--- a
+++ b/fetal_net/model/fetal_net.py
@@ -0,0 +1,91 @@
+from functools import partial
+
+from keras import Model, Input
+from keras.layers import BatchNormalization, Conv2D, Softmax, MaxPooling2D
+from keras.losses import binary_crossentropy
+from keras.optimizers import RMSprop
+from tensorflow import Tensor
+
+from fetal_net.metrics import vod_coefficient
+
+
+def fetal_envelope_model(input_shape=(5, 64, 64),
+                         optimizer=RMSprop,
+                         initial_learning_rate=5e-4,
+                         loss_function=binary_crossentropy):
+    """
+    :param input_shape:
+    :param n_base_filters:
+    :param depth:
+    :param dropout_rate:
+    :param n_segmentation_levels:
+    :param n_labels:
+    :param optimizer:
+    :param initial_learning_rate:
+    :param loss_function:
+    :param activation_name:
+    :return:
+    """
+    kernel_size = (3, 3)
+    padding = 'same'
+    batch_norm = True
+
+    Conv2D_ = partial(Conv2D, kernel_size=kernel_size, padding=padding, data_format='channels_last')
+
+    def conv_block(input_layer, batch_norm=batch_norm):
+        output = Conv2D_(16, activation='relu')(input_layer)
+        output = MaxPooling2D(data_format='channels_last', padding='same')(output)
+        if batch_norm:
+            output = BatchNormalization()(output)
+        return output
+
+    def fc_block(input_layer: Tensor, output_channels, batch_norm=batch_norm,
+                 activation='tanh'):
+        output = Conv2D_(output_channels,
+                         kernel_size=input_layer.shape[1:3].as_list(),  # input_layer.output_shape[:-1],
+                         padding='valid',
+                         activation=activation)(input_layer)
+        if batch_norm:
+            output = BatchNormalization()(output)
+        return output
+
+    input_layer = Input(input_shape)
+
+    # net = addConvBlock(net, opts, '1', [3 3], 5, 16, 'pool', true, 'relu', true, 'bnorm', bnorm);
+    conv_block_1 = conv_block(input_layer)
+
+    # net = addConvBlock(net, opts, '2', [3 3], 16, 16, 'pool', true, 'relu', true, 'bnorm', bnorm);
+    conv_block_2 = conv_block(conv_block_1)
+
+    # net = addConvBlock(net, opts, '3', [3 3], 16, 16, 'pool', true, 'relu', true, 'bnorm', bnorm);
+    conv_block_3 = conv_block(conv_block_2)
+
+    # net = addConvBlock(net, opts, '4', 'fc', 16, 1000, 'pool', false, 'relu', false, 'bnorm', bnorm);
+    fc_block_1 = fc_block(conv_block_3, 1000)
+
+    # net = addConvBlock(net, opts, '5', 'fc', 1000, 2, 'pool', false, 'relu', false, 'bnorm', false); 
+    fc_block_2 = fc_block(fc_block_1, 2, batch_norm=False)
+
+    # net.layers{end + 1} = struct('type', 'softmax');
+    output_layer = Softmax(name='softmax_last_layer')(fc_block_2)
+
+    model = Model(inputs=input_layer, output=output_layer)
+    model.compile(optimizer=optimizer(lr=initial_learning_rate),
+                  loss=loss_function,
+                  metrics=['binary_accuracy', vod_coefficient])  # 'binary_crossentropy')#loss_function)
+    return model
+
+# Sequential Model
+# model = Sequential()
+# model.add(Conv2D_(16, (3, 3), activation='relu'))
+# model.add(BatchNormalization())
+# model.add(AveragePooling2D())
+# model.add(Conv2D_(16, (3, 3), padding='same', activation='relu'))
+# model.add(AveragePooling2D())
+# model.add(BatchNormalization())
+# model.add(Conv2D_(16, (3, 3), padding='same', activation='relu'))
+# model.add(AveragePooling2D())
+# model.add(BatchNormalization())
+# model.add(Conv2D_(1000, (3, 3), padding='same', activation='tanh'))
+# model.add(BatchNormalization())
+# model.add(Conv2D_(2, (3, 3), padding='same', activation='tanh'))