xxmaj so bad that i ca n't bear to xxunk anything other than just a few words. If you don't want to feel slighted you'll sit through this horrible film and develop a real sense of pity for the actors involved, they've all seen better days, but then you realize they actually got paid quite a bit of money to do this and you'll lose pity for them just like you've alr.
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It almost seems as a series of challenges set up to determine whether or not you are willing to walk out of the movie and give up the money you just paid. However this comes from the fact that you can't make heads or tails of this mess. This movie succeeds at being one of the most unique movies you've seen. The theme of the movie "Duty, Honor, Country" are not just mere words blathered from the lips of a high-brassed offic. The disappointing thing about this film is that it only concentrate on a short period of the man's life - interestingly enough the man's entire life would have made such an epic bio-pic that it is staggering to imagine the cost for production.Some posters elude to the flawed characteristics about the man, which are cheap shots.
I watched this movie with my dad when it came out and having served in Korea he had great admiration for the man. The production value was so incredibly low that it felt li. The script felt as if it were being written as the movie was being shot. We had swearing guy, fat guy who eats donuts, goofy foreign guy, etc. For starters, there was a musical montage every five minutes. If I labor all my days and I can save but one soul from watching this movie, how great will be my joy.Where to begin my discussion of pain.
There is som.Įvery once in a long while a movie will come along that will be so awful that I feel compelled to warn people. It is now fashionable to blame the British for setting Hindus and Muslims against each other, and then cruelly separating them into two countries. The entire cast does an excellent job with the script.But it is hard to watch, because there is no good end to a situation like the one presented. There are no really superstars in the cast, though several faces will be familiar. The music is good, too, though it is mostly early in the film, when things are still relatively cheery. The acting, script and camera-work are all first-rate. Banzai!!! Finally this was directed by the guy who did Big Chill? Must be a replay of Jonestown - hollywood style.
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Plus Kevin Kline: what kind of suicide trip has his career been on? Whoosh. Hard to believe she was the producer on this dog. Un-bleeping-believable! Meg Ryan doesn't even look her usual pert lovable self in this, which normally makes me forgive her shallow ticky acting schtick. But we will need to do something to the raw dataframe to get our inputs, read the first column and add the proper folder before the filename. Notice how there is one more question compared to before: we wont have to use a get_items function here because we already have all our data in one place.
So it looks like we have one column with filenames, one column with the labels (separated by space) and one column that tells us if the filename should go in the validation set or not. do we want to apply a function to a batch after it's created? Yes, we want data augmentation.do we want to apply a function to a given sample? Yes, we need to resize everything to a given size.how do we know the label of an image? By looking at the parent folder.how do we know if a sample is in the training or the validation set? We'll take a random split.what are the types of our inputs and targets? Images and labels.So let's go over the same questions as before and add two more:
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We will also see how to add data augmentation. Therefore, we will need to add something to make them all the same size to be able to assemble them together in a batch. In MNIST they were all 28 by 28 pixels, but here they have different aspect ratios or dimensions.
A slight (but very) important difference with MNIST is that images are now not all of the same size. The Oxford IIIT Pets dataset is a dataset of pictures of dogs and cats, with 37 different breeds. (TensorImageBW of size 4x1x28x28, TensorCategory(, device='cuda:6')) Setting up Pipeline: parent_label -> Categorize - gives Collecting items from /home/jhoward/.fastai/data/mnist_tiny