UNET Segmentation

Minor fixes in UNET Code

	#minor fixes of https://github.com/zhixuhao/unet
	import tensorflow as tf
	from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Conv2DTranspose, concatenate, BatchNormalization, Dropout
	from tensorflow.keras.models import Model
	from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
	from tensorflow.keras.optimizers import Adam
	from tensorflow.keras import regularizers
	from tensorflow.keras.callbacks import ReduceLROnPlateau
	def unet_newmodel():
	input_size = (256,256,1)
	inputs = Input(input_size)
	conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
	conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
	pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
	conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
	conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
	pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
	conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
	conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
	pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
	conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
	conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
	drop4 = Dropout(0.5)(conv4)
	pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
	conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
	conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
	drop5 = Dropout(0.5)(conv5)
	up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
	merge6 = concatenate([drop4,up6], axis = 3)
	conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
	conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
	up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
	merge7 = concatenate([conv3,up7], axis = 3)
	conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
	conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
	up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
	merge8 = concatenate([conv2,up8], axis = 3)
	conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
	conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
	up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
	merge9 = concatenate([conv1,up9], axis = 3)
	conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
	conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
	conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
	conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
	model = Model(inputs = inputs, outputs = conv10)
	model.compile(optimizer = Adam(learning_rate = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
	return model

view raw unetexample.py hosted with ❤ by GitHub

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