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)