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91 | 91 | image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
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92 | 92 | data_transforms[x])
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93 | 93 | for x in ['train', 'val']}
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94 |
| -dataloders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, |
| 94 | +dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, |
95 | 95 | shuffle=True, num_workers=4)
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96 | 96 | for x in ['train', 'val']}
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97 | 97 | dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
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@@ -119,7 +119,7 @@ def imshow(inp, title=None):
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119 | 119 |
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120 | 120 |
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121 | 121 | # Get a batch of training data
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122 |
| -inputs, classes = next(iter(dataloders['train'])) |
| 122 | +inputs, classes = next(iter(dataloaders['train'])) |
123 | 123 |
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124 | 124 | # Make a grid from batch
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125 | 125 | out = torchvision.utils.make_grid(inputs)
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@@ -163,7 +163,7 @@ def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
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163 | 163 | running_corrects = 0
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164 | 164 |
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165 | 165 | # Iterate over data.
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166 |
| - for data in dataloders[phase]: |
| 166 | + for data in dataloaders[phase]: |
167 | 167 | # get the inputs
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168 | 168 | inputs, labels = data
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169 | 169 |
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@@ -225,7 +225,7 @@ def visualize_model(model, num_images=6):
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225 | 225 | images_so_far = 0
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226 | 226 | fig = plt.figure()
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227 | 227 |
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228 |
| - for i, data in enumerate(dataloders['val']): |
| 228 | + for i, data in enumerate(dataloaders['val']): |
229 | 229 | inputs, labels = data
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230 | 230 | if use_gpu:
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231 | 231 | inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())
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