From 9cecaeb9d6036335b1ed5b953a01cc3758429b69 Mon Sep 17 00:00:00 2001 From: Jordon Brooks Date: Fri, 25 Aug 2023 01:54:22 +0100 Subject: [PATCH] update model --- video_compression_model.py | 29 ++++++++++++----------------- 1 file changed, 12 insertions(+), 17 deletions(-) diff --git a/video_compression_model.py b/video_compression_model.py index 6e7c9a2..2247aa9 100644 --- a/video_compression_model.py +++ b/video_compression_model.py @@ -89,31 +89,26 @@ class VideoCompressionModel(tf.keras.Model): # Encoder part of the model self.encoder = tf.keras.Sequential([ layers.InputLayer(input_shape=input_shape), - layers.Conv2D(64, (3, 3), padding='same'), - #layers.BatchNormalization(), + layers.Conv2D(32, (3, 3), padding='same'), layers.LeakyReLU(), layers.MaxPooling2D((2, 2), padding='same'), - layers.SeparableConv2D(32, (3, 3), padding='same'), # Using Separable Convolution - #layers.BatchNormalization(), + layers.Dropout(0.4), + layers.SeparableConv2D(16, (3, 3), padding='same'), layers.LeakyReLU(), - layers.MaxPooling2D((2, 2), padding='same') + layers.MaxPooling2D((2, 2), padding='same'), + layers.Dropout(0.4), ]) - # Decoder part of the model + # Decoder part of the model using Transposed Convolutions for upsampling self.decoder = tf.keras.Sequential([ - layers.Conv2DTranspose(32, (3, 3), padding='same'), - #layers.BatchNormalization(), + layers.Conv2DTranspose(16, (3, 3), padding='same'), layers.LeakyReLU(), - layers.Conv2DTranspose(64, (3, 3), dilation_rate=2, padding='same'), # Using Dilated Convolution - #layers.BatchNormalization(), + layers.Dropout(0.4), + layers.Conv2DTranspose(32, (3, 3), strides=(2, 2), padding='same'), layers.LeakyReLU(), - # First Sub-Pixel Convolutional Layer - layers.Conv2DTranspose(NUM_COLOUR_CHANNELS * 4, (3, 3), padding='same'), # 4 times the number of color channels for first upscaling by 2 - layers.Lambda(lambda x: tf.nn.depth_to_space(x, block_size=2)), # Sub-Pixel Convolutional Layer with block_size=2 - # Second Sub-Pixel Convolutional Layer - layers.Conv2DTranspose(NUM_COLOUR_CHANNELS * 4, (3, 3), padding='same'), # 4 times the number of color channels for second upscaling by 2 - layers.Lambda(lambda x: tf.nn.depth_to_space(x, block_size=2)), # Sub-Pixel Convolutional Layer with block_size=2 - layers.Activation('sigmoid') + layers.Dropout(0.4), + layers.UpSampling2D((2, 2)), + layers.Conv2D(NUM_COLOUR_CHANNELS, (3, 3), padding='same', activation='sigmoid') ])