{"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":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-08-10T08:24:46.738281Z","iopub.execute_input":"2021-08-10T08:24:46.738598Z","iopub.status.idle":"2021-08-10T08:24:46.747943Z","shell.execute_reply.started":"2021-08-10T08:24:46.738568Z","shell.execute_reply":"2021-08-10T08:24:46.746788Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Importing important libraries","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\nimport os\nimport random\nimport numpy as np\nfrom tqdm import tqdm\nfrom skimage.io import imread,imshow\nfrom skimage.transform import resize\nimport matplotlib.pyplot as plt","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:25:14.548448Z","iopub.execute_input":"2021-08-10T08:25:14.548786Z","iopub.status.idle":"2021-08-10T08:25:14.555367Z","shell.execute_reply.started":"2021-08-10T08:25:14.548753Z","shell.execute_reply":"2021-08-10T08:25:14.554373Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Unzipping the files","metadata":{}},{"cell_type":"code","source":"# importing required modules \nfrom zipfile import ZipFile \n  \n# specifying the zip file name \nfile_name = \"../input/data-science-bowl-2018/stage1_train.zip\"\n  \n# opening the zip file in READ mode \nwith ZipFile(file_name, 'r') as zip: \n    # printing all the contents of the zip file \n     \n  \n    # extracting all the files \n    print('Extracting all the files now...') \n    zip.extractall(\"stage1_train\") \n    print('Done!') ","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:25:16.057649Z","iopub.execute_input":"2021-08-10T08:25:16.058062Z","iopub.status.idle":"2021-08-10T08:25:22.118262Z","shell.execute_reply.started":"2021-08-10T08:25:16.058001Z","shell.execute_reply":"2021-08-10T08:25:22.116509Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# importing required modules \nfrom zipfile import ZipFile \n  \n# specifying the zip file name \nfile_name = \"../input/data-science-bowl-2018/stage1_test.zip\"\n  \n# opening the zip file in READ mode \nwith ZipFile(file_name, 'r') as zip: \n    # printing all the contents of the zip file \n     \n  \n    # extracting all the files \n    print('Extracting all the files now...') \n    zip.extractall(\"stage1_test\") \n    print('Done!') ","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:25:22.120140Z","iopub.execute_input":"2021-08-10T08:25:22.120504Z","iopub.status.idle":"2021-08-10T08:25:22.400626Z","shell.execute_reply.started":"2021-08-10T08:25:22.120466Z","shell.execute_reply":"2021-08-10T08:25:22.399903Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Performing preprocessing, Loading the data, and Getting data ready for our model","metadata":{}},{"cell_type":"code","source":"TRAIN_PATH=\"stage1_train/\"\nTEST_PATH='stage1_test/'","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:25:26.647712Z","iopub.execute_input":"2021-08-10T08:25:26.648079Z","iopub.status.idle":"2021-08-10T08:25:26.652267Z","shell.execute_reply.started":"2021-08-10T08:25:26.648048Z","shell.execute_reply":"2021-08-10T08:25:26.651259Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_ids=next(os.walk(TRAIN_PATH))[1]\ntest_ids=next(os.walk(TEST_PATH))[1]\nprint(len(train_ids))","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:25:29.308082Z","iopub.execute_input":"2021-08-10T08:25:29.308401Z","iopub.status.idle":"2021-08-10T08:25:29.317097Z","shell.execute_reply.started":"2021-08-10T08:25:29.308372Z","shell.execute_reply":"2021-08-10T08:25:29.315020Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"IMG_HEIGHT=128\nIMG_WIDTH=128\nIMG_CHANNELS=3","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:25:30.517518Z","iopub.execute_input":"2021-08-10T08:25:30.517875Z","iopub.status.idle":"2021-08-10T08:25:30.521501Z","shell.execute_reply.started":"2021-08-10T08:25:30.517832Z","shell.execute_reply":"2021-08-10T08:25:30.520615Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\nY_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)\n\nprint('Resizing training images and masks')\nfor n, id_ in tqdm(enumerate(train_ids), total=len(train_ids)):   \n    path = TRAIN_PATH + id_\n    img = imread(path + '/images/' + id_ + '.png')[:,:,:IMG_CHANNELS]  \n    img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n    X_train[n] = img  #Fill empty X_train with values from img\n    mask = np.zeros((IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)\n    for mask_file in next(os.walk(path + '/masks/'))[2]:\n        mask_ = imread(path + '/masks/' + mask_file)\n        mask_ = np.expand_dims(resize(mask_, (IMG_HEIGHT, IMG_WIDTH), mode='constant',  \n                                      preserve_range=True), axis=-1)\n        mask = np.maximum(mask, mask_)  \n            \n    Y_train[n] = mask   \n\n# test images\nX_test = np.zeros((len(test_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\nsizes_test = []\nprint('Resizing test images') \nfor n, id_ in tqdm(enumerate(test_ids), total=len(test_ids)):\n    path = TEST_PATH + id_\n    img = imread(path + '/images/' + id_ + '.png')[:,:,:IMG_CHANNELS]\n    sizes_test.append([img.shape[0], img.shape[1]])\n    img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n    X_test[n] = img\n\nprint('Done!')","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:25:31.667789Z","iopub.execute_input":"2021-08-10T08:25:31.668145Z","iopub.status.idle":"2021-08-10T08:29:53.038071Z","shell.execute_reply.started":"2021-08-10T08:25:31.668114Z","shell.execute_reply":"2021-08-10T08:29:53.036862Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Having a look at our training images and the segmented images related to it respectively","metadata":{}},{"cell_type":"code","source":"image_x=random.randint(0,len(train_ids))\nimshow(X_train[image_x])\nplt.show()\nimshow(np.squeeze(Y_train[image_x]))\n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:29:56.817738Z","iopub.execute_input":"2021-08-10T08:29:56.818137Z","iopub.status.idle":"2021-08-10T08:29:57.160277Z","shell.execute_reply.started":"2021-08-10T08:29:56.818101Z","shell.execute_reply":"2021-08-10T08:29:57.159021Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"seed=42\nnp.random.seed=seed","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:30:05.508532Z","iopub.execute_input":"2021-08-10T08:30:05.508894Z","iopub.status.idle":"2021-08-10T08:30:05.512957Z","shell.execute_reply.started":"2021-08-10T08:30:05.508853Z","shell.execute_reply":"2021-08-10T08:30:05.511993Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Building a U-Net","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:image.png)","metadata":{},"attachments":{"image.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"### Introduction:\nU-Net is a convolutional neural network that was developed for biomedical image segmentation at the Computer Science Department of the University of Freiburg, Germany. The network is based on the fully convolutional network and its architecture was modified and extended to work with fewer training images and to yield more precise segmentations. Segmentation of a 512 × 512 image takes less than a second on a modern GPU.\n\nThe U-Net architecture stems from the so-called “fully convolutional network” first proposed by Long and Shelhamer.\n\nThe main idea is to supplement a usual contracting network by successive layers, where pooling operations are replaced by upsampling operators. Hence these layers increase the resolution of the output. What's more, a successive convolutional layer can then learn to assemble a precise output based on this information.\n\nOne important modification in U-Net is that there are a large number of feature channels in the upsampling part, which allow the network to propagate context information to higher resolution layers. As a consequence, the expansive path is more or less symmetric to the contracting part, and yields a u-shaped architecture. The network only uses the valid part of each convolution without any fully connected layers. To predict the pixels in the border region of the image, the missing context is extrapolated by mirroring the input image. This tiling strategy is important to apply the network to large images, since otherwise the resolution would be limited by the GPU memory.\n\nSource: https://en.wikipedia.org/wiki/U-Net","metadata":{}},{"cell_type":"code","source":"\n#Build the model\ninputs = tf.keras.layers.Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))\ns = tf.keras.layers.Lambda(lambda x: x / 255)(inputs)\n\n#Contraction path\nc1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(s)\nc1 = tf.keras.layers.Dropout(0.1)(c1)\nc1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c1)\np1 = tf.keras.layers.MaxPooling2D((2, 2))(c1)\n\nc2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p1)\nc2 = tf.keras.layers.Dropout(0.1)(c2)\nc2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c2)\np2 = tf.keras.layers.MaxPooling2D((2, 2))(c2)\n \nc3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p2)\nc3 = tf.keras.layers.Dropout(0.2)(c3)\nc3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c3)\np3 = tf.keras.layers.MaxPooling2D((2, 2))(c3)\n \nc4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p3)\nc4 = tf.keras.layers.Dropout(0.2)(c4)\nc4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c4)\np4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c4)\n \nc5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p4)\nc5 = tf.keras.layers.Dropout(0.3)(c5)\nc5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c5)\n\n#Expansive path \nu6 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5)\nu6 = tf.keras.layers.concatenate([u6, c4])\nc6 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u6)\nc6 = tf.keras.layers.Dropout(0.2)(c6)\nc6 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c6)\n \nu7 = tf.keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6)\nu7 = tf.keras.layers.concatenate([u7, c3])\nc7 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u7)\nc7 = tf.keras.layers.Dropout(0.2)(c7)\nc7 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c7)\n \nu8 = tf.keras.layers.Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7)\nu8 = tf.keras.layers.concatenate([u8, c2])\nc8 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u8)\nc8 = tf.keras.layers.Dropout(0.1)(c8)\nc8 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c8)\n \nu9 = tf.keras.layers.Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8)\nu9 = tf.keras.layers.concatenate([u9, c1], axis=3)\nc9 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u9)\nc9 = tf.keras.layers.Dropout(0.1)(c9)\nc9 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c9)\n \noutputs = tf.keras.layers.Conv2D(1, (1, 1), activation='sigmoid')(c9)\nmodelu = tf.keras.Model(inputs=[inputs], outputs=[outputs])\nmodelu.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\nmodelu.summary()\n","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:50:55.744795Z","iopub.execute_input":"2021-08-10T08:50:55.745188Z","iopub.status.idle":"2021-08-10T08:50:56.354735Z","shell.execute_reply.started":"2021-08-10T08:50:55.745152Z","shell.execute_reply":"2021-08-10T08:50:56.353822Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#callbacks\ncallbacks=[tf.keras.callbacks.EarlyStopping(patience=2,monitor='val_loss'),\n           tf.keras.callbacks.TensorBoard(log_dir='logs')]","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:51:04.447683Z","iopub.execute_input":"2021-08-10T08:51:04.448038Z","iopub.status.idle":"2021-08-10T08:51:04.453342Z","shell.execute_reply.started":"2021-08-10T08:51:04.448007Z","shell.execute_reply":"2021-08-10T08:51:04.452521Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Training the model","metadata":{}},{"cell_type":"code","source":"results=model.fit(X_train,Y_train,validation_split=0.1,batch_size=16,epochs=30,callbacks=callbacks)","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:51:10.527589Z","iopub.execute_input":"2021-08-10T08:51:10.527989Z","iopub.status.idle":"2021-08-10T08:51:42.049630Z","shell.execute_reply.started":"2021-08-10T08:51:10.527953Z","shell.execute_reply":"2021-08-10T08:51:42.048835Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Applying the segmentation on the test images using our trained model.","metadata":{}},{"cell_type":"code","source":"predict_test=model.predict(X_test,verbose=1)","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:53:29.528899Z","iopub.execute_input":"2021-08-10T08:53:29.529358Z","iopub.status.idle":"2021-08-10T08:53:29.865428Z","shell.execute_reply.started":"2021-08-10T08:53:29.529316Z","shell.execute_reply":"2021-08-10T08:53:29.864535Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predict_test_t=(predict_test>0.5).astype(np.uint8)","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:53:30.867765Z","iopub.execute_input":"2021-08-10T08:53:30.868130Z","iopub.status.idle":"2021-08-10T08:53:30.874295Z","shell.execute_reply.started":"2021-08-10T08:53:30.868100Z","shell.execute_reply":"2021-08-10T08:53:30.873253Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"imshow(X_test[1])\nplt.show()\nimshow(np.mean(predict_test_t[1], axis=2))\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:54:03.728442Z","iopub.execute_input":"2021-08-10T08:54:03.728764Z","iopub.status.idle":"2021-08-10T08:54:04.156697Z","shell.execute_reply.started":"2021-08-10T08:54:03.728733Z","shell.execute_reply":"2021-08-10T08:54:04.155770Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"imshow(X_test[2])\nplt.show()\nimshow(np.mean(predict_test_t[2], axis=2))\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:56:22.267725Z","iopub.execute_input":"2021-08-10T08:56:22.268094Z","iopub.status.idle":"2021-08-10T08:56:22.557546Z","shell.execute_reply.started":"2021-08-10T08:56:22.268063Z","shell.execute_reply":"2021-08-10T08:56:22.556530Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Building a Double Unet","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras.layers import *\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.applications import *\n\ndef squeeze_excite_block(inputs, ratio=8):\n    init = inputs\n    channel_axis = -1\n    filters = init.shape[channel_axis]\n    se_shape = (1, 1, filters)\n\n    se = GlobalAveragePooling2D()(init)\n    se = Reshape(se_shape)(se)\n    se = Dense(filters // ratio, activation='relu', kernel_initializer='he_normal', use_bias=False)(se)\n    se = Dense(filters, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(se)\n\n    x = Multiply()([init, se])\n    return x\n\ndef conv_block(inputs, filters):\n    x = inputs\n\n    x = Conv2D(filters, (3, 3), padding=\"same\")(x)\n    x = BatchNormalization()(x)\n    x = Activation('relu')(x)\n\n    x = Conv2D(filters, (3, 3), padding=\"same\")(x)\n    x = BatchNormalization()(x)\n    x = Activation('relu')(x)\n\n    x = squeeze_excite_block(x)\n\n    return x\n\ndef encoder1(inputs):\n    skip_connections = []\n\n    model = VGG19(include_top=False, weights='imagenet', input_tensor=inputs)\n    names = [\"block1_conv2\", \"block2_conv2\", \"block3_conv4\", \"block4_conv4\"]\n    for name in names:\n        skip_connections.append(model.get_layer(name).output)\n\n    output = model.get_layer(\"block5_conv4\").output\n    return output, skip_connections\n\ndef decoder1(inputs, skip_connections):\n    num_filters = [256, 128, 64, 32]\n    skip_connections.reverse()\n    x = inputs\n\n    for i, f in enumerate(num_filters):\n        x = UpSampling2D((2, 2), interpolation='bilinear')(x)\n        x = Concatenate()([x, skip_connections[i]])\n        x = conv_block(x, f)\n\n    return x\n\ndef encoder2(inputs):\n    num_filters = [32, 64, 128, 256]\n    skip_connections = []\n    x = inputs\n\n    for i, f in enumerate(num_filters):\n        x = conv_block(x, f)\n        skip_connections.append(x)\n        x = MaxPool2D((2, 2))(x)\n\n    return x, skip_connections\n\ndef decoder2(inputs, skip_1, skip_2):\n    num_filters = [256, 128, 64, 32]\n    skip_2.reverse()\n    x = inputs\n\n    for i, f in enumerate(num_filters):\n        x = UpSampling2D((2, 2), interpolation='bilinear')(x)\n        x = Concatenate()([x, skip_1[i], skip_2[i]])\n        x = conv_block(x, f)\n\n    return x\n\ndef output_block(inputs):\n    x = Conv2D(1, (1, 1), padding=\"same\")(inputs)\n    x = Activation('sigmoid')(x)\n    return x\n\ndef Upsample(tensor, size):\n    \"\"\"Bilinear upsampling\"\"\"\n    def _upsample(x, size):\n        return tf.image.resize(images=x, size=size)\n    return Lambda(lambda x: _upsample(x, size), output_shape=size)(tensor)\n\ndef ASPP(x, filter):\n    shape = x.shape\n\n    y1 = AveragePooling2D(pool_size=(shape[1], shape[2]))(x)\n    y1 = Conv2D(filter, 1, padding=\"same\")(y1)\n    y1 = BatchNormalization()(y1)\n    y1 = Activation(\"relu\")(y1)\n    y1 = UpSampling2D((shape[1], shape[2]), interpolation='bilinear')(y1)\n\n    y2 = Conv2D(filter, 1, dilation_rate=1, padding=\"same\", use_bias=False)(x)\n    y2 = BatchNormalization()(y2)\n    y2 = Activation(\"relu\")(y2)\n\n    y3 = Conv2D(filter, 3, dilation_rate=6, padding=\"same\", use_bias=False)(x)\n    y3 = BatchNormalization()(y3)\n    y3 = Activation(\"relu\")(y3)\n\n    y4 = Conv2D(filter, 3, dilation_rate=12, padding=\"same\", use_bias=False)(x)\n    y4 = BatchNormalization()(y4)\n    y4 = Activation(\"relu\")(y4)\n\n    y5 = Conv2D(filter, 3, dilation_rate=18, padding=\"same\", use_bias=False)(x)\n    y5 = BatchNormalization()(y5)\n    y5 = Activation(\"relu\")(y5)\n\n    y = Concatenate()([y1, y2, y3, y4, y5])\n\n    y = Conv2D(filter, 1, dilation_rate=1, padding=\"same\", use_bias=False)(y)\n    y = BatchNormalization()(y)\n    y = Activation(\"relu\")(y)\n\n    return y\n\ndef build_model(shape):\n    inputs = Input(shape)\n    x, skip_1 = encoder1(inputs)\n    x = ASPP(x, 64)\n    x = decoder1(x, skip_1)\n    outputs1 = output_block(x)\n\n    x = inputs * outputs1\n\n    x, skip_2 = encoder2(x)\n    x = ASPP(x, 64)\n    x = decoder2(x, skip_1, skip_2)\n    outputs2 = output_block(x)\n    outputs = Concatenate()([outputs1, outputs2])\n\n    model = Model(inputs, outputs)\n    return model","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:17:18.395776Z","iopub.execute_input":"2021-08-10T08:17:18.396141Z","iopub.status.idle":"2021-08-10T08:17:22.968667Z","shell.execute_reply.started":"2021-08-10T08:17:18.396112Z","shell.execute_reply":"2021-08-10T08:17:22.967933Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = build_model((128, 128, 3))\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:18:52.932888Z","iopub.execute_input":"2021-08-10T08:18:52.933228Z","iopub.status.idle":"2021-08-10T08:18:57.597885Z","shell.execute_reply.started":"2021-08-10T08:18:52.933196Z","shell.execute_reply":"2021-08-10T08:18:57.596849Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['accuracy'])\ncallbacks=[tf.keras.callbacks.EarlyStopping(patience=5,monitor='val_loss'),\n           tf.keras.callbacks.TensorBoard(log_dir='logs')]","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:32:58.628593Z","iopub.execute_input":"2021-08-10T08:32:58.628938Z","iopub.status.idle":"2021-08-10T08:32:58.664847Z","shell.execute_reply.started":"2021-08-10T08:32:58.628904Z","shell.execute_reply":"2021-08-10T08:32:58.663973Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"results=model.fit(X_train,Y_train,validation_split=0.1,batch_size=16,epochs=30,callbacks=callbacks)","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:32:59.567625Z","iopub.execute_input":"2021-08-10T08:32:59.568003Z","iopub.status.idle":"2021-08-10T08:36:52.447076Z","shell.execute_reply.started":"2021-08-10T08:32:59.567957Z","shell.execute_reply":"2021-08-10T08:36:52.446346Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predict_test=model.predict(X_test,verbose=1)","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:37:33.862574Z","iopub.execute_input":"2021-08-10T08:37:33.862920Z","iopub.status.idle":"2021-08-10T08:37:37.250226Z","shell.execute_reply.started":"2021-08-10T08:37:33.862885Z","shell.execute_reply":"2021-08-10T08:37:37.249463Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predict_test_t=(predict_test>0.5).astype(np.uint8)","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:37:39.447555Z","iopub.execute_input":"2021-08-10T08:37:39.447891Z","iopub.status.idle":"2021-08-10T08:37:39.455014Z","shell.execute_reply.started":"2021-08-10T08:37:39.447858Z","shell.execute_reply":"2021-08-10T08:37:39.454030Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_test.shape\npredict_test_t.shape","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:43:02.008175Z","iopub.execute_input":"2021-08-10T08:43:02.008496Z","iopub.status.idle":"2021-08-10T08:43:02.013637Z","shell.execute_reply.started":"2021-08-10T08:43:02.008464Z","shell.execute_reply":"2021-08-10T08:43:02.012734Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.imshow(X_test[1])\nplt.show()\nplt.imshow(np.mean(predict_test_t[1], axis=2))\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:47:45.068100Z","iopub.execute_input":"2021-08-10T08:47:45.068446Z","iopub.status.idle":"2021-08-10T08:47:45.364034Z","shell.execute_reply.started":"2021-08-10T08:47:45.068412Z","shell.execute_reply":"2021-08-10T08:47:45.363049Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"imshow(X_test[2])\nplt.show()\nimshow(np.mean(predict_test_t[2], axis=2))\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-10T08:48:15.008271Z","iopub.execute_input":"2021-08-10T08:48:15.008639Z","iopub.status.idle":"2021-08-10T08:48:15.345997Z","shell.execute_reply.started":"2021-08-10T08:48:15.008602Z","shell.execute_reply":"2021-08-10T08:48:15.344865Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}