def test_simple_rnn(self):
np.random.seed(12082518)
x = np.random.rand(128, 8, 32)
#
X = K.placeholder(shape=(None, 8, 32))
X1 = K.placeholder(shape=(None, 8, 32))
X2 = K.placeholder(shape=(None, 8, 32))
X3 = K.placeholder(shape=(None, 8, 33))
f = N.RNN(32, activation=K.relu, input_mode='skip')
#
y = f(X, mask=K.ones(shape=(128, 8)))
graph = K.ComputationGraph(y)
self.assertEqual(len(graph.inputs), 1)
f1 = K.function([X], y)
x1 = f1(x)
# ====== different placeholder ====== #
y = f(X1)
f2 = K.function([X1], y)
x2 = f1(x)
self.assertEqual(np.sum(x1[0] == x2[0]), np.prod(x1[0].shape))
# ====== pickle load ====== #
f = cPickle.loads(cPickle.dumps(f))
y = f(X2)
f2 = K.function([X2], y)
x3 = f2(x)
self.assertEqual(np.sum(x2[0] == x3[0]), np.prod(x2[0].shape))
# ====== other input shape ====== #
error_happen = False
try:
y = f(X3)
f3 = K.function([X3], y)
x3 = f3(np.random.rand(128, 8, 33))
except (ValueError, Exception):
error_happen = True
self.assertTrue(error_happen)
def odin_net3():
"RNN"
W = [random(28, 32), random(32, 32), random(32), random_bin(12, 28)]
f = N.Sequence([
N.Dense(num_units=32, W_init=W[0], b_init=W[2],
activation=K.linear),
N.RNN(num_units=32, activation=K.relu,
W_init=W[1])
])
return X1, f(X1, hid_init=zeros(1, 32))