diff --git a/src/icosagon/model.py b/src/icosagon/model.py
new file mode 100644
index 0000000..ba28bb7
--- /dev/null
+++ b/src/icosagon/model.py
@@ -0,0 +1,127 @@
+from .data import Data
+from typing import List
+from .trainprep import prepare_training, \
+    TrainValTest
+import torch
+from .convlayer import DecagonLayer
+from .input import OneHotInputLayer
+from types import FunctionType
+from .declayer import DecodeLayer
+from .batch import PredictionsBatch
+
+
+class Model(object):
+    def __init__(self, data: Data,
+        layer_dimensions: List[int] = [32, 64],
+        ratios: TrainValTest = TrainValTest(.8, .1, .1),
+        keep_prob: float = 1.,
+        rel_activation: Callable[[torch.Tensor], torch.Tensor] = lambda x: x,
+        layer_activation: Callable[[torch.Tensor], torch.Tensor] = torch.nn.functional.relu,
+        dec_activation: Callable[[torch.Tensor], torch.Tensor] = lambda x: x,
+        lr: float = 0.001,
+        loss = Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = torch.nn.functional.binary_cross_entropy_with_logits,
+        batch_size: int = 100) -> None:
+
+        if not isinstance(data, Data):
+            raise TypeError('data must be an instance of Data')
+
+        if not isinstance(layer_sizes, list):
+            raise TypeError('layer_dimensions must be a list')
+
+        if not isinstance(ratios, TrainValTest):
+            raise TypeError('ratios must be an instance of TrainValTest')
+
+        keep_prob = float(keep_prob)
+
+        if not isinstance(rel_activation, FunctionType):
+            raise TypeError('rel_activation must be a function')
+
+        if not isinstance(layer_activation, FunctionType):
+            raise TypeError('layer_activation must be a function')
+
+        if not isinstance(dec_activation, FunctionType):
+            raise TypeError('dec_activation must be a function')
+
+        lr = float(lr)
+
+        if not isinstance(loss, FunctionType):
+            raise TypeError('loss must be a function')
+
+        batch_size = int(batch_size)
+
+        self.data = data
+        self.layer_dimensions = layer_dimensions
+        self.ratios = ratios
+        self.keep_prob = keep_prob
+        self.rel_activation = rel_activation
+        self.layer_activation = layer_activation
+        self.dec_activation = dec_activation
+        self.lr = lr
+        self.loss = loss
+        self.batch_size = batch_size
+
+        self.build()
+
+    def build(self):
+        self.prep_d = prepare_training(self.data, self.ratios)
+
+        in_layer = OneHotInputLayer(self.prep_d)
+        last_output_dim = in_layer.output_dim
+        seq = [ in_layer ]
+
+        for dim in self.layer_dimensions:
+            conv_layer = DecagonLayer(input_dim = last_output_dim,
+                output_dim = [ dim ] * len(self.prep_d.node_types),
+                data = self.prep_d,
+                keep_prob = self.keep_prob,
+                rel_activation = self.rel_activation,
+                layer_activation = self.layer_activation)
+            last_output_dim = conv_layer.output_dim
+            seq.append(conv_layer)
+
+        dec_layer = DecodeLayer(input_dim = last_output_dim,
+            data = self.prep_d,
+            keep_prob = self.keep_prob,
+            activation = self.dec_activation)
+        seq.append(dec_layer)
+
+        seq = torch.nn.Sequential(*seq)
+        self.seq = seq
+
+        opt = torch.optim.Adam(seq.parameters(), lr=self.lr)
+        self.opt = opt
+
+
+    def run_epoch(self):
+        pred = self.seq(None)
+        batch = PredictionsBatch(pred, self.batch_size)
+        n = len(list(iter(batch)))
+        loss_sum = 0
+        for i in range(n - 1):
+            self.opt.zero_grad()
+            pred = self.seq(None)
+            batch = PredictionsBatch(pred, self.batch_size)
+            seed = torch.rand(1).item()
+            rng_state = torch.get_rng_state()
+            torch.manual_seed(seed)
+            it = iter(batch)
+            torch.set_rng_state(rng_state)
+            for k in range(i):
+                _ = next(it)
+            (input, target) = next(it)
+            loss = self.loss(input, target)
+            loss.backward()
+            self.opt.optimize()
+            loss_sum += loss.detach().cpu().item()
+        return loss_sum
+
+
+    def train(self, max_epochs):
+        best_loss = None
+        best_epoch = None
+        for i in range(max_epochs):
+            loss = self.run_epoch()
+            if best_loss is None or loss < best_loss:
+                best_loss = loss
+                best_epoch = i
+        return loss, best_loss, best_epoch