Value error while concatenating the numpy arrays

Question

Im loading mnist dataset as follows,

(X_train, y_train), (X_test, y_test) = mnist.load_data()

However since I need to load and train my own dataset, I wrote the little script as follows which will give the exact train and test values

def load_train(path):
X_train = []
y_train = []
print('Read train images')
for j in range(10):
    files = glob(path + "*.jpeg")
    for fl in files:
        img = get_im(fl)
        print(fl)
        X_train.append(img)
        y_train.append(j)

return np.asarray(X_train), np.asarray(y_train)

the pertained model generates a numpy array of size (64, 28, 28, 1) while training. Im concatenating the image_batch from the generated image as follows,

    X = np.concatenate((image_batch, generated_images))

However im getting the following error,

ValueError: all the input arrays must have same number of dimensions

img_batch is of size (64, 28, 28) generated_images is of size (64, 28, 28, 1)

How do I expand the dimension of the img_batch in X_train so as to concatenate with generated_images? or is there any other ways to load the custom images in place of loadmnist?

Answer 1

There is a function in python called np.expand_dims() which can expand the dimension of any array along the axis provided in arguments. In your case use, img_batch = np.expand_dims(img_batch, axis=3).

One other approach would be to use reshape function as suggested by @Ioannis Nasios. img_batch = img_batch.reshape(64,28,28,1)