| 90 | |
| 91 | |
| 92 | class Data(object): |
| 93 | |
| 94 | def __init__(self, fpath, batch_size=32, seq_length=100, train_ratio=0.8): |
| 95 | '''Data object for loading a plain text file. |
| 96 | |
| 97 | Args: |
| 98 | fpath, path to the text file. |
| 99 | train_ratio, split the text file into train and test sets, where |
| 100 | train_ratio of the characters are in the train set. |
| 101 | ''' |
| 102 | self.raw_data = open(fpath, 'r', |
| 103 | encoding='iso-8859-1').read() # read text file |
| 104 | chars = list(set(self.raw_data)) |
| 105 | self.vocab_size = len(chars) |
| 106 | self.char_to_idx = {ch: i for i, ch in enumerate(chars)} |
| 107 | self.idx_to_char = {i: ch for i, ch in enumerate(chars)} |
| 108 | data = [self.char_to_idx[c] for c in self.raw_data] |
| 109 | # seq_length + 1 for the data + label |
| 110 | nsamples = len(data) // (1 + seq_length) |
| 111 | data = data[0:nsamples * (1 + seq_length)] |
| 112 | data = np.asarray(data, dtype=np.int32) |
| 113 | data = np.reshape(data, (-1, seq_length + 1)) |
| 114 | # shuffle all sequences |
| 115 | np.random.shuffle(data) |
| 116 | self.train_dat = data[0:int(data.shape[0] * train_ratio)] |
| 117 | self.num_train_batch = self.train_dat.shape[0] // batch_size |
| 118 | self.val_dat = data[self.train_dat.shape[0]:] |
| 119 | self.num_test_batch = self.val_dat.shape[0] // batch_size |
| 120 | print('train dat', self.train_dat.shape) |
| 121 | print('val dat', self.val_dat.shape) |
| 122 | |
| 123 | |
| 124 | def numpy2tensors(npx, npy, dev, inputs=None, labels=None): |