Invalid dimension for image data in plt.imshow()
As per the comment of @sdcbr using np.sqeeze reduces unnecessary dimension. If image is 2 dimensions then imshow function works fine. If image has 3 dimensions then you have to reduce extra 1 dimension. But, for higher dim data you will have to reduce it to 2 dims, so np.sqeeze may be applied multiple times. (Or you may use some other dim reduction functions for higher dim data)
import numpy as np import matplotlib.pyplot as pltimg_path = x_test[1] print(img_path.shape)if(len(img_path.shape) == 3): plt.imshow(np.squeeze(img_path))elif(len(img_path.shape) == 2): plt.imshow(img_path)else: print("Higher dimensional data")
Example:
plt.imshow(test_images[0])TypeError: Invalid shape (28, 28, 1) for image data
Correction:
plt.imshow((tf.squeeze(test_images[0])))
You can use tf.squeeze for removing dimensions of size 1 from the shape of a tensor.
plt.imshow( tf.shape( tf.squeeze(x_train) ) )