So I have a training set and a test set both in h5py format. I also have a data_load function that loads the files and returns NumPy arrays. The main problem is I don’t need NumPy as I am working with Tensors. I am expecting to have an x&y tensor of size N(batch size) and D_in(input size for each image) and D_out(Output size of each tensor).
The problem:
x&y do not get converted to tensors of dimensions mentioned below.If anything their types remain to be numpy.ndarray. Any help is appreciated.


# Load the training data

train_dataset =h5py.File(train_file, 'r')


# Separate features(x) and labels(y) for training set
train_set_x_orig =np.array(train_dataset["train_set_x"][:])
train_set_y_orig =np.array(train_dataset["train_set_y"][:])

# Load the test data
test_dataset =h5py.File(test_file,'r') 

# Separate features(x) and labels(y) for training set
test_set_x_orig =np.array(test_dataset["test_set_x"][:]) 
test_set_y_orig =np.array(test_dataset["test_set_y"][:])

classes = np.array(test_dataset["list_classes"][:]) # the list of classes

train_set_y_orig = torch.from_numpy(train_set_y_orig.reshape((1, train_set_y_orig.shape[0])))
test_set_y_orig = torch.from_numpy(test_set_y_orig.reshape((1, test_set_y_orig.shape[0])))

return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes






x = torch.Tensor(N, D_in)
y = torch.Tensor(N, D_out)
train_file="data/train_catvnoncat.h5"
test_file="data/test_catvnoncat.h5"
x,y,_,_,_=load_data(train_file,test_file)

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