Jack Burdick

We double checked and all images are the same dimensions. If we run `test_x, test_y = data_provider(4)` we get the following; ``` >>> print(x_test.shape) (4, 767, 1022, 3) >>> print(y_test.shape)...

We assumed the line `data_provider(4)` on line 367 of `master/tf_unet/unet.py` was left over from the toy demo.. We can revert back and see what the error is if needed though

Ok.. We've reverted back/updated, thank you. If we follow this [example](https://tf-unet.readthedocs.io/en/latest/usage.html) where should `data` come from? I think our logic must be wrong.

Right, ok so if we try this (not sure we're using it correctly): ``` data_provider = image_util.ImageDataProvider('reduced_segmentation_dataset/*', data_suffix=".jpg", mask_suffix='_Segmentation.png') net = unet.Unet(channels=3, n_class=2, layers=3, features_root=64) trainer = unet.Trainer(net, optimizer="momentum", opt_kwargs=dict(momentum=0.2))...

we also tried: ``` path = trainer.train(data_provider, output_path, training_iters=16, epochs=4) data, _ = data_provider(1) prediction = net.predict(path, data) ``` but were met with what looks like the same error