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@ -64,14 +64,14 @@ def fc(input,
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is flattened: the first `num_flatten_dims`
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dimensions will be flatten to form the first
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dimension of the final matrix (height of the
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matrix), and the rest `rank(X) - num_col_dims`
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matrix), and the rest `rank(X) - num_flatten_dims`
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dimensions are flattened to form the second
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dimension of the final matrix (width of the matrix).
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For example, suppose `X` is a 6-dimensional tensor
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with a shape [2, 3, 4, 5, 6], and
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`x_num_col_dims` = 3. Then, the flattened matrix
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`num_flatten_dims` = 3. Then, the flattened matrix
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will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
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By default, `x_num_col_dims` is set to 1.
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By default, `num_flatten_dims` is set to 1.
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param_attr(ParamAttr|list): The parameter attribute for learnable
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parameters/weights of the fully connected
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layer.
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