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}