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- What does . shape [] do in for i in range (Y. shape [0])?
shape is a tuple that gives you an indication of the number of dimensions in the array So in your case, since the index value of Y shape[0] is 0, your are working along the first dimension of your array
- arrays - what does numpy ndarray shape do? - Stack Overflow
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() (i e it gives the n of the ndarray since all arrays in NumPy are just n-dimensional arrays (shortly called as ndarrays))
- python - x. shape [0] vs x [0]. shape in NumPy - Stack Overflow
x[0] shape will give the Length of 1st row of an array x shape[0] will give the number of rows in an array In your case it will give output 10 If you will type x shape[1], it will print out the number of columns i e 1024 If you would type x shape[2], it will give an error, since we are working on a 2-d array and we are out of index Let me
- What does shape[0] and shape[1] do in python? - Stack Overflow
In this case your training set will be of the shape (n,x,y) The fourth dimension in your data is the number of channels (3, or RGB in this case) In your dataset the height and the width of each image is the same and thus the size of the image can be retrieved merely by the third line: num_px = train_set_x_orig shape[1]
- python - What does -1 mean in numpy reshape? - Stack Overflow
When reshaping an array, the new shape must contain the same number of elements as the old shape, meaning the products of the two shapes' dimensions must be equal When using a -1, the dimension corresponding to the -1 will be the product of the dimensions of the original array divided by the product of the dimensions given to reshape so as to
- I want to know what it means (height,width)=img. shape [:2]
Then the shape of the object holds a tuple (rows, columns, channels) (height,width)=img shape[:2] is an example of tuple unpacking, with it you extract the rows and columns values from the shape tuple
- AttributeError: NoneType object has no attribute shape
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- Keras input explanation: input_shape, units, batch_size, dim, etc
Given the input shape, all other shapes are results of layers calculations The "units" of each layer will define the output shape (the shape of the tensor that is produced by the layer and that will be the input of the next layer) Each type of layer works in a particular way
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