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| @@ -1 +1,3 @@ | ||
| Data/* | ||
| *.ckpt* | ||
| .ipynb_checkpoints/* |
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| # coding: utf-8 | ||
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| # In[1]: | ||
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| import tensorflow as tf | ||
| import numpy as np | ||
| import scipy.io as io | ||
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| # In[2]: | ||
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| """ Functions for handling the IndianPines data""" | ||
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| class DataSet(object): | ||
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| def __init__(self, images, labels, dtype=tf.float32): | ||
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| """Construct a DataSet. | ||
| FIXME: fake_data options | ||
| one_hot arg is used only if fake_data is true. `dtype` can be either | ||
| `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into | ||
| `[0, 1]`. | ||
| """ | ||
| #COnvert the shape from [num_exmaple,channels, height, width] | ||
| #to [num_exmaple, height, width, channels] | ||
| images = np.transpose(images,(0,2,3,1)) | ||
| #labels[:] = [i - 1 for i in labels] | ||
| labels = np.transpose(labels) | ||
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| dtype = tf.as_dtype(dtype).base_dtype | ||
| if dtype not in (tf.uint8, tf.float32): | ||
| raise TypeError('Invalid image dtype %r, expected uint8 or float32' % | ||
| dtype) | ||
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| assert images.shape[0] == labels.shape[0], ( | ||
| 'images.shape: %s labels.shape: %s' % (images.shape, labels.shape)) | ||
| self._num_examples = images.shape[0] | ||
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| # Convert shape from [num examples, rows, columns, depth] | ||
| # to [num examples, rows*columns*depth] | ||
| images = images.reshape(images.shape[0],images.shape[1] * images.shape[2] * images.shape[3]) | ||
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| # if dtype == tf.float32: | ||
| # # Convert from [0, 255] -> [0.0, 1.0]. | ||
| # images = images.astype(numpy.float32) | ||
| # images = numpy.multiply(images, 1.0 / 255.0) | ||
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| self._images = images | ||
| self._labels = labels | ||
| self._epochs_completed = 0 | ||
| self._index_in_epoch = 0 | ||
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| @property | ||
| def images(self): | ||
| return self._images | ||
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| @property | ||
| def labels(self): | ||
| return self._labels | ||
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| @property | ||
| def num_examples(self): | ||
| return self._num_examples | ||
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| @property | ||
| def epochs_completed(self): | ||
| return self._epochs_completed | ||
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| def next_batch(self, batch_size): | ||
| """Return the next `batch_size` examples from this data set.""" | ||
| start = self._index_in_epoch | ||
| self._index_in_epoch += batch_size | ||
| if self._index_in_epoch > self._num_examples: | ||
| # Finished epoch | ||
| self._epochs_completed += 1 | ||
| # Shuffle the data | ||
| perm = np.arange(self._num_examples) | ||
| np.random.shuffle(perm) | ||
| self._images = self._images[perm] | ||
| self._labels = self._labels[perm] | ||
| # Start next epoch | ||
| start = 0 | ||
| self._index_in_epoch = batch_size | ||
| assert batch_size <= self._num_examples | ||
| end = self._index_in_epoch | ||
| return self._images[start:end], np.reshape(self._labels[start:end],len(self._labels[start:end])) | ||
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| # In[20]: | ||
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| def read_data_sets(directory,value, dtype=tf.float32): | ||
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| images = io.loadmat(directory)[value+'_patch'] | ||
| labels = io.loadmat(directory)[value+'_labels'] | ||
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| data_sets = DataSet(images, labels, dtype=dtype) | ||
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| return data_sets | ||
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| # In[ ]: | ||
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| # In[ ]: | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,5 @@ | ||
| model_checkpoint_path: "model-spatial-21X21.ckpt-3999" | ||
| all_model_checkpoint_paths: "model-spatial-21X21.ckpt-999" | ||
| all_model_checkpoint_paths: "model-spatial-21X21.ckpt-1999" | ||
| all_model_checkpoint_paths: "model-spatial-21X21.ckpt-2999" | ||
| all_model_checkpoint_paths: "model-spatial-21X21.ckpt-3999" |
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