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code to make an end-to-end image segmentation model using keras and tensorflow

Energetic Eel answered on June 24, 2023 Popularity 1/10 Helpfulness 1/10

answer code to make an end-to-end image segmentation model using keras and tensorflow

code to make an end-to-end image segmentation model using keras and tensorflow

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import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Dropout, UpSampling2D

# Define the U-Net architecture
def unet(input_size):
    inputs = Input(input_size)
    
    # Encoder
    conv1 = Conv2D(64, 3, activation='relu', padding='same')(inputs)
    conv1 = Conv2D(64, 3, activation='relu', padding='same')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    
    conv2 = Conv2D(128, 3, activation='relu', padding='same')(pool1)
    conv2 = Conv2D(128, 3, activation='relu', padding='same')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    
    conv3 = Conv2D(256, 3, activation='relu', padding='same')(pool2)
    conv3 = Conv2D(256, 3, activation='relu', padding='same')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    
    conv4 = Conv2D(512, 3, activation='relu', padding='same')(pool3)
    conv4 = Conv2D(512, 3, activation='relu', padding='same')(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
    
    # Bridge
    conv5 = Conv2D(1024, 3, activation='relu', padding='same')(pool4)
    conv5 = Conv2D(1024, 3, activation='relu', padding='same')(conv5)
    drop5 = Dropout(0.5)(conv5)
    
    # Decoder
    up6 = Conv2D(512, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(drop5))
    merge6 = tf.keras.layers.concatenate([drop4, up6], axis=3)
    conv6 = Conv2D(512, 3, activation='relu', padding='same')(merge6)
    conv6 = Conv2D(512, 3, activation='relu', padding='same')(conv6)
    
    up7 = Conv2D(256, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv6))
    merge7 = tf.keras.layers.concatenate([conv3, up7], axis=3)
    conv7 = Conv2D(256, 3, activation='relu', padding='same')(merge7)
    conv7 = Conv2D(256, 3, activation='relu', padding='same')(conv7)
    
    up8 = Conv2D(128, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv7))
    merge8 = tf.keras.layers.concatenate([conv2, up8], axis=3)
    conv8 = Conv2D(128, 3, activation='relu', padding='same')(merge8)
    conv8 = Conv2D(128, 3, activation='relu', padding='same')(conv8)
    
    up9 = Conv2D(64, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv8))
    merge9 = tf.keras.layers.concatenate([conv1, up9], axis=3)
    conv9 = Conv2D(64, 3, activation='relu', padding='same')(merge9)
    conv9 = Conv2D(64, 3, activation='relu', padding='same')(conv9)
    conv9 = Conv2D(2, 3, activation='relu', padding='same')(conv9)
    
    # Output
    outputs = Conv2D(1, 1, activation='sigmoid')(conv9)
    
    model = Model(inputs=inputs, outputs=outputs)
    
    return model

# Create the model
input_size = (256, 256, 3)  # Adjust the input size as per your requirements
model = unet(input_size)

# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Train the model
# X_train and Y_train are your input images and corresponding segmentation masks
model.fit(X_train, Y_train, batch_size=16, epochs=10)

# Once the model is trained, you can use it for inference
# X_test is your test image for segmentation
predicted_masks = model.predict(X_test)

Popularity 1/10 Helpfulness 1/10 Language python

Source: chat.openai.com

Tags: end-to-end image-segmentation keras model python tensorflow using

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Contributed on Jun 24 2023

Energetic Eel

0 Answers Avg Quality 2/10

code to make an end-to-end image segmentation model using keras and tensorflow

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