keras/backend/tensorflow_backend.py [4356:4430]:
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def local_conv1d(inputs, kernel, kernel_size, strides, data_format=None):
    """Apply 1D conv with un-shared weights.

    # Arguments
        inputs: 3D tensor with shape: (batch_size, steps, input_dim)
        kernel: the unshared weight for convolution,
                with shape (output_length, feature_dim, filters)
        kernel_size: a tuple of a single integer,
                     specifying the length of the 1D convolution window
        strides: a tuple of a single integer,
                 specifying the stride length of the convolution
        data_format: the data format, channels_first or channels_last

    # Returns
        the tensor after 1d conv with un-shared weights, with shape (batch_size, output_length, filters)

    # Raises
        ValueError: If `data_format` is neither
            `"channels_last"` nor `"channels_first"`.
    """
    data_format = normalize_data_format(data_format)

    stride = strides[0]
    kernel_shape = int_shape(kernel)
    output_length, feature_dim, filters = kernel_shape

    xs = []
    for i in range(output_length):
        slice_length = py_slice(i * stride,
                                i * stride + kernel_size[0])
        xs.append(reshape(inputs[:, slice_length, :],
                          (1, -1, feature_dim)))
    x_aggregate = concatenate(xs, axis=0)
    # Shape: `(output_length, batch_size, filters)`.
    output = batch_dot(x_aggregate, kernel)
    return permute_dimensions(output, (1, 0, 2))


def local_conv2d(inputs, kernel, kernel_size, strides, output_shape, data_format=None):
    """Apply 2D conv with un-shared weights.

    # Arguments
        inputs: 4D tensor with shape:
                (batch_size, filters, new_rows, new_cols)
                if data_format='channels_first'
                or 4D tensor with shape:
                (batch_size, new_rows, new_cols, filters)
                if data_format='channels_last'.
        kernel: the unshared weight for convolution,
                with shape (output_items, feature_dim, filters)
        kernel_size: a tuple of 2 integers, specifying the
                     width and height of the 2D convolution window.
        strides: a tuple of 2 integers, specifying the strides
                 of the convolution along the width and height.
        output_shape: a tuple with (output_row, output_col)
        data_format: the data format, channels_first or channels_last

    # Returns
        A 4d tensor with shape:
        (batch_size, filters, new_rows, new_cols)
        if data_format='channels_first'
        or 4D tensor with shape:
        (batch_size, new_rows, new_cols, filters)
        if data_format='channels_last'.

    # Raises
        ValueError: if `data_format` is neither
                    `channels_last` or `channels_first`.
    """
    data_format = normalize_data_format(data_format)

    stride_row, stride_col = strides
    output_row, output_col = output_shape
    kernel_shape = int_shape(kernel)
    _, feature_dim, filters = kernel_shape
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keras/backend/theano_backend.py [2799:2824]:
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def local_conv1d(inputs, kernel, kernel_size, strides, data_format=None):
    data_format = normalize_data_format(data_format)

    stride = strides[0]
    kernel_shape = int_shape(kernel)
    output_length, feature_dim, filters = kernel_shape

    xs = []
    for i in range(output_length):
        slice_length = py_slice(i * stride,
                                i * stride + kernel_size[0])
        xs.append(reshape(inputs[:, slice_length, :],
                          (1, -1, feature_dim)))
    x_aggregate = concatenate(xs, axis=0)
    # Shape: `(output_length, batch_size, filters)`.
    output = batch_dot(x_aggregate, kernel)
    return permute_dimensions(output, (1, 0, 2))


def local_conv2d(inputs, kernel, kernel_size, strides, output_shape, data_format=None):
    data_format = normalize_data_format(data_format)

    stride_row, stride_col = strides
    output_row, output_col = output_shape
    kernel_shape = int_shape(kernel)
    _, feature_dim, filters = kernel_shape
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