Module ImportFrom alias Import alias Import alias identifier ImportFrom alias ImportFrom alias Assign NameStore ListLoad Str ClassDef bases NameLoad body Expr Str FunctionDef arguments args NameParam NameParam NameParam NameParam defaults NameLoad NameLoad kwarg body Assign AttributeStore NameLoad attr NameLoad If Call NameLoad GeneratorExp BoolOpAnd CompareIsNot NameLoad NameLoad CompareLtE NameLoad Num comprehension NameStore AttributeLoad NameLoad attr body Raise Call NameLoad BinOpMod Str TupleLoad AttributeLoad NameLoad attr NameLoad Assign NameStore Call NameLoad NameLoad If CompareIs NameLoad NameLoad body Assign NameStore Call AttributeLoad NameLoad attr AttributeLoad AttributeLoad NameLoad attr attr BinOpMult ListLoad NameLoad NameLoad Assign NameStore IfExp CompareIsNot NameLoad NameLoad BinOpMod Str NameLoad Str Assign NameStore Call NameLoad NameLoad orelse If CompareNotEq AttributeLoad NameLoad attr NameLoad body Raise Call NameLoad BinOpMod Str TupleLoad NameLoad AttributeLoad NameLoad attr Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr Call NameLoad decorator_list FunctionDef arguments args NameParam defaults body Return AttributeLoad NameLoad attr decorator_list AttributeLoad AttributeLoad NameLoad attr attr decorator_list
N/A collections OrderedDict N/A theano N/A theano.tensor T N/A utils base Layer N/A __all__ N/A InputLayer InputLayer N/A Layer N/A N/A 
    This layer holds a symbolic variable that represents a network input. A
    variable can be specified when the layer is instantiated, else it is
    created.

    Parameters
    ----------
    shape : tuple of `int` or `None` elements
        The shape of the input. Any element can be `None` to indicate that the
        size of that dimension is not fixed at compile time.

    input_var : Theano symbolic variable or `None` (default: `None`)
        A variable representing a network input. If it is not provided, a
        variable will be created.

    Raises
    ------
    ValueError
        If the dimension of `input_var` is not equal to `len(shape)`

    Notes
    -----
    The first dimension usually indicates the batch size. If you specify it,
    Theano may apply more optimizations while compiling the training or
    prediction function, but the compiled function will not accept data of a
    different batch size at runtime. To compile for a variable batch size, set
    the first shape element to `None` instead.

    Examples
    --------
    >>> from lasagne.layers import InputLayer
    >>> l_in = InputLayer((100, 20))
     __init__ N/A N/A self shape input_var name N/A None None kwargs N/A N/A N/A self shape shape N/A N/A any N/A N/A N/A d None N/A d 0 N/A d N/A self shape N/A N/A N/A ValueError N/A Cannot create InputLayer with a non-positive shape dimension. shape=%r, self.name=%r N/A N/A self shape name N/A ndim N/A len shape N/A N/A input_var None N/A N/A input_var_type N/A N/A T TensorType N/A N/A theano config floatX N/A N/A False ndim N/A var_name N/A N/A name None N/A %s.input name input N/A input_var N/A input_var_type var_name N/A N/A N/A N/A input_var ndim ndim N/A N/A N/A ValueError N/A shape has %d dimensions, but variable has %d N/A ndim N/A input_var ndim N/A N/A self input_var input_var N/A N/A self name name N/A N/A self params N/A OrderedDict N/A output_shape N/A N/A self N/A N/A N/A N/A self shape N/A N/A N/A Layer output_shape getter N/A