![]() Output WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor("img:0", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 784, 784). Print("predicted : ", result) # always result is 2 Img = cv2.imread('C:/Users/Horseman.mini/Desktop/7.jpg', 0) I changed to 0.jpg (that is 0), and predicted result is still 2. But it returns me, that my 7.jpg number is 2. IRS-1A image is classified with five different models using k-NN. Tried to change w and h to 784, now no previous error, except a new one, probably the next is because of that one error. However, the algorithms are implemented on the original (poorly illuminated) image. What should I do for predicting numbers (in this case) from the images with different w x h sizes, not only images with the size of 28x28 pixels? How do I reshape my image properly for this model to work without such an error. Now we will create the Confusion Matrix for our K-NN model to see the. The ind properly route k-nn the q accessed in comparison to t dex. Img = np.resize(img, (-1, 28, 28, 1)) # if I use this - error 2 occuresĮrror 1 ValueError: Input 0 of layer dense is incompatible with the layer: expected axis -1 of input shape to have value 784 but received input with shape Įrror 2 ValueError: cannot reshape array of size 3498416 into shape (28,28,1) Example: Suppose, we have an image of a creature that looks similar to cat and dog. o indices to evaluate both models: the penetration rate m of and Penetration Rate. ![]() # img = cv2.resize(img, (28, 28)) # if I use this - error 1 occures Img = cv2.imread('C:/Users/Horseman.mini/Desktop/7.jpg', 0) # it is a picture of number 7 View samples of S-Log2 and S-Log3 from the camera, plus see how it performs at a bit of Seattle street photography. But when I load my trained model and trying to predict the value on my custom image ( 1620x2160 pixels) that is taken from my phone it returns me an error: model = _model('myFirstModel.model') Jason Hendardy provides an overview of the Sony ZV-1 Mark II, highlighting its capabilities as a tool for vloggers and content creators in video and still photography. So as you could see training image sizes are 28x28 pixels. Test_scores = model.evaluate(x_test, y_test, verbose=2) pile(loss='sparse_categorical_crossentropy', Model = keras.Model(inputs=inputs, outputs=outputs, name='mnist_model') Outputs = layers.Dense(10, activation='softmax')(x) Download and use 100,000+ Webe Model Pics stock photos for free. X = layers.Dense(64, activation='relu')(x) Thousands of new, high-quality pictures added every day. X = layers.Dense(64, activation='relu')(inputs) Find Nn stock images in HD and millions of other royalty-free stock photos, illustrations and. I trained my NN model & saved it using next code: from tensorflow import keras
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