27 lines
No EOL
1 KiB
Python
27 lines
No EOL
1 KiB
Python
import tensorflow as tf
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class VideoCompressionModel(tf.keras.Model):
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def __init__(self, NUM_CHANNELS=3):
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super(VideoCompressionModel, self).__init__()
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# Encoder layers
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self.encoder = tf.keras.Sequential([
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tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(None, None, NUM_CHANNELS)),
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# Add more encoder layers as needed
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])
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# Decoder layers
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self.decoder = tf.keras.Sequential([
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tf.keras.layers.Conv2DTranspose(32, (3, 3), activation='relu', padding='same'),
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# Add more decoder layers as needed
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tf.keras.layers.Conv2D(NUM_CHANNELS, (3, 3), activation='sigmoid', padding='same') # Output layer for video frames
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])
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def call(self, inputs):
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# Encoding the video frames
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compressed_representation = self.encoder(inputs)
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# Decoding to generate compressed video frames
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reconstructed_frames = self.decoder(compressed_representation)
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return reconstructed_frames |