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Shape Eyes Chart - In your case it will give output 10. Shape is a tuple that gives you an indication of the number of dimensions in the array. 10 x[0].shape will give the length of 1st row of 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.? 82 yourarray.shape or np.shape() or np.ma.shape() returns the shape of your ndarray as a tuple; 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. It's useful to know the usual numpy. 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?

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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. When reshaping an array, the new shape must contain the same number of elements. In your case it will give output 10. Instead of calling list, does.

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Please can someone tell me work of shape [0] and shape [1]? Shape is a tuple that gives you an indication of the number of dimensions in the array. 10 x[0].shape will give the length of 1st row of an array. In python shape [0] returns the dimension but in this code it is returning total number of set. And.

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For any keras layer (layer class), can someone explain how to understand the difference between input_shape, units, dim, etc.? What numpy calls the dimension is 2, in your case (ndim). For example the doc says units specify the. And you can get the (number of) dimensions of your array using. Shape is a tuple that gives you an indication of.

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Please can someone tell me work of shape [0] and shape [1]? And you can get the (number of) dimensions of your array using. In your case it will give output 10. When reshaping an array, the new shape must contain the same number of elements. For example the doc says units specify the.

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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. 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.

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

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For any keras layer (layer class), can someone explain how to understand the difference between input_shape, units, dim, etc.? 82 yourarray.shape or np.shape() or np.ma.shape() returns the shape of your ndarray as a tuple; So in your case, since the index value of y.shape[0] is 0, your are working along the first. If you will type x.shape[1], it will. Shape.

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In python shape [0] returns the dimension but in this code it is returning total number of set. For example the doc says units specify the. (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. What numpy calls the dimension.

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In your case it will give output 10. 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. For example the doc says units specify the.

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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? If you will type x.shape[1], it will. 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.