def test_cross_entropy_loss_basic():
"""
Some simple tests of cross_entropy_loss to get you started.
Warning: these are not exhaustive.
"""
y = np.array([[0, 1], [1, 0], [1, 0]])
yhat = np.array([[.5, .5], [.5, .5], [.5, .5]])
test1 = cross_entropy_loss(tf.constant(y, dtype=tf.int32),
tf.constant(yhat, dtype=tf.float32))
with tf.Session() as sess:
test1 = sess.run(test1)
expected = -3 * np.log(.5)
test_all_close("Cross-entropy test 1", test1, expected)
print "Basic (non-exhaustive) cross-entropy tests pass"
def test_cross_entropy_loss_basic():
"""
Some simple tests of cross_entropy_loss to get you started.
Warning: these are not exhaustive.
"""
y = np.array([[0, 1], [1, 0], [1, 0]])
yhat = np.array([[.5, .5], [.5, .5], [.5, .5]])
test1 = cross_entropy_loss(
tf.constant(y, dtype=tf.int32),
tf.constant(yhat, dtype=tf.float32))
with tf.Session() as sess:
test1 = sess.run(test1)
expected = -3 * np.log(.5)
test_all_close("Cross-entropy test 1", test1, expected)
print "Basic (non-exhaustive) cross-entropy tests pass"
def test_softmax_basic():
"""
Some simple tests of softmax to get you started. Warning: these are not exhaustive.
"""
test1 = softmax(tf.constant(np.array([[1001, 1002], [3, 4]]), dtype=tf.float32))
with tf.Session() as sess:
test1 = sess.run(test1)
test_all_close("Softmax test 1", test1, np.array([[0.26894142, 0.73105858],
[0.26894142, 0.73105858]]))
test2 = softmax(tf.constant(np.array([[-1001, -1002]]), dtype=tf.float32))
with tf.Session() as sess:
test2 = sess.run(test2)
test_all_close("Softmax test 2", test2, np.array([[0.73105858, 0.26894142]]))
print "Basic (non-exhaustive) softmax tests pass\n"
def test_softmax_basic():
"""
Some simple tests of softmax to get you started.
Warning: these are not exhaustive.
"""
test1 = softmax(tf.constant(np.array([[1001, 1002], [3, 4]]), dtype=tf.float32))
with tf.Session() as sess:
test1 = sess.run(test1)
test_all_close("Softmax test 1", test1, np.array([[0.26894142, 0.73105858],
[0.26894142, 0.73105858]]))
test2 = softmax(tf.constant(np.array([[-1001, -1002]]), dtype=tf.float32))
with tf.Session() as sess:
test2 = sess.run(test2)
test_all_close("Softmax test 2", test2, np.array([[0.73105858, 0.26894142]]))
print "Basic (non-exhaustive) softmax tests pass\n"
def test_softmax_basic():
"""
Some simple tests of softmax to get you started.
Warning: these are not exhaustive.
"""
test1 = softmax(t.tensor(np.array([[1001, 1002], [3, 4]]),
dtype=t.float32))
test_all_close(
"Softmax test 1", test1,
np.array([[0.26894142, 0.73105858], [0.26894142, 0.73105858]]))
test2 = softmax(t.tensor(np.array([[-1001, -1002]]), dtype=t.float32))
test_all_close("Softmax test 2", test2, np.array([[0.73105858,
0.26894142]]))
print("Basic (non-exhaustive) softmax tests pass\n")
def test_cross_entropy_loss_basic():
"""
Some simple tests of cross_entropy_loss to get you started.
Warning: these are not exhaustive.
"""
y = np.array([[0, 1], [1, 0], [1, 0]])
yhat = np.array([[.5, .5], [.5, .5], [.5, .5]])
# test1 = cross_entropy_loss(
# torch.Tensor([[0, 1], [1, 0], [1, 0]]),
# torch.Tensor([[.5, .5], [.5, .5], [.5, .5]]))
# test1 = np.array(test1)
test1 = cross_entropy_loss(
dy.inputTensor([[0, 1], [1, 0], [1, 0]]),
dy.inputTensor([[.5, .5], [.5, .5], [.5, .5]]))
test1 = np.array(test1.value())
expected = -3 * np.log(.5)
test_all_close("Cross-entropy test 1", test1, expected)
print "Basic (non-exhaustive) cross-entropy tests pass"
def test_softmax_basic():
"""
Some simple tests of softmax to get you started.
Warning: these are not exhaustive.
"""
# test1 = softmax(torch.Tensor([[1001, 1002], [3, 4]]))
# test1 = test1.numpy()
test1 = softmax(dy.inputTensor([[1001, 1002], [3, 4]]))
test1 = test1.npvalue();
test_all_close("Softmax test 1", test1, np.array([[0.26894142, 0.73105858],
[0.26894142, 0.73105858]]))
# test2 = softmax(torch.Tensor([[-1001, -1002]]))
# test2 = test2.numpy()
test2 = softmax(dy.inputTensor([[-1001, -1002]]))
test2 = test2.npvalue();
test_all_close("Softmax test 2", test2, np.array([[0.73105858, 0.26894142]]))
print "Basic (non-exhaustive) softmax tests pass\n"
def test_cross_entropy_loss_basic():
"""
Some simple tests of cross_entropy_loss to get you started.
Warning: these are not exhaustive.
"""
dy.renew_cg()
#y = np.array([[0, 1], [1, 0], [1, 0]])
#yhat = np.array([[.5, .5], [.5, .5], [.5, .5]])
y = np.array([[0, 1], [1, 0], [1, 0]], dtype=np.float32)
yhat = np.array([[.5, .5], [.5, .5], [.5, .5]], dtype=np.float32)
test1 = cross_entropy_loss(dy.inputTensor(y), dy.inputTensor(yhat))
#tf.constant(y, dtype=tf.int32),
#tf.constant(yhat, dtype=tf.float32))
#with tf.Session() as sess:
# test1 = sess.run(test1)
expected = -3 * np.log(.5)
test_all_close("Cross-entropy test 1",
test1.npvalue().reshape([]), expected)
print "Basic (non-exhaustive) cross-entropy tests pass"
def test_softmax_basic():
"""
Some simple tests of softmax to get you started.
Warning: these are not exhaustive.
"""
test1 = softmax(
tf.constant(np.array([[1001, 1002], [3, 4]]), dtype=tf.float32))
with tf.Session() as sess:
test1 = sess.run(test1)
test_all_close(
"Softmax test 1", test1,
np.array([[0.26894142, 0.73105858], [0.26894142, 0.73105858]]))
test2 = softmax(tf.constant(np.array([[-1001, -1002]]), dtype=tf.float32))
with tf.Session() as sess:
test2 = sess.run(test2)
test_all_close("Softmax test 2", test2, np.array([[0.73105858,
0.26894142]]))
# [CL] added some more tests for exhaustive
test3 = softmax(tf.constant(np.array([[23,-2, 30], \
[3,4,2], \
[-10,-2,-8], \
[1,20,-4]]), dtype=tf.float32))
with tf.Session() as sess:
test3 = sess.run(test3)
test_all_close(
"Softmax test 3", test3,
np.array([[0.000911051, 1.26526E-14, 0.999088949],
[0.244728471, 0.665240956, 0.090030573],
[3.3452123e-04, 0.99719369, 2.4717962e-03],
[5.6028E-09, 0.999999994, 3.77513E-11]]))
print "Basic (non-exhaustive) softmax tests pass\n"
# test_all_close("Softmax test 2", test2, np.array([[0.73105858, 0.26894142]]))
#
# print "Basic (non-exhaustive) softmax tests pass\n"
# def test_cross_entropy_loss_basic():
# """
# Some simple tests of cross_entropy_loss to get you started.
# Warning: these are not exhaustive.
# """
# y = np.array([[0, 1], [1, 0], [1, 0]])
# yhat = np.array([[.5, .5], [.5, .5], [.5, .5]])
#
# test1 = cross_entropy_loss(tf.constant(y, dtype=tf.int32), tf.constant(yhat, dtype=tf.float32))
# with tf.Session() as sess:
# test1 = sess.run(test1)
# expected = -3 * np.log(.5)
# test_all_close("Cross-entropy test 1", test1, expected)
#
# print "Basic (non-exhaustive) cross-entropy tests pass"
if __name__ == "__main__":
y = np.array([[0, 1], [1, 0], [1, 0]])
yhat = np.array([[.5, .5], [.5, .5], [.5, .5]])
test1 = cross_entropy_loss(tf.constant(y, dtype=tf.int32),
tf.constant(yhat, dtype=tf.float32))
with tf.Session() as sess:
test1 = sess.run(test1)
expected = -3 * np.log(.5)
test_all_close("Cross-entropy test 1", test1, expected)
