CODEWITHSUNDEEP
conv-neural-network
user157522
user157522

Reputation: 371

Using Convolution neural network for semantic image segmentation

I am naive to python and this is my first attempt so please bear with me if my question seems to be too basic. I would like to use convolutional neural network to perform semantic segmentation to 9 satellite images. I have so far been successful in importing the images one by one and converting them to grayscale.

I would like to do the following process :

convolution- 16 filters, 33 filter size pooling- 22 filter size output- 4 classes testing and validation 80:20

any lead on this could be helpful. kindly guide !

Upvotes: 1

Views: 189

Answers (1)

Minh-Long Luu
Minh-Long Luu

Reputation: 2751

Here is the code, only the model part:

import tensorflow as tf
from tensorflow.keras import Conv2D, Dense, Flatten, MaxPool2D

# Declare your desired things here
num_filter=32
kernel_size=(3,3)
strides=(1,1)
padding="valid"
input_shape=(width,height,channel)

model = tf.keras.Sequential([
   Conv2D(num_filter, kernel_size, strides=strides, input_shape=input_shape),
   MaxPool2D(),
   Flatten(),
   Dense(4, activation="softmax")
])

Here is a useful link: ConvNet TensorFlow guide

Upvotes: 2

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