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| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2017-06-26 14:00:17 -0700 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2017-06-26 14:04:35 -0700 |
| commit | 1fa73c53ab95693f070ce70e6be0c644d83c163a (patch) | |
| tree | ffbedf825daf1f3453c695a433c8a9cdf93f6019 /tensorflow/examples/tutorials | |
| parent | b13e96e21c1229a905a623111dd89d2bd0cba53b (diff) | |
Automated g4 rollback of changelist 160182040
PiperOrigin-RevId: 160190881
Diffstat (limited to 'tensorflow/examples/tutorials')
| -rw-r--r-- | tensorflow/examples/tutorials/mnist/mnist.py | 2 | ||||
| -rw-r--r-- | tensorflow/examples/tutorials/word2vec/word2vec_basic.py | 16 |
2 files changed, 7 insertions, 11 deletions
diff --git a/tensorflow/examples/tutorials/mnist/mnist.py b/tensorflow/examples/tutorials/mnist/mnist.py index 3585043a2a..d533697976 100644 --- a/tensorflow/examples/tutorials/mnist/mnist.py +++ b/tensorflow/examples/tutorials/mnist/mnist.py @@ -17,7 +17,7 @@ Implements the inference/loss/training pattern for model building. -1. inference() - Builds the model as far as required for running the network +1. inference() - Builds the model as far as is required for running the network forward to make predictions. 2. loss() - Adds to the inference model the layers required to generate loss. 3. training() - Adds to the loss model the Ops required to generate and diff --git a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py index aee482fda5..13e5717b0d 100644 --- a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py +++ b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py @@ -91,6 +91,7 @@ print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]]) data_index = 0 + # Step 3: Function to generate a training batch for the skip-gram model. def generate_batch(batch_size, num_skips, skip_window): global data_index @@ -100,10 +101,9 @@ def generate_batch(batch_size, num_skips, skip_window): labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32) span = 2 * skip_window + 1 # [ skip_window target skip_window ] buffer = collections.deque(maxlen=span) - if data_index + span > len(data): - data_index = 0 - buffer.extend(data[data_index:data_index + span]) - data_index += span + for _ in range(span): + buffer.append(data[data_index]) + data_index = (data_index + 1) % len(data) for i in range(batch_size // num_skips): target = skip_window # target label at the center of the buffer targets_to_avoid = [skip_window] @@ -113,12 +113,8 @@ def generate_batch(batch_size, num_skips, skip_window): targets_to_avoid.append(target) batch[i * num_skips + j] = buffer[skip_window] labels[i * num_skips + j, 0] = buffer[target] - if data_index == len(data): - buffer[:] = data[:span] - data_index = span - else: - buffer.append(data[data_index]) - data_index += 1 + buffer.append(data[data_index]) + data_index = (data_index + 1) % len(data) # Backtrack a little bit to avoid skipping words in the end of a batch data_index = (data_index + len(data) - span) % len(data) return batch, labels |