Module ImportFrom alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias alias ImportFrom alias ImportFrom alias ImportFrom alias ImportFrom alias ImportFrom alias ImportFrom alias alias alias alias alias alias ImportFrom alias Import alias ImportFrom alias identifier FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults NameLoad NameLoad Str TupleLoad Num Num NameLoad NameLoad kwarg body Expr Str If BoolOpOr CompareIn Str NameLoad CompareIn Str NameLoad body Raise Call NameLoad Str If CompareGt Call NameLoad Call AttributeLoad NameLoad attr Num body Expr Call AttributeLoad NameLoad attr BinOpAdd Call NameLoad Call AttributeLoad NameLoad attr Str keyword Num If CompareIsNot NameLoad NameLoad body Expr Call AttributeLoad NameLoad attr Str keyword Num If CompareIs NameLoad NameLoad body Assign NameStore NameLoad orelse Raise Call NameLoad Str Return Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad decorator_list
N/A nnet CrossentropyCategorical1Hot CrossentropyCategorical1HotGrad CrossentropySoftmax1HotWithBiasDx CrossentropySoftmaxArgmax1HotWithBias LogSoftmax Prepend_scalar_constant_to_each_row Prepend_scalar_to_each_row Softmax SoftmaxGrad SoftmaxWithBias binary_crossentropy categorical_crossentropy crossentropy_categorical_1hot crossentropy_categorical_1hot_grad crossentropy_softmax_1hot crossentropy_softmax_1hot_with_bias crossentropy_softmax_1hot_with_bias_dx crossentropy_softmax_argmax_1hot_with_bias crossentropy_softmax_max_and_argmax_1hot crossentropy_softmax_max_and_argmax_1hot_with_bias crossentropy_to_crossentropy_with_softmax crossentropy_to_crossentropy_with_softmax_with_bias graph_merge_softmax_with_crossentropy_softmax h_softmax logsoftmax logsoftmax_op prepend_0_to_each_row prepend_1_to_each_row prepend_scalar_to_each_row relu softmax softmax_grad softmax_graph softmax_op softmax_simplifier softmax_with_bias elu N/A opt conv ConvOp Conv3D * ConvGrad3D * ConvTransp3D * sigm softplus sigmoid sigmoid_inplace scalar_sigmoid ultra_fast_sigmoid hard_sigmoid bn batch_normalization N/A warnings abstract_conv conv2d abstract_conv2d conv2d N/A N/A input filters input_shape filter_shape border_mode subsample filter_flip image_shape N/A None None valid N/A 1 1 True None kwargs N/A N/A 
    This function will build the symbolic graph for convolving a mini-batch of a
    stack of 2D inputs with a set of 2D filters. The implementation is modelled
    after Convolutional Neural Networks (CNN).


    Parameters
    ----------
    input: symbolic 4D tensor
        Mini-batch of feature map stacks, of shape
        (batch size, input channels, input rows, input columns).
        See the optional parameter ``input_shape``.

    filters: symbolic 4D tensor
        Set of filters used in CNN layer of shape
        (output channels, input channels, filter rows, filter columns).
        See the optional parameter ``filter_shape``.

    input_shape: None, tuple/list of len 4 of int or Constant variable
        The shape of the input parameter.
        Optional, possibly used to choose an optimal implementation.
        You can give ``None`` for any element of the list to specify that this
        element is not known at compile time.

    filter_shape: None, tuple/list of len 4 of int or Constant variable
        The shape of the filters parameter.
        Optional, possibly used to choose an optimal implementation.
        You can give ``None`` for any element of the list to specify that this
        element is not known at compile time.

    border_mode: str, int or tuple of two int
        Either of the following:

        ``'valid'``: apply filter wherever it completely overlaps with the
            input. Generates output of shape: input shape - filter shape + 1
        ``'full'``: apply filter wherever it partly overlaps with the input.
            Generates output of shape: input shape + filter shape - 1
        ``'half'``: pad input with a symmetric border of ``filter rows // 2``
            rows and ``filter columns // 2`` columns, then perform a valid
            convolution. For filters with an odd number of rows and columns, this
            leads to the output shape being equal to the input shape.
        ``int``: pad input with a symmetric border of zeros of the given
            width, then perform a valid convolution.
        ``(int1, int2)``: pad input with a symmetric border of ``int1`` rows
            and ``int2`` columns, then perform a valid convolution.

    subsample: tuple of len 2
        Factor by which to subsample the output.
        Also called strides elsewhere.

    filter_flip: bool
        If ``True``, will flip the filter rows and columns
        before sliding them over the input. This operation is normally referred
        to as a convolution, and this is the default. If ``False``, the filters
        are not flipped and the operation is referred to as a cross-correlation.

    image_shape: None, tuple/list of len 4 of int or Constant variable
        Deprecated alias for input_shape.

    kwargs: Any other keyword arguments are accepted for backwards
            compatibility, but will be ignored.

    Returns
    -------
    Symbolic 4D tensor
        Set of feature maps generated by convolutional layer. Tensor is
        of shape (batch size, output channels, output rows, output columns)

    Notes
    -----
        If CuDNN is available, it will be used on the
        GPU. Otherwise, it is the *CorrMM* convolution that will be used
        "caffe style convolution".

        This is only supported in Theano 0.8 or the development
        version until it is released.

     N/A N/A N/A imshp_logical kwargs N/A kshp_logical kwargs N/A N/A N/A ValueError Keyword arguments 'imshp_logical' and 'kshp_logical' for conv2d are not supported anymore (and have not been a reliable way to perform upsampling). That feature is still available by calling theano.tensor.nnet.conv.conv2d() for the time being. N/A N/A N/A len N/A N/A kwargs keys 0 N/A N/A N/A N/A warnings warn N/A N/A str N/A N/A kwargs keys  are now deprecated in `tensor.nnet.abstract_conv.conv2d` interface and will be ignored. stacklevel 2 N/A N/A image_shape None N/A N/A N/A N/A warnings warn The `image_shape` keyword argument to `tensor.nnet.conv2d` is deprecated, it has been renamed to `input_shape`. stacklevel 2 N/A N/A input_shape None N/A N/A input_shape image_shape N/A N/A N/A ValueError input_shape and image_shape should not be provided at the same time. N/A N/A abstract_conv2d input filters input_shape filter_shape border_mode subsample filter_flip N/A