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model_architectures.py
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"""
Created on 15 Jun, 2022 at 10:28
Title: model_architectures.py - ...
Description:
- CNN model for classificatoin
- Unet model for segmentation
@author: Supantha Sen, nrsc, ISRO
"""
# Importing Modules
from tensorflow import keras
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model
# Importing Custom Modules
...
def cnn_model():
model = keras.Sequential()
model.add(Rescaling(1./255, input_shape=(254, 254, 3)))
# Convolutional layer and maxpool layer 1
model.add(keras.layers.Conv2D(32, (3, 3), activation='relu'))
model.add(keras.layers.MaxPool2D(2, 2))
# Convolutional layer and maxpool layer 2
model.add(keras.layers.Conv2D(64, (3, 3), activation='relu'))
model.add(keras.layers.MaxPool2D(2, 2))
# Convolutional layer and maxpool layer 3
model.add(keras.layers.Conv2D(128, (3, 3), activation='relu'))
model.add(keras.layers.MaxPool2D(2, 2))
# Convolutional layer and maxpool layer 4
model.add(keras.layers.Conv2D(128, (3, 3), activation='relu'))
model.add(keras.layers.MaxPool2D(2, 2))
# This layer flattens the resulting image array to 1D array
model.add(keras.layers.Flatten())
# Hidden layer with 512 neurons and Rectified Linear Unit activation function
model.add(keras.layers.Dense(512, activation='relu'))
# Output layer with single neuron which gives 0 or 1
# Here we use sigmoid activation function which makes our model output to lie between 0 and 1
model.add(keras.layers.Dense(1, activation='sigmoid'))
return model
def unet_model():
inputs = Input((512, 512, 3))
s = Lambda(lambda x: x / 255)(inputs)
c1 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(s)
c1 = Dropout(0.1)(c1)
c1 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(c1)
p1 = MaxPooling2D((2, 2))(c1)
c2 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(p1)
c2 = Dropout(0.1)(c2)
c2 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(c2)
p2 = MaxPooling2D((2, 2))(c2)
c3 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(p2)
c3 = Dropout(0.2)(c3)
c3 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(c3)
p3 = MaxPooling2D((2, 2))(c3)
c4 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(p3)
c4 = Dropout(0.2)(c4)
c4 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(c4)
p4 = MaxPooling2D(pool_size=(2, 2))(c4)
c5 = Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(p4)
c5 = Dropout(0.3)(c5)
c5 = Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(c5)
u6 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5)
u6 = concatenate([u6, c4])
c6 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(u6)
c6 = Dropout(0.2)(c6)
c6 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(c6)
u7 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6)
u7 = concatenate([u7, c3])
c7 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(u7)
c7 = Dropout(0.2)(c7)
c7 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(c7)
u8 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7)
u8 = concatenate([u8, c2])
c8 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(u8)
c8 = Dropout(0.1)(c8)
c8 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(c8)
u9 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8)
u9 = concatenate([u9, c1], axis=3)
c9 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(u9)
c9 = Dropout(0.1)(c9)
c9 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same')(c9)
outputs = Conv2D(1, (1, 1), activation='sigmoid')(c9)
model = Model(inputs=[inputs], outputs=[outputs])
return model