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March 15, 2021 13:47
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A simple auto-encoder using the Subpixel1D layer for upsampling
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# down- and up-sampling by a factor of 4 | |
strides = 4 | |
inputs = tf.keras.Input(shape=(16384, 1)) | |
d = tf.keras.layers.Conv1D(16, kernel_size=64, strides=strides, | |
padding='same', activation='elu', | |
kernel_initializer='he_normal')(inputs) | |
d = tf.keras.layers.Conv1D(32, kernel_size=32, strides=strides, padding='same', | |
activation='elu', kernel_initializer='he_normal')(d) | |
d = tf.keras.layers.Conv1D(64, kernel_size=16, strides=strides, padding='same', | |
activation='elu', kernel_initializer='he_normal')(d) | |
bottleneck = tf.keras.layers.Conv1D(32, kernel_size=8, | |
strides=1, padding='same', | |
activation='elu', | |
kernel_initializer='he_normal', | |
name='bottleneck')(d) | |
u = tf.keras.layers.Conv1D(64, kernel_size=16, strides=1, | |
padding='same', activation='elu', | |
kernel_initializer='he_normal')(bottleneck) | |
u = Subpixel1D(r=strides)(u) | |
u = tf.keras.layers.Conv1D(32, kernel_size=32, strides=1, padding='same', | |
activation='elu', kernel_initializer='he_normal')(u) | |
u = Subpixel1D(r=strides)(u) | |
u = tf.keras.layers.Conv1D(strides, kernel_size=64, strides=1, padding='same', | |
activation='elu', kernel_initializer='he_normal')(u) | |
outputs = Subpixel1D(r=strides)(u) | |
model = tf.keras.Model(inputs=inputs, outputs=outputs) |
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