Resolve "release v2.3.0"

Closes #440 (closed)

mlair/model_modules/branched_input_networks.py

@@ -10,6 +10,7 @@ from mlair.helpers import select_from_dict, to_list
 from mlair.model_modules.loss import var_loss
 from mlair.model_modules.recurrent_networks import RNN
 from mlair.model_modules.convolutional_networks import CNNfromConfig
+from mlair.model_modules.residual_networks import ResNet
 from mlair.model_modules.u_networks import UNet
 
 
@@ -522,3 +523,36 @@ class BranchedInputUNet(UNet, BranchedInputCNN):  # pragma: no cover
         print(self.model.summary())
 
 
+class BranchedInputResNet(ResNet, BranchedInputCNN):  # pragma: no cover
+    """
+    A convolutional neural network with multiple input branches and residual blocks (skip connections).
+
+    ```python
+    input_shape = [(65,1,9), (65,1,9)]
+    output_shape = [(4, )]
+
+    # model
+    layer_configuration=[
+        {"type": "Conv2D", "activation": "relu", "kernel_size": (7, 1), "filters": 32, "padding": "same"},
+        {"type": "MaxPooling2D", "pool_size": (2, 1), "strides": (2, 1)},
+        {"type": "residual_block", "activation": "relu", "kernel_size": (3, 1), "filters": 32, "strides": (1, 1), "kernel_regularizer": "l2"},
+        {"type": "residual_block", "activation": "relu", "kernel_size": (3, 1), "filters": 32, "strides": (1, 1), "kernel_regularizer": "l2"},
+        {"type": "residual_block", "activation": "relu", "kernel_size": (3, 1), "filters": 64, "strides": (1, 1), "kernel_regularizer": "l2", "use_1x1conv": True},
+        {"type": "residual_block", "activation": "relu", "kernel_size": (3, 1), "filters": 64, "strides": (1, 1), "kernel_regularizer": "l2"},
+        {"type": "residual_block", "activation": "relu", "kernel_size": (3, 1), "filters": 128, "strides": (1, 1), "kernel_regularizer": "l2", "use_1x1conv": True},
+        {"type": "residual_block", "activation": "relu", "kernel_size": (3, 1), "filters": 128, "strides": (1, 1), "kernel_regularizer": "l2"},
+        {"type": "MaxPooling2D", "pool_size": (2, 1), "strides": (2, 1)},
+        {"type": "Dropout", "rate": 0.25},
+        {"type": "Flatten"},
+        {"type": "Concatenate"},
+        {"type": "Dense", "units": 128, "activation": "relu"}
+    ]
+
+    model = BranchedInputResNet(input_shape, output_shape, layer_configuration)
+    ```
+
+    """
+
+    def __init__(self, input_shape: list, output_shape: list, layer_configuration: list, optimizer="adam", **kwargs):
+
+        super().__init__(input_shape, output_shape, layer_configuration, optimizer=optimizer, **kwargs)