I try to train a tensorflow model. But I got error.

Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray).

Here my fit codes:

model.fit(self.datas.trainImages, self.datas.trainLabels,self.datas.batch_size, epochs =self.datas.epochs)

My self.datas.trainImages is numpy.array() its shape is (16,) it has 16 sample and their sizes is 28x28, it is mnist dataset.

self.train_dataset = [[cv2.imread(image0),0],[cv2.imread(image1),1],[cv2.imread(image2),2],[...],[...]]
self.trainDataset = numpy.array(self.train_dataset)
        
self.trainImages, self.trainLabels = numpy.asarray(self.trainDataset[:,0])/255,self.trainDataset[:,1] #.astype(numpy.float32)/



self.val_dataset = [[cv2.imread(image0),0],[cv2.imread(image1),1],[cv2.imread(image2),2],[...],[...]]

self.valDataset = numpy.array(self.val_dataset)#.astype(numpy.float32)
self.valImages, self.valLabels = numpy.asarray(self.valDataset[:,0])/255,self.valDataset[:,1] #.astype(numpy.float32)/255
        

I tried to use astype or numpy.ndarray but I got another errors. I am sure of that all datas in the self.datas.trainImages is float numbers and has same shape.

CodePudding user response：

Would it be possible to print out some type of error output?

Personally, I was having a similar issue and by coating my input with "np.stack()" it added an extra dimension, changed the shape of the array and allowed it to work.

i.e.

images = np.stack(self.data.trainImages)

Furthermore, I'm not sure if you're using a custom Model.fit() method, but I believe for multiple inputs it's best to use square brackets, i.e.

model.fit(x = [input1, input2], y = output1, batch_size = batch_sizing, epochs = epoch_quantity)