Transfer Learning — Part — 7.3!! Densenet Architecture in Keras

 Becoming Human: Artificial Intelligence Magazine – Medium Transfer Learning — Part — 7.3!! Densenet Architecture in KerasIn Part 7.0 of the Transfer Learning series we have discussed about Densenet pre-trained model in depth so in this series we will implement the above mentioned pre-trained model in Keras. We will be implementing the pre-trained Densenet model in 4 ways which we will discuss further in this article. For setting- up the Colab notebook it will be advisable to go through the below mentioned article of Transfer Learning Series. In Part 2 of the Transfer Learning series we have discussed how we can set-up our environment below is the link for the article.Launching Soon!!!Ravi Shekhar TiwariFigure.1 Transfer LearningTransfer Learning —Part -3!! Datasets and repositoriesIt is also advisable to go through the article of Densenet before reading this article which is mentioned below:Transfer Learning -Part -7.0 !! Dense net1.2 Using Densenet Architecture(without weights)In this section we will see how we can implement Densenet as a architecture in Keras. We will use state of the art Densenet network architecture and train it with our dataset from scratch i.e. we will not use pre-trained weights in this architecture the weights will be optimized while training from scratch. The code is explained below:1.2.1 DatasetFor feature extraction we will use CIFAR-10 dataset composed of 60K images, 50K for training and 10K for testing/evaluation.(trainX, trainy), (testX, testy) = tf.keras.datasets.cifar10.load_data() #Line 1Line 1: The above snippet used to import the datasets into separate variable and labels.from matplotlib import pyplot #Line 2print(‘Train: X=%s, y=%s’ % (trainX.shape, trainy.shape)) #Line 3print(‘Test: X=%s, y=%s’ % (testX.shape, testy.shape)) #Line 4for i in range(9): #Line 5# define subplotpyplot.subplot(330 + 1 + i) #Line 6# plot raw pixel datapyplot.imshow(trainX[i], cmap=pyplot.get_cmap(‘gray’)) #Line 7# show the figurepyplot.show() #Line 8Line 2: This code snippet is used to import the Matplot library for plotting.Line 3 and Line 4: This code snippet is used to display the training and testing dataset size as shown below:Train: X=(50000, 32, 32, 3), y=(50000, 1)Test: X=(10000, 32, 32, 3), y=(10000, 1)Line 5 to Line 8: These code snippets are used to display the samples from the dataset as shown below:Figure 2. Sample CIFDAR-10 datasetIf you want to have the insight of the visualization library please follow the below mention article series:Visualisation Libraries — ConclusiontrainY=tf.keras.utils.to_categorical(trainy, num_classes=10) #Line 9testY=tf.keras.utils.to_categorical(testy, num_classes=10) #Line 10Line 9 and Line 10: Since we have 10 classes and labels are number from 0 to 9 so we have to hot encoded these labels this has been done by the help of this snippets.1.2.2 Densenet Architecture (code)In this section we will see how we can implement Densenet as a architecture in Keras.import tensorflow as tf #Line 1Line 1: The above snippet is used to import the TensorFlow library which we use use to implement Keras.image_input = tf.keras.layers.Input(shape=(32,32, 3)) #Line 2baseModel = tf.keras.applications.Densenet121(include_top=False,weights=None,input_tensor=image_input) #Line 3baseModel.summary() #Line 4Line 2 : We have specified out datasets to be of shape (32,32,3) i.e. in channel last format where channel number is 3, Height and Width of the Images are 32 respectively.Line 3: We have imported the pre-trained Densenet with noweight by specifying weights=None, we have excluded the Dense layer by include_top=False since we have to get the features from the image though there is option available to us where we can use dense layer to get 1d- feature tensor from this model. also we have used Line 2 in Line 3 to specify the input shape of the model by input_tensor=image_input.Line 4: This snippet is used to display the Summary of theDensenet model which will be used to extract feature from the image shown below.Model: “densenet121″__________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) [(None, 224, 224, 3 0 [] )] zero_padding2d (ZeroPadding2D) (None, 230, 230, 3) 0 [‘input_1[0][0]’] conv1/conv (Conv2D) (None, 112, 112, 64 9408 [‘zero_padding2d[0][0]’] ) conv1/bn (BatchNormalization) (None, 112, 112, 64 256 [‘conv1/conv[0][0]’] ) conv1/relu (Activation) (None, 112, 112, 64 0 [‘conv1/bn[0][0]’] ) zero_padding2d_1 (ZeroPadding2 (None, 114, 114, 64 0 [‘conv1/relu[0][0]’] D) ) pool1 (MaxPooling2D) (None, 56, 56, 64) 0 [‘zero_padding2d_1[0][0]’] conv2_block1_0_bn (BatchNormal (None, 56, 56, 64) 256 [‘pool1[0][0]’] ization) conv2_block1_0_relu (Activatio (None, 56, 56, 64) 0 [‘conv2_block1_0_bn[0][0]’] n) conv2_block1_1_conv (Conv2D) (None, 56, 56, 128) 8192 [‘conv2_block1_0_relu[0][0]’] conv2_block1_1_bn (BatchNormal (None, 56, 56, 128) 512 [‘conv2_block1_1_conv[0][0]’] ization) conv2_block1_1_relu (Activatio (None, 56, 56, 128) 0 [‘conv2_block1_1_bn[0][0]’] n) conv2_block1_2_conv (Conv2D) (None, 56, 56, 32) 36864 [‘conv2_block1_1_relu[0][0]’] conv2_block1_concat (Concatena (None, 56, 56, 96) 0 [‘pool1[0][0]’, te) ‘conv2_block1_2_conv[0][0]’] conv2_block2_0_bn (BatchNormal (None, 56, 56, 96) 384 [‘conv2_block1_concat[0][0]’] ization) conv2_block2_0_relu (Activatio (None, 56, 56, 96) 0 [‘conv2_block2_0_bn[0][0]’] n) conv2_block2_1_conv (Conv2D) (None, 56, 56, 128) 12288 [‘conv2_block2_0_relu[0][0]’] conv2_block2_1_bn (BatchNormal (None, 56, 56, 128) 512 [‘conv2_block2_1_conv[0][0]’] ization) conv2_block2_1_relu (Activatio (None, 56, 56, 128) 0 [‘conv2_block2_1_bn[0][0]’] n) conv2_block2_2_conv (Conv2D) (None, 56, 56, 32) 36864 [‘conv2_block2_1_relu[0][0]’] conv2_block2_concat (Concatena (None, 56, 56, 128) 0 [‘conv2_block1_concat[0][0]’, te) ‘conv2_block2_2_conv[0][0]’] conv2_block3_0_bn (BatchNormal (None, 56, 56, 128) 512 [‘conv2_block2_concat[0][0]’] ization) conv2_block3_0_relu (Activatio (None, 56, 56, 128) 0 [‘conv2_block3_0_bn[0][0]’] n) conv2_block3_1_conv (Conv2D) (None, 56, 56, 128) 16384 [‘conv2_block3_0_relu[0][0]’] conv2_block3_1_bn (BatchNormal (None, 56, 56, 128) 512 [‘conv2_block3_1_conv[0][0]’] ization) conv2_block3_1_relu (Activatio (None, 56, 56, 128) 0 [‘conv2_block3_1_bn[0][0]’] n) conv2_block3_2_conv (Conv2D) (None, 56, 56, 32) 36864 [‘conv2_block3_1_relu[0][0]’] conv2_block3_concat (Concatena (None, 56, 56, 160) 0 [‘conv2_block2_concat[0][0]’, te) ‘conv2_block3_2_conv[0][0]’] conv2_block4_0_bn (BatchNormal (None, 56, 56, 160) 640 [‘conv2_block3_concat[0][0]’] ization) conv2_block4_0_relu (Activatio (None, 56, 56, 160) 0 [‘conv2_block4_0_bn[0][0]’] n) conv2_block4_1_conv (Conv2D) (None, 56, 56, 128) 20480 [‘conv2_block4_0_relu[0][0]’] conv2_block4_1_bn (BatchNormal (None, 56, 56, 128) 512 [‘conv2_block4_1_conv[0][0]’] ization) conv2_block4_1_relu (Activatio (None, 56, 56, 128) 0 [‘conv2_block4_1_bn[0][0]’] n) conv2_block4_2_conv (Conv2D) (None, 56, 56, 32) 36864 [‘conv2_block4_1_relu[0][0]’] conv2_block4_concat (Concatena (None, 56, 56, 192) 0 [‘conv2_block3_concat[0][0]’, te) ‘conv2_block4_2_conv[0][0]’] conv2_block5_0_bn (BatchNormal (None, 56, 56, 192) 768 [‘conv2_block4_concat[0][0]’] ization) conv2_block5_0_relu (Activatio (None, 56, 56, 192) 0 [‘conv2_block5_0_bn[0][0]’] n) conv2_block5_1_conv (Conv2D) (None, 56, 56, 128) 24576 [‘conv2_block5_0_relu[0][0]’] conv2_block5_1_bn (BatchNormal (None, 56, 56, 128) 512 [‘conv2_block5_1_conv[0][0]’] ization) conv2_block5_1_relu (Activatio (None, 56, 56, 128) 0 [‘conv2_block5_1_bn[0][0]’] n) conv2_block5_2_conv (Conv2D) (None, 56, 56, 32) 36864 [‘conv2_block5_1_relu[0][0]’] conv2_block5_concat (Concatena (None, 56, 56, 224) 0 [‘conv2_block4_concat[0][0]’, te) ‘conv2_block5_2_conv[0][0]’] conv2_block6_0_bn (BatchNormal (None, 56, 56, 224) 896 [‘conv2_block5_concat[0][0]’] ization) conv2_block6_0_relu (Activatio (None, 56, 56, 224) 0 [‘conv2_block6_0_bn[0][0]’] n) conv2_block6_1_conv (Conv2D) (None, 56, 56, 128) 28672 [‘conv2_block6_0_relu[0][0]’] conv2_block6_1_bn (BatchNormal (None, 56, 56, 128) 512 [‘conv2_block6_1_conv[0][0]’] ization) conv2_block6_1_relu (Activatio (None, 56, 56, 128) 0 [‘conv2_block6_1_bn[0][0]’] n) conv2_block6_2_conv (Conv2D) (None, 56, 56, 32) 36864 [‘conv2_block6_1_relu[0][0]’] conv2_block6_concat (Concatena (None, 56, 56, 256) 0 [‘conv2_block5_concat[0][0]’, te) ‘conv2_block6_2_conv[0][0]’] pool2_bn (BatchNormalization) (None, 56, 56, 256) 1024 [‘conv2_block6_concat[0][0]’] pool2_relu (Activation) (None, 56, 56, 256) 0 [‘pool2_bn[0][0]’] pool2_conv (Conv2D) (None, 56, 56, 128) 32768 [‘pool2_relu[0][0]’] pool2_pool (AveragePooling2D) (None, 28, 28, 128) 0 [‘pool2_conv[0][0]’] conv3_block1_0_bn (BatchNormal (None, 28, 28, 128) 512 [‘pool2_pool[0][0]’] ization) conv3_block1_0_relu (Activatio (None, 28, 28, 128) 0 [‘conv3_block1_0_bn[0][0]’] n) conv3_block1_1_conv (Conv2D) (None, 28, 28, 128) 16384 [‘conv3_block1_0_relu[0][0]’] conv3_block1_1_bn (BatchNormal (None, 28, 28, 128) 512 [‘conv3_block1_1_conv[0][0]’] ization) conv3_block1_1_relu (Activatio (None, 28, 28, 128) 0 [‘conv3_block1_1_bn[0][0]’] n) conv3_block1_2_conv (Conv2D) (None, 28, 28, 32) 36864 [‘conv3_block1_1_relu[0][0]’] conv3_block1_concat (Concatena (None, 28, 28, 160) 0 [‘pool2_pool[0][0]’, te) ‘conv3_block1_2_conv[0][0]’] conv3_block2_0_bn (BatchNormal (None, 28, 28, 160) 640 [‘conv3_block1_concat[0][0]’] ization) conv3_block2_0_relu (Activatio (None, 28, 28, 160) 0 [‘conv3_block2_0_bn[0][0]’] n) conv3_block2_1_conv (Conv2D) (None, 28, 28, 128) 20480 [‘conv3_block2_0_relu[0][0]’] conv3_block2_1_bn (BatchNormal (None, 28, 28, 128) 512 [‘conv3_block2_1_conv[0][0]’] ization) conv3_block2_1_relu (Activatio (None, 28, 28, 128) 0 [‘conv3_block2_1_bn[0][0]’] n) conv3_block2_2_conv (Conv2D) (None, 28, 28, 32) 36864 [‘conv3_block2_1_relu[0][0]’] conv3_block2_concat (Concatena (None, 28, 28, 192) 0 [‘conv3_block1_concat[0][0]’, te) ‘conv3_block2_2_conv[0][0]’] conv3_block3_0_bn (BatchNormal (None, 28, 28, 192) 768 [‘conv3_block2_concat[0][0]’] ization) conv3_block3_0_relu (Activatio (None, 28, 28, 192) 0 [‘conv3_block3_0_bn[0][0]’] n) conv3_block3_1_conv (Conv2D) (None, 28, 28, 128) 24576 [‘conv3_block3_0_relu[0][0]’] conv3_block3_1_bn (BatchNormal (None, 28, 28, 128) 512 [‘conv3_block3_1_conv[0][0]’] ization) conv3_block3_1_relu (Activatio (None, 28, 28, 128) 0 [‘conv3_block3_1_bn[0][0]’] n) conv3_block3_2_conv (Conv2D) (None, 28, 28, 32) 36864 [‘conv3_block3_1_relu[0][0]’] conv3_block3_concat (Concatena (None, 28, 28, 224) 0 [‘conv3_block2_concat[0][0]’, te) ‘conv3_block3_2_conv[0][0]’] conv3_block4_0_bn (BatchNormal (None, 28, 28, 224) 896 [‘conv3_block3_concat[0][0]’] ization) conv3_block4_0_relu (Activatio (None, 28, 28, 224) 0 [‘conv3_block4_0_bn[0][0]’] n) conv3_block4_1_conv (Conv2D) (None, 28, 28, 128) 28672 [‘conv3_block4_0_relu[0][0]’] conv3_block4_1_bn (BatchNormal (None, 28, 28, 128) 512 [‘conv3_block4_1_conv[0][0]’] ization) conv3_block4_1_relu (Activatio (None, 28, 28, 128) 0 [‘conv3_block4_1_bn[0][0]’] n) conv3_block4_2_conv (Conv2D) (None, 28, 28, 32) 36864 [‘conv3_block4_1_relu[0][0]’] conv3_block4_concat (Concatena (None, 28, 28, 256) 0 [‘conv3_block3_concat[0][0]’, te) ‘conv3_block4_2_conv[0][0]’] conv3_block5_0_bn (BatchNormal (None, 28, 28, 256) 1024 [‘conv3_block4_concat[0][0]’] ization) conv3_block5_0_relu (Activatio (None, 28, 28, 256) 0 [‘conv3_block5_0_bn[0][0]’] n) conv3_block5_1_conv (Conv2D) (None, 28, 28, 128) 32768 [‘conv3_block5_0_relu[0][0]’] conv3_block5_1_bn (BatchNormal (None, 28, 28, 128) 512 [‘conv3_block5_1_conv[0][0]’] ization) conv3_block5_1_relu (Activatio (None, 28, 28, 128) 0 [‘conv3_block5_1_bn[0][0]’] n) conv3_block5_2_conv (Conv2D) (None, 28, 28, 32) 36864 [‘conv3_block5_1_relu[0][0]’] conv3_block5_concat (Concatena (None, 28, 28, 288) 0 [‘conv3_block4_concat[0][0]’, te) ‘conv3_block5_2_conv[0][0]’] conv3_block6_0_bn (BatchNormal (None, 28, 28, 288) 1152 [‘conv3_block5_concat[0][0]’] ization) conv3_block6_0_relu (Activatio (None, 28, 28, 288) 0 [‘conv3_block6_0_bn[0][0]’] n) conv3_block6_1_conv (Conv2D) (None, 28, 28, 128) 36864 [‘conv3_block6_0_relu[0][0]’] conv3_block6_1_bn (BatchNormal (None, 28, 28, 128) 512 [‘conv3_block6_1_conv[0][0]’] ization) conv3_block6_1_relu (Activatio (None, 28, 28, 128) 0 [‘conv3_block6_1_bn[0][0]’] n) conv3_block6_2_conv (Conv2D) (None, 28, 28, 32) 36864 [‘conv3_block6_1_relu[0][0]’] conv3_block6_concat (Concatena (None, 28, 28, 320) 0 [‘conv3_block5_concat[0][0]’, te) ‘conv3_block6_2_conv[0][0]’] conv3_block7_0_bn (BatchNormal (None, 28, 28, 320) 1280 [‘conv3_block6_concat[0][0]’] ization) conv3_block7_0_relu (Activatio (None, 28, 28, 320) 0 [‘conv3_block7_0_bn[0][0]’] n) conv3_block7_1_conv (Conv2D) (None, 28, 28, 128) 40960 [‘conv3_block7_0_relu[0][0]’] conv3_block7_1_bn (BatchNormal (None, 28, 28, 128) 512 [‘conv3_block7_1_conv[0][0]’] ization) conv3_block7_1_relu (Activatio (None, 28, 28, 128) 0 [‘conv3_block7_1_bn[0][0]’] n) conv3_block7_2_conv (Conv2D) (None, 28, 28, 32) 36864 [‘conv3_block7_1_relu[0][0]’] conv3_block7_concat (Concatena (None, 28, 28, 352) 0 [‘conv3_block6_concat[0][0]’, te) ‘conv3_block7_2_conv[0][0]’] conv3_block8_0_bn (BatchNormal (None, 28, 28, 352) 1408 [‘conv3_block7_concat[0][0]’] ization) conv3_block8_0_relu (Activatio (None, 28, 28, 352) 0 [‘conv3_block8_0_bn[0][0]’] n) conv3_block8_1_conv (Conv2D) (None, 28, 28, 128) 45056 [‘conv3_block8_0_relu[0][0]’] conv3_block8_1_bn (BatchNormal (None, 28, 28, 128) 512 [‘conv3_block8_1_conv[0][0]’] ization) conv3_block8_1_relu (Activatio (None, 28, 28, 128) 0 [‘conv3_block8_1_bn[0][0]’] n) conv3_block8_2_conv (Conv2D) (None, 28, 28, 32) 36864 [‘conv3_block8_1_relu[0][0]’] conv3_block8_concat (Concatena (None, 28, 28, 384) 0 [‘conv3_block7_concat[0][0]’, te) ‘conv3_block8_2_conv[0][0]’] conv3_block9_0_bn (BatchNormal (None, 28, 28, 384) 1536 [‘conv3_block8_concat[0][0]’] ization) conv3_block9_0_relu (Activatio (None, 28, 28, 384) 0 [‘conv3_block9_0_bn[0][0]’] n) conv3_block9_1_conv (Conv2D) (None, 28, 28, 128) 49152 [‘conv3_block9_0_relu[0][0]’] conv3_block9_1_bn (BatchNormal (None, 28, 28, 128) 512 [‘conv3_block9_1_conv[0][0]’] ization) conv3_block9_1_relu (Activatio (None, 28, 28, 128) 0 [‘conv3_block9_1_bn[0][0]’] n) conv3_block9_2_conv (Conv2D) (None, 28, 28, 32) 36864 [‘conv3_block9_1_relu[0][0]’] conv3_block9_concat (Concatena (None, 28, 28, 416) 0 [‘conv3_block8_concat[0][0]’, te) ‘conv3_block9_2_conv[0][0]’] conv3_block10_0_bn (BatchNorma (None, 28, 28, 416) 1664 [‘conv3_block9_concat[0][0]’] lization) conv3_block10_0_relu (Activati (None, 28, 28, 416) 0 [‘conv3_block10_0_bn[0][0]’] on) conv3_block10_1_conv (Conv2D) (None, 28, 28, 128) 53248 [‘conv3_block10_0_relu[0][0]’] conv3_block10_1_bn (BatchNorma (None, 28, 28, 128) 512 [‘conv3_block10_1_conv[0][0]’] lization) conv3_block10_1_relu (Activati (None, 28, 28, 128) 0 [‘conv3_block10_1_bn[0][0]’] on) conv3_block10_2_conv (Conv2D) (None, 28, 28, 32) 36864 [‘conv3_block10_1_relu[0][0]’] conv3_block10_concat (Concaten (None, 28, 28, 448) 0 [‘conv3_block9_concat[0][0]’, ate) ‘conv3_block10_2_conv[0][0]’] conv3_block11_0_bn (BatchNorma (None, 28, 28, 448) 1792 [‘conv3_block10_concat[0][0]’] lization) conv3_block11_0_relu (Activati (None, 28, 28, 448) 0 [‘conv3_block11_0_bn[0][0]’] on) conv3_block11_1_conv (Conv2D) (None, 28, 28, 128) 57344 [‘conv3_block11_0_relu[0][0]’] conv3_block11_1_bn (BatchNorma (None, 28, 28, 128) 512 [‘conv3_block11_1_conv[0][0]’] lization) conv3_block11_1_relu (Activati (None, 28, 28, 128) 0 [‘conv3_block11_1_bn[0][0]’] on) conv3_block11_2_conv (Conv2D) (None, 28, 28, 32) 36864 [‘conv3_block11_1_relu[0][0]’] conv3_block11_concat (Concaten (None, 28, 28, 480) 0 [‘conv3_block10_concat[0][0]’, ate) ‘conv3_block11_2_conv[0][0]’] conv3_block12_0_bn (BatchNorma (None, 28, 28, 480) 1920 [‘conv3_block11_concat[0][0]’] lization) conv3_block12_0_relu (Activati (None, 28, 28, 480) 0 [‘conv3_block12_0_bn[0][0]’] on) conv3_block12_1_conv (Conv2D) (None, 28, 28, 128) 61440 [‘conv3_block12_0_relu[0][0]’] conv3_block12_1_bn (BatchNorma (None, 28, 28, 128) 512 [‘conv3_block12_1_conv[0][0]’] lization) conv3_block12_1_relu (Activati (None, 28, 28, 128) 0 [‘conv3_block12_1_bn[0][0]’] on) conv3_block12_2_conv (Conv2D) (None, 28, 28, 32) 36864 [‘conv3_block12_1_relu[0][0]’] conv3_block12_concat (Concaten (None, 28, 28, 512) 0 [‘conv3_block11_concat[0][0]’, ate) ‘conv3_block12_2_conv[0][0]’] pool3_bn (BatchNormalization) (None, 28, 28, 512) 2048 [‘conv3_block12_concat[0][0]’] pool3_relu (Activation) (None, 28, 28, 512) 0 [‘pool3_bn[0][0]’] pool3_conv (Conv2D) (None, 28, 28, 256) 131072 [‘pool3_relu[0][0]’] pool3_pool (AveragePooling2D) (None, 14, 14, 256) 0 [‘pool3_conv[0][0]’] conv4_block1_0_bn (BatchNormal (None, 14, 14, 256) 1024 [‘pool3_pool[0][0]’] ization) conv4_block1_0_relu (Activatio (None, 14, 14, 256) 0 [‘conv4_block1_0_bn[0][0]’] n) conv4_block1_1_conv (Conv2D) (None, 14, 14, 128) 32768 [‘conv4_block1_0_relu[0][0]’] conv4_block1_1_bn (BatchNormal (None, 14, 14, 128) 512 [‘conv4_block1_1_conv[0][0]’] ization) conv4_block1_1_relu (Activatio (None, 14, 14, 128) 0 [‘conv4_block1_1_bn[0][0]’] n) conv4_block1_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block1_1_relu[0][0]’] conv4_block1_concat (Concatena (None, 14, 14, 288) 0 [‘pool3_pool[0][0]’, te) ‘conv4_block1_2_conv[0][0]’] conv4_block2_0_bn (BatchNormal (None, 14, 14, 288) 1152 [‘conv4_block1_concat[0][0]’] ization) conv4_block2_0_relu (Activatio (None, 14, 14, 288) 0 [‘conv4_block2_0_bn[0][0]’] n) conv4_block2_1_conv (Conv2D) (None, 14, 14, 128) 36864 [‘conv4_block2_0_relu[0][0]’] conv4_block2_1_bn (BatchNormal (None, 14, 14, 128) 512 [‘conv4_block2_1_conv[0][0]’] ization) conv4_block2_1_relu (Activatio (None, 14, 14, 128) 0 [‘conv4_block2_1_bn[0][0]’] n) conv4_block2_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block2_1_relu[0][0]’] conv4_block2_concat (Concatena (None, 14, 14, 320) 0 [‘conv4_block1_concat[0][0]’, te) ‘conv4_block2_2_conv[0][0]’] conv4_block3_0_bn (BatchNormal (None, 14, 14, 320) 1280 [‘conv4_block2_concat[0][0]’] ization) conv4_block3_0_relu (Activatio (None, 14, 14, 320) 0 [‘conv4_block3_0_bn[0][0]’] n) conv4_block3_1_conv (Conv2D) (None, 14, 14, 128) 40960 [‘conv4_block3_0_relu[0][0]’] conv4_block3_1_bn (BatchNormal (None, 14, 14, 128) 512 [‘conv4_block3_1_conv[0][0]’] ization) conv4_block3_1_relu (Activatio (None, 14, 14, 128) 0 [‘conv4_block3_1_bn[0][0]’] n) conv4_block3_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block3_1_relu[0][0]’] conv4_block3_concat (Concatena (None, 14, 14, 352) 0 [‘conv4_block2_concat[0][0]’, te) ‘conv4_block3_2_conv[0][0]’] conv4_block4_0_bn (BatchNormal (None, 14, 14, 352) 1408 [‘conv4_block3_concat[0][0]’] ization) conv4_block4_0_relu (Activatio (None, 14, 14, 352) 0 [‘conv4_block4_0_bn[0][0]’] n) conv4_block4_1_conv (Conv2D) (None, 14, 14, 128) 45056 [‘conv4_block4_0_relu[0][0]’] conv4_block4_1_bn (BatchNormal (None, 14, 14, 128) 512 [‘conv4_block4_1_conv[0][0]’] ization) conv4_block4_1_relu (Activatio (None, 14, 14, 128) 0 [‘conv4_block4_1_bn[0][0]’] n) conv4_block4_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block4_1_relu[0][0]’] conv4_block4_concat (Concatena (None, 14, 14, 384) 0 [‘conv4_block3_concat[0][0]’, te) ‘conv4_block4_2_conv[0][0]’] conv4_block5_0_bn (BatchNormal (None, 14, 14, 384) 1536 [‘conv4_block4_concat[0][0]’] ization) conv4_block5_0_relu (Activatio (None, 14, 14, 384) 0 [‘conv4_block5_0_bn[0][0]’] n) conv4_block5_1_conv (Conv2D) (None, 14, 14, 128) 49152 [‘conv4_block5_0_relu[0][0]’] conv4_block5_1_bn (BatchNormal (None, 14, 14, 128) 512 [‘conv4_block5_1_conv[0][0]’] ization) conv4_block5_1_relu (Activatio (None, 14, 14, 128) 0 [‘conv4_block5_1_bn[0][0]’] n) conv4_block5_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block5_1_relu[0][0]’] conv4_block5_concat (Concatena (None, 14, 14, 416) 0 [‘conv4_block4_concat[0][0]’, te) ‘conv4_block5_2_conv[0][0]’] conv4_block6_0_bn (BatchNormal (None, 14, 14, 416) 1664 [‘conv4_block5_concat[0][0]’] ization) conv4_block6_0_relu (Activatio (None, 14, 14, 416) 0 [‘conv4_block6_0_bn[0][0]’] n) conv4_block6_1_conv (Conv2D) (None, 14, 14, 128) 53248 [‘conv4_block6_0_relu[0][0]’] conv4_block6_1_bn (BatchNormal (None, 14, 14, 128) 512 [‘conv4_block6_1_conv[0][0]’] ization) conv4_block6_1_relu (Activatio (None, 14, 14, 128) 0 [‘conv4_block6_1_bn[0][0]’] n) conv4_block6_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block6_1_relu[0][0]’] conv4_block6_concat (Concatena (None, 14, 14, 448) 0 [‘conv4_block5_concat[0][0]’, te) ‘conv4_block6_2_conv[0][0]’] conv4_block7_0_bn (BatchNormal (None, 14, 14, 448) 1792 [‘conv4_block6_concat[0][0]’] ization) conv4_block7_0_relu (Activatio (None, 14, 14, 448) 0 [‘conv4_block7_0_bn[0][0]’] n) conv4_block7_1_conv (Conv2D) (None, 14, 14, 128) 57344 [‘conv4_block7_0_relu[0][0]’] conv4_block7_1_bn (BatchNormal (None, 14, 14, 128) 512 [‘conv4_block7_1_conv[0][0]’] ization) conv4_block7_1_relu (Activatio (None, 14, 14, 128) 0 [‘conv4_block7_1_bn[0][0]’] n) conv4_block7_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block7_1_relu[0][0]’] conv4_block7_concat (Concatena (None, 14, 14, 480) 0 [‘conv4_block6_concat[0][0]’, te) ‘conv4_block7_2_conv[0][0]’] conv4_block8_0_bn (BatchNormal (None, 14, 14, 480) 1920 [‘conv4_block7_concat[0][0]’] ization) conv4_block8_0_relu (Activatio (None, 14, 14, 480) 0 [‘conv4_block8_0_bn[0][0]’] n) conv4_block8_1_conv (Conv2D) (None, 14, 14, 128) 61440 [‘conv4_block8_0_relu[0][0]’] conv4_block8_1_bn (BatchNormal (None, 14, 14, 128) 512 [‘conv4_block8_1_conv[0][0]’] ization) conv4_block8_1_relu (Activatio (None, 14, 14, 128) 0 [‘conv4_block8_1_bn[0][0]’] n) conv4_block8_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block8_1_relu[0][0]’] conv4_block8_concat (Concatena (None, 14, 14, 512) 0 [‘conv4_block7_concat[0][0]’, te) ‘conv4_block8_2_conv[0][0]’] conv4_block9_0_bn (BatchNormal (None, 14, 14, 512) 2048 [‘conv4_block8_concat[0][0]’] ization) conv4_block9_0_relu (Activatio (None, 14, 14, 512) 0 [‘conv4_block9_0_bn[0][0]’] n) conv4_block9_1_conv (Conv2D) (None, 14, 14, 128) 65536 [‘conv4_block9_0_relu[0][0]’] conv4_block9_1_bn (BatchNormal (None, 14, 14, 128) 512 [‘conv4_block9_1_conv[0][0]’] ization) conv4_block9_1_relu (Activatio (None, 14, 14, 128) 0 [‘conv4_block9_1_bn[0][0]’] n) conv4_block9_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block9_1_relu[0][0]’] conv4_block9_concat (Concatena (None, 14, 14, 544) 0 [‘conv4_block8_concat[0][0]’, te) ‘conv4_block9_2_conv[0][0]’] conv4_block10_0_bn (BatchNorma (None, 14, 14, 544) 2176 [‘conv4_block9_concat[0][0]’] lization) conv4_block10_0_relu (Activati (None, 14, 14, 544) 0 [‘conv4_block10_0_bn[0][0]’] on) conv4_block10_1_conv (Conv2D) (None, 14, 14, 128) 69632 [‘conv4_block10_0_relu[0][0]’] conv4_block10_1_bn (BatchNorma (None, 14, 14, 128) 512 [‘conv4_block10_1_conv[0][0]’] lization) conv4_block10_1_relu (Activati (None, 14, 14, 128) 0 [‘conv4_block10_1_bn[0][0]’] on) conv4_block10_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block10_1_relu[0][0]’] conv4_block10_concat (Concaten (None, 14, 14, 576) 0 [‘conv4_block9_concat[0][0]’, ate) ‘conv4_block10_2_conv[0][0]’] conv4_block11_0_bn (BatchNorma (None, 14, 14, 576) 2304 [‘conv4_block10_concat[0][0]’] lization) conv4_block11_0_relu (Activati (None, 14, 14, 576) 0 [‘conv4_block11_0_bn[0][0]’] on) conv4_block11_1_conv (Conv2D) (None, 14, 14, 128) 73728 [‘conv4_block11_0_relu[0][0]’] conv4_block11_1_bn (BatchNorma (None, 14, 14, 128) 512 [‘conv4_block11_1_conv[0][0]’] lization) conv4_block11_1_relu (Activati (None, 14, 14, 128) 0 [‘conv4_block11_1_bn[0][0]’] on) conv4_block11_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block11_1_relu[0][0]’] conv4_block11_concat (Concaten (None, 14, 14, 608) 0 [‘conv4_block10_concat[0][0]’, ate) ‘conv4_block11_2_conv[0][0]’] conv4_block12_0_bn (BatchNorma (None, 14, 14, 608) 2432 [‘conv4_block11_concat[0][0]’] lization) conv4_block12_0_relu (Activati (None, 14, 14, 608) 0 [‘conv4_block12_0_bn[0][0]’] on) conv4_block12_1_conv (Conv2D) (None, 14, 14, 128) 77824 [‘conv4_block12_0_relu[0][0]’] conv4_block12_1_bn (BatchNorma (None, 14, 14, 128) 512 [‘conv4_block12_1_conv[0][0]’] lization) conv4_block12_1_relu (Activati (None, 14, 14, 128) 0 [‘conv4_block12_1_bn[0][0]’] on) conv4_block12_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block12_1_relu[0][0]’] conv4_block12_concat (Concaten (None, 14, 14, 640) 0 [‘conv4_block11_concat[0][0]’, ate) ‘conv4_block12_2_conv[0][0]’] conv4_block13_0_bn (BatchNorma (None, 14, 14, 640) 2560 [‘conv4_block12_concat[0][0]’] lization) conv4_block13_0_relu (Activati (None, 14, 14, 640) 0 [‘conv4_block13_0_bn[0][0]’] on) conv4_block13_1_conv (Conv2D) (None, 14, 14, 128) 81920 [‘conv4_block13_0_relu[0][0]’] conv4_block13_1_bn (BatchNorma (None, 14, 14, 128) 512 [‘conv4_block13_1_conv[0][0]’] lization) conv4_block13_1_relu (Activati (None, 14, 14, 128) 0 [‘conv4_block13_1_bn[0][0]’] on) conv4_block13_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block13_1_relu[0][0]’] conv4_block13_concat (Concaten (None, 14, 14, 672) 0 [‘conv4_block12_concat[0][0]’, ate) ‘conv4_block13_2_conv[0][0]’] conv4_block14_0_bn (BatchNorma (None, 14, 14, 672) 2688 [‘conv4_block13_concat[0][0]’] lization) conv4_block14_0_relu (Activati (None, 14, 14, 672) 0 [‘conv4_block14_0_bn[0][0]’] on) conv4_block14_1_conv (Conv2D) (None, 14, 14, 128) 86016 [‘conv4_block14_0_relu[0][0]’] conv4_block14_1_bn (BatchNorma (None, 14, 14, 128) 512 [‘conv4_block14_1_conv[0][0]’] lization) conv4_block14_1_relu (Activati (None, 14, 14, 128) 0 [‘conv4_block14_1_bn[0][0]’] on) conv4_block14_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block14_1_relu[0][0]’] conv4_block14_concat (Concaten (None, 14, 14, 704) 0 [‘conv4_block13_concat[0][0]’, ate) ‘conv4_block14_2_conv[0][0]’] conv4_block15_0_bn (BatchNorma (None, 14, 14, 704) 2816 [‘conv4_block14_concat[0][0]’] lization) conv4_block15_0_relu (Activati (None, 14, 14, 704) 0 [‘conv4_block15_0_bn[0][0]’] on) conv4_block15_1_conv (Conv2D) (None, 14, 14, 128) 90112 [‘conv4_block15_0_relu[0][0]’] conv4_block15_1_bn (BatchNorma (None, 14, 14, 128) 512 [‘conv4_block15_1_conv[0][0]’] lization) conv4_block15_1_relu (Activati (None, 14, 14, 128) 0 [‘conv4_block15_1_bn[0][0]’] on) conv4_block15_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block15_1_relu[0][0]’] conv4_block15_concat (Concaten (None, 14, 14, 736) 0 [‘conv4_block14_concat[0][0]’, ate) ‘conv4_block15_2_conv[0][0]’] conv4_block16_0_bn (BatchNorma (None, 14, 14, 736) 2944 [‘conv4_block15_concat[0][0]’] lization) conv4_block16_0_relu (Activati (None, 14, 14, 736) 0 [‘conv4_block16_0_bn[0][0]’] on) conv4_block16_1_conv (Conv2D) (None, 14, 14, 128) 94208 [‘conv4_block16_0_relu[0][0]’] conv4_block16_1_bn (BatchNorma (None, 14, 14, 128) 512 [‘conv4_block16_1_conv[0][0]’] lization) conv4_block16_1_relu (Activati (None, 14, 14, 128) 0 [‘conv4_block16_1_bn[0][0]’] on) conv4_block16_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block16_1_relu[0][0]’] conv4_block16_concat (Concaten (None, 14, 14, 768) 0 [‘conv4_block15_concat[0][0]’, ate) ‘conv4_block16_2_conv[0][0]’] conv4_block17_0_bn (BatchNorma (None, 14, 14, 768) 3072 [‘conv4_block16_concat[0][0]’] lization) conv4_block17_0_relu (Activati (None, 14, 14, 768) 0 [‘conv4_block17_0_bn[0][0]’] on) conv4_block17_1_conv (Conv2D) (None, 14, 14, 128) 98304 [‘conv4_block17_0_relu[0][0]’] conv4_block17_1_bn (BatchNorma (None, 14, 14, 128) 512 [‘conv4_block17_1_conv[0][0]’] lization) conv4_block17_1_relu (Activati (None, 14, 14, 128) 0 [‘conv4_block17_1_bn[0][0]’] on) conv4_block17_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block17_1_relu[0][0]’] conv4_block17_concat (Concaten (None, 14, 14, 800) 0 [‘conv4_block16_concat[0][0]’, ate) ‘conv4_block17_2_conv[0][0]’] conv4_block18_0_bn (BatchNorma (None, 14, 14, 800) 3200 [‘conv4_block17_concat[0][0]’] lization) conv4_block18_0_relu (Activati (None, 14, 14, 800) 0 [‘conv4_block18_0_bn[0][0]’] on) conv4_block18_1_conv (Conv2D) (None, 14, 14, 128) 102400 [‘conv4_block18_0_relu[0][0]’] conv4_block18_1_bn (BatchNorma (None, 14, 14, 128) 512 [‘conv4_block18_1_conv[0][0]’] lization) conv4_block18_1_relu (Activati (None, 14, 14, 128) 0 [‘conv4_block18_1_bn[0][0]’] on) conv4_block18_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block18_1_relu[0][0]’] conv4_block18_concat (Concaten (None, 14, 14, 832) 0 [‘conv4_block17_concat[0][0]’, ate) ‘conv4_block18_2_conv[0][0]’] conv4_block19_0_bn (BatchNorma (None, 14, 14, 832) 3328 [‘conv4_block18_concat[0][0]’] lization) conv4_block19_0_relu (Activati (None, 14, 14, 832) 0 [‘conv4_block19_0_bn[0][0]’] on) conv4_block19_1_conv (Conv2D) (None, 14, 14, 128) 106496 [‘conv4_block19_0_relu[0][0]’] conv4_block19_1_bn (BatchNorma (None, 14, 14, 128) 512 [‘conv4_block19_1_conv[0][0]’] lization) conv4_block19_1_relu (Activati (None, 14, 14, 128) 0 [‘conv4_block19_1_bn[0][0]’] on) conv4_block19_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block19_1_relu[0][0]’] conv4_block19_concat (Concaten (None, 14, 14, 864) 0 [‘conv4_block18_concat[0][0]’, ate) ‘conv4_block19_2_conv[0][0]’] conv4_block20_0_bn (BatchNorma (None, 14, 14, 864) 3456 [‘conv4_block19_concat[0][0]’] lization) conv4_block20_0_relu (Activati (None, 14, 14, 864) 0 [‘conv4_block20_0_bn[0][0]’] on) conv4_block20_1_conv (Conv2D) (None, 14, 14, 128) 110592 [‘conv4_block20_0_relu[0][0]’] conv4_block20_1_bn (BatchNorma (None, 14, 14, 128) 512 [‘conv4_block20_1_conv[0][0]’] lization) conv4_block20_1_relu (Activati (None, 14, 14, 128) 0 [‘conv4_block20_1_bn[0][0]’] on) conv4_block20_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block20_1_relu[0][0]’] conv4_block20_concat (Concaten (None, 14, 14, 896) 0 [‘conv4_block19_concat[0][0]’, ate) ‘conv4_block20_2_conv[0][0]’] conv4_block21_0_bn (BatchNorma (None, 14, 14, 896) 3584 [‘conv4_block20_concat[0][0]’] lization) conv4_block21_0_relu (Activati (None, 14, 14, 896) 0 [‘conv4_block21_0_bn[0][0]’] on) conv4_block21_1_conv (Conv2D) (None, 14, 14, 128) 114688 [‘conv4_block21_0_relu[0][0]’] conv4_block21_1_bn (BatchNorma (None, 14, 14, 128) 512 [‘conv4_block21_1_conv[0][0]’] lization) conv4_block21_1_relu (Activati (None, 14, 14, 128) 0 [‘conv4_block21_1_bn[0][0]’] on) conv4_block21_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block21_1_relu[0][0]’] conv4_block21_concat (Concaten (None, 14, 14, 928) 0 [‘conv4_block20_concat[0][0]’, ate) ‘conv4_block21_2_conv[0][0]’] conv4_block22_0_bn (BatchNorma (None, 14, 14, 928) 3712 [‘conv4_block21_concat[0][0]’] lization) conv4_block22_0_relu (Activati (None, 14, 14, 928) 0 [‘conv4_block22_0_bn[0][0]’] on) conv4_block22_1_conv (Conv2D) (None, 14, 14, 128) 118784 [‘conv4_block22_0_relu[0][0]’] conv4_block22_1_bn (BatchNorma (None, 14, 14, 128) 512 [‘conv4_block22_1_conv[0][0]’] lization) conv4_block22_1_relu (Activati (None, 14, 14, 128) 0 [‘conv4_block22_1_bn[0][0]’] on) conv4_block22_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block22_1_relu[0][0]’] conv4_block22_concat (Concaten (None, 14, 14, 960) 0 [‘conv4_block21_concat[0][0]’, ate) ‘conv4_block22_2_conv[0][0]’] conv4_block23_0_bn (BatchNorma (None, 14, 14, 960) 3840 [‘conv4_block22_concat[0][0]’] lization) conv4_block23_0_relu (Activati (None, 14, 14, 960) 0 [‘conv4_block23_0_bn[0][0]’] on) conv4_block23_1_conv (Conv2D) (None, 14, 14, 128) 122880 [‘conv4_block23_0_relu[0][0]’] conv4_block23_1_bn (BatchNorma (None, 14, 14, 128) 512 [‘conv4_block23_1_conv[0][0]’] lization) conv4_block23_1_relu (Activati (None, 14, 14, 128) 0 [‘conv4_block23_1_bn[0][0]’] on) conv4_block23_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block23_1_relu[0][0]’] conv4_block23_concat (Concaten (None, 14, 14, 992) 0 [‘conv4_block22_concat[0][0]’, ate) ‘conv4_block23_2_conv[0][0]’] conv4_block24_0_bn (BatchNorma (None, 14, 14, 992) 3968 [‘conv4_block23_concat[0][0]’] lization) conv4_block24_0_relu (Activati (None, 14, 14, 992) 0 [‘conv4_block24_0_bn[0][0]’] on) conv4_block24_1_conv (Conv2D) (None, 14, 14, 128) 126976 [‘conv4_block24_0_relu[0][0]’] conv4_block24_1_bn (BatchNorma (None, 14, 14, 128) 512 [‘conv4_block24_1_conv[0][0]’] lization) conv4_block24_1_relu (Activati (None, 14, 14, 128) 0 [‘conv4_block24_1_bn[0][0]’] on) conv4_block24_2_conv (Conv2D) (None, 14, 14, 32) 36864 [‘conv4_block24_1_relu[0][0]’] conv4_block24_concat (Concaten (None, 14, 14, 1024 0 [‘conv4_block23_concat[0][0]’, ate) ) ‘conv4_block24_2_conv[0][0]’] pool4_bn (BatchNormalization) (None, 14, 14, 1024 4096 [‘conv4_block24_concat[0][0]’] ) pool4_relu (Activation) (None, 14, 14, 1024 0 [‘pool4_bn[0][0]’] ) pool4_conv (Conv2D) (None, 14, 14, 512) 524288 [‘pool4_relu[0][0]’] pool4_pool (AveragePooling2D) (None, 7, 7, 512) 0 [‘pool4_conv[0][0]’] conv5_block1_0_bn (BatchNormal (None, 7, 7, 512) 2048 [‘pool4_pool[0][0]’] ization) conv5_block1_0_relu (Activatio (None, 7, 7, 512) 0 [‘conv5_block1_0_bn[0][0]’] n) conv5_block1_1_conv (Conv2D) (None, 7, 7, 128) 65536 [‘conv5_block1_0_relu[0][0]’] conv5_block1_1_bn (BatchNormal (None, 7, 7, 128) 512 [‘conv5_block1_1_conv[0][0]’] ization) conv5_block1_1_relu (Activatio (None, 7, 7, 128) 0 [‘conv5_block1_1_bn[0][0]’] n) conv5_block1_2_conv (Conv2D) (None, 7, 7, 32) 36864 [‘conv5_block1_1_relu[0][0]’] conv5_block1_concat (Concatena (None, 7, 7, 544) 0 [‘pool4_pool[0][0]’, te) ‘conv5_block1_2_conv[0][0]’] conv5_block2_0_bn (BatchNormal (None, 7, 7, 544) 2176 [‘conv5_block1_concat[0][0]’] ization) conv5_block2_0_relu (Activatio (None, 7, 7, 544) 0 [‘conv5_block2_0_bn[0][0]’] n) conv5_block2_1_conv (Conv2D) (None, 7, 7, 128) 69632 [‘conv5_block2_0_relu[0][0]’] conv5_block2_1_bn (BatchNormal (None, 7, 7, 128) 512 [‘conv5_block2_1_conv[0][0]’] ization) conv5_block2_1_relu (Activatio (None, 7, 7, 128) 0 [‘conv5_block2_1_bn[0][0]’] n) conv5_block2_2_conv (Conv2D) (None, 7, 7, 32) 36864 [‘conv5_block2_1_relu[0][0]’] conv5_block2_concat (Concatena (None, 7, 7, 576) 0 [‘conv5_block1_concat[0][0]’, te) ‘conv5_block2_2_conv[0][0]’] conv5_block3_0_bn (BatchNormal (None, 7, 7, 576) 2304 [‘conv5_block2_concat[0][0]’] ization) conv5_block3_0_relu (Activatio (None, 7, 7, 576) 0 [‘conv5_block3_0_bn[0][0]’] n) conv5_block3_1_conv (Conv2D) (None, 7, 7, 128) 73728 [‘conv5_block3_0_relu[0][0]’] conv5_block3_1_bn (BatchNormal (None, 7, 7, 128) 512 [‘conv5_block3_1_conv[0][0]’] ization) conv5_block3_1_relu (Activatio (None, 7, 7, 128) 0 [‘conv5_block3_1_bn[0][0]’] n) conv5_block3_2_conv (Conv2D) (None, 7, 7, 32) 36864 [‘conv5_block3_1_relu[0][0]’] conv5_block3_concat (Concatena (None, 7, 7, 608) 0 [‘conv5_block2_concat[0][0]’, te) ‘conv5_block3_2_conv[0][0]’] conv5_block4_0_bn (BatchNormal (None, 7, 7, 608) 2432 [‘conv5_block3_concat[0][0]’] ization) conv5_block4_0_relu (Activatio (None, 7, 7, 608) 0 [‘conv5_block4_0_bn[0][0]’] n) conv5_block4_1_conv (Conv2D) (None, 7, 7, 128) 77824 [‘conv5_block4_0_relu[0][0]’] conv5_block4_1_bn (BatchNormal (None, 7, 7, 128) 512 [‘conv5_block4_1_conv[0][0]’] ization) conv5_block4_1_relu (Activatio (None, 7, 7, 128) 0 [‘conv5_block4_1_bn[0][0]’] n) conv5_block4_2_conv (Conv2D) (None, 7, 7, 32) 36864 [‘conv5_block4_1_relu[0][0]’] conv5_block4_concat (Concatena (None, 7, 7, 640) 0 [‘conv5_block3_concat[0][0]’, te) ‘conv5_block4_2_conv[0][0]’] conv5_block5_0_bn (BatchNormal (None, 7, 7, 640) 2560 [‘conv5_block4_concat[0][0]’] ization) conv5_block5_0_relu (Activatio (None, 7, 7, 640) 0 [‘conv5_block5_0_bn[0][0]’] n) conv5_block5_1_conv (Conv2D) (None, 7, 7, 128) 81920 [‘conv5_block5_0_relu[0][0]’] conv5_block5_1_bn (BatchNormal (None, 7, 7, 128) 512 [‘conv5_block5_1_conv[0][0]’] ization) conv5_block5_1_relu (Activatio (None, 7, 7, 128) 0 [‘conv5_block5_1_bn[0][0]’] n) conv5_block5_2_conv (Conv2D) (None, 7, 7, 32) 36864 [‘conv5_block5_1_relu[0][0]’] conv5_block5_concat (Concatena (None, 7, 7, 672) 0 [‘conv5_block4_concat[0][0]’, te) ‘conv5_block5_2_conv[0][0]’] conv5_block6_0_bn (BatchNormal (None, 7, 7, 672) 2688 [‘conv5_block5_concat[0][0]’] ization) conv5_block6_0_relu (Activatio (None, 7, 7, 672) 0 [‘conv5_block6_0_bn[0][0]’] n) conv5_block6_1_conv (Conv2D) (None, 7, 7, 128) 86016 [‘conv5_block6_0_relu[0][0]’] conv5_block6_1_bn (BatchNormal (None, 7, 7, 128) 512 [‘conv5_block6_1_conv[0][0]’] ization) conv5_block6_1_relu (Activatio (None, 7, 7, 128) 0 [‘conv5_block6_1_bn[0][0]’] n) conv5_block6_2_conv (Conv2D) (None, 7, 7, 32) 36864 [‘conv5_block6_1_relu[0][0]’] conv5_block6_concat (Concatena (None, 7, 7, 704) 0 [‘conv5_block5_concat[0][0]’, te) ‘conv5_block6_2_conv[0][0]’] conv5_block7_0_bn (BatchNormal (None, 7, 7, 704) 2816 [‘conv5_block6_concat[0][0]’] ization) conv5_block7_0_relu (Activatio (None, 7, 7, 704) 0 [‘conv5_block7_0_bn[0][0]’] n) conv5_block7_1_conv (Conv2D) (None, 7, 7, 128) 90112 [‘conv5_block7_0_relu[0][0]’] conv5_block7_1_bn (BatchNormal (None, 7, 7, 128) 512 [‘conv5_block7_1_conv[0][0]’] ization) conv5_block7_1_relu (Activatio (None, 7, 7, 128) 0 [‘conv5_block7_1_bn[0][0]’] n) conv5_block7_2_conv (Conv2D) (None, 7, 7, 32) 36864 [‘conv5_block7_1_relu[0][0]’] conv5_block7_concat (Concatena (None, 7, 7, 736) 0 [‘conv5_block6_concat[0][0]’, te) ‘conv5_block7_2_conv[0][0]’] conv5_block8_0_bn (BatchNormal (None, 7, 7, 736) 2944 [‘conv5_block7_concat[0][0]’] ization) conv5_block8_0_relu (Activatio (None, 7, 7, 736) 0 [‘conv5_block8_0_bn[0][0]’] n) conv5_block8_1_conv (Conv2D) (None, 7, 7, 128) 94208 [‘conv5_block8_0_relu[0][0]’] conv5_block8_1_bn (BatchNormal (None, 7, 7, 128) 512 [‘conv5_block8_1_conv[0][0]’] ization) conv5_block8_1_relu (Activatio (None, 7, 7, 128) 0 [‘conv5_block8_1_bn[0][0]’] n) conv5_block8_2_conv (Conv2D) (None, 7, 7, 32) 36864 [‘conv5_block8_1_relu[0][0]’] conv5_block8_concat (Concatena (None, 7, 7, 768) 0 [‘conv5_block7_concat[0][0]’, te) ‘conv5_block8_2_conv[0][0]’] conv5_block9_0_bn (BatchNormal (None, 7, 7, 768) 3072 [‘conv5_block8_concat[0][0]’] ization) conv5_block9_0_relu (Activatio (None, 7, 7, 768) 0 [‘conv5_block9_0_bn[0][0]’] n) conv5_block9_1_conv (Conv2D) (None, 7, 7, 128) 98304 [‘conv5_block9_0_relu[0][0]’] conv5_block9_1_bn (BatchNormal (None, 7, 7, 128) 512 [‘conv5_block9_1_conv[0][0]’] ization) conv5_block9_1_relu (Activatio (None, 7, 7, 128) 0 [‘conv5_block9_1_bn[0][0]’] n) conv5_block9_2_conv (Conv2D) (None, 7, 7, 32) 36864 [‘conv5_block9_1_relu[0][0]’] conv5_block9_concat (Concatena (None, 7, 7, 800) 0 [‘conv5_block8_concat[0][0]’, te) ‘conv5_block9_2_conv[0][0]’] conv5_block10_0_bn (BatchNorma (None, 7, 7, 800) 3200 [‘conv5_block9_concat[0][0]’] lization) conv5_block10_0_relu (Activati (None, 7, 7, 800) 0 [‘conv5_block10_0_bn[0][0]’] on) conv5_block10_1_conv (Conv2D) (None, 7, 7, 128) 102400 [‘conv5_block10_0_relu[0][0]’] conv5_block10_1_bn (BatchNorma (None, 7, 7, 128) 512 [‘conv5_block10_1_conv[0][0]’] lization) conv5_block10_1_relu (Activati (None, 7, 7, 128) 0 [‘conv5_block10_1_bn[0][0]’] on) conv5_block10_2_conv (Conv2D) (None, 7, 7, 32) 36864 [‘conv5_block10_1_relu[0][0]’] conv5_block10_concat (Concaten (None, 7, 7, 832) 0 [‘conv5_block9_concat[0][0]’, ate) ‘conv5_block10_2_conv[0][0]’] conv5_block11_0_bn (BatchNorma (None, 7, 7, 832) 3328 [‘conv5_block10_concat[0][0]’] lization) conv5_block11_0_relu (Activati (None, 7, 7, 832) 0 [‘conv5_block11_0_bn[0][0]’] on) conv5_block11_1_conv (Conv2D) (None, 7, 7, 128) 106496 [‘conv5_block11_0_relu[0][0]’] conv5_block11_1_bn (BatchNorma (None, 7, 7, 128) 512 [‘conv5_block11_1_conv[0][0]’] lization) conv5_block11_1_relu (Activati (None, 7, 7, 128) 0 [‘conv5_block11_1_bn[0][0]’] on) conv5_block11_2_conv (Conv2D) (None, 7, 7, 32) 36864 [‘conv5_block11_1_relu[0][0]’] conv5_block11_concat (Concaten (None, 7, 7, 864) 0 [‘conv5_block10_concat[0][0]’, ate) ‘conv5_block11_2_conv[0][0]’] conv5_block12_0_bn (BatchNorma (None, 7, 7, 864) 3456 [‘conv5_block11_concat[0][0]’] lization) conv5_block12_0_relu (Activati (None, 7, 7, 864) 0 [‘conv5_block12_0_bn[0][0]’] on) conv5_block12_1_conv (Conv2D) (None, 7, 7, 128) 110592 [‘conv5_block12_0_relu[0][0]’] conv5_block12_1_bn (BatchNorma (None, 7, 7, 128) 512 [‘conv5_block12_1_conv[0][0]’] lization) conv5_block12_1_relu (Activati (None, 7, 7, 128) 0 [‘conv5_block12_1_bn[0][0]’] on) conv5_block12_2_conv (Conv2D) (None, 7, 7, 32) 36864 [‘conv5_block12_1_relu[0][0]’] conv5_block12_concat (Concaten (None, 7, 7, 896) 0 [‘conv5_block11_concat[0][0]’, ate) ‘conv5_block12_2_conv[0][0]’] conv5_block13_0_bn (BatchNorma (None, 7, 7, 896) 3584 [‘conv5_block12_concat[0][0]’] lization) conv5_block13_0_relu (Activati (None, 7, 7, 896) 0 [‘conv5_block13_0_bn[0][0]’] on) conv5_block13_1_conv (Conv2D) (None, 7, 7, 128) 114688 [‘conv5_block13_0_relu[0][0]’] conv5_block13_1_bn (BatchNorma (None, 7, 7, 128) 512 [‘conv5_block13_1_conv[0][0]’] lization) conv5_block13_1_relu (Activati (None, 7, 7, 128) 0 [‘conv5_block13_1_bn[0][0]’] on) conv5_block13_2_conv (Conv2D) (None, 7, 7, 32) 36864 [‘conv5_block13_1_relu[0][0]’] conv5_block13_concat (Concaten (None, 7, 7, 928) 0 [‘conv5_block12_concat[0][0]’, ate) ‘conv5_block13_2_conv[0][0]’] conv5_block14_0_bn (BatchNorma (None, 7, 7, 928) 3712 [‘conv5_block13_concat[0][0]’] lization) conv5_block14_0_relu (Activati (None, 7, 7, 928) 0 [‘conv5_block14_0_bn[0][0]’] on) conv5_block14_1_conv (Conv2D) (None, 7, 7, 128) 118784 [‘conv5_block14_0_relu[0][0]’] conv5_block14_1_bn (BatchNorma (None, 7, 7, 128) 512 [‘conv5_block14_1_conv[0][0]’] lization) conv5_block14_1_relu (Activati (None, 7, 7, 128) 0 [‘conv5_block14_1_bn[0][0]’] on) conv5_block14_2_conv (Conv2D) (None, 7, 7, 32) 36864 [‘conv5_block14_1_relu[0][0]’] conv5_block14_concat (Concaten (None, 7, 7, 960) 0 [‘conv5_block13_concat[0][0]’, ate) ‘conv5_block14_2_conv[0][0]’] conv5_block15_0_bn (BatchNorma (None, 7, 7, 960) 3840 [‘conv5_block14_concat[0][0]’] lization) conv5_block15_0_relu (Activati (None, 7, 7, 960) 0 [‘conv5_block15_0_bn[0][0]’] on) conv5_block15_1_conv (Conv2D) (None, 7, 7, 128) 122880 [‘conv5_block15_0_relu[0][0]’] conv5_block15_1_bn (BatchNorma (None, 7, 7, 128) 512 [‘conv5_block15_1_conv[0][0]’] lization) conv5_block15_1_relu (Activati (None, 7, 7, 128) 0 [‘conv5_block15_1_bn[0][0]’] on) conv5_block15_2_conv (Conv2D) (None, 7, 7, 32) 36864 [‘conv5_block15_1_relu[0][0]’] conv5_block15_concat (Concaten (None, 7, 7, 992) 0 [‘conv5_block14_concat[0][0]’, ate) ‘conv5_block15_2_conv[0][0]’] conv5_block16_0_bn (BatchNorma (None, 7, 7, 992) 3968 [‘conv5_block15_concat[0][0]’] lization) conv5_block16_0_relu (Activati (None, 7, 7, 992) 0 [‘conv5_block16_0_bn[0][0]’] on) conv5_block16_1_conv (Conv2D) (None, 7, 7, 128) 126976 [‘conv5_block16_0_relu[0][0]’] conv5_block16_1_bn (BatchNorma (None, 7, 7, 128) 512 [‘conv5_block16_1_conv[0][0]’] lization) conv5_block16_1_relu (Activati (None, 7, 7, 128) 0 [‘conv5_block16_1_bn[0][0]’] on) conv5_block16_2_conv (Conv2D) (None, 7, 7, 32) 36864 [‘conv5_block16_1_relu[0][0]’] conv5_block16_concat (Concaten (None, 7, 7, 1024) 0 [‘conv5_block15_concat[0][0]’, ate) ‘conv5_block16_2_conv[0][0]’] bn (BatchNormalization) (None, 7, 7, 1024) 4096 [‘conv5_block16_concat[0][0]’] relu (Activation) (None, 7, 7, 1024) 0 [‘bn[0][0]’] avg_pool (GlobalAveragePooling (None, 1024) 0 [‘relu[0][0]’] 2D) predictions (Dense) (None, 1000) 1025000 [‘avg_pool[0][0]’] ==================================================================================================Total params: 8,062,504Trainable params: 7,978,856Non-trainable params: 83,648__________________________________________________________________________________________________NoneSince we are using theDensenet as a architecture with our custom dataset so we have to add our custom dense layer so that we can classify the objects from the datasets objects the snippet is mentioned below:FC_layer_Flatten = tf.keras.layers.Flatten()(baseModel.output) #Line 5Dense=tf.keras.layers.Dense(units=1000,activation=”relu”)(FC_layer_Flatten) #Line 6Dense=tf.keras.layers.Dense(units=800,activation=”relu”)(Dense)#Line 7Dense=tf.keras.layers.Dense(units=400,activation=”relu”)(Dense)#Line 8Dense=tf.keras.layers.Dense(units=200,activation=”relu”)(Dense) #Line 9Dense=tf.keras.layers.Dense(units=100,activation=”relu”)(Dense)#Line 10Classification=tf.keras.layers.Dense(units=10,activation=”softmax”)(Dense) #Line 11Line 5: This line is used to flatten the layer of the Densenet network, already we have output as a form of 1d-tensor, then also i have flatten it for demonstration purpose , which will feed into further layer.Line 6 to Line 10: These following mentioned line are artificial neural network with relu activation.Line 11: The line has 10 neurons with Softmax activation function which allow us to predict the probabilities of each classes from the neural network.model_final = tf.keras.Model(inputs=image_input,outputs=Classification) #Line 12model_final.summary() #Line 13Line 12: This line is used to create the custom model which has Dernsenet architecture as well as our custom fully classification layer. We have specified our input layer as image_input and output layer as Classification so that the model is aware of the input and output layer to do further calculations.Line 13: This snippets shows the full summary of the model which is shown below:Model: “model”__________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_2 (InputLayer) [(None, 32, 32, 3)] 0 [] zero_padding2d_2 (ZeroPadding2 (None, 38, 38, 3) 0 [‘input_2[0][0]’] D) conv1/conv (Conv2D) (None, 16, 16, 64) 9408 [‘zero_padding2d_2[0][0]’] conv1/bn (BatchNormalization) (None, 16, 16, 64) 256 [‘conv1/conv[0][0]’] conv1/relu (Activation) (None, 16, 16, 64) 0 [‘conv1/bn[0][0]’] zero_padding2d_3 (ZeroPadding2 (None, 18, 18, 64) 0 [‘conv1/relu[0][0]’] D) pool1 (MaxPooling2D) (None, 8, 8, 64) 0 [‘zero_padding2d_3[0][0]’] conv2_block1_0_bn (BatchNormal (None, 8, 8, 64) 256 [‘pool1[0][0]’] ization) conv2_block1_0_relu (Activatio (None, 8, 8, 64) 0 [‘conv2_block1_0_bn[0][0]’] n) conv2_block1_1_conv (Conv2D) (None, 8, 8, 128) 8192 [‘conv2_block1_0_relu[0][0]’] conv2_block1_1_bn (BatchNormal (None, 8, 8, 128) 512 [‘conv2_block1_1_conv[0][0]’] ization) conv2_block1_1_relu (Activatio (None, 8, 8, 128) 0 [‘conv2_block1_1_bn[0][0]’] n) conv2_block1_2_conv (Conv2D) (None, 8, 8, 32) 36864 [‘conv2_block1_1_relu[0][0]’] conv2_block1_concat (Concatena (None, 8, 8, 96) 0 [‘pool1[0][0]’, te) ‘conv2_block1_2_conv[0][0]’] conv2_block2_0_bn (BatchNormal (None, 8, 8, 96) 384 [‘conv2_block1_concat[0][0]’] ization) conv2_block2_0_relu (Activatio (None, 8, 8, 96) 0 [‘conv2_block2_0_bn[0][0]’] n) conv2_block2_1_conv (Conv2D) (None, 8, 8, 128) 12288 [‘conv2_block2_0_relu[0][0]’] conv2_block2_1_bn (BatchNormal (None, 8, 8, 128) 512 [‘conv2_block2_1_conv[0][0]’] ization) conv2_block2_1_relu (Activatio (None, 8, 8, 128) 0 [‘conv2_block2_1_bn[0][0]’] n) conv2_block2_2_conv (Conv2D) (None, 8, 8, 32) 36864 [‘conv2_block2_1_relu[0][0]’] conv2_block2_concat (Concatena (None, 8, 8, 128) 0 [‘conv2_block1_concat[0][0]’, te) ‘conv2_block2_2_conv[0][0]’] conv2_block3_0_bn (BatchNormal (None, 8, 8, 128) 512 [‘conv2_block2_concat[0][0]’] ization) conv2_block3_0_relu (Activatio (None, 8, 8, 128) 0 [‘conv2_block3_0_bn[0][0]’] n) conv2_block3_1_conv (Conv2D) (None, 8, 8, 128) 16384 [‘conv2_block3_0_relu[0][0]’] conv2_block3_1_bn (BatchNormal (None, 8, 8, 128) 512 [‘conv2_block3_1_conv[0][0]’] ization) conv2_block3_1_relu (Activatio (None, 8, 8, 128) 0 [‘conv2_block3_1_bn[0][0]’] n) conv2_block3_2_conv (Conv2D) (None, 8, 8, 32) 36864 [‘conv2_block3_1_relu[0][0]’] conv2_block3_concat (Concatena (None, 8, 8, 160) 0 [‘conv2_block2_concat[0][0]’, te) ‘conv2_block3_2_conv[0][0]’] conv2_block4_0_bn (BatchNormal (None, 8, 8, 160) 640 [‘conv2_block3_concat[0][0]’] ization) conv2_block4_0_relu (Activatio (None, 8, 8, 160) 0 [‘conv2_block4_0_bn[0][0]’] n) conv2_block4_1_conv (Conv2D) (None, 8, 8, 128) 20480 [‘conv2_block4_0_relu[0][0]’] conv2_block4_1_bn (BatchNormal (None, 8, 8, 128) 512 [‘conv2_block4_1_conv[0][0]’] ization) conv2_block4_1_relu (Activatio (None, 8, 8, 128) 0 [‘conv2_block4_1_bn[0][0]’] n) conv2_block4_2_conv (Conv2D) (None, 8, 8, 32) 36864 [‘conv2_block4_1_relu[0][0]’] conv2_block4_concat (Concatena (None, 8, 8, 192) 0 [‘conv2_block3_concat[0][0]’, te) ‘conv2_block4_2_conv[0][0]’] conv2_block5_0_bn (BatchNormal (None, 8, 8, 192) 768 [‘conv2_block4_concat[0][0]’] ization) conv2_block5_0_relu (Activatio (None, 8, 8, 192) 0 [‘conv2_block5_0_bn[0][0]’] n) conv2_block5_1_conv (Conv2D) (None, 8, 8, 128) 24576 [‘conv2_block5_0_relu[0][0]’] conv2_block5_1_bn (BatchNormal (None, 8, 8, 128) 512 [‘conv2_block5_1_conv[0][0]’] ization) conv2_block5_1_relu (Activatio (None, 8, 8, 128) 0 [‘conv2_block5_1_bn[0][0]’] n) conv2_block5_2_conv (Conv2D) (None, 8, 8, 32) 36864 [‘conv2_block5_1_relu[0][0]’] conv2_block5_concat (Concatena (None, 8, 8, 224) 0 [‘conv2_block4_concat[0][0]’, te) ‘conv2_block5_2_conv[0][0]’] conv2_block6_0_bn (BatchNormal (None, 8, 8, 224) 896 [‘conv2_block5_concat[0][0]’] ization) conv2_block6_0_relu (Activatio (None, 8, 8, 224) 0 [‘conv2_block6_0_bn[0][0]’] n) conv2_block6_1_conv (Conv2D) (None, 8, 8, 128) 28672 [‘conv2_block6_0_relu[0][0]’] conv2_block6_1_bn (BatchNormal (None, 8, 8, 128) 512 [‘conv2_block6_1_conv[0][0]’] ization) conv2_block6_1_relu (Activatio (None, 8, 8, 128) 0 [‘conv2_block6_1_bn[0][0]’] n) conv2_block6_2_conv (Conv2D) (None, 8, 8, 32) 36864 [‘conv2_block6_1_relu[0][0]’] conv2_block6_concat (Concatena (None, 8, 8, 256) 0 [‘conv2_block5_concat[0][0]’, te) ‘conv2_block6_2_conv[0][0]’] pool2_bn (BatchNormalization) (None, 8, 8, 256) 1024 [‘conv2_block6_concat[0][0]’] pool2_relu (Activation) (None, 8, 8, 256) 0 [‘pool2_bn[0][0]’] pool2_conv (Conv2D) (None, 8, 8, 128) 32768 [‘pool2_relu[0][0]’] pool2_pool (AveragePooling2D) (None, 4, 4, 128) 0 [‘pool2_conv[0][0]’] conv3_block1_0_bn (BatchNormal (None, 4, 4, 128) 512 [‘pool2_pool[0][0]’] ization) conv3_block1_0_relu (Activatio (None, 4, 4, 128) 0 [‘conv3_block1_0_bn[0][0]’] n) conv3_block1_1_conv (Conv2D) (None, 4, 4, 128) 16384 [‘conv3_block1_0_relu[0][0]’] conv3_block1_1_bn (BatchNormal (None, 4, 4, 128) 512 [‘conv3_block1_1_conv[0][0]’] ization) conv3_block1_1_relu (Activatio (None, 4, 4, 128) 0 [‘conv3_block1_1_bn[0][0]’] n) conv3_block1_2_conv (Conv2D) (None, 4, 4, 32) 36864 [‘conv3_block1_1_relu[0][0]’] conv3_block1_concat (Concatena (None, 4, 4, 160) 0 [‘pool2_pool[0][0]’, te) ‘conv3_block1_2_conv[0][0]’] conv3_block2_0_bn (BatchNormal (None, 4, 4, 160) 640 [‘conv3_block1_concat[0][0]’] ization) conv3_block2_0_relu (Activatio (None, 4, 4, 160) 0 [‘conv3_block2_0_bn[0][0]’] n) conv3_block2_1_conv (Conv2D) (None, 4, 4, 128) 20480 [‘conv3_block2_0_relu[0][0]’] conv3_block2_1_bn (BatchNormal (None, 4, 4, 128) 512 [‘conv3_block2_1_conv[0][0]’] ization) conv3_block2_1_relu (Activatio (None, 4, 4, 128) 0 [‘conv3_block2_1_bn[0][0]’] n) conv3_block2_2_conv (Conv2D) (None, 4, 4, 32) 36864 [‘conv3_block2_1_relu[0][0]’] conv3_block2_concat (Concatena (None, 4, 4, 192) 0 [‘conv3_block1_concat[0][0]’, te) ‘conv3_block2_2_conv[0][0]’] conv3_block3_0_bn (BatchNormal (None, 4, 4, 192) 768 [‘conv3_block2_concat[0][0]’] ization) conv3_block3_0_relu (Activatio (None, 4, 4, 192) 0 [‘conv3_block3_0_bn[0][0]’] n) conv3_block3_1_conv (Conv2D) (None, 4, 4, 128) 24576 [‘conv3_block3_0_relu[0][0]’] conv3_block3_1_bn (BatchNormal (None, 4, 4, 128) 512 [‘conv3_block3_1_conv[0][0]’] ization) conv3_block3_1_relu (Activatio (None, 4, 4, 128) 0 [‘conv3_block3_1_bn[0][0]’] n) conv3_block3_2_conv (Conv2D) (None, 4, 4, 32) 36864 [‘conv3_block3_1_relu[0][0]’] conv3_block3_concat (Concatena (None, 4, 4, 224) 0 [‘conv3_block2_concat[0][0]’, te) ‘conv3_block3_2_conv[0][0]’] conv3_block4_0_bn (BatchNormal (None, 4, 4, 224) 896 [‘conv3_block3_concat[0][0]’] ization) conv3_block4_0_relu (Activatio (None, 4, 4, 224) 0 [‘conv3_block4_0_bn[0][0]’] n) conv3_block4_1_conv (Conv2D) (None, 4, 4, 128) 28672 [‘conv3_block4_0_relu[0][0]’] conv3_block4_1_bn (BatchNormal (None, 4, 4, 128) 512 [‘conv3_block4_1_conv[0][0]’] ization) conv3_block4_1_relu (Activatio (None, 4, 4, 128) 0 [‘conv3_block4_1_bn[0][0]’] n) conv3_block4_2_conv (Conv2D) (None, 4, 4, 32) 36864 [‘conv3_block4_1_relu[0][0]’] conv3_block4_concat (Concatena (None, 4, 4, 256) 0 [‘conv3_block3_concat[0][0]’, te) ‘conv3_block4_2_conv[0][0]’] conv3_block5_0_bn (BatchNormal (None, 4, 4, 256) 1024 [‘conv3_block4_concat[0][0]’] ization) conv3_block5_0_relu (Activatio (None, 4, 4, 256) 0 [‘conv3_block5_0_bn[0][0]’] n) conv3_block5_1_conv (Conv2D) (None, 4, 4, 128) 32768 [‘conv3_block5_0_relu[0][0]’] conv3_block5_1_bn (BatchNormal (None, 4, 4, 128) 512 [‘conv3_block5_1_conv[0][0]’] ization) conv3_block5_1_relu (Activatio (None, 4, 4, 128) 0 [‘conv3_block5_1_bn[0][0]’] n) conv3_block5_2_conv (Conv2D) (None, 4, 4, 32) 36864 [‘conv3_block5_1_relu[0][0]’] conv3_block5_concat (Concatena (None, 4, 4, 288) 0 [‘conv3_block4_concat[0][0]’, te) ‘conv3_block5_2_conv[0][0]’] conv3_block6_0_bn (BatchNormal (None, 4, 4, 288) 1152 [‘conv3_block5_concat[0][0]’] ization) conv3_block6_0_relu (Activatio (None, 4, 4, 288) 0 [‘conv3_block6_0_bn[0][0]’] n) conv3_block6_1_conv (Conv2D) (None, 4, 4, 128) 36864 [‘conv3_block6_0_relu[0][0]’] conv3_block6_1_bn (BatchNormal (None, 4, 4, 128) 512 [‘conv3_block6_1_conv[0][0]’] ization) conv3_block6_1_relu (Activatio (None, 4, 4, 128) 0 [‘conv3_block6_1_bn[0][0]’] n) conv3_block6_2_conv (Conv2D) (None, 4, 4, 32) 36864 [‘conv3_block6_1_relu[0][0]’] conv3_block6_concat (Concatena (None, 4, 4, 320) 0 [‘conv3_block5_concat[0][0]’, te) ‘conv3_block6_2_conv[0][0]’] conv3_block7_0_bn (BatchNormal (None, 4, 4, 320) 1280 [‘conv3_block6_concat[0][0]’] ization) conv3_block7_0_relu (Activatio (None, 4, 4, 320) 0 [‘conv3_block7_0_bn[0][0]’] n) conv3_block7_1_conv (Conv2D) (None, 4, 4, 128) 40960 [‘conv3_block7_0_relu[0][0]’] conv3_block7_1_bn (BatchNormal (None, 4, 4, 128) 512 [‘conv3_block7_1_conv[0][0]’] ization) conv3_block7_1_relu (Activatio (None, 4, 4, 128) 0 [‘conv3_block7_1_bn[0][0]’] n) conv3_block7_2_conv (Conv2D) (None, 4, 4, 32) 36864 [‘conv3_block7_1_relu[0][0]’] conv3_block7_concat (Concatena (None, 4, 4, 352) 0 [‘conv3_block6_concat[0][0]’, te) ‘conv3_block7_2_conv[0][0]’] conv3_block8_0_bn (BatchNormal (None, 4, 4, 352) 1408 [‘conv3_block7_concat[0][0]’] ization) conv3_block8_0_relu (Activatio (None, 4, 4, 352) 0 [‘conv3_block8_0_bn[0][0]’] n) conv3_block8_1_conv (Conv2D) (None, 4, 4, 128) 45056 [‘conv3_block8_0_relu[0][0]’] conv3_block8_1_bn (BatchNormal (None, 4, 4, 128) 512 [‘conv3_block8_1_conv[0][0]’] ization) conv3_block8_1_relu (Activatio (None, 4, 4, 128) 0 [‘conv3_block8_1_bn[0][0]’] n) conv3_block8_2_conv (Conv2D) (None, 4, 4, 32) 36864 [‘conv3_block8_1_relu[0][0]’] conv3_block8_concat (Concatena (None, 4, 4, 384) 0 [‘conv3_block7_concat[0][0]’, te) ‘conv3_block8_2_conv[0][0]’] conv3_block9_0_bn (BatchNormal (None, 4, 4, 384) 1536 [‘conv3_block8_concat[0][0]’] ization) conv3_block9_0_relu (Activatio (None, 4, 4, 384) 0 [‘conv3_block9_0_bn[0][0]’] n) conv3_block9_1_conv (Conv2D) (None, 4, 4, 128) 49152 [‘conv3_block9_0_relu[0][0]’] conv3_block9_1_bn (BatchNormal (None, 4, 4, 128) 512 [‘conv3_block9_1_conv[0][0]’] ization) conv3_block9_1_relu (Activatio (None, 4, 4, 128) 0 [‘conv3_block9_1_bn[0][0]’] n) conv3_block9_2_conv (Conv2D) (None, 4, 4, 32) 36864 [‘conv3_block9_1_relu[0][0]’] conv3_block9_concat (Concatena (None, 4, 4, 416) 0 [‘conv3_block8_concat[0][0]’, te) ‘conv3_block9_2_conv[0][0]’] conv3_block10_0_bn (BatchNorma (None, 4, 4, 416) 1664 [‘conv3_block9_concat[0][0]’] lization) conv3_block10_0_relu (Activati (None, 4, 4, 416) 0 [‘conv3_block10_0_bn[0][0]’] on) conv3_block10_1_conv (Conv2D) (None, 4, 4, 128) 53248 [‘conv3_block10_0_relu[0][0]’] conv3_block10_1_bn (BatchNorma (None, 4, 4, 128) 512 [‘conv3_block10_1_conv[0][0]’] lization) conv3_block10_1_relu (Activati (None, 4, 4, 128) 0 [‘conv3_block10_1_bn[0][0]’] on) conv3_block10_2_conv (Conv2D) (None, 4, 4, 32) 36864 [‘conv3_block10_1_relu[0][0]’] conv3_block10_concat (Concaten (None, 4, 4, 448) 0 [‘conv3_block9_concat[0][0]’, ate) ‘conv3_block10_2_conv[0][0]’] conv3_block11_0_bn (BatchNorma (None, 4, 4, 448) 1792 [‘conv3_block10_concat[0][0]’] lization) conv3_block11_0_relu (Activati (None, 4, 4, 448) 0 [‘conv3_block11_0_bn[0][0]’] on) conv3_block11_1_conv (Conv2D) (None, 4, 4, 128) 57344 [‘conv3_block11_0_relu[0][0]’] conv3_block11_1_bn (BatchNorma (None, 4, 4, 128) 512 [‘conv3_block11_1_conv[0][0]’] lization) conv3_block11_1_relu (Activati (None, 4, 4, 128) 0 [‘conv3_block11_1_bn[0][0]’] on) conv3_block11_2_conv (Conv2D) (None, 4, 4, 32) 36864 [‘conv3_block11_1_relu[0][0]’] conv3_block11_concat (Concaten (None, 4, 4, 480) 0 [‘conv3_block10_concat[0][0]’, ate) ‘conv3_block11_2_conv[0][0]’] conv3_block12_0_bn (BatchNorma (None, 4, 4, 480) 1920 [‘conv3_block11_concat[0][0]’] lization) conv3_block12_0_relu (Activati (None, 4, 4, 480) 0 [‘conv3_block12_0_bn[0][0]’] on) conv3_block12_1_conv (Conv2D) (None, 4, 4, 128) 61440 [‘conv3_block12_0_relu[0][0]’] conv3_block12_1_bn (BatchNorma (None, 4, 4, 128) 512 [‘conv3_block12_1_conv[0][0]’] lization) conv3_block12_1_relu (Activati (None, 4, 4, 128) 0 [‘conv3_block12_1_bn[0][0]’] on) conv3_block12_2_conv (Conv2D) (None, 4, 4, 32) 36864 [‘conv3_block12_1_relu[0][0]’] conv3_block12_concat (Concaten (None, 4, 4, 512) 0 [‘conv3_block11_concat[0][0]’, ate) ‘conv3_block12_2_conv[0][0]’] pool3_bn (BatchNormalization) (None, 4, 4, 512) 2048 [‘conv3_block12_concat[0][0]’] pool3_relu (Activation) (None, 4, 4, 512) 0 [‘pool3_bn[0][0]’] pool3_conv (Conv2D) (None, 4, 4, 256) 131072 [‘pool3_relu[0][0]’] pool3_pool (AveragePooling2D) (None, 2, 2, 256) 0 [‘pool3_conv[0][0]’] conv4_block1_0_bn (BatchNormal (None, 2, 2, 256) 1024 [‘pool3_pool[0][0]’] ization) conv4_block1_0_relu (Activatio (None, 2, 2, 256) 0 [‘conv4_block1_0_bn[0][0]’] n) conv4_block1_1_conv (Conv2D) (None, 2, 2, 128) 32768 [‘conv4_block1_0_relu[0][0]’] conv4_block1_1_bn (BatchNormal (None, 2, 2, 128) 512 [‘conv4_block1_1_conv[0][0]’] ization) conv4_block1_1_relu (Activatio (None, 2, 2, 128) 0 [‘conv4_block1_1_bn[0][0]’] n) conv4_block1_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block1_1_relu[0][0]’] conv4_block1_concat (Concatena (None, 2, 2, 288) 0 [‘pool3_pool[0][0]’, te) ‘conv4_block1_2_conv[0][0]’] conv4_block2_0_bn (BatchNormal (None, 2, 2, 288) 1152 [‘conv4_block1_concat[0][0]’] ization) conv4_block2_0_relu (Activatio (None, 2, 2, 288) 0 [‘conv4_block2_0_bn[0][0]’] n) conv4_block2_1_conv (Conv2D) (None, 2, 2, 128) 36864 [‘conv4_block2_0_relu[0][0]’] conv4_block2_1_bn (BatchNormal (None, 2, 2, 128) 512 [‘conv4_block2_1_conv[0][0]’] ization) conv4_block2_1_relu (Activatio (None, 2, 2, 128) 0 [‘conv4_block2_1_bn[0][0]’] n) conv4_block2_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block2_1_relu[0][0]’] conv4_block2_concat (Concatena (None, 2, 2, 320) 0 [‘conv4_block1_concat[0][0]’, te) ‘conv4_block2_2_conv[0][0]’] conv4_block3_0_bn (BatchNormal (None, 2, 2, 320) 1280 [‘conv4_block2_concat[0][0]’] ization) conv4_block3_0_relu (Activatio (None, 2, 2, 320) 0 [‘conv4_block3_0_bn[0][0]’] n) conv4_block3_1_conv (Conv2D) (None, 2, 2, 128) 40960 [‘conv4_block3_0_relu[0][0]’] conv4_block3_1_bn (BatchNormal (None, 2, 2, 128) 512 [‘conv4_block3_1_conv[0][0]’] ization) conv4_block3_1_relu (Activatio (None, 2, 2, 128) 0 [‘conv4_block3_1_bn[0][0]’] n) conv4_block3_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block3_1_relu[0][0]’] conv4_block3_concat (Concatena (None, 2, 2, 352) 0 [‘conv4_block2_concat[0][0]’, te) ‘conv4_block3_2_conv[0][0]’] conv4_block4_0_bn (BatchNormal (None, 2, 2, 352) 1408 [‘conv4_block3_concat[0][0]’] ization) conv4_block4_0_relu (Activatio (None, 2, 2, 352) 0 [‘conv4_block4_0_bn[0][0]’] n) conv4_block4_1_conv (Conv2D) (None, 2, 2, 128) 45056 [‘conv4_block4_0_relu[0][0]’] conv4_block4_1_bn (BatchNormal (None, 2, 2, 128) 512 [‘conv4_block4_1_conv[0][0]’] ization) conv4_block4_1_relu (Activatio (None, 2, 2, 128) 0 [‘conv4_block4_1_bn[0][0]’] n) conv4_block4_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block4_1_relu[0][0]’] conv4_block4_concat (Concatena (None, 2, 2, 384) 0 [‘conv4_block3_concat[0][0]’, te) ‘conv4_block4_2_conv[0][0]’] conv4_block5_0_bn (BatchNormal (None, 2, 2, 384) 1536 [‘conv4_block4_concat[0][0]’] ization) conv4_block5_0_relu (Activatio (None, 2, 2, 384) 0 [‘conv4_block5_0_bn[0][0]’] n) conv4_block5_1_conv (Conv2D) (None, 2, 2, 128) 49152 [‘conv4_block5_0_relu[0][0]’] conv4_block5_1_bn (BatchNormal (None, 2, 2, 128) 512 [‘conv4_block5_1_conv[0][0]’] ization) conv4_block5_1_relu (Activatio (None, 2, 2, 128) 0 [‘conv4_block5_1_bn[0][0]’] n) conv4_block5_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block5_1_relu[0][0]’] conv4_block5_concat (Concatena (None, 2, 2, 416) 0 [‘conv4_block4_concat[0][0]’, te) ‘conv4_block5_2_conv[0][0]’] conv4_block6_0_bn (BatchNormal (None, 2, 2, 416) 1664 [‘conv4_block5_concat[0][0]’] ization) conv4_block6_0_relu (Activatio (None, 2, 2, 416) 0 [‘conv4_block6_0_bn[0][0]’] n) conv4_block6_1_conv (Conv2D) (None, 2, 2, 128) 53248 [‘conv4_block6_0_relu[0][0]’] conv4_block6_1_bn (BatchNormal (None, 2, 2, 128) 512 [‘conv4_block6_1_conv[0][0]’] ization) conv4_block6_1_relu (Activatio (None, 2, 2, 128) 0 [‘conv4_block6_1_bn[0][0]’] n) conv4_block6_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block6_1_relu[0][0]’] conv4_block6_concat (Concatena (None, 2, 2, 448) 0 [‘conv4_block5_concat[0][0]’, te) ‘conv4_block6_2_conv[0][0]’] conv4_block7_0_bn (BatchNormal (None, 2, 2, 448) 1792 [‘conv4_block6_concat[0][0]’] ization) conv4_block7_0_relu (Activatio (None, 2, 2, 448) 0 [‘conv4_block7_0_bn[0][0]’] n) conv4_block7_1_conv (Conv2D) (None, 2, 2, 128) 57344 [‘conv4_block7_0_relu[0][0]’] conv4_block7_1_bn (BatchNormal (None, 2, 2, 128) 512 [‘conv4_block7_1_conv[0][0]’] ization) conv4_block7_1_relu (Activatio (None, 2, 2, 128) 0 [‘conv4_block7_1_bn[0][0]’] n) conv4_block7_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block7_1_relu[0][0]’] conv4_block7_concat (Concatena (None, 2, 2, 480) 0 [‘conv4_block6_concat[0][0]’, te) ‘conv4_block7_2_conv[0][0]’] conv4_block8_0_bn (BatchNormal (None, 2, 2, 480) 1920 [‘conv4_block7_concat[0][0]’] ization) conv4_block8_0_relu (Activatio (None, 2, 2, 480) 0 [‘conv4_block8_0_bn[0][0]’] n) conv4_block8_1_conv (Conv2D) (None, 2, 2, 128) 61440 [‘conv4_block8_0_relu[0][0]’] conv4_block8_1_bn (BatchNormal (None, 2, 2, 128) 512 [‘conv4_block8_1_conv[0][0]’] ization) conv4_block8_1_relu (Activatio (None, 2, 2, 128) 0 [‘conv4_block8_1_bn[0][0]’] n) conv4_block8_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block8_1_relu[0][0]’] conv4_block8_concat (Concatena (None, 2, 2, 512) 0 [‘conv4_block7_concat[0][0]’, te) ‘conv4_block8_2_conv[0][0]’] conv4_block9_0_bn (BatchNormal (None, 2, 2, 512) 2048 [‘conv4_block8_concat[0][0]’] ization) conv4_block9_0_relu (Activatio (None, 2, 2, 512) 0 [‘conv4_block9_0_bn[0][0]’] n) conv4_block9_1_conv (Conv2D) (None, 2, 2, 128) 65536 [‘conv4_block9_0_relu[0][0]’] conv4_block9_1_bn (BatchNormal (None, 2, 2, 128) 512 [‘conv4_block9_1_conv[0][0]’] ization) conv4_block9_1_relu (Activatio (None, 2, 2, 128) 0 [‘conv4_block9_1_bn[0][0]’] n) conv4_block9_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block9_1_relu[0][0]’] conv4_block9_concat (Concatena (None, 2, 2, 544) 0 [‘conv4_block8_concat[0][0]’, te) ‘conv4_block9_2_conv[0][0]’] conv4_block10_0_bn (BatchNorma (None, 2, 2, 544) 2176 [‘conv4_block9_concat[0][0]’] lization) conv4_block10_0_relu (Activati (None, 2, 2, 544) 0 [‘conv4_block10_0_bn[0][0]’] on) conv4_block10_1_conv (Conv2D) (None, 2, 2, 128) 69632 [‘conv4_block10_0_relu[0][0]’] conv4_block10_1_bn (BatchNorma (None, 2, 2, 128) 512 [‘conv4_block10_1_conv[0][0]’] lization) conv4_block10_1_relu (Activati (None, 2, 2, 128) 0 [‘conv4_block10_1_bn[0][0]’] on) conv4_block10_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block10_1_relu[0][0]’] conv4_block10_concat (Concaten (None, 2, 2, 576) 0 [‘conv4_block9_concat[0][0]’, ate) ‘conv4_block10_2_conv[0][0]’] conv4_block11_0_bn (BatchNorma (None, 2, 2, 576) 2304 [‘conv4_block10_concat[0][0]’] lization) conv4_block11_0_relu (Activati (None, 2, 2, 576) 0 [‘conv4_block11_0_bn[0][0]’] on) conv4_block11_1_conv (Conv2D) (None, 2, 2, 128) 73728 [‘conv4_block11_0_relu[0][0]’] conv4_block11_1_bn (BatchNorma (None, 2, 2, 128) 512 [‘conv4_block11_1_conv[0][0]’] lization) conv4_block11_1_relu (Activati (None, 2, 2, 128) 0 [‘conv4_block11_1_bn[0][0]’] on) conv4_block11_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block11_1_relu[0][0]’] conv4_block11_concat (Concaten (None, 2, 2, 608) 0 [‘conv4_block10_concat[0][0]’, ate) ‘conv4_block11_2_conv[0][0]’] conv4_block12_0_bn (BatchNorma (None, 2, 2, 608) 2432 [‘conv4_block11_concat[0][0]’] lization) conv4_block12_0_relu (Activati (None, 2, 2, 608) 0 [‘conv4_block12_0_bn[0][0]’] on) conv4_block12_1_conv (Conv2D) (None, 2, 2, 128) 77824 [‘conv4_block12_0_relu[0][0]’] conv4_block12_1_bn (BatchNorma (None, 2, 2, 128) 512 [‘conv4_block12_1_conv[0][0]’] lization) conv4_block12_1_relu (Activati (None, 2, 2, 128) 0 [‘conv4_block12_1_bn[0][0]’] on) conv4_block12_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block12_1_relu[0][0]’] conv4_block12_concat (Concaten (None, 2, 2, 640) 0 [‘conv4_block11_concat[0][0]’, ate) ‘conv4_block12_2_conv[0][0]’] conv4_block13_0_bn (BatchNorma (None, 2, 2, 640) 2560 [‘conv4_block12_concat[0][0]’] lization) conv4_block13_0_relu (Activati (None, 2, 2, 640) 0 [‘conv4_block13_0_bn[0][0]’] on) conv4_block13_1_conv (Conv2D) (None, 2, 2, 128) 81920 [‘conv4_block13_0_relu[0][0]’] conv4_block13_1_bn (BatchNorma (None, 2, 2, 128) 512 [‘conv4_block13_1_conv[0][0]’] lization) conv4_block13_1_relu (Activati (None, 2, 2, 128) 0 [‘conv4_block13_1_bn[0][0]’] on) conv4_block13_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block13_1_relu[0][0]’] conv4_block13_concat (Concaten (None, 2, 2, 672) 0 [‘conv4_block12_concat[0][0]’, ate) ‘conv4_block13_2_conv[0][0]’] conv4_block14_0_bn (BatchNorma (None, 2, 2, 672) 2688 [‘conv4_block13_concat[0][0]’] lization) conv4_block14_0_relu (Activati (None, 2, 2, 672) 0 [‘conv4_block14_0_bn[0][0]’] on) conv4_block14_1_conv (Conv2D) (None, 2, 2, 128) 86016 [‘conv4_block14_0_relu[0][0]’] conv4_block14_1_bn (BatchNorma (None, 2, 2, 128) 512 [‘conv4_block14_1_conv[0][0]’] lization) conv4_block14_1_relu (Activati (None, 2, 2, 128) 0 [‘conv4_block14_1_bn[0][0]’] on) conv4_block14_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block14_1_relu[0][0]’] conv4_block14_concat (Concaten (None, 2, 2, 704) 0 [‘conv4_block13_concat[0][0]’, ate) ‘conv4_block14_2_conv[0][0]’] conv4_block15_0_bn (BatchNorma (None, 2, 2, 704) 2816 [‘conv4_block14_concat[0][0]’] lization) conv4_block15_0_relu (Activati (None, 2, 2, 704) 0 [‘conv4_block15_0_bn[0][0]’] on) conv4_block15_1_conv (Conv2D) (None, 2, 2, 128) 90112 [‘conv4_block15_0_relu[0][0]’] conv4_block15_1_bn (BatchNorma (None, 2, 2, 128) 512 [‘conv4_block15_1_conv[0][0]’] lization) conv4_block15_1_relu (Activati (None, 2, 2, 128) 0 [‘conv4_block15_1_bn[0][0]’] on) conv4_block15_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block15_1_relu[0][0]’] conv4_block15_concat (Concaten (None, 2, 2, 736) 0 [‘conv4_block14_concat[0][0]’, ate) ‘conv4_block15_2_conv[0][0]’] conv4_block16_0_bn (BatchNorma (None, 2, 2, 736) 2944 [‘conv4_block15_concat[0][0]’] lization) conv4_block16_0_relu (Activati (None, 2, 2, 736) 0 [‘conv4_block16_0_bn[0][0]’] on) conv4_block16_1_conv (Conv2D) (None, 2, 2, 128) 94208 [‘conv4_block16_0_relu[0][0]’] conv4_block16_1_bn (BatchNorma (None, 2, 2, 128) 512 [‘conv4_block16_1_conv[0][0]’] lization) conv4_block16_1_relu (Activati (None, 2, 2, 128) 0 [‘conv4_block16_1_bn[0][0]’] on) conv4_block16_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block16_1_relu[0][0]’] conv4_block16_concat (Concaten (None, 2, 2, 768) 0 [‘conv4_block15_concat[0][0]’, ate) ‘conv4_block16_2_conv[0][0]’] conv4_block17_0_bn (BatchNorma (None, 2, 2, 768) 3072 [‘conv4_block16_concat[0][0]’] lization) conv4_block17_0_relu (Activati (None, 2, 2, 768) 0 [‘conv4_block17_0_bn[0][0]’] on) conv4_block17_1_conv (Conv2D) (None, 2, 2, 128) 98304 [‘conv4_block17_0_relu[0][0]’] conv4_block17_1_bn (BatchNorma (None, 2, 2, 128) 512 [‘conv4_block17_1_conv[0][0]’] lization) conv4_block17_1_relu (Activati (None, 2, 2, 128) 0 [‘conv4_block17_1_bn[0][0]’] on) conv4_block17_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block17_1_relu[0][0]’] conv4_block17_concat (Concaten (None, 2, 2, 800) 0 [‘conv4_block16_concat[0][0]’, ate) ‘conv4_block17_2_conv[0][0]’] conv4_block18_0_bn (BatchNorma (None, 2, 2, 800) 3200 [‘conv4_block17_concat[0][0]’] lization) conv4_block18_0_relu (Activati (None, 2, 2, 800) 0 [‘conv4_block18_0_bn[0][0]’] on) conv4_block18_1_conv (Conv2D) (None, 2, 2, 128) 102400 [‘conv4_block18_0_relu[0][0]’] conv4_block18_1_bn (BatchNorma (None, 2, 2, 128) 512 [‘conv4_block18_1_conv[0][0]’] lization) conv4_block18_1_relu (Activati (None, 2, 2, 128) 0 [‘conv4_block18_1_bn[0][0]’] on) conv4_block18_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block18_1_relu[0][0]’] conv4_block18_concat (Concaten (None, 2, 2, 832) 0 [‘conv4_block17_concat[0][0]’, ate) ‘conv4_block18_2_conv[0][0]’] conv4_block19_0_bn (BatchNorma (None, 2, 2, 832) 3328 [‘conv4_block18_concat[0][0]’] lization) conv4_block19_0_relu (Activati (None, 2, 2, 832) 0 [‘conv4_block19_0_bn[0][0]’] on) conv4_block19_1_conv (Conv2D) (None, 2, 2, 128) 106496 [‘conv4_block19_0_relu[0][0]’] conv4_block19_1_bn (BatchNorma (None, 2, 2, 128) 512 [‘conv4_block19_1_conv[0][0]’] lization) conv4_block19_1_relu (Activati (None, 2, 2, 128) 0 [‘conv4_block19_1_bn[0][0]’] on) conv4_block19_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block19_1_relu[0][0]’] conv4_block19_concat (Concaten (None, 2, 2, 864) 0 [‘conv4_block18_concat[0][0]’, ate) ‘conv4_block19_2_conv[0][0]’] conv4_block20_0_bn (BatchNorma (None, 2, 2, 864) 3456 [‘conv4_block19_concat[0][0]’] lization) conv4_block20_0_relu (Activati (None, 2, 2, 864) 0 [‘conv4_block20_0_bn[0][0]’] on) conv4_block20_1_conv (Conv2D) (None, 2, 2, 128) 110592 [‘conv4_block20_0_relu[0][0]’] conv4_block20_1_bn (BatchNorma (None, 2, 2, 128) 512 [‘conv4_block20_1_conv[0][0]’] lization) conv4_block20_1_relu (Activati (None, 2, 2, 128) 0 [‘conv4_block20_1_bn[0][0]’] on) conv4_block20_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block20_1_relu[0][0]’] conv4_block20_concat (Concaten (None, 2, 2, 896) 0 [‘conv4_block19_concat[0][0]’, ate) ‘conv4_block20_2_conv[0][0]’] conv4_block21_0_bn (BatchNorma (None, 2, 2, 896) 3584 [‘conv4_block20_concat[0][0]’] lization) conv4_block21_0_relu (Activati (None, 2, 2, 896) 0 [‘conv4_block21_0_bn[0][0]’] on) conv4_block21_1_conv (Conv2D) (None, 2, 2, 128) 114688 [‘conv4_block21_0_relu[0][0]’] conv4_block21_1_bn (BatchNorma (None, 2, 2, 128) 512 [‘conv4_block21_1_conv[0][0]’] lization) conv4_block21_1_relu (Activati (None, 2, 2, 128) 0 [‘conv4_block21_1_bn[0][0]’] on) conv4_block21_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block21_1_relu[0][0]’] conv4_block21_concat (Concaten (None, 2, 2, 928) 0 [‘conv4_block20_concat[0][0]’, ate) ‘conv4_block21_2_conv[0][0]’] conv4_block22_0_bn (BatchNorma (None, 2, 2, 928) 3712 [‘conv4_block21_concat[0][0]’] lization) conv4_block22_0_relu (Activati (None, 2, 2, 928) 0 [‘conv4_block22_0_bn[0][0]’] on) conv4_block22_1_conv (Conv2D) (None, 2, 2, 128) 118784 [‘conv4_block22_0_relu[0][0]’] conv4_block22_1_bn (BatchNorma (None, 2, 2, 128) 512 [‘conv4_block22_1_conv[0][0]’] lization) conv4_block22_1_relu (Activati (None, 2, 2, 128) 0 [‘conv4_block22_1_bn[0][0]’] on) conv4_block22_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block22_1_relu[0][0]’] conv4_block22_concat (Concaten (None, 2, 2, 960) 0 [‘conv4_block21_concat[0][0]’, ate) ‘conv4_block22_2_conv[0][0]’] conv4_block23_0_bn (BatchNorma (None, 2, 2, 960) 3840 [‘conv4_block22_concat[0][0]’] lization) conv4_block23_0_relu (Activati (None, 2, 2, 960) 0 [‘conv4_block23_0_bn[0][0]’] on) conv4_block23_1_conv (Conv2D) (None, 2, 2, 128) 122880 [‘conv4_block23_0_relu[0][0]’] conv4_block23_1_bn (BatchNorma (None, 2, 2, 128) 512 [‘conv4_block23_1_conv[0][0]’] lization) conv4_block23_1_relu (Activati (None, 2, 2, 128) 0 [‘conv4_block23_1_bn[0][0]’] on) conv4_block23_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block23_1_relu[0][0]’] conv4_block23_concat (Concaten (None, 2, 2, 992) 0 [‘conv4_block22_concat[0][0]’, ate) ‘conv4_block23_2_conv[0][0]’] conv4_block24_0_bn (BatchNorma (None, 2, 2, 992) 3968 [‘conv4_block23_concat[0][0]’] lization) conv4_block24_0_relu (Activati (None, 2, 2, 992) 0 [‘conv4_block24_0_bn[0][0]’] on) conv4_block24_1_conv (Conv2D) (None, 2, 2, 128) 126976 [‘conv4_block24_0_relu[0][0]’] conv4_block24_1_bn (BatchNorma (None, 2, 2, 128) 512 [‘conv4_block24_1_conv[0][0]’] lization) conv4_block24_1_relu (Activati (None, 2, 2, 128) 0 [‘conv4_block24_1_bn[0][0]’] on) conv4_block24_2_conv (Conv2D) (None, 2, 2, 32) 36864 [‘conv4_block24_1_relu[0][0]’] conv4_block24_concat (Concaten (None, 2, 2, 1024) 0 [‘conv4_block23_concat[0][0]’, ate) ‘conv4_block24_2_conv[0][0]’] pool4_bn (BatchNormalization) (None, 2, 2, 1024) 4096 [‘conv4_block24_concat[0][0]’] pool4_relu (Activation) (None, 2, 2, 1024) 0 [‘pool4_bn[0][0]’] pool4_conv (Conv2D) (None, 2, 2, 512) 524288 [‘pool4_relu[0][0]’] pool4_pool (AveragePooling2D) (None, 1, 1, 512) 0 [‘pool4_conv[0][0]’] conv5_block1_0_bn (BatchNormal (None, 1, 1, 512) 2048 [‘pool4_pool[0][0]’] ization) conv5_block1_0_relu (Activatio (None, 1, 1, 512) 0 [‘conv5_block1_0_bn[0][0]’] n) conv5_block1_1_conv (Conv2D) (None, 1, 1, 128) 65536 [‘conv5_block1_0_relu[0][0]’] conv5_block1_1_bn (BatchNormal (None, 1, 1, 128) 512 [‘conv5_block1_1_conv[0][0]’] ization) conv5_block1_1_relu (Activatio (None, 1, 1, 128) 0 [‘conv5_block1_1_bn[0][0]’] n) conv5_block1_2_conv (Conv2D) (None, 1, 1, 32) 36864 [‘conv5_block1_1_relu[0][0]’] conv5_block1_concat (Concatena (None, 1, 1, 544) 0 [‘pool4_pool[0][0]’, te) ‘conv5_block1_2_conv[0][0]’] conv5_block2_0_bn (BatchNormal (None, 1, 1, 544) 2176 [‘conv5_block1_concat[0][0]’] ization) conv5_block2_0_relu (Activatio (None, 1, 1, 544) 0 [‘conv5_block2_0_bn[0][0]’] n) conv5_block2_1_conv (Conv2D) (None, 1, 1, 128) 69632 [‘conv5_block2_0_relu[0][0]’] conv5_block2_1_bn (BatchNormal (None, 1, 1, 128) 512 [‘conv5_block2_1_conv[0][0]’] ization) conv5_block2_1_relu (Activatio (None, 1, 1, 128) 0 [‘conv5_block2_1_bn[0][0]’] n) conv5_block2_2_conv (Conv2D) (None, 1, 1, 32) 36864 [‘conv5_block2_1_relu[0][0]’] conv5_block2_concat (Concatena (None, 1, 1, 576) 0 [‘conv5_block1_concat[0][0]’, te) ‘conv5_block2_2_conv[0][0]’] conv5_block3_0_bn (BatchNormal (None, 1, 1, 576) 2304 [‘conv5_block2_concat[0][0]’] ization) conv5_block3_0_relu (Activatio (None, 1, 1, 576) 0 [‘conv5_block3_0_bn[0][0]’] n) conv5_block3_1_conv (Conv2D) (None, 1, 1, 128) 73728 [‘conv5_block3_0_relu[0][0]’] conv5_block3_1_bn (BatchNormal (None, 1, 1, 128) 512 [‘conv5_block3_1_conv[0][0]’] ization) conv5_block3_1_relu (Activatio (None, 1, 1, 128) 0 [‘conv5_block3_1_bn[0][0]’] n) conv5_block3_2_conv (Conv2D) (None, 1, 1, 32) 36864 [‘conv5_block3_1_relu[0][0]’] conv5_block3_concat (Concatena (None, 1, 1, 608) 0 [‘conv5_block2_concat[0][0]’, te) ‘conv5_block3_2_conv[0][0]’] conv5_block4_0_bn (BatchNormal (None, 1, 1, 608) 2432 [‘conv5_block3_concat[0][0]’] ization) conv5_block4_0_relu (Activatio (None, 1, 1, 608) 0 [‘conv5_block4_0_bn[0][0]’] n) conv5_block4_1_conv (Conv2D) (None, 1, 1, 128) 77824 [‘conv5_block4_0_relu[0][0]’] conv5_block4_1_bn (BatchNormal (None, 1, 1, 128) 512 [‘conv5_block4_1_conv[0][0]’] ization) conv5_block4_1_relu (Activatio (None, 1, 1, 128) 0 [‘conv5_block4_1_bn[0][0]’] n) conv5_block4_2_conv (Conv2D) (None, 1, 1, 32) 36864 [‘conv5_block4_1_relu[0][0]’] conv5_block4_concat (Concatena (None, 1, 1, 640) 0 [‘conv5_block3_concat[0][0]’, te) ‘conv5_block4_2_conv[0][0]’] conv5_block5_0_bn (BatchNormal (None, 1, 1, 640) 2560 [‘conv5_block4_concat[0][0]’] ization) conv5_block5_0_relu (Activatio (None, 1, 1, 640) 0 [‘conv5_block5_0_bn[0][0]’] n) conv5_block5_1_conv (Conv2D) (None, 1, 1, 128) 81920 [‘conv5_block5_0_relu[0][0]’] conv5_block5_1_bn (BatchNormal (None, 1, 1, 128) 512 [‘conv5_block5_1_conv[0][0]’] ization) conv5_block5_1_relu (Activatio (None, 1, 1, 128) 0 [‘conv5_block5_1_bn[0][0]’] n) conv5_block5_2_conv (Conv2D) (None, 1, 1, 32) 36864 [‘conv5_block5_1_relu[0][0]’] conv5_block5_concat (Concatena (None, 1, 1, 672) 0 [‘conv5_block4_concat[0][0]’, te) ‘conv5_block5_2_conv[0][0]’] conv5_block6_0_bn (BatchNormal (None, 1, 1, 672) 2688 [‘conv5_block5_concat[0][0]’] ization) conv5_block6_0_relu (Activatio (None, 1, 1, 672) 0 [‘conv5_block6_0_bn[0][0]’] n) conv5_block6_1_conv (Conv2D) (None, 1, 1, 128) 86016 [‘conv5_block6_0_relu[0][0]’] conv5_block6_1_bn (BatchNormal (None, 1, 1, 128) 512 [‘conv5_block6_1_conv[0][0]’] ization) conv5_block6_1_relu (Activatio (None, 1, 1, 128) 0 [‘conv5_block6_1_bn[0][0]’] n) conv5_block6_2_conv (Conv2D) (None, 1, 1, 32) 36864 [‘conv5_block6_1_relu[0][0]’] conv5_block6_concat (Concatena (None, 1, 1, 704) 0 [‘conv5_block5_concat[0][0]’, te) ‘conv5_block6_2_conv[0][0]’] conv5_block7_0_bn (BatchNormal (None, 1, 1, 704) 2816 [‘conv5_block6_concat[0][0]’] ization) conv5_block7_0_relu (Activatio (None, 1, 1, 704) 0 [‘conv5_block7_0_bn[0][0]’] n) conv5_block7_1_conv (Conv2D) (None, 1, 1, 128) 90112 [‘conv5_block7_0_relu[0][0]’] conv5_block7_1_bn (BatchNormal (None, 1, 1, 128) 512 [‘conv5_block7_1_conv[0][0]’] ization) conv5_block7_1_relu (Activatio (None, 1, 1, 128) 0 [‘conv5_block7_1_bn[0][0]’] n) conv5_block7_2_conv (Conv2D) (None, 1, 1, 32) 36864 [‘conv5_block7_1_relu[0][0]’] conv5_block7_concat (Concatena (None, 1, 1, 736) 0 [‘conv5_block6_concat[0][0]’, te) ‘conv5_block7_2_conv[0][0]’] conv5_block8_0_bn (BatchNormal (None, 1, 1, 736) 2944 [‘conv5_block7_concat[0][0]’] ization) conv5_block8_0_relu (Activatio (None, 1, 1, 736) 0 [‘conv5_block8_0_bn[0][0]’] n) conv5_block8_1_conv (Conv2D) (None, 1, 1, 128) 94208 [‘conv5_block8_0_relu[0][0]’] conv5_block8_1_bn (BatchNormal (None, 1, 1, 128) 512 [‘conv5_block8_1_conv[0][0]’] ization) conv5_block8_1_relu (Activatio (None, 1, 1, 128) 0 [‘conv5_block8_1_bn[0][0]’] n) conv5_block8_2_conv (Conv2D) (None, 1, 1, 32) 36864 [‘conv5_block8_1_relu[0][0]’] conv5_block8_concat (Concatena (None, 1, 1, 768) 0 [‘conv5_block7_concat[0][0]’, te) ‘conv5_block8_2_conv[0][0]’] conv5_block9_0_bn (BatchNormal (None, 1, 1, 768) 3072 [‘conv5_block8_concat[0][0]’] ization) conv5_block9_0_relu (Activatio (None, 1, 1, 768) 0 [‘conv5_block9_0_bn[0][0]’] n) conv5_block9_1_conv (Conv2D) (None, 1, 1, 128) 98304 [‘conv5_block9_0_relu[0][0]’] conv5_block9_1_bn (BatchNormal (None, 1, 1, 128) 512 [‘conv5_block9_1_conv[0][0]’] ization) conv5_block9_1_relu (Activatio (None, 1, 1, 128) 0 [‘conv5_block9_1_bn[0][0]’] n) conv5_block9_2_conv (Conv2D) (None, 1, 1, 32) 36864 [‘conv5_block9_1_relu[0][0]’] conv5_block9_concat (Concatena (None, 1, 1, 800) 0 [‘conv5_block8_concat[0][0]’, te) ‘conv5_block9_2_conv[0][0]’] conv5_block10_0_bn (BatchNorma (None, 1, 1, 800) 3200 [‘conv5_block9_concat[0][0]’] lization) conv5_block10_0_relu (Activati (None, 1, 1, 800) 0 [‘conv5_block10_0_bn[0][0]’] on) conv5_block10_1_conv (Conv2D) (None, 1, 1, 128) 102400 [‘conv5_block10_0_relu[0][0]’] conv5_block10_1_bn (BatchNorma (None, 1, 1, 128) 512 [‘conv5_block10_1_conv[0][0]’] lization) conv5_block10_1_relu (Activati (None, 1, 1, 128) 0 [‘conv5_block10_1_bn[0][0]’] on) conv5_block10_2_conv (Conv2D) (None, 1, 1, 32) 36864 [‘conv5_block10_1_relu[0][0]’] conv5_block10_concat (Concaten (None, 1, 1, 832) 0 [‘conv5_block9_concat[0][0]’, ate) ‘conv5_block10_2_conv[0][0]’] conv5_block11_0_bn (BatchNorma (None, 1, 1, 832) 3328 [‘conv5_block10_concat[0][0]’] lization) conv5_block11_0_relu (Activati (None, 1, 1, 832) 0 [‘conv5_block11_0_bn[0][0]’] on) conv5_block11_1_conv (Conv2D) (None, 1, 1, 128) 106496 [‘conv5_block11_0_relu[0][0]’] conv5_block11_1_bn (BatchNorma (None, 1, 1, 128) 512 [‘conv5_block11_1_conv[0][0]’] lization) conv5_block11_1_relu (Activati (None, 1, 1, 128) 0 [‘conv5_block11_1_bn[0][0]’] on) conv5_block11_2_conv (Conv2D) (None, 1, 1, 32) 36864 [‘conv5_block11_1_relu[0][0]’] conv5_block11_concat (Concaten (None, 1, 1, 864) 0 [‘conv5_block10_concat[0][0]’, ate) ‘conv5_block11_2_conv[0][0]’] conv5_block12_0_bn (BatchNorma (None, 1, 1, 864) 3456 [‘conv5_block11_concat[0][0]’] lization) conv5_block12_0_relu (Activati (None, 1, 1, 864) 0 [‘conv5_block12_0_bn[0][0]’] on) conv5_block12_1_conv (Conv2D) (None, 1, 1, 128) 110592 [‘conv5_block12_0_relu[0][0]’] conv5_block12_1_bn (BatchNorma (None, 1, 1, 128) 512 [‘conv5_block12_1_conv[0][0]’] lization) conv5_block12_1_relu (Activati (None, 1, 1, 128) 0 [‘conv5_block12_1_bn[0][0]’] on) conv5_block12_2_conv (Conv2D) (None, 1, 1, 32) 36864 [‘conv5_block12_1_relu[0][0]’] conv5_block12_concat (Concaten (None, 1, 1, 896) 0 [‘conv5_block11_concat[0][0]’, ate) ‘conv5_block12_2_conv[0][0]’] conv5_block13_0_bn (BatchNorma (None, 1, 1, 896) 3584 [‘conv5_block12_concat[0][0]’] lization) conv5_block13_0_relu (Activati (None, 1, 1, 896) 0 [‘conv5_block13_0_bn[0][0]’] on) conv5_block13_1_conv (Conv2D) (None, 1, 1, 128) 114688 [‘conv5_block13_0_relu[0][0]’] conv5_block13_1_bn (BatchNorma (None, 1, 1, 128) 512 [‘conv5_block13_1_conv[0][0]’] lization) conv5_block13_1_relu (Activati (None, 1, 1, 128) 0 [‘conv5_block13_1_bn[0][0]’] on) conv5_block13_2_conv (Conv2D) (None, 1, 1, 32) 36864 [‘conv5_block13_1_relu[0][0]’] conv5_block13_concat (Concaten (None, 1, 1, 928) 0 [‘conv5_block12_concat[0][0]’, ate) ‘conv5_block13_2_conv[0][0]’] conv5_block14_0_bn (BatchNorma (None, 1, 1, 928) 3712 [‘conv5_block13_concat[0][0]’] lization) conv5_block14_0_relu (Activati (None, 1, 1, 928) 0 [‘conv5_block14_0_bn[0][0]’] on) conv5_block14_1_conv (Conv2D) (None, 1, 1, 128) 118784 [‘conv5_block14_0_relu[0][0]’] conv5_block14_1_bn (BatchNorma (None, 1, 1, 128) 512 [‘conv5_block14_1_conv[0][0]’] lization) conv5_block14_1_relu (Activati (None, 1, 1, 128) 0 [‘conv5_block14_1_bn[0][0]’] on) conv5_block14_2_conv (Conv2D) (None, 1, 1, 32) 36864 [‘conv5_block14_1_relu[0][0]’] conv5_block14_concat (Concaten (None, 1, 1, 960) 0 [‘conv5_block13_concat[0][0]’, ate) ‘conv5_block14_2_conv[0][0]’] conv5_block15_0_bn (BatchNorma (None, 1, 1, 960) 3840 [‘conv5_block14_concat[0][0]’] lization) conv5_block15_0_relu (Activati (None, 1, 1, 960) 0 [‘conv5_block15_0_bn[0][0]’] on) conv5_block15_1_conv (Conv2D) (None, 1, 1, 128) 122880 [‘conv5_block15_0_relu[0][0]’] conv5_block15_1_bn (BatchNorma (None, 1, 1, 128) 512 [‘conv5_block15_1_conv[0][0]’] lization) conv5_block15_1_relu (Activati (None, 1, 1, 128) 0 [‘conv5_block15_1_bn[0][0]’] on) conv5_block15_2_conv (Conv2D) (None, 1, 1, 32) 36864 [‘conv5_block15_1_relu[0][0]’] conv5_block15_concat (Concaten (None, 1, 1, 992) 0 [‘conv5_block14_concat[0][0]’, ate) ‘conv5_block15_2_conv[0][0]’] conv5_block16_0_bn (BatchNorma (None, 1, 1, 992) 3968 [‘conv5_block15_concat[0][0]’] lization) conv5_block16_0_relu (Activati (None, 1, 1, 992) 0 [‘conv5_block16_0_bn[0][0]’] on) conv5_block16_1_conv (Conv2D) (None, 1, 1, 128) 126976 [‘conv5_block16_0_relu[0][0]’] conv5_block16_1_bn (BatchNorma (None, 1, 1, 128) 512 [‘conv5_block16_1_conv[0][0]’] lization) conv5_block16_1_relu (Activati (None, 1, 1, 128) 0 [‘conv5_block16_1_bn[0][0]’] on) conv5_block16_2_conv (Conv2D) (None, 1, 1, 32) 36864 [‘conv5_block16_1_relu[0][0]’] conv5_block16_concat (Concaten (None, 1, 1, 1024) 0 [‘conv5_block15_concat[0][0]’, ate) ‘conv5_block16_2_conv[0][0]’] bn (BatchNormalization) (None, 1, 1, 1024) 4096 [‘conv5_block16_concat[0][0]’] relu (Activation) (None, 1, 1, 1024) 0 [‘bn[0][0]’] flatten (Flatten) (None, 1024) 0 [‘relu[0][0]’] dense (Dense) (None, 1000) 1025000 [‘flatten[0][0]’] dense_1 (Dense) (None, 800) 800800 [‘dense[0][0]’] dense_2 (Dense) (None, 400) 320400 [‘dense_1[0][0]’] dense_3 (Dense) (None, 200) 80200 [‘dense_2[0][0]’] dense_4 (Dense) (None, 100) 20100 [‘dense_3[0][0]’] dense_5 (Dense) (None, 10) 1010 [‘dense_4[0][0]’] ==================================================================================================Total params: 9,285,014Trainable params: 9,201,366Non-trainable params: 83,648Now we have to compile the model which is shown below:base_learning_rate = 0.0001 #Line 13model_final.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),loss=tf.keras.losses.CategoricalCrossentropy(),metrics=[‘accuracy’]) #Line 14Line 13: We have set the learning rate for the optimizer i.e. 0.0001Line 14: In this snippet we have selected our desired parameters such as accuracy, Optimizer : ADam, Loss: CategoricalCrossentrophy.Finally we can try and predict the model by using the following sbippets:history = model_final.fit(trainX,trainY,epochs=10,batch_size=32,validation_data=(testX, testY)) #Line 15prediction=model_final.predict(testX) #Line 16Line 15: This snippet is used to train the model on train datasets.Line 16: This snippet is used to predict from the model on test datasetsIn next article, we will have hands on experience with Densenet as an Pre-trained Densener model weights as a Feature Extracter in Keras.Stay Tuned !!! Happy Learning :)Special Thanks:As we say “Car is useless if it doesn’t have a good engine” similarly student is useless without proper guidance and motivation. I will like to thank my Guru as well as my Idol “Dr. P. Supraja” and “A. Helen Victoria”- guided me throughout the journey, from the bottom of my heart. As a Guru, she has lighted the best available path for me, motivated me whenever I encountered failure or roadblock- without her support and motivation this was an impossible task for me.WebsiteRavi Shekhar Tiwari: HomePortfolioRavi Shekhar TiwariReferencesPytorch: LinkKeras: LinkTensorflow: Linkif you have any query feel free to contact me with any of the -below mentioned options:YouTube : LinkWebsite: www.rstiwari.comMedium: https://tiwari11-rst.medium.comPortfolio: https://portfolio.rstiwari.com/Articles: https://laptrinhx.com/author/ravi-shekhar-tiwari/Transfer Learning — Part — 7.3!! Densenet Architecture in Keras was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story. Read More