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authorGravatar A. Unique TensorFlower <gardener@tensorflow.org>2017-06-27 16:33:00 -0700
committerGravatar TensorFlower Gardener <gardener@tensorflow.org>2017-06-27 16:37:09 -0700
commit50b999a8336d19400ab75aea66fe46eca2f5fe0b (patch)
tree7cba4f4af6b131c253b65ff9f2923e851184668c /tensorflow/examples/tutorials
parentd6d58a3a1785785679af56c0f8f131e7312b8226 (diff)
Merge changes from github.
PiperOrigin-RevId: 160344052
Diffstat (limited to 'tensorflow/examples/tutorials')
-rw-r--r--tensorflow/examples/tutorials/mnist/mnist.py2
-rw-r--r--tensorflow/examples/tutorials/word2vec/word2vec_basic.py16
2 files changed, 11 insertions, 7 deletions
diff --git a/tensorflow/examples/tutorials/mnist/mnist.py b/tensorflow/examples/tutorials/mnist/mnist.py
index d533697976..3585043a2a 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 is required for running the network
+1. inference() - Builds the model as far as 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 13e5717b0d..aee482fda5 100644
--- a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
+++ b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
@@ -91,7 +91,6 @@ 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
@@ -101,9 +100,10 @@ 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)
- for _ in range(span):
- buffer.append(data[data_index])
- data_index = (data_index + 1) % len(data)
+ if data_index + span > len(data):
+ data_index = 0
+ buffer.extend(data[data_index:data_index + span])
+ data_index += span
for i in range(batch_size // num_skips):
target = skip_window # target label at the center of the buffer
targets_to_avoid = [skip_window]
@@ -113,8 +113,12 @@ 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]
- buffer.append(data[data_index])
- data_index = (data_index + 1) % len(data)
+ if data_index == len(data):
+ buffer[:] = data[:span]
+ data_index = span
+ else:
+ buffer.append(data[data_index])
+ data_index += 1
# 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