WebMay 12, 2024 · May 12, 2024 at 17:41 Your input is in RGB not grayscale but you are defining only 1 channel for inputs: X_train = X_train.reshape (-1, 28, 28, 1). You need to either transform your images into grayscale or set the channel dimension to 3. – Erfan May 12, 2024 at 17:59 Thank you so much for your help @Erfan. WebMar 25, 2024 · Without those brackets, the i [0]...check is interpreted as a generator comprehension (gives a generator not an iterator) and so just generates the 1st element …
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WebAug 29, 2024 · You're trying to reshape a 4096-dimensional image to an image having the shape of (64, 64, 3) -- which denotes an image with RGB color (or BGR color in OpenCV). However, the images being read are grayscale. This means you should not reshape it to (64, 64, 3) but instead to (64, 64, 1). data = img.reshape (1, IMG_SIZE, IMG_SIZE, 1) … WebMay 12, 2024 · ValueError: cannot reshape array of size 50176 into shape (1,224,224,3) I am doing image classification and I trained a model and saved a model. When I try to …
WebNov 23, 2024 · The LSTM input needs to be of shape (num sample, time steps, num features) if you are using tensorflow backend. Assuming that you want to split the data into sequences of 5 time steps you will need to do something like the following: X_data = X_data.reshape((20000,5,30)) I think you mean: X_data = X_data.reshape((10000,5,30)) WebSep 20, 2024 · To reshape with, X = numpy.reshape (dataX, (n_patterns, seq_length, 1)) the dimensions should be consistent. 5342252 x 200 x 1 = 1,064,505,600 should be the number of elements in dataX if you want that shape. It is not clear what you are trying to accomplish but my guess is that n_patterns = len (dataX) should be n_patterns = len …
WebOct 4, 2024 · 1 Answer Sorted by: 2 You need 2734 × 132 × 126 × 1 = 45, 471, 888 values in order to reshape into that tensor. Since you have 136, 415, 664 values, the reshaping is impossible. If your fourth dimension is 4, then the reshape will be possible. Share Improve this answer Follow answered Oct 4, 2024 at 15:30 Dave 3,744 1 7 22 Add a comment … WebSep 10, 2024 · This then gives a problem with the reshape: state = np.reshape (state, [1, state_size]) because reshape cannot process a tuple. If you use the gym library 0.12.5, …
WebFeb 2, 2024 · You can only reshape an array of one size to another size if the new size has the same number of elements as the old size. In this case, you are attempting to …
WebJun 16, 2024 · cannot reshape array of size 1 into shape (48,48) I have this code that generates an error, the error is in the reconstruct function. def reconstruct (pix_str, size= … how can rigor mortis determine time of deathWebDec 1, 2024 · 1. When reshaping, if you are keeping the same data contiguity and just reshaping the box, you can reshape your data with. data_reconstructed = … how can risk be reduced in outdoor activitiesWebFirst of all, you don't need to reshape an array. The shape attribute of a numpy array simply determines how the underlying data is displayed to you and how the data is … how many people in the world are savedWebSep 10, 2024 · This then gives a problem with the reshape: state = np.reshape (state, [1, state_size]) because reshape cannot process a tuple. If you use the gym library 0.12.5, a numpy.ndarray is returned, which the reshape function likes. so, use gym==0.12.5 and it works. Share Follow answered Sep 22, 2024 at 9:38 Patrick Huber 1 3 Add a comment 0 how many people in the world are orphansWebJun 25, 2024 · The problem is that in the line that is supposed to grab the data from the file ( all_pixels = np.frombuffer (f.read (), dtype=np.uint8) ), the call to f.read () does not read … how many people in the world are neurodiverseWebMar 17, 2024 · import numpy as np n = 10160 #n = 10083 X = np.arange (n).reshape (1,-1) np.shape (X) X = X.reshape ( [X.shape [0], X.shape [1],1]) X_train_1 = X [:,0:10080,:] X_train_2 = X [:,10080:10160,:].reshape (1,80) np.shape (X_train_2) If you cannot make sure that X is 10160 long I suggest one of the following solutions: how can risk be managedWeb6. You can reshape the numpy matrix arrays such that before (a x b x c..n) = after (a x b x c..n). i.e the total elements in the matrix should be same as before, In your case, you can … how can risk management improve performance