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(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)