from keras import backend as K
from tensorflow.keras.layers import MaxPooling2D,Conv2D,Input,Add,Flatten,AveragePooling2D,Dense,BatchNormalization,ZeroPadding2D,Activation
from tensorflow.keras.models import Model
def Dense_Layer(x,k):
x = BatchNormalization(axis = 3)(x)
x = Activation('relu')(x)
x = Conv2D(4*k,(1,1),strides = (1,1))(x)
x = BatchNormalization(axis = 3)(x)
x = Activation('relu')(x)
x = Conv2D(k,(1,1),strides = (1,1))(x)
return x
def Dense_Block(x,k):
x1 = Dense_Layer(x,k)
x1_add = keras.layers.Concatenate()([x1,x])
x2 = Dense_Layer(x1_add,k)
x2_add = keras.layers.Concatenate()([x1,x2])
return x2_add
def Dilated_Spatial_Pyramid_Pooling(x,k):
x = BatchNormalization(axis = 3)(x)
d1 = Conv2D(k, (1,1), dilation_rate = 2)(x)
d2 = Conv2D(k, (1,1), dilation_rate = …Run Code Online (Sandbox Code Playgroud) neural-network image-segmentation keras tensorflow unity3d-unet