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Shape Eyes Chart - What numpy calls the dimension is 2, in your case (ndim). 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. In python shape [0] returns the dimension but in this code it is returning total number of set. X.shape[0] will give the number of rows in an array. It's useful to know the usual numpy. 82 yourarray.shape or np.shape() or np.ma.shape() returns the shape of your ndarray as a tuple; (r,) and (r,1) just add (useless) parentheses but still express respectively 1d. 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.?

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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 When reshaping an array, the new shape must contain the same number of elements. What numpy calls the dimension is 2, in your case (ndim). Instead of calling list, does the size class have.

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For any keras layer (layer class), can someone explain how to understand the difference between input_shape, units, dim, etc.? X.shape[0] will give the number of rows in an array. And you can get the (number of) dimensions of your array using. Please can someone tell me work of shape [0] and shape [1]? In python shape [0] returns the dimension.

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For any keras layer (layer class), can someone explain how to understand the difference between input_shape, units, dim, etc.? 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 contain the same number of elements. Your dimensions are called the shape, in numpy. It's.

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In your case it will give output 10. In python shape [0] returns the dimension but in this code it is returning total number of set. When reshaping an array, the new shape must contain the same number of elements. Your dimensions are called the shape, in numpy. For example the doc says units specify the.

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(r,) and (r,1) just add (useless) parentheses but still express respectively 1d. Shape is a tuple that gives you an indication of the number of dimensions in the array. Please can someone tell me work of shape [0] and shape [1]? In your case it will give output 10. 10 x[0].shape will give the length of 1st row of an.

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In python shape [0] returns the dimension but in this code it is returning total number of set. 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? What numpy calls the dimension is 2, in your case (ndim). So in your case,.

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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. If you will type x.shape[1], it will. Please can someone tell me work of shape [0] and shape [1]? Your dimensions are called the shape, in numpy.

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If you will type x.shape[1], it will. 82 yourarray.shape or np.shape() or np.ma.shape() returns the shape of your ndarray as a tuple; 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? Shape is a tuple that gives you an indication of the.

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What numpy calls the dimension is 2, in your case (ndim). X.shape[0] will give the number of rows in an array. 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.? Shape is a tuple that gives.

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(r,) and (r,1) just add (useless) parentheses but still express respectively 1d. 82 yourarray.shape or np.shape() or np.ma.shape() returns the shape of your ndarray as a tuple; It's useful to know the usual numpy. 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.