Priti Shaw pritishaw1
pritishaw1
/ Explaining_SARS_Cov2_Diagnosis_Using_LIME.py
Created
May 12, 2021 14:42
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| temp_1, mask_1 = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=True, num_features=5, hide_rest=True) | |
| temp_2, mask_2 = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=False, num_features=10, hide_rest=False) |
pritishaw1
/ Explaining_SARS_Cov2_Diagnosis_Using_LIME
Created
May 12, 2021 12:36
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| preds = model.predict(images) | |
| prediction_class = model.predict_classes(images) |
pritishaw1
/ Explaining_SARS_Cov2_Diagnosis_Using_LIME
Created
May 12, 2021 12:28
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| explainer = lime_image.LimeImageExplainer() |
pritishaw1
/ Explaining_SARS_Cov2_Diagnosis_Using_LIME.py
Created
May 12, 2021 07:38
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| hist = model.fit_generator( | |
| train_generator, | |
| epochs = 10, | |
| validation_data = validation_generator, | |
| validation_steps=2 | |
| ) |
pritishaw1
/ Explaining_SARS_Cov2_Diagnosis_Using_LIME.py
Created
May 12, 2021 06:30
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| model = Sequential() | |
| model.add(Conv2D(32,kernel_size=(3,3),activation='relu',input_shape=(224,224,3))) | |
| model.add(Conv2D(128,(3,3),activation='relu')) | |
| model.add(MaxPooling2D(pool_size=(2,2))) | |
| model.add(Dropout(0.25)) | |
| model.add(Conv2D(64,(3,3),activation='relu')) | |
| model.add(MaxPooling2D(pool_size=(2,2))) | |
| model.add(Dropout(0.25)) |
pritishaw1
/ Explaining_SARS_Cov2_Diagnosis_Using_LIME.py
Last active
May 12, 2021 06:28
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| train_generator = train_datagen.flow_from_directory( | |
| '/content/drive/MyDrive/CovidDataset/Train', | |
| target_size = (224,224), | |
| batch_size = 32, | |
| class_mode = 'binary') | |
| validation_generator = test_datagen.flow_from_directory( | |
| '/content/drive/MyDrive/CovidDataset/Val', | |
| target_size = (224,224), | |
| batch_size = 32, | |
| class_mode = 'binary') |
pritishaw1
/ Explaining_SARS_Cov2_Diagnosis_Using_LIME.py
Created
May 12, 2021 06:15
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| train_datagen = image.ImageDataGenerator(rescale = 1./255, shear_range = 0.2,zoom_range = 0.2, horizontal_flip = True) | |
| test_datagen = image.ImageDataGenerator(rescale=1./255) |
pritishaw1
/ model.py
Created
May 11, 2021 17:32
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| model = Sequential() | |
| model.add(Conv2D(32,kernel_size=(3,3),activation='relu',input_shape=(224,224,3))) | |
| model.add(Conv2D(128,(3,3),activation='relu')) | |
| model.add(MaxPooling2D(pool_size=(2,2))) | |
| model.add(Dropout(0.25)) | |
| model.add(Conv2D(64,(3,3),activation='relu')) | |
| model.add(MaxPooling2D(pool_size=(2,2))) | |
| model.add(Dropout(0.25)) |
pritishaw1
/ model.ipynb
Created
May 11, 2021 17:24
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pritishaw1
/ gist:e21ae0a2528ebf492cb9e49cb1070e07
Created
May 11, 2021 17:23
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| model = Sequential() | |
| model.add(Conv2D(32,kernel_size=(3,3),activation='relu',input_shape=(224,224,3))) | |
| model.add(Conv2D(128,(3,3),activation='relu')) | |
| model.add(MaxPooling2D(pool_size=(2,2))) | |
| model.add(Dropout(0.25)) | |
| model.add(Conv2D(64,(3,3),activation='relu')) | |
| model.add(MaxPooling2D(pool_size=(2,2))) | |
| model.add(Dropout(0.25)) |
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