{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Visualizing Activation from a CNN\n\n![image.png](attachment:image.png)\n\nSkin cancer is the most prevalent type of cancer. Melanoma, specifically, is responsible for 75% of skin cancer deaths, despite being the least common skin cancer. The American Cancer Society estimates over 100,000 new melanoma cases will be diagnosed in 2020. It's also expected that almost 7,000 people will die from the disease. As with other cancers, early and accurate detection—potentially aided by data science—can make treatment more effective.\n\nCurrently, dermatologists evaluate every one of a patient's moles to identify outlier lesions or “ugly ducklings” that are most likely to be melanoma. Existing AI approaches have not adequately considered this clinical frame of reference. Dermatologists could enhance their diagnostic accuracy if detection algorithms take into account “contextual” images within the same patient to determine which images represent a melanoma. If successful, classifiers would be more accurate and could better support dermatological clinic work.\n\nAs the leading healthcare organization for informatics in medical imaging, the Society for Imaging Informatics in Medicine (SIIM)'s mission is to advance medical imaging informatics through education, research, and innovation in a multi-disciplinary community. SIIM is joined by the International Skin Imaging Collaboration (ISIC), an international effort to improve melanoma diagnosis. The ISIC Archive contains the largest publicly available collection of quality-controlled dermoscopic images of skin lesions.\n\nIn this competition, you’ll identify melanoma in images of skin lesions. In particular, you’ll use images within the same patient and determine which are likely to represent a melanoma. Using patient-level contextual information may help the development of image analysis tools, which could better support clinical dermatologists.\n\nMelanoma is a deadly disease, but if caught early, most melanomas can be cured with minor surgery. Image analysis tools that automate the diagnosis of melanoma will improve dermatologists' diagnostic accuracy. Better detection of melanoma has the opportunity to positively impact millions of 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"}}},{"cell_type":"markdown","source":"## Purpose of this notebook\n\nThe purpose of this notebook is to show how convnets view malignant and benign moles within this competition.  \n\nFrançois Chollet states, \"It is often said that deep learning models are \"black boxes\", learning representations that are difficult to extract and present in a human-readable form. While this is partially true for certain types of deep learning models, it is definitely not true for convnets. The representations learned by convnets are highly amenable to visualization, in large part because they are representations of visual concepts. Since 2013, a wide array of techniques have been developed for visualizing and interpreting these representations.\" \n\nThis notebook will focus specifically on activation visualization, we will train a small convnet and visualize how this networks views different images.  \n\nThis notebook has been adapted from https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/5.4-visualizing-what-convnets-learn.ipynb.  ","metadata":{}},{"cell_type":"code","source":"# load libraries\nimport os\n####*IMPORANT*: Have to do this line *before* importing tensorflow\nos.environ['PYTHONHASHSEED']=str(1)\n\nimport tensorflow as tf\nimport numpy as np\nimport random\n\ndef reset_random_seeds():\n   os.environ['PYTHONHASHSEED']=str(1)\n   tf.random.set_seed(1)\n   np.random.seed(1)\n   random.seed(1)\n    \nreset_random_seeds()\n\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom keras.utils.np_utils import to_categorical\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.callbacks import LearningRateScheduler\nfrom keras import models\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom tensorflow.keras import models, layers\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Flatten\nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D, BatchNormalization\nfrom tensorflow.keras.layers import Dropout, Flatten, Input, Dense","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-input":true,"_kg_hide-output":false,"execution":{"iopub.status.busy":"2023-09-08T17:07:02.948348Z","iopub.execute_input":"2023-09-08T17:07:02.949032Z","iopub.status.idle":"2023-09-08T17:07:10.704739Z","shell.execute_reply.started":"2023-09-08T17:07:02.948966Z","shell.execute_reply":"2023-09-08T17:07:10.703376Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Load Data","metadata":{"_kg_hide-input":false}},{"cell_type":"code","source":"#https://www.kaggle.com/ibtesama/siim-baseline-keras-vgg16\ntrain_dir='/kaggle/input/siim-isic-melanoma-classification/jpeg/train/'\ntest_dir='/kaggle/input/siim-isic-melanoma-classification/jpeg/test/'\ntrain=pd.read_csv('/kaggle/input/siim-isic-melanoma-classification/train.csv')\ntest=pd.read_csv('/kaggle/input/siim-isic-melanoma-classification/test.csv')\nsubmission=pd.read_csv('/kaggle/input/siim-isic-melanoma-classification/sample_submission.csv')\ntrain.head()","metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-09-08T17:07:10.707000Z","iopub.execute_input":"2023-09-08T17:07:10.707415Z","iopub.status.idle":"2023-09-08T17:07:10.899626Z","shell.execute_reply.started":"2023-09-08T17:07:10.707376Z","shell.execute_reply":"2023-09-08T17:07:10.898548Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(train.columns)","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:18:46.238207Z","iopub.execute_input":"2023-09-08T17:18:46.238641Z","iopub.status.idle":"2023-09-08T17:18:46.244970Z","shell.execute_reply.started":"2023-09-08T17:18:46.238603Z","shell.execute_reply":"2023-09-08T17:18:46.243803Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.describe()","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:22:34.344918Z","iopub.execute_input":"2023-09-08T17:22:34.345502Z","iopub.status.idle":"2023-09-08T17:22:34.370740Z","shell.execute_reply.started":"2023-09-08T17:22:34.345448Z","shell.execute_reply":"2023-09-08T17:22:34.369198Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(8, 6))\nsns.boxplot(x=train['target'], color='blue')\nplt.title('Box Plot - target', fontsize=16)\nplt.xlabel('target', fontsize=14)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:19:25.537036Z","iopub.execute_input":"2023-09-08T17:19:25.537564Z","iopub.status.idle":"2023-09-08T17:19:25.694459Z","shell.execute_reply.started":"2023-09-08T17:19:25.537521Z","shell.execute_reply":"2023-09-08T17:19:25.692965Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Preprocess images","metadata":{}},{"cell_type":"code","source":"labels=[]\ndata=[]\nfor i in range(train.shape[0]):\n    data.append(train_dir + train['image_name'].iloc[i]+'.jpg')\n    labels.append(train['target'].iloc[i])\ndf=pd.DataFrame(data)\ndf.columns=['images']\ndf['target']=labels\n\ntest_data=[]\nfor i in range(test.shape[0]):\n    test_data.append(test_dir + test['image_name'].iloc[i]+'.jpg')\ndf_test=pd.DataFrame(test_data)\ndf_test.columns=['images']\n\nX_train, X_val, y_train, y_val = train_test_split(df['images'],df['target'], test_size=0.2, random_state=1234)\n\ntrain=pd.DataFrame(X_train)\ntrain.columns=['images']\ntrain['target']=y_train\n\nvalidation=pd.DataFrame(X_val)\nvalidation.columns=['images']\nvalidation['target']=y_val\n\ntrain_datagen = ImageDataGenerator(rescale=1./255,rotation_range=20,\n    width_shift_range=0.2,\n    height_shift_range=0.2,horizontal_flip=True)\nval_datagen=ImageDataGenerator(rescale=1./255)\ntrain_generator = train_datagen.flow_from_dataframe(\n    train,\n    x_col='images',\n    y_col='target',\n    target_size=(224, 224),\n    batch_size=8,\n    shuffle=True,\n    class_mode='raw')\n\nvalidation_generator = val_datagen.flow_from_dataframe(\n    validation,\n    x_col='images',\n    y_col='target',\n    target_size=(224, 224),\n    shuffle=False,\n    batch_size=8,\n    class_mode='raw')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-09-08T17:07:10.900873Z","iopub.execute_input":"2023-09-08T17:07:10.901211Z","iopub.status.idle":"2023-09-08T17:08:56.295521Z","shell.execute_reply.started":"2023-09-08T17:07:10.901178Z","shell.execute_reply":"2023-09-08T17:08:56.294604Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Build Convnet","metadata":{}},{"cell_type":"code","source":"model = models.Sequential()\nmodel.add(layers.Conv2D(filters=32, kernel_size=(3, 3), strides=(1, 1), activation='relu',\n                        input_shape=(224, 224, 3)))\nmodel.add(layers.MaxPooling2D((2, 2),strides=2))\nmodel.add(layers.Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), activation='relu'))\nmodel.add(layers.MaxPooling2D(pool_size=(2, 2),strides=2))\nmodel.add(layers.Conv2D(filters=128, kernel_size=(3, 3), strides=(1, 1), activation='relu'))\nmodel.add(layers.MaxPooling2D(pool_size=(2, 2),strides=2))\nmodel.add(layers.Flatten())\nmodel.add(layers.Dense(units=1, activation='sigmoid'))\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:08:56.296731Z","iopub.execute_input":"2023-09-08T17:08:56.297180Z","iopub.status.idle":"2023-09-08T17:08:56.487218Z","shell.execute_reply.started":"2023-09-08T17:08:56.297146Z","shell.execute_reply":"2023-09-08T17:08:56.485858Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Train Convnet","metadata":{}},{"cell_type":"code","source":"model.compile(optimizer='adam',\n              loss='binary_crossentropy',\n              metrics=['accuracy'])\nfit = model.fit_generator(train_generator, steps_per_epoch=5, epochs=2,\n                         validation_data=validation_generator, validation_steps=5)","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:08:56.495634Z","iopub.execute_input":"2023-09-08T17:08:56.496031Z","iopub.status.idle":"2023-09-08T17:09:37.860443Z","shell.execute_reply.started":"2023-09-08T17:08:56.495995Z","shell.execute_reply":"2023-09-08T17:09:37.859195Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Evaluate Convnet","metadata":{}},{"cell_type":"code","source":"metrics = list(fit.history.keys())\nloss_values = fit.history[metrics[2]]\nval_loss_values = fit.history[metrics[0]]\nacc_values = fit.history[metrics[3]]\nval_acc_values = fit.history[metrics[1]]\nprint(\"\\nFinal validation loss function is\", val_loss_values[-1])\nprint(\"Final validation accuracy is\", val_acc_values[-1])\n\n# summarize history for accuracy\nplt.plot(fit.history['accuracy'])\nplt.plot(fit.history['val_accuracy'])\nplt.title('model accuracy')\nplt.ylabel('accuracy')\nplt.xlabel('epoch')\nplt.legend(['train', 'test'], loc='upper left')\nplt.show()\n\n# summarize history for loss\nplt.plot(fit.history['loss'])\nplt.plot(fit.history['val_loss'])\nplt.title('model loss')\nplt.ylabel('loss')\nplt.xlabel('epoch')\nplt.legend(['train', 'test'], loc='upper left')\nplt.show()","metadata":{"_kg_hide-input":false,"execution":{"iopub.status.busy":"2023-09-08T17:09:37.863537Z","iopub.execute_input":"2023-09-08T17:09:37.863893Z","iopub.status.idle":"2023-09-08T17:09:38.319725Z","shell.execute_reply.started":"2023-09-08T17:09:37.863861Z","shell.execute_reply":"2023-09-08T17:09:38.318532Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Visualize Activations - Benign\n\nFirst lets extract all of the layer names from our network:","metadata":{}},{"cell_type":"code","source":"# Extracts the outputs of the all the layers\nlayer_outputs = [layer.output for layer in model.layers]\n# Creates a model that will return these outputs, given the model input:\nactivation_model = models.Model(inputs=model.input, outputs=layer_outputs)\n\nlayer_names = []\nfor layer in model.layers:\n    layer_names.append(layer.name)\n    \nlayer_names","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:38.321313Z","iopub.execute_input":"2023-09-08T17:09:38.321680Z","iopub.status.idle":"2023-09-08T17:09:38.341534Z","shell.execute_reply.started":"2023-09-08T17:09:38.321643Z","shell.execute_reply":"2023-09-08T17:09:38.340486Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import cv2\n\nimg=cv2.imread(validation.images[0])\nimg = cv2.resize(img, (224,224))\nimg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\nimg = img.astype(np.float32)/255.\n\nclass_names = ['Benign', 'Malignant']\n\nplt.imshow(img)\nplt.axis('off')\nplt.title(class_names[validation.target[0]], fontsize=12)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:38.343057Z","iopub.execute_input":"2023-09-08T17:09:38.343434Z","iopub.status.idle":"2023-09-08T17:09:39.157430Z","shell.execute_reply.started":"2023-09-08T17:09:38.343400Z","shell.execute_reply":"2023-09-08T17:09:39.156143Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"This is the input image that will be used for the convnet visualization.  It is a picture, from the validation set, of a benign mole.  The model is fed the input image, which then returns the layer activations of the original modle.  The model has 1 input and 8 outputs.  ","metadata":{}},{"cell_type":"code","source":"img1 = cv2.imread(validation.images[0])/255.\nimg1 = cv2.resize(img1, (224, 224), 3)\nimg1 = img1.reshape((1, 224, 224, 3))\nimg1.shape","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:39.159341Z","iopub.execute_input":"2023-09-08T17:09:39.160121Z","iopub.status.idle":"2023-09-08T17:09:39.811052Z","shell.execute_reply.started":"2023-09-08T17:09:39.160068Z","shell.execute_reply":"2023-09-08T17:09:39.810049Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"activations = activation_model.predict(img1)\nlen(activations)","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:39.812577Z","iopub.execute_input":"2023-09-08T17:09:39.813165Z","iopub.status.idle":"2023-09-08T17:09:39.998316Z","shell.execute_reply.started":"2023-09-08T17:09:39.813124Z","shell.execute_reply":"2023-09-08T17:09:39.997298Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"This is the activation of the first convolution layer for the benign image:","metadata":{}},{"cell_type":"code","source":"first_layer_activation = activations[0]\nprint(first_layer_activation.shape)","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:39.999947Z","iopub.execute_input":"2023-09-08T17:09:40.000592Z","iopub.status.idle":"2023-09-08T17:09:40.007678Z","shell.execute_reply.started":"2023-09-08T17:09:40.000551Z","shell.execute_reply":"2023-09-08T17:09:40.006044Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"It is a 222x222 feature map with 32 channels.  Below we can visualize some of the different channels:  ","metadata":{}},{"cell_type":"code","source":"plt.matshow(first_layer_activation[0, :, :, 4], cmap='viridis')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:40.009212Z","iopub.execute_input":"2023-09-08T17:09:40.009570Z","iopub.status.idle":"2023-09-08T17:09:40.181010Z","shell.execute_reply.started":"2023-09-08T17:09:40.009529Z","shell.execute_reply":"2023-09-08T17:09:40.179793Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The 5th channel looks like it is detecting hair (see green lines around hair) or the darker color, with the image of the mole basically removed.   ","metadata":{}},{"cell_type":"code","source":"plt.matshow(first_layer_activation[0, :, :, 27], cmap='viridis')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:40.182781Z","iopub.execute_input":"2023-09-08T17:09:40.183648Z","iopub.status.idle":"2023-09-08T17:09:40.320203Z","shell.execute_reply.started":"2023-09-08T17:09:40.183556Z","shell.execute_reply":"2023-09-08T17:09:40.319262Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The 28th channel appears to focus on the white coloring around the mole on the right side.  ","metadata":{}},{"cell_type":"code","source":"plt.matshow(first_layer_activation[0, :, :, 10], cmap='viridis')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:40.321410Z","iopub.execute_input":"2023-09-08T17:09:40.321873Z","iopub.status.idle":"2023-09-08T17:09:40.475848Z","shell.execute_reply.started":"2023-09-08T17:09:40.321838Z","shell.execute_reply":"2023-09-08T17:09:40.474664Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The 11th channel appears to focus on the mole itself.","metadata":{}},{"cell_type":"code","source":"plt.matshow(first_layer_activation[0, :, :, 7], cmap='viridis')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:40.477467Z","iopub.execute_input":"2023-09-08T17:09:40.478072Z","iopub.status.idle":"2023-09-08T17:09:40.645376Z","shell.execute_reply.started":"2023-09-08T17:09:40.478019Z","shell.execute_reply":"2023-09-08T17:09:40.644143Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.matshow(first_layer_activation[0, :, :, 15], cmap='viridis')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:40.646918Z","iopub.execute_input":"2023-09-08T17:09:40.647310Z","iopub.status.idle":"2023-09-08T17:09:40.810986Z","shell.execute_reply.started":"2023-09-08T17:09:40.647271Z","shell.execute_reply":"2023-09-08T17:09:40.809789Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.matshow(first_layer_activation[0, :, :, 19], cmap='viridis')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:40.812634Z","iopub.execute_input":"2023-09-08T17:09:40.813347Z","iopub.status.idle":"2023-09-08T17:09:40.961746Z","shell.execute_reply.started":"2023-09-08T17:09:40.813295Z","shell.execute_reply":"2023-09-08T17:09:40.960572Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.matshow(first_layer_activation[0, :, :, 22], cmap='viridis')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:40.963368Z","iopub.execute_input":"2023-09-08T17:09:40.963986Z","iopub.status.idle":"2023-09-08T17:09:41.115985Z","shell.execute_reply.started":"2023-09-08T17:09:40.963939Z","shell.execute_reply":"2023-09-08T17:09:41.114646Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.matshow(first_layer_activation[0, :, :, 23], cmap='viridis')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:41.117586Z","iopub.execute_input":"2023-09-08T17:09:41.117946Z","iopub.status.idle":"2023-09-08T17:09:41.243738Z","shell.execute_reply.started":"2023-09-08T17:09:41.117911Z","shell.execute_reply":"2023-09-08T17:09:41.242641Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Next we can plot a visualization of all the activations within the netowrk.  ","metadata":{}},{"cell_type":"code","source":"layer_names = []\nfor layer in model.layers:\n    layer_names.append(layer.name)\n\nimages_per_row = 16\n\n# Now let's display our feature maps\nfor layer_name, layer_activation in zip(layer_names, activations):\n    \n    if layer_name == 'flatten': \n        break\n    # This is the number of features in the feature map\n    n_features = layer_activation.shape[-1]\n\n    # The feature map has shape (1, size, size, n_features)\n    size = layer_activation.shape[1]\n\n    # We will tile the activation channels in this matrix\n    n_cols = n_features // images_per_row\n    display_grid = np.zeros((size * n_cols, images_per_row * size))\n\n    # We'll tile each filter into this big horizontal grid\n    for col in range(n_cols):\n        for row in range(images_per_row):\n            channel_image = layer_activation[0,\n                                             :, :,\n                                             col * images_per_row + row]\n            # Post-process the feature to make it visually palatable\n            channel_image -= channel_image.mean()\n            channel_image /= channel_image.std()\n            channel_image *= 64\n            channel_image += 128\n            channel_image = np.clip(channel_image, 0, 255).astype('uint8')\n            display_grid[col * size : (col + 1) * size,\n                         row * size : (row + 1) * size] = channel_image\n\n    # Display the grid\n    scale = 1. / size\n    plt.figure(figsize=(scale * display_grid.shape[1],\n                        scale * display_grid.shape[0]))\n    plt.title(layer_name)\n    plt.grid(False)\n    plt.imshow(display_grid, aspect='auto', cmap='viridis')\n    \nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:41.245831Z","iopub.execute_input":"2023-09-08T17:09:41.246338Z","iopub.status.idle":"2023-09-08T17:09:43.563561Z","shell.execute_reply.started":"2023-09-08T17:09:41.246286Z","shell.execute_reply":"2023-09-08T17:09:43.562479Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The deeper the layer of the neural network, the more abstract the features become. The first layer detects edges.  This particular network is very sparse, and become more so as the depth increases (blank filters).  This means the pattern encoded by the filter isn't in the input image.   ","metadata":{}},{"cell_type":"markdown","source":"## Now Lets Visualize A Malignant Mole\n## Visualize Activations - Malignant","metadata":{}},{"cell_type":"code","source":"img=cv2.imread(validation.images.iloc[32])\nimg = cv2.resize(img, (224,224))\nimg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\nimg = img.astype(np.float32)/255.\n\nclass_names = ['Benign', 'Malignant']\n\nplt.imshow(img)\nplt.axis('off')\nplt.title(class_names[validation.target.iloc[32]], fontsize=12)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:43.565138Z","iopub.execute_input":"2023-09-08T17:09:43.565545Z","iopub.status.idle":"2023-09-08T17:09:43.801077Z","shell.execute_reply.started":"2023-09-08T17:09:43.565508Z","shell.execute_reply":"2023-09-08T17:09:43.800147Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"img1 = cv2.imread(validation.images.iloc[32])/255.\nimg1 = cv2.resize(img1, (224, 224), 3)\nimg1 = img1.reshape((1, 224, 224, 3))\nprint(img1.shape)\n\nactivations = activation_model.predict(img1)\nprint(len(activations))\n\nfirst_layer_activation = activations[0]\nprint(first_layer_activation.shape)","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:43.802269Z","iopub.execute_input":"2023-09-08T17:09:43.802740Z","iopub.status.idle":"2023-09-08T17:09:44.045487Z","shell.execute_reply.started":"2023-09-08T17:09:43.802705Z","shell.execute_reply":"2023-09-08T17:09:44.044104Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.matshow(first_layer_activation[0, :, :, 4], cmap='viridis')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:44.047494Z","iopub.execute_input":"2023-09-08T17:09:44.047989Z","iopub.status.idle":"2023-09-08T17:09:44.169566Z","shell.execute_reply.started":"2023-09-08T17:09:44.047935Z","shell.execute_reply":"2023-09-08T17:09:44.168594Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Similar to the benign mole, the 5th channel in the malignant image confirms that this channel is detecting a darker color, not hair.  ","metadata":{}},{"cell_type":"code","source":"plt.matshow(first_layer_activation[0, :, :, 27], cmap='viridis')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:44.170954Z","iopub.execute_input":"2023-09-08T17:09:44.171485Z","iopub.status.idle":"2023-09-08T17:09:44.322774Z","shell.execute_reply.started":"2023-09-08T17:09:44.171439Z","shell.execute_reply":"2023-09-08T17:09:44.321697Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The 28th channel is focusing on the color white, just like in the benign image.","metadata":{}},{"cell_type":"code","source":"plt.matshow(first_layer_activation[0, :, :, 10], cmap='viridis')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:44.324418Z","iopub.execute_input":"2023-09-08T17:09:44.324789Z","iopub.status.idle":"2023-09-08T17:09:44.480197Z","shell.execute_reply.started":"2023-09-08T17:09:44.324753Z","shell.execute_reply":"2023-09-08T17:09:44.479002Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The 11th channel is focusing on lighter colors moles, and not the darker color malignant mole.","metadata":{}},{"cell_type":"code","source":"layer_names = []\nfor layer in model.layers:\n    layer_names.append(layer.name)\n\nimages_per_row = 16\n\n# Now let's display our feature maps\nfor layer_name, layer_activation in zip(layer_names, activations):\n    \n    if layer_name == 'flatten': \n        break\n    # This is the number of features in the feature map\n    n_features = layer_activation.shape[-1]\n\n    # The feature map has shape (1, size, size, n_features)\n    size = layer_activation.shape[1]\n\n    # We will tile the activation channels in this matrix\n    n_cols = n_features // images_per_row\n    display_grid = np.zeros((size * n_cols, images_per_row * size))\n\n    # We'll tile each filter into this big horizontal grid\n    for col in range(n_cols):\n        for row in range(images_per_row):\n            channel_image = layer_activation[0,\n                                             :, :,\n                                             col * images_per_row + row]\n            # Post-process the feature to make it visually palatable\n            channel_image -= channel_image.mean()\n            channel_image /= channel_image.std()\n            channel_image *= 64\n            channel_image += 128\n            channel_image = np.clip(channel_image, 0, 255).astype('uint8')\n            display_grid[col * size : (col + 1) * size,\n                         row * size : (row + 1) * size] = channel_image\n\n    # Display the grid\n    scale = 1. / size\n    plt.figure(figsize=(scale * display_grid.shape[1],\n                        scale * display_grid.shape[0]))\n    plt.title(layer_name)\n    plt.grid(False)\n    plt.imshow(display_grid, aspect='auto', cmap='viridis')\n    \nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-09-08T17:09:44.481984Z","iopub.execute_input":"2023-09-08T17:09:44.482473Z","iopub.status.idle":"2023-09-08T17:09:46.559257Z","shell.execute_reply.started":"2023-09-08T17:09:44.482426Z","shell.execute_reply":"2023-09-08T17:09:46.558176Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Conclusion\n\nVizualizing activations of a convnet is useful to understand the \"black box\".  As François Chollet stated, and we have now seen, the represenations learned by convets are able to be visualized fairly easily because they are representations of visual concepts.  ","metadata":{}}]}