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Commit e37ca30f authored by tomrink's avatar tomrink
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modules/deeplearning/espcn.py
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...@@ -436,11 +436,11 @@ class ESPCN: ...@@ -436,11 +436,11 @@ class ESPCN:
conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=4, strides=2, padding=padding, activation=activation)(conv) conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=4, strides=2, padding=padding, activation=activation)(conv)
print(conv.shape) print(conv.shape)
# conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=3, strides=1, padding=padding, activation=activation)(conv) conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)
# print(conv.shape) print(conv.shape)
#
# conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=3, strides=1, padding=padding, activation=activation)(conv) conv = tf.keras.layers.Conv2DTranspose(num_filters // 8, kernel_size=1, strides=1, padding=padding, activation=activation)(conv)
# print(conv.shape) print(conv.shape)
#self.logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, name='probability', activation=tf.nn.sigmoid)(conv) #self.logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, name='probability', activation=tf.nn.sigmoid)(conv)
self.logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, name='probability')(conv) self.logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, name='probability')(conv)
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