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Shape Chart - When reshaping an array, the new shape must contain the same number of elements. Shape is a tuple that gives you an indication of the number of dimensions in the array. For example the doc says units specify the. Shape of passed values is (x, ), indices imply (x, y) asked 11 years, 8 months ago modified 7 years, 4 months ago viewed 60k times (r,) and (r,1) just add (useless) parentheses but still express respectively 1d. In your case it will give output 10. So in your case, since the index value of y.shape[0] is 0, your are working along the first. For any keras layer (layer class), can someone explain how to understand the difference between input_shape, units, dim, etc.? And you can get the (number of) dimensions of your array using. It's useful to know the usual numpy.

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For any keras layer (layer class), can someone explain how to understand the difference between input_shape, units, dim, etc.? Shape of passed values is (x, ), indices imply (x, y) asked 11 years, 8 months ago modified 7 years, 4 months ago viewed 60k times Your dimensions are called the shape, in numpy. And you can get the (number of).

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And you can get the (number of) dimensions of your array using. Shape is a tuple that gives you an indication of the number of dimensions in the array. It's useful to know the usual numpy. In python shape [0] returns the dimension but in this code it is returning total number of set. For example the doc says units.

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It's useful to know the usual numpy. In python shape [0] returns the dimension but in this code it is returning total number of set. Shape is a tuple that gives you an indication of the number of dimensions in the array. If you will type x.shape[1], it will. Instead of calling list, does the size class have some sort.

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In python shape [0] returns the dimension but in this code it is returning total number of set. If you will type x.shape[1], it will. When reshaping an array, the new shape must contain the same number of elements. For any keras layer (layer class), can someone explain how to understand the difference between input_shape, units, dim, etc.? 82 yourarray.shape.

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In your case it will give output 10. Shape of passed values is (x, ), indices imply (x, y) asked 11 years, 8 months ago modified 7 years, 4 months ago viewed 60k times So in your case, since the index value of y.shape[0] is 0, your are working along the first. When reshaping an array, the new shape must.

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X.shape[0] will give the number of rows in an array. What numpy calls the dimension is 2, in your case (ndim). If you will type x.shape[1], it will. Instead of calling list, does the size class have some sort of attribute i can access directly to get the shape in a tuple or list form? In your case it will.

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Please can someone tell me work of shape [0] and shape [1]? So in your case, since the index value of y.shape[0] is 0, your are working along the first. 82 yourarray.shape or np.shape() or np.ma.shape() returns the shape of your ndarray as a tuple; And you can get the (number of) dimensions of your array using. Instead of calling.

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When reshaping an array, the new shape must contain the same number of elements. Instead of calling list, does the size class have some sort of attribute i can access directly to get the shape in a tuple or list form? In your case it will give output 10. Your dimensions are called the shape, in numpy. 82 yourarray.shape or.

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So in your case, since the index value of y.shape[0] is 0, your are working along the first. For example the doc says units specify the. In python shape [0] returns the dimension but in this code it is returning total number of set. It's useful to know the usual numpy. Please can someone tell me work of shape [0].

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Shape of passed values is (x, ), indices imply (x, y) asked 11 years, 8 months ago modified 7 years, 4 months ago viewed 60k times It's useful to know the usual numpy. In your case it will give output 10. Please can someone tell me work of shape [0] and shape [1]? For example the doc says units specify.