def test_relu():
'''
Relu implementation doesn't depend on the value being
a theano variable. Testing ints, floats and theano tensors.
'''
from keras.activations import relu as r
assert r(5) == 5
assert r(-5) == 0
assert r(-0.1) == 0
assert r(0.1) == 0.1
x = T.vector()
exp = r(x)
f = theano.function([x], exp)
test_values = get_standard_values()
result = f(test_values)
list_assert_equal(result, test_values) # because no negatives in test values
def test_relu():
'''
Relu implementation doesn't depend on the value being
a theano variable. Testing ints, floats and theano tensors.
'''
from keras.activations import relu as r
assert r(5) == 5
assert r(-5) == 0
assert r(-0.1) == 0
assert r(0.1) == 0.1
x = T.vector()
exp = r(x)
f = theano.function([x], exp)
test_values = get_standard_values()
result = f(test_values)
list_assert_equal(result,
test_values) # because no negatives in test values
def test_relu(self):
"""
Relu implementation doesn't depend on the value being
a theano variable. Testing ints, floats and theano tensors.
"""
from keras.activations import relu as r
x = K.placeholder(ndim=2)
exp = r(x)
f = K.function([x], [exp])
test_values = get_standard_values()
result = f([test_values])[0]
# because no negatives in test values
assert_allclose(result, test_values, rtol=1e-05)
def test_relu(self):
'''
Relu implementation doesn't depend on the value being
a theano variable. Testing ints, floats and theano tensors.
'''
from keras.activations import relu as r
x = K.placeholder(ndim=2)
exp = r(x)
f = K.function([x], [exp])
test_values = get_standard_values()
result = f([test_values])[0]
# because no negatives in test values
assert_allclose(result, test_values, rtol=1e-05)