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Merge pull request #30422 from siju-samuel:deprecated_dropout_keepprob_removed

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PiperOrigin-RevId: 257149096
This commit is contained in:
TensorFlower Gardener 2019-07-09 02:29:35 -07:00
commit 36ac0a1ac5
3 changed files with 19 additions and 16 deletions
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2
tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
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@ -103,7 +103,7 @@ def train():
with tf.name_scope('dropout'):
keep_prob = tf.placeholder(tf.float32)
tf.summary.scalar('dropout_keep_probability', keep_prob)
dropped = tf.nn.dropout(hidden1, keep_prob)
dropped = tf.nn.dropout(hidden1, rate=(1 - keep_prob))
# Do not apply softmax activation yet, see below.
y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)

2
tensorflow/python/grappler/hierarchical_controller.py
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@ -883,7 +883,7 @@ class HierarchicalController(Controller):
actions.read(i - 1))
)
if self.hparams.keep_prob is not None:
signal = nn_ops.dropout(signal, self.hparams.keep_prob)
signal = nn_ops.dropout(signal, rate=(1 - self.hparams.keep_prob))
next_c, next_h = lstm(signal, prev_c, prev_h, w_lstm, forget_bias)
query = math_ops.matmul(next_h, attn_w_2)
query = array_ops.reshape(

31
tensorflow/python/ops/nn_test.py
View File

@ -313,7 +313,7 @@ class DropoutTest(test_lib.TestCase):
num_iter = 10
for keep_prob in [0.1, 0.5, 0.8]:
t = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32)
dropout = nn_ops.dropout(t, keep_prob)
dropout = nn_ops.dropout(t, rate=(1 - keep_prob))
final_count = 0
self.assertEqual([x_dim, y_dim], dropout.get_shape())
for _ in xrange(0, num_iter):
@ -340,7 +340,7 @@ class DropoutTest(test_lib.TestCase):
num_iter = 10
for keep_prob in [0.1, 0.5, 0.8]:
t = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32)
dropout = nn_ops.dropout(t, keep_prob, noise_shape=[x_dim, 1])
dropout = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[x_dim, 1])
self.assertEqual([x_dim, y_dim], dropout.get_shape())
final_count = 0
for _ in xrange(0, num_iter):
@ -364,7 +364,7 @@ class DropoutTest(test_lib.TestCase):
num_iter = 10
for keep_prob in [0.1, 0.5, 0.8]:
t = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32)
dropout = nn_ops.dropout(t, keep_prob, noise_shape=[x_dim, 1])
dropout = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[x_dim, 1])
self.assertEqual([x_dim, y_dim], dropout.get_shape())
for _ in xrange(0, num_iter):
value = self.evaluate(dropout)
@ -409,7 +409,9 @@ class DropoutTest(test_lib.TestCase):
keep_prob = 0.5
x = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32)
dropout_x = nn_ops.dropout(
x, keep_prob, noise_shape=array_ops.placeholder(dtypes.int32))
x,
rate=(1 - keep_prob),
noise_shape=array_ops.placeholder(dtypes.int32))
self.assertEqual(x.get_shape(), dropout_x.get_shape())
def testPartialShapedDropout(self):
@ -419,7 +421,7 @@ class DropoutTest(test_lib.TestCase):
for keep_prob in [0.1, 0.5, 0.8]:
t = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32)
# Set noise_shape=[None, 1] which means [x_dim, 1].
dropout = nn_ops.dropout(t, keep_prob, noise_shape=[None, 1])
dropout = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[None, 1])
self.assertEqual([x_dim, y_dim], dropout.get_shape())
final_count = 0
for _ in xrange(0, num_iter):
@ -478,22 +480,23 @@ class DropoutTest(test_lib.TestCase):
keep_prob = 0.5
t = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32)
with self.assertRaises(ValueError):
_ = nn_ops.dropout(t, keep_prob, noise_shape=[x_dim, y_dim + 10])
_ = nn_ops.dropout(
t, rate=(1 - keep_prob), noise_shape=[x_dim, y_dim + 10])
with self.assertRaises(ValueError):
_ = nn_ops.dropout(t, keep_prob, noise_shape=[x_dim, y_dim, 5])
_ = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[x_dim, y_dim, 5])
with self.assertRaises(ValueError):
_ = nn_ops.dropout(t, keep_prob, noise_shape=[x_dim + 3])
_ = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[x_dim + 3])
with self.assertRaises(ValueError):
_ = nn_ops.dropout(t, keep_prob, noise_shape=[x_dim])
_ = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[x_dim])
# test that broadcasting proceeds
_ = nn_ops.dropout(t, keep_prob, noise_shape=[y_dim])
_ = nn_ops.dropout(t, keep_prob, noise_shape=[1, y_dim])
_ = nn_ops.dropout(t, keep_prob, noise_shape=[x_dim, 1])
_ = nn_ops.dropout(t, keep_prob, noise_shape=[1, 1])
_ = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[y_dim])
_ = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[1, y_dim])
_ = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[x_dim, 1])
_ = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[1, 1])
def testNoDropoutFast(self):
x = array_ops.zeros((5,))
y = nn_ops.dropout(x, keep_prob=1)
y = nn_ops.dropout(x, rate=0)
self.assertTrue(x is y)
y = nn_ops.dropout_v2(x, rate=0)