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Quick doc and script fixes + missing folder #2

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Quick doc and script fixes + missing folder #2

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745c4f2
Quick doc fixes. The specified version of TensorFlow won't install wi…
rswezey May 31, 2019
1496df6
Variable fix
rswezey May 31, 2019
a2ec211
elif fix
rswezey May 31, 2019
53b88ac
Merge branch 'master' into master
rswezey Jun 11, 2019
e582d66
Merge branch 'master' into master
rswezey Jun 24, 2019
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2 changes: 1 addition & 1 deletion README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@ This repository provides a reference implementation for learning NeuralSort-base

## Requirements

The codebase is implemented in Python 3.7. To install the necessary requirements, run the following commands:
The codebase is implemented in Python 3.6. To install the necessary requirements, run the following commands:

```
pip install -r requirements.txt
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6 changes: 3 additions & 3 deletions tf/run_median.py
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Expand Up @@ -231,7 +231,7 @@ def prnt(*args):
TRAIN_PER_EPOCH = mnist_input.TRAIN_SET_SIZE // (l * M)
VAL_PER_EPOCH = mnist_input.VAL_SET_SIZE // (l * M)
TEST_PER_EPOCH = mnist_input.TEST_SET_SIZE // (l * M)
best_val = float('inf')
best_correct_val = float('inf')
tiebreaker_val = -1


Expand Down Expand Up @@ -272,8 +272,8 @@ def test(epoch, val=False):
if val:
prnt("Validation set: correctly identified %f, mean squared error %f" %
(c_i, l_v))
if l_v < best_val:
best_val = l_v
if l_v < best_correct_val:
best_correct_val = l_v
prnt('Saving...')
save_model(epoch)
else:
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8 changes: 4 additions & 4 deletions tf/run_sort.py
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Expand Up @@ -73,7 +73,7 @@
losses = tf.reduce_mean(losses, axis=-1)
loss = tf.reduce_mean(losses)

if method == 'sinkhorn':
elif method == 'sinkhorn':
representations = multi_mnist_cnn.deepnn(l, X, n)
pre_sinkhorn = tf.reshape(representations, [M, n, n])
P_hat = sinkhorn_operator(pre_sinkhorn, temp=temperature)
Expand All @@ -84,7 +84,7 @@
losses = tf.reduce_mean(losses, axis=-1)
loss = tf.reduce_mean(losses)

if method == 'gumbel_sinkhorn':
elif method == 'gumbel_sinkhorn':
representations = multi_mnist_cnn.deepnn(l, X, n)
pre_sinkhorn = tf.reshape(representations, [M, n, n])
P_hat = sinkhorn_operator(pre_sinkhorn, temp=temperature)
Expand All @@ -102,7 +102,7 @@
losses = tf.reshape(losses, [-1])
loss = tf.reduce_mean(losses)

if method == 'deterministic_neuralsort':
elif method == 'deterministic_neuralsort':
scores = multi_mnist_cnn.deepnn(l, X, 1)
scores = tf.reshape(scores, [M, n, 1])
P_hat = util.neuralsort(scores, temperature)
Expand All @@ -112,7 +112,7 @@
losses = tf.reduce_mean(losses, axis=-1)
loss = tf.reduce_mean(losses)

if method == 'stochastic_neuralsort':
elif method == 'stochastic_neuralsort':
scores = multi_mnist_cnn.deepnn(l, X, 1)
scores = tf.reshape(scores, [M, n, 1])
P_hat = util.neuralsort(scores, temperature)
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