def test_output(self):
n_inputs = 10
n_hidden = 20
x = tf.placeholder(dtype=tf.float32, shape=[None,n_inputs])
input_layer = Layer(n_units=n_inputs,activation=x)
nn = NeuralNetwork(input_layer)
# add an identity layer
id_layer = Layer(n_hidden, activation=tf.identity)
nn.add_layer(id_layer,biased=True)
wij = nn.weights(0,1)
expected_out = id_layer.activation(tf.add(tf.matmul(x,wij), nn.biases(1)))
init = tf.initialize_all_variables()
with tf.Session() as ss:
ss.run(init)
feed = {x: np.ones((1,n_inputs),dtype=np.float32)}
r1 = ss.run(nn.output(), feed_dict=feed)
r2 = ss.run(expected_out,feed_dict=feed)
self.assertTrue(np.array_equal(r1,r2))
def test_biases(self):
n_inputs = 10
n_hidden = 20
x = tf.ones([1,n_inputs])
input_layer = Layer(n_units=n_inputs,activation=x)
nn = NeuralNetwork(input_layer)
# add a layer with biases
id_layer = Layer(n_hidden,activation=tf.identity)
nn.add_layer(id_layer, biased=True)
# get biases
b = nn.biases(1)
self.assertIsInstance(b,tf.Variable)
init = tf.initialize_all_variables()
with tf.Session() as ss:
ss.run(init)
(bs) = ss.run(tf.shape(b))
self.assertEqual(bs,n_hidden)
