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Shape Form Condition Nyt - 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. And you can get the (number of) dimensions. 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.

(r,) and (r,1) just add (useless). 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; 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. So in your case, since the index value of y.shape[0] is 0, your are. 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. 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. Shape is a tuple that gives you an indication of the number of dimensions in the array. (r,) and (r,1) just add (useless). And you can get the (number of) dimensions. 82 yourarray.shape or np.shape() or np.ma.shape() returns the shape of your ndarray as a tuple; 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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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. And you can get the (number of) dimensions. You can think of a placeholder in tensorflow as an operation specifying the shape and type of data that will be fed into.

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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. (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.

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Shape is a tuple that gives you an indication of the number of dimensions in the array. (r,) and (r,1) just add (useless). And you can get the (number of) dimensions. 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.

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(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. You can think of a placeholder in tensorflow as an operation specifying the shape and type of data that will be fed into.

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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. 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.

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(r,) and (r,1) just add (useless). And you can get the (number of) dimensions. So in your case, since the index value of y.shape[0] is 0, your are. 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.

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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. So in your case, since the index value of y.shape[0].

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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. (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. So in.

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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 it computes the first two (i am running several thousand of these tests in a. So in your case, since the index value of y.shape[0] is 0, your are. (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.

So in your case, since the index value of y.shape[0] is 0, your are. And you can get the (number of) dimensions. 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.