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Layers are nodes which consist of multiple horizontally parallel nodes. The incoming data is split up according to the dimensions of the internal nodes. For example if the first node has an input_dim of 50 and the second node 100 then the layer will have an input_dim of 150. The first node gets x[:,:50], the second one x[:,50:]. Any additional arguments are forwarded unaltered to each node. Warning: This might change in the next release (2.5). Since they are nodes themselves layers can be stacked in a flow (e.g. to build a layered network). If one would like to use flows instead of nodes inside of a layer one can use a FlowNode.
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__metaclass__ This Metaclass is meant to overwrite doc strings of methods like execute, stop_training, inverse with the ones defined in the corresponding private methods _execute, _stop_training, _inverse, etc... |
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_train_seq List of tuples: [(training-phase1, stop-training-phase1), (training-phase2, stop_training-phase2), ... |
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dtype dtype |
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input_dim Input dimensions |
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output_dim Output dimensions |
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supported_dtypes Supported dtypes |
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Setup the layer with the given list of nodes. The input and output dimensions for the nodes must be already set (the output dimensions for simplicity reasons). The training phases for the nodes are allowed to differ. Keyword arguments: nodes -- List of the nodes to be used.
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Check the compatibility of the properties of the internal nodes. Return the found dtype and check the dimensions. dtype -- The specified layer dtype. |
Process the data through the internal nodes.
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Calculate the output_dim from the nodes and return it. If the output_dim of a node is not set the None is returned. |
Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.
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Return the train sequence. The length is set by the node with maximum length.
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Combine the inverse of all the internal nodes.
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Make sure that output_dim is set and then perform normal checks.
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Stop training of the internal nodes.
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Perform single training step by training the internal nodes.
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Process the data through the internal nodes.
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Combine the inverse of all the internal nodes.
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Return True if the node can be inverted, False otherwise.
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Return True if the node can be trained, False otherwise.
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Stop training of the internal nodes.
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Perform single training step by training the internal nodes.
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