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model.py
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# SNSR by codedcosmos
#
# SNSR is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License 3 as published by
# the Free Software Foundation.
# SNSR is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License 3 for more details.
# You should have received a copy of the GNU General Public License 3
# along with SNSR. If not, see <https://www.gnu.org/licenses/>.
import warnings
warnings.filterwarnings('ignore',category=FutureWarning)
import tensorflow as tf
from tensorflow import keras
def generate_model():
model = keras.Sequential([
# Input
keras.layers.Conv2DTranspose(128, (3, 3), strides=(1, 1), use_bias=False, padding='same',
input_shape=(None, None, 3)),
keras.layers.BatchNormalization(),
keras.layers.LeakyReLU(),
# Detection
keras.layers.Conv2DTranspose(128, (3, 3), strides=(1, 1), use_bias=False, padding='same'),
keras.layers.BatchNormalization(),
keras.layers.LeakyReLU(),
keras.layers.Conv2DTranspose(128, (3, 3), strides=(1, 1), use_bias=False, padding='same'),
keras.layers.BatchNormalization(),
keras.layers.LeakyReLU(),
keras.layers.Conv2DTranspose(128, (3, 3), strides=(1, 1), use_bias=False, padding='same'),
keras.layers.BatchNormalization(),
keras.layers.LeakyReLU(),
keras.layers.Conv2DTranspose(128, (3, 3), strides=(1, 1), use_bias=False, padding='same'),
keras.layers.BatchNormalization(),
keras.layers.LeakyReLU(),
keras.layers.Conv2DTranspose(128, (3, 3), strides=(1, 1), use_bias=False, padding='same'),
keras.layers.BatchNormalization(),
keras.layers.LeakyReLU(),
keras.layers.Conv2DTranspose(128, (3, 3), strides=(1, 1), use_bias=False, padding='same'),
keras.layers.BatchNormalization(),
keras.layers.LeakyReLU(),
# Upscale 2x
keras.layers.Conv2DTranspose(64, (3, 3), strides=(2, 2), use_bias=False, padding='same'),
keras.layers.BatchNormalization(),
keras.layers.LeakyReLU(),
# Interpolate upscaled result
keras.layers.Conv2DTranspose(64, (3, 3), strides=(1, 1), use_bias=False, padding='same'),
keras.layers.BatchNormalization(),
keras.layers.LeakyReLU(),
keras.layers.Conv2DTranspose(64, (3, 3), strides=(1, 1), use_bias=False, padding='same'),
keras.layers.BatchNormalization(),
keras.layers.LeakyReLU(),
keras.layers.Conv2DTranspose(64, (3, 3), strides=(1, 1), use_bias=False, padding='same'),
keras.layers.BatchNormalization(),
keras.layers.LeakyReLU(),
keras.layers.Conv2DTranspose(64, (3, 3), strides=(1, 1), use_bias=False, padding='same'),
keras.layers.BatchNormalization(),
keras.layers.LeakyReLU(),
keras.layers.Conv2DTranspose(64, (3, 3), strides=(1, 1), use_bias=False, padding='same'),
keras.layers.BatchNormalization(),
keras.layers.LeakyReLU(),
keras.layers.Conv2DTranspose(64, (3, 3), strides=(1, 1), use_bias=False, padding='same'),
keras.layers.BatchNormalization(),
keras.layers.LeakyReLU(),
# RGB Output
keras.layers.Conv2DTranspose(3, (5, 5), strides=(1, 1), padding='same', use_bias=False, activation='tanh'),
])
return model