def test_merge():
layer_1 = core.Layer()
layer_2 = core.Layer()
layer_1.set_input_shape((None,))
layer_2.set_input_shape((None,))
layer = core.Merge([layer_1, layer_2])
_runner(layer)
def test_connections(self):
nb_samples = 10
input_dim = 5
layer1 = core.Layer()
layer2 = core.Layer()
input = np.ones((nb_samples, input_dim))
layer1.input = theano.shared(value=input)
# After connecting, input of layer1 should be passed through
layer2.set_previous(layer1)
for train in [True, False]:
assert_allclose(layer2.get_input(train).eval(), input)
assert_allclose(layer2.get_output(train).eval(), input)
def test_connections():
nb_samples = 10
input_dim = 5
layer1 = core.Layer()
layer2 = core.Layer()
input = np.ones((nb_samples, input_dim))
layer1.input = K.variable(input)
# After connecting, input of layer1 should be passed through
layer2.set_previous(layer1)
for train in [True, False]:
assert_allclose(K.eval(layer2.get_input(train)), input)
assert_allclose(K.eval(layer2.get_output(train)), input)
def test_connections(self):
nb_samples = 10
input_dim = 5
layer1 = core.Layer()
layer2 = core.Layer()
input = np.ones((nb_samples, input_dim))
layer1.input = theano.shared(value=input)
# As long as there is no previous layer, an error should be raised.
for train in [True, False]:
self.assertRaises(AttributeError, layer2.get_input, train)
# After connecting, input of layer1 should be passed through
layer2.set_previous(layer1)
for train in [True, False]:
assert_allclose(layer2.get_input(train).eval(), input)
assert_allclose(layer2.get_output(train).eval(), input)
def test_input_output():
nb_samples = 10
input_dim = 5
layer = core.Layer()
# Once an input is provided, it should be reachable through the
# appropriate getters
input = np.ones((nb_samples, input_dim))
layer.input = K.variable(input)
for train in [True, False]:
assert_allclose(K.eval(layer.get_input(train)), input)
assert_allclose(K.eval(layer.get_output(train)), input)
def test_input_output(self):
nb_samples = 10
input_dim = 5
layer = core.Layer()
# As long as there is no input, an error should be raised.
for train in [True, False]:
self.assertRaises(AttributeError, layer.get_input, train)
self.assertRaises(AttributeError, layer.get_output, train)
# Once an input is provided, it should be reachable through the
# appropriate getters
input = np.ones((nb_samples, input_dim))
layer.input = theano.shared(value=input)
for train in [True, False]:
assert_allclose(layer.get_input(train).eval(), input)
assert_allclose(layer.get_output(train).eval(), input)
def test_base():
layer = core.Layer()
_runner(layer)
def test_autoencoder():
layer_1 = core.Layer()
layer_2 = core.Layer()
layer = core.AutoEncoder(layer_1, layer_2)
_runner(layer)
def test_base(self):
layer = core.Layer()
self._runner(layer)
def test_merge(self):
layer_1 = core.Layer()
layer_2 = core.Layer()
layer = core.Merge([layer_1, layer_2])
self._runner(layer)
