c25fa38deb4efc5445f64af3ec17eae0eb660d2f,keras/models.py,Sequential,add,#Sequential#Any#,409

Before Change

                // to the input layer we just created.
                layer(x)

            if len(layer.inbound_nodes) != 1:
                raise ValueError("A layer added to a Sequential model must "
                                 "not already be connected somewhere else. "
                                 "Model received layer " + layer.name +
                                 " which has " +
                                 str(len(layer.inbound_nodes)) +
                                 " pre-existing inbound connections.")

            if len(layer.inbound_nodes[0].output_tensors) != 1:
                raise ValueError("All layers in a Sequential model "
                                 "should have a single output tensor. "
                                 "For multi-output layers, "

After Change

                            "Found: " + str(layer))
        if not self.outputs:
            // First layer in model: check that it is an input layer.
            if not isinstance(layer, (InputLayer, legacy_layers.Merge)):
                // Create an input layer.
                // First, we need to infer its expected input shape and dtype.
                if isinstance(layer, (Model, Sequential)):
                    // We were passed a model as first layer.
                    // This requires a specific way to figure out the
                    // input shape and dtype.
                    if not layer.layers:
                        raise ValueError("Cannot add an empty model "
                                         "to a `Sequential` model.")
                    // In case of nested models: recover the first layer
                    // of the deepest model to infer input shape and dtype.
                    first_layer = layer.layers[0]
                    while isinstance(first_layer, (Model, Sequential)):
                        first_layer = first_layer.layers[0]
                    batch_shape = first_layer.batch_input_shape
                    dtype = first_layer.dtype
                else:
                    // We were passed a regular layer, and it should
                    // know about its input shape. Otherwise, that"s an error.
                    if not hasattr(layer, "batch_input_shape"):
                        raise ValueError("The first layer in a "
                                         "Sequential model must "
                                         "get an `input_shape` or "
                                         "`batch_input_shape` argument.")
                    batch_shape = layer.batch_input_shape
                    dtype = layer.dtype
                // Instantiate the input layer.
                x = Input(batch_shape=batch_shape,
                          dtype=dtype,
                          name=layer.name + "_input")
                // This will build the current layer
                // and create the node connecting the current layer
                // to the input layer we just created.
                layer(x)

            if len(layer.inbound_nodes[-1].output_tensors) != 1:
                raise ValueError("All layers in a Sequential model "
                                 "should have a single output tensor. "
                                 "For multi-output layers, "

Instances