From 1764e10da4352a0e4e0a1128eb86f344cda91a30 Mon Sep 17 00:00:00 2001 From: Jim Martens Date: Fri, 12 Apr 2019 14:26:43 +0200 Subject: [PATCH] Removed unnecessary layers and fixed the names of the remaining ones Signed-off-by: Jim Martens --- src/twomartens/masterthesis/aae/model.py | 30 +++++++----------------- 1 file changed, 8 insertions(+), 22 deletions(-) diff --git a/src/twomartens/masterthesis/aae/model.py b/src/twomartens/masterthesis/aae/model.py index 0263e7f..b45a099 100644 --- a/src/twomartens/masterthesis/aae/model.py +++ b/src/twomartens/masterthesis/aae/model.py @@ -49,29 +49,21 @@ class Encoder(keras.Model): self.conv1 = keras.layers.Conv2D(filters=zsize * 2, kernel_size=7, strides=1, name='conv1', padding='same', kernel_initializer=weight_init) self.conv1_a = keras.layers.ReLU() - self.dropout = keras.layers.Dropout(rate=0.25) - self.pool1 = keras.layers.MaxPool2D(pool_size=(2, 2), padding='same', name='pool1') - self.conv3 = keras.layers.Conv2D(filters=zsize * 2, kernel_size=7, strides=1, name='conv3', + self.conv2 = keras.layers.Conv2D(filters=zsize * 2, kernel_size=7, strides=1, name='conv2', + padding='same', kernel_initializer=weight_init) + self.conv2_a = keras.layers.ReLU() + self.conv3 = keras.layers.Conv2D(filters=zsize, kernel_size=7, strides=1, name='conv3', padding='same', kernel_initializer=weight_init) self.conv3_a = keras.layers.ReLU() - self.pool3 = keras.layers.MaxPool2D(pool_size=(2, 2), padding='same', name='pool3') - self.conv4 = keras.layers.Conv2D(filters=zsize, kernel_size=7, strides=1, name='conv4', - padding='same', kernel_initializer=weight_init) - self.conv4_a = keras.layers.ReLU() - self.pool4 = keras.layers.MaxPool2D(pool_size=(2, 2), padding='same', name='pool4') def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor: """See base class.""" result = self.conv1(inputs) result = self.conv1_a(result) - # result = self.dropout(result) - # result = self.pool1(result) + result = self.conv2(result) + result = self.conv2_a(result) result = self.conv3(result) result = self.conv3_a(result) - # result = self.pool3(result) - result = self.conv4(result) - result = self.conv4_a(result) - # result = self.pool4(result) return result @@ -91,30 +83,24 @@ class Decoder(keras.Model): self.deconv1 = keras.layers.Conv2D(filters=zsize, kernel_size=7, strides=1, name='deconv1', padding='same', kernel_initializer=weight_init) self.deconv1_a = keras.layers.ReLU() - self.upsample1 = keras.layers.UpSampling2D(size=(2, 2), name='upsample1') self.deconv2 = keras.layers.Conv2D(filters=zsize * 2, kernel_size=7, strides=1, name='deconv2', padding='same', kernel_initializer=weight_init) self.deconv2_a = keras.layers.ReLU() - self.upsample2 = keras.layers.UpSampling2D(size=(2, 2), name='upsample2') self.deconv3 = keras.layers.Conv2D(filters=zsize * 2, kernel_size=7, strides=1, name='deconv3', padding='same', kernel_initializer=weight_init) self.deconv3_a = keras.layers.ReLU() - self.upsample3 = keras.layers.UpSampling2D(size=(2, 2), name='upsample3') - self.deconv5 = keras.layers.Conv2D(filters=channels, kernel_size=7, strides=1, name='deconv5', + self.deconv4 = keras.layers.Conv2D(filters=channels, kernel_size=7, strides=1, name='deconv4', padding='same', kernel_initializer=weight_init) def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor: """See base class.""" result = self.deconv1(inputs) result = self.deconv1_a(result) - # result = self.upsample1(result) result = self.deconv2(result) result = self.deconv2_a(result) - # result = self.upsample2(result) result = self.deconv3(result) result = self.deconv3_a(result) - # result = self.upsample3(result) - result = self.deconv5(result) + result = self.deconv4(result) result = k.sigmoid(result) return result