Build Functional Model, remove subclassed model

Functional models are way easier to work with,
and I don't need any advanced features that would
require model subclassing

main.py

@@ -1,70 +1,6 @@
-import tensorflow as tf
-from tensorflow import keras
-
 import tensorflow_datasets as tfds
 
 
-class DecodeConv(keras.layers.Layer):
-    def __init__(self, out_filters, **kwargs):
-        super().__init__(**kwargs)
-        # Should be depthwise followed by batchnorm and relu.
-        self.depthwise = keras.layers.DepthwiseConv2D(5)
-        self.batch_norm = keras.layers.BatchNormalization()
-        self.relu = keras.layers.ReLU(6.)
-        self.pointwise = keras.layers.Conv2D(out_filters, 1)
-        self.pointwise_bn = keras.layers.BatchNormalization()
-        self.pointwise_rl = keras.layers.ReLU(6.)
-
-    def call(self, inputs, **kwargs):
-        inputs = self.depthwise(inputs, **kwargs)
-        inputs = self.batch_norm(inputs, **kwargs)
-        inputs = self.relu(inputs, **kwargs)
-        inputs = self.pointwise(inputs, **kwargs)
-        inputs = self.pointwise_bn(inputs, **kwargs)
-        return self.pointwise_rl(inputs, **kwargs)
-
-
-class FastDepth(keras.Model):
-    def get_config(self):
-        # TODO: What to put here?
-        pass
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.mobile_net = keras.applications.MobileNet(include_top=False)
-
-        # TODO: Try keras.layers.SeparableConv2D as well, should do the same thing if relu is used as activation
-        # It probably doesn't, since
-        self.decode_conv1 = DecodeConv(512)
-        self.decode_conv2 = DecodeConv(256)
-        self.decode_conv3 = DecodeConv(128)
-        self.decode_conv4 = DecodeConv(64)
-        self.decode_conv5 = DecodeConv(32)
-
-        self.final_pointwise = keras.layers.Conv2D(1, 1)
-        self.final_pointwise_bn = keras.layers.BatchNormalization()
-        self.final_pointwise_relu = keras.layers.ReLU()
-
-    def call(self, inputs, is_training=False, **kwargs):
-        # Go through mobilenet, then each decode layer, including skip connections using:
-        # keras.layers.Add()
-        inputs = self.mobile_net(inputs, is_training=is_training, **kwargs)
-
-        # FastDepth Additive Decoder
-        inputs = self.decode_conv1(inputs, is_training=is_training, **kwargs)
-        inputs = self.decode_conv2(inputs, is_training=is_training, **kwargs)
-        inputs = inputs + self.mobile_net.get_layer('conv_pw_5_relu').output
-        inputs = self.decode_conv3(inputs, is_training=is_training, **kwargs)
-        inputs = inputs + self.mobile_net.get_layer('conv_pw_3_relu').output
-        inputs = self.decode_conv4(inputs, is_training=is_training, **kwargs)
-        inputs = inputs + self.mobile_net.get_layer('conv_pw_1_relu').output
-        inputs = self.decode_conv5(inputs, is_training=is_training, **kwargs)
-
-        inputs = self.final_pointwise(inputs, is_training=is_training, **kwargs)
-        inputs = self.final_pointwise_bn(inputs, is_training=is_training, **kwargs)
-        return self.final_pointwise_relu(inputs, is_training=is_training, **kwargs)
-
-
 def load_nyu():
     builder = tfds.builder('nyu_depth_v2')
     builder.download_and_prepare(download_dir='../nyu')