def reconstruction_loss_for(self, output_nodes):
data = self.mnist_data
bw_layer1 = Layer(InputLayer(784), output_nodes, session=self.session, noise_std=1.0, bactivate=True)
cost = bw_layer1.unsupervised_cost_train()
optimizer = tf.train.AdamOptimizer(0.1).minimize(cost)
self.session.run(tf.initialize_all_variables())
end_epoch = data.train.epochs_completed + 3
while data.train.epochs_completed <= end_epoch:
train_x, train_y = data.train.next_batch(100)
self.session.run(optimizer, feed_dict={bw_layer1.input_placeholder: train_x})
result = self.session.run(bw_layer1.unsupervised_cost_predict(),
feed_dict={bw_layer1.input_placeholder: data.test.images})
print("denoising with %s hidden layer had cost %s" % (output_nodes, result))
return result
def test_noise_reconstruction(self):
INPUT_DIM = 10
HIDDEN_NODES = 1
bw_layer1 = Layer(InputLayer(INPUT_DIM), HIDDEN_NODES, session=self.session, noise_std=1.0, bactivate=True)
# single cluster reconstruct
data = []
for i in range(10):
data.append([i*.1]*INPUT_DIM)
cost = bw_layer1.unsupervised_cost_train()
optimizer = tf.train.AdamOptimizer(0.1).minimize(cost)
self.session.run(tf.initialize_all_variables())
for j in range(200):
self.session.run(optimizer, feed_dict={bw_layer1.input_placeholder: data})
result = self.session.run(bw_layer1.unsupervised_cost_predict(),
feed_dict={bw_layer1.input_placeholder: data})
print("denoising with %s hidden layer had cost %s" % (HIDDEN_NODES, result))
