Error after generating ant 1 about deepswarm HOT 6 CLOSED

dataset = Dataset(training_examples=x_train, training_labels=y_train, testing_examples=x_train, testing_labels=y_train)
backend = TFKerasBackend(dataset=dataset, optimizer=tf.keras.optimizers.Adam(1e-4))
deepswarm = DeepSwarm(backend=backend)
topology = deepswarm.find_topology()
trained_topology = deepswarm.train_topology(topology, 50)

DeepSwarm:
    save_folder:
    metrics: accuracy
    max_depth: 15
    reuse_patience: 1

    aco:
        pheromone:
            start: 0.1
            decay: 0.1
            evaporation: 0.1
            verbose: False
        greediness: 0.5
        ant_count: 16

    backend:
        epochs: 15
        batch_size: 16
        patience: 5
        loss: binary_crossentropy
        verbose: True

    spatial_nodes: [InputNode, Conv2DNode, DropoutSpatialNode, BatchNormalizationNode, Pool2DNode]
    flat_nodes: [FlattenNode, DenseNode, DropoutFlatNode, BatchNormalizationFlatNode]

Nodes:

    InputNode:
        type: Input
        attributes: 
            shape: [!!python/tuple [256, 256, 1]]
        transitions:
            Conv2DNode: 1.0

    Conv2DNode:
        type: Conv2D
        attributes:
            filter_count: [32, 64, 128]
            kernel_size: [1, 3, 5]
            activation: [ReLU]
        transitions:
            Conv2DNode: 0.8
            Pool2DNode: 1.2
            FlattenNode: 1.0
            DropoutSpatialNode: 1.1
            BatchNormalizationNode: 1.2
    
    DropoutSpatialNode:
        type: Dropout
        attributes:
            rate: [0.1, 0.3]
        transitions:
            Conv2DNode: 1.1
            Pool2DNode: 1.0
            FlattenNode: 1.0
            BatchNormalizationNode: 1.1

    BatchNormalizationNode:
        type: BatchNormalization
        attributes: {}
        transitions:
            Conv2DNode: 1.1
            Pool2DNode: 1.1
            DropoutSpatialNode: 1.0
            FlattenNode: 1.0

    Pool2DNode:
        type: Pool2D
        attributes:
            pool_type: [max, average]
            pool_size: [2]
            stride: [2, 3]
        transitions:
            Conv2DNode: 1.1
            FlattenNode: 1.0
            BatchNormalizationNode: 1.1

    FlattenNode:
        type: Flatten
        attributes: {}
        transitions:
            DenseNode: 1.0
            OutputNode: 0.8
            BatchNormalizationFlatNode: 0.9

    DenseNode:
        type: Dense
        attributes:
            output_size: [64, 128]
            activation: [ReLU, Sigmoid]
        transitions:
            DenseNode: 0.8
            DropoutFlatNode: 1.2
            BatchNormalizationFlatNode: 1.2
            OutputNode: 1.0

    DropoutFlatNode:
        type: Dropout
        attributes:
            rate: [0.1, 0.3]
        transitions:
            DenseNode: 1.0
            BatchNormalizationFlatNode: 1.0
            OutputNode: 0.9

    BatchNormalizationFlatNode:
        type: BatchNormalization
        attributes: {}
        transitions:
            DenseNode: 1.1
            DropoutFlatNode: 1.1
            OutputNode: 0.9

    OutputNode:
        type: Output
        attributes:
            output_size: [1]
            activation: [Sigmoid]
        transitions: {}