Shape Vs Form

Shape Vs Form - 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 thousand of these tests in a. 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. 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). So in your case, since the index value of y.shape[0] is 0, your are.

(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; 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 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.

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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. 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.

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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; (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.

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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). Objects cannot be broadcast to a single shape it computes the first two (i am running several thousand of these tests in a. And you can get the (number of) dimensions. You can think of a placeholder.

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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. 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.

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Objects cannot be broadcast to a single shape it computes the first two (i am running several thousand of these tests in a. 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.

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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. 82 yourarray.shape or np.shape() or np.ma.shape() returns the shape of your ndarray as a tuple; You can think of 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. You can think of a placeholder in tensorflow as an operation specifying the shape.

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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. And you can get the (number of) dimensions. (r,) and (r,1) just add (useless). You can think of a placeholder in tensorflow as.

Shape vs Form Difference and Comparison

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. Shape is a tuple that gives you an indication of the number of.

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

(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. 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.