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_equivalent_networks(self):
"""
Create two equivalent neural networks
nn1 = x -> 1 -> div(x/in) -> w -> sigm -> o
nn2 = x -> w -> sigm -> 0
(with shared weights)
"""
x = tf.ones([1, 4])
input_layer = Layer(n_units=4,activation=x)
weights = tf.ones([4,4])
shared_w = tf.Variable(normalised_weight_init(4,4))
nn1 = NeuralNetwork(input_layer)
l11 = Layer(4, lambda x_in: tf.div(x_in, 4))
l21 = Layer(4, tf.nn.sigmoid)
nn1.add_layer(l11,biased=False,shared_weights=weights)
nn1.add_layer(l21,biased=False,shared_weights=shared_w)
self.assertEqual(nn1.graph.number_of_edges(), 2)
self.assertEqual(nn1.size(),3)
nn2 = NeuralNetwork(Layer(4,x))
l12 = Layer(4, tf.nn.sigmoid)
nn2.add_layer(l12,biased=False,shared_weights=shared_w)
self.assertEqual(nn2.size(),2)
self.assertEqual(nn2.graph.number_of_edges(),1)
init_vars = tf.initialize_all_variables()
with tf.Session() as ss:
ss.run(init_vars)
w1 = ss.run(nn1.weights(1,2))
w2 = ss.run(nn2.weights(0,1))
# shared weights should be the same in both networks
self.assertTrue(np.array_equal(w1,w2))
out_nn1 = ss.run(nn1.output())
out_nn2 = ss.run(nn2.output())
# outputs should be the same since the networks are equivalent
self.assertTrue(np.array_equal(out_nn1,out_nn2))
ss = tf.InteractiveSession()
n_inputs = 784
x = tf.placeholder(tf.float32, [None, n_inputs], name = "x")
# create neural network with tensorx
input_layer = Layer(n_units = n_inputs, activation = x)
network = NeuralNetwork(input_layer)
output_layer = Layer(10, tf.nn.softmax)
network.add_layer(output_layer, biased=True)
# train
target_output = tf.placeholder("float", shape=[None, 10])
# loss function
network_output = network.output()
cross_entropy = -tf.reduce_sum(target_output*tf.log(tf.clip_by_value(network_output,1e-50,1.0)))
train_step_rate = 0.003 # default: 0.003 -> accuracy ~ 0.9162 (step 999)
train_step = tf.train.GradientDescentOptimizer(train_step_rate).minimize(cross_entropy)
# test
correct_prediction = tf.equal(tf.argmax(network_output,1), tf.argmax(target_output,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
# run train and test
tf.initialize_all_variables().run()
n_steps = 1000
for i in range(n_steps):
batch_xs, batch_ys = mnist.train.next_batch(100)
feed = {x: batch_xs, target_output: batch_ys}