The shape attribute for numpy arrays returns the dimensions of the array. If Y has n rows and m columns, then Y.shape is (n,m). So Y.shape[0] is n.

Shape n, expresses the shape of a 1D array with n items, and n, 1 the shape of a n-row x 1-column array. (R,) and (R,1) just add (useless) parentheses but still express respectively 1D and 2D array …

Nov 30, 2017 · 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 of your array using yourarray.ndim or np.ndim(). …

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 that an unspecified number of rows of shape (128, …

ValueError: shape mismatch: objects cannot be broadcast to a single shape It computes the first two (I am running several thousand of these tests in a loop) and then dies.

May 24, 2016 · Still, performance-wise, the difference should be negligible except for a giant giant 2D dataframe. So in line with the previous answers, df.shape is good if you need both dimensions, for a …

I'm creating a plot in ggplot from a 2 x 2 study design and would like to use 2 colors and 2 symbols to classify my 4 different treatment combinations. Currently I have 2 legends, one for the colo...

Oct 22, 2018 · Shape (in the numpy context) seems to me the better option for an argument name. The actual relation between the two is size = np.prod(shape) so the distinction should indeed be a bit …

Mar 1, 2014 · I'm new to python and numpy in general. I read several tutorials and still so confused between the differences in dim, ranks, shape, aixes and dimensions. My mind seems to be stuck at …

Sep 5, 2016 · But isn't the input_shape defined as (sample_size,timestep, features). ? That's tensorflow site mentions about input_shape.