def test_cosinedense():
from keras import regularizers
from keras import constraints
from keras.models import Sequential
layer_test(core.CosineDense,
kwargs={'output_dim': 3},
input_shape=(3, 2))
layer_test(core.CosineDense,
kwargs={'output_dim': 3},
input_shape=(3, 4, 2))
layer_test(core.CosineDense,
kwargs={'output_dim': 3},
input_shape=(None, None, 2))
layer_test(core.CosineDense,
kwargs={'output_dim': 3},
input_shape=(3, 4, 5, 2))
layer_test(core.CosineDense,
kwargs={'output_dim': 3,
'W_regularizer': regularizers.l2(0.01),
'b_regularizer': regularizers.l1(0.01),
'activity_regularizer': regularizers.activity_l2(0.01),
'W_constraint': constraints.MaxNorm(1),
'b_constraint': constraints.MaxNorm(1)},
input_shape=(3, 2))
X = np.random.randn(1, 20)
model = Sequential()
model.add(core.CosineDense(1, bias=True, input_shape=(20,)))
model.compile(loss='mse', optimizer='rmsprop')
W = model.get_weights()
W[0] = X.T
W[1] = np.asarray([1.])
model.set_weights(W)
out = model.predict(X)
assert_allclose(out, np.ones((1, 1), dtype=K.floatx()), atol=1e-5)
X = np.random.randn(1, 20)
model = Sequential()
model.add(core.CosineDense(1, bias=False, input_shape=(20,)))
model.compile(loss='mse', optimizer='rmsprop')
W = model.get_weights()
W[0] = -2 * X.T
model.set_weights(W)
out = model.predict(X)
assert_allclose(out, -np.ones((1, 1), dtype=K.floatx()), atol=1e-5)
def test_cosinedense_correctness():
X = np.random.randn(1, 20)
model = Sequential()
model.add(core.CosineDense(1, use_bias=True, input_shape=(20, )))
model.compile(loss='mse', optimizer='rmsprop')
W = model.get_weights()
W[0] = X.T
W[1] = np.asarray([1.])
model.set_weights(W)
out = model.predict(X)
assert_allclose(out, np.ones((1, 1), dtype=K.floatx()), atol=1e-5)
X = np.random.randn(1, 20)
model = Sequential()
model.add(core.CosineDense(1, use_bias=False, input_shape=(20, )))
model.compile(loss='mse', optimizer='rmsprop')
W = model.get_weights()
W[0] = -2 * X.T
model.set_weights(W)
out = model.predict(X)
assert_allclose(out, -np.ones((1, 1), dtype=K.floatx()), atol=1e-5)