Shape And Form Difference - So in your case, since the index value of y.shape[0] is 0, your are. And you can get the (number of) dimensions. Shape is a tuple that gives you an indication of the number of dimensions in the array. 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). You can think of a placeholder in tensorflow as an operation specifying the shape and type of data that will be fed into the graph.placeholder x defines. Objects cannot be broadcast to a single shape it computes the first two (i am running several thousand of these tests in a.
You can think of a placeholder in tensorflow as an operation specifying the shape and type of data that will be fed into the graph.placeholder x defines. So in your case, since the index value of y.shape[0] is 0, your are. 82 yourarray.shape or np.shape() or np.ma.shape() returns the shape of your ndarray as a tuple; Shape is a tuple that gives you an indication of the number of dimensions in the array. And you can get the (number of) dimensions. Objects cannot be broadcast to a single shape it computes the first two (i am running several thousand of these tests in a. (r,) and (r,1) just add (useless).
So in your case, since the index value of y.shape[0] is 0, your are. Shape is a tuple that gives you an indication of the number of dimensions in the array. You can think of a placeholder in tensorflow as an operation specifying the shape and type of data that will be fed into the graph.placeholder x defines. 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). And you can get the (number of) dimensions. Objects cannot be broadcast to a single shape it computes the first two (i am running several thousand of these tests in a.
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(r,) and (r,1) just add (useless). Objects cannot be broadcast to a single shape it computes the first two (i am running several thousand of these tests in a. Shape is a tuple that gives you an indication of the number of dimensions in the array. 82 yourarray.shape or np.shape() or np.ma.shape() returns the shape of your ndarray as a.
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You can think of a placeholder in tensorflow as an operation specifying the shape and type of data that will be fed into the graph.placeholder x defines. Objects cannot be broadcast to a single shape it computes the first two (i am running several thousand of these tests in a. Shape is a tuple that gives you an indication of.
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So in your case, since the index value of y.shape[0] is 0, your are. (r,) and (r,1) just add (useless). Objects cannot be broadcast to a single shape it computes the first two (i am running several thousand of these tests in a. You can think of a placeholder in tensorflow as an operation specifying the shape and type of.
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And you can get the (number of) dimensions. Shape is a tuple that gives you an indication of the number of dimensions in the array. (r,) and (r,1) just add (useless). Objects cannot be broadcast to a single shape it computes the first two (i am running several thousand of these tests in a. So in your case, since the.
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So in your case, since the index value of y.shape[0] is 0, your are. 82 yourarray.shape or np.shape() or np.ma.shape() returns the shape of your ndarray as a tuple; Shape is a tuple that gives you an indication of the number of dimensions in the array. (r,) and (r,1) just add (useless). You can think of a placeholder in tensorflow.
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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. (r,) and (r,1) just add (useless). Shape is a tuple that gives you an indication of the number of dimensions in the array. Objects cannot be broadcast to a single shape.
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(r,) and (r,1) just add (useless). 82 yourarray.shape or np.shape() or np.ma.shape() returns the shape of your ndarray as a tuple; Shape is a tuple that gives you an indication of the number of dimensions in the array. You can think of a placeholder in tensorflow as an operation specifying the shape and type of data that will be fed.
Shape And Form Difference
You can think of a placeholder in tensorflow as an operation specifying the shape and type of data that will be fed into the graph.placeholder x defines. And you can get the (number of) dimensions. So in your case, since the index value of y.shape[0] is 0, your are. (r,) and (r,1) just add (useless). 82 yourarray.shape or np.shape() or.
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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). So in your case, since the index value of y.shape[0] is 0, your are. And you can get the (number of) dimensions. Objects cannot be broadcast to a single shape it computes the first two (i am running several.
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And you can get the (number of) dimensions. (r,) and (r,1) just add (useless). You can think of a placeholder in tensorflow as an operation specifying the shape and type of data that will be fed into the graph.placeholder x defines. Objects cannot be broadcast to a single shape it computes the first two (i am running several thousand of.
(R,) And (R,1) Just Add (Useless).
So in your case, since the index value of y.shape[0] is 0, your are. Shape is a tuple that gives you an indication of the number of dimensions in the array. You can think of a placeholder in tensorflow as an operation specifying the shape and type of data that will be fed into the graph.placeholder x defines. 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.
Objects cannot be broadcast to a single shape it computes the first two (i am running several thousand of these tests in a.