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 …

Jan 7, 2018 · On the other hand, x.shape is a 2-tuple which represents the shape of x, which in this case is (10, 1024). x.shape[0] gives the first element in that tuple, which is 10. Here's a demo with some …

Why doesn't Pyspark Dataframe simply store the shape values like pandas dataframe does with .shape? Having to call count seems incredibly resource-intensive for such a common and simple operation.

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 …

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 …

Jun 25, 2017 · For any Keras layer (Layer class), can someone explain how to understand the difference between input_shape, units, dim, etc.? For example the doc says units specify the output shape of a …

Sep 5, 2016 · At the moment my dataset is in the shape X: [4000,20], Y: [4000]. I want to train an LSTM using TensorFlow to predict the value of Y (regression), given the 10 previous inputs of d features, …

For example, output shape of Dense layer is based on units defined in the layer where as output shape of Conv layer depends on filters. Another thing to remember is, by default, last dimension of any …

Mar 5, 2021 · input_shape = first_parameter.size () this is for the weight size, if you save the model and open it in neuron, you would see that the weight size is the same as the input shape.