experiments/STT-tensorflow
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deprecated keep_prob removed from dropout in nn_test.py

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This commit is contained in:
Siju Samuel 2019-07-05 09:53:27 +05:30
parent fc7bce9b4a
commit 26ef37bef3
1 changed files with 14 additions and 14 deletions
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28
tensorflow/python/ops/nn_test.py
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@ -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 +409,7 @@ 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 +419,7 @@ 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 +478,22 @@ 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)