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createNetwork.py
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from tensorflow.keras.layers import Input, concatenate, Dropout
from tensorflow.keras.layers import Conv2D, MaxPooling2D, UpSampling2D, LeakyReLU, BatchNormalization, Softmax
import tensorflow as tf
def createUnet_modified():
alpha = 0.01
inputs = Input(shape=(None, None, 6))
conv1 = Conv2D(64, kernel_size=(3, 3), padding='same')(inputs)
conv1 = BatchNormalization(axis=-1)(conv1)
conv1 = LeakyReLU(alpha=alpha)(conv1)
conv1 = Dropout(0.5)(conv1)
conv1 = Conv2D(64, kernel_size=(3, 3), padding='same')(conv1)
conv1 = BatchNormalization(axis=-1)(conv1)
conv1 = LeakyReLU(alpha=alpha)(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, kernel_size=(3, 3), padding='same')(pool1)
conv2 = BatchNormalization(axis=-1)(conv2)
conv2 = LeakyReLU(alpha=alpha)(conv2)
conv2 = Dropout(0.5)(conv2)
conv2 = Conv2D(128, kernel_size=(3, 3), padding='same')(conv2)
conv2 = BatchNormalization(axis=-1)(conv2)
conv2 = LeakyReLU(alpha=alpha)(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, kernel_size=(3, 3), padding='same')(pool2)
conv3 = BatchNormalization(axis=-1)(conv3)
conv3 = LeakyReLU(alpha=alpha)(conv3)
conv3 = Dropout(0.5)(conv3)
conv3 = Conv2D(256, kernel_size=(3, 3), padding='same')(conv3)
conv3 = BatchNormalization(axis=-1)(conv3)
conv3 = LeakyReLU(alpha=alpha)(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, kernel_size=(3, 3), padding='same')(pool3)
conv4 = BatchNormalization(axis=-1)(conv4)
conv4 = LeakyReLU(alpha=alpha)(conv4)
conv4 = Dropout(0.5)(conv4)
conv4 = Conv2D(512, kernel_size=(3, 3), padding='same')(conv4)
conv4 = BatchNormalization(axis=-1)(conv4)
conv4 = LeakyReLU(alpha=alpha)(conv4)
up1 = concatenate([UpSampling2D(size=(2, 2))(conv4), conv3], axis=-1)
conv5 = Conv2D(256, kernel_size=(3, 3), padding='same')(up1)
conv5 = BatchNormalization(axis=-1)(conv5)
conv5 = LeakyReLU(alpha=alpha)(conv5)
conv5 = Dropout(0.5)(conv5)
conv5 = Conv2D(256, kernel_size=(3, 3), padding='same')(conv5)
conv5 = BatchNormalization(axis=-1)(conv5)
conv5 = LeakyReLU(alpha=alpha)(conv5)
up2 = concatenate([UpSampling2D(size=(2, 2))(conv5), conv2], axis=-1)
conv6 = Conv2D(128, kernel_size=(3, 3), padding='same')(up2)
conv6 = BatchNormalization(axis=-1)(conv6)
conv6 = LeakyReLU(alpha=alpha)(conv6)
conv6 = Dropout(0.5)(conv6)
conv6 = Conv2D(128, kernel_size=(3, 3), padding='same')(conv6)
conv6 = BatchNormalization(axis=-1)(conv6)
conv6 = LeakyReLU(alpha=alpha)(conv6)
up3 = concatenate([UpSampling2D(size=(2, 2))(conv6), conv1], axis=-1)
conv7 = Conv2D(64, kernel_size=(3, 3), padding='same')(up3)
conv7 = BatchNormalization(axis=-1)(conv7)
conv7 = LeakyReLU(alpha=alpha)(conv7)
conv7 = Conv2D(64, kernel_size=(3, 3), padding='same')(conv7)
conv7 = BatchNormalization(axis=-1)(conv7)
conv7 = LeakyReLU(alpha=alpha)(conv7)
out = Conv2D(3, kernel_size=(1, 1), padding='same')(conv7)
out = Softmax(axis=-1)(out)
model = tf.keras.models.Model(inputs=inputs, outputs=out)
return model
if __name__ == "__main__":
model = createUnet_modified()