I am new to TensorFlow and machine learning. I am trying to classify two objects a cup and a pendrive (jpeg images). I have trained and exported a model.ckpt successfully. N
image
has a shape of (64,64,3)
.
Your input placeholder _x
have a shape of (?, 64,64,3)
.
The problem is that you're feeding the placeholder with a value of a different shape.
You have to feed it with a value of (1, 64, 64, 3)
= a batch of 1 image.
Just reshape your image
value to a batch with size one.
image = array(img).reshape(1, 64,64,3)
P.S: the fact that the input placeholder accepts a batch of images, means that you can run predicions for a batch of images in parallel.
You can try to read more than 1 image (N images) and than build a batch of N image, using a tensor with shape (N, 64,64,3)
Powder's comment may go undetected like I missed it so many times,. So with the hope of making it more visible, I will re-iterate his point.
Sometimes using image = array(img).reshape(a,b,c,d)
will reshape alright but from experience, my kernel crashes every time I try to use the new dimension in an operation. The safest to use is
np.expand_dims(img, axis=0)
It works perfect every time. I just can't explain why. This link has a great explanation and examples regarding its usage.