"plt.legend(['training_set', 'test_set'], loc='upper left')\n",
"plt.show()\n"
]
}
],
"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
}