def test_reconstruction_of_single_input(self):
input_layer = InputLayer(1)
layer = BackWeightLayer(input_layer, 1, non_liniarity=tf.nn.sigmoid, session=self.session, noise_std=0.3)
cost = layer.unsupervised_cost_train()
optimizer = tf.train.AdamOptimizer(0.1).minimize(cost)
self.session.run(tf.initialize_all_variables())
data = np.random.normal(0.5, 0.5, size=[200, 1])
for x in range(100):
self.session.run([optimizer], feed_dict={input_layer.input_placeholder: data})
result = self.session.run([cost], feed_dict={input_layer.input_placeholder: data})
print result
def test_reconstruction_of_single_input(self):
input_layer = InputLayer(1)
layer = BackWeightLayer(input_layer, 1, non_liniarity=tf.nn.sigmoid, session=self.session, noise_std=0.3)
cost = layer.unsupervised_cost_train()
optimizer = tf.train.AdamOptimizer(0.1).minimize(cost)
self.session.run(tf.initialize_all_variables())
data = np.random.normal(0.5, 0.5, size=[200, 1])
for x in range(100):
self.session.run([optimizer], feed_dict={input_layer.input_placeholder: data})
result = self.session.run([cost], feed_dict={input_layer.input_placeholder: data})
print result
def reconstruction_loss_for(self, output_nodes):
data = self.mnist_data
bw_layer1 = BackWeightLayer(InputLayer(784), output_nodes, non_liniarity=tf.nn.sigmoid, session=self.session,
noise_std=0.3)
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 + 5
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.train.images})
print("denoising with %s hidden nodes had cost %s" % (output_nodes, result))
return result
def reconstruction_loss_for(self, output_nodes):
data = self.mnist_data
bw_layer1 = BackWeightLayer(InputLayer(784), output_nodes, non_liniarity=tf.nn.sigmoid, session=self.session,
noise_std=0.3)
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 + 5
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.train.features})
print("denoising with %s hidden nodes had cost %s" % (output_nodes, result))
return result
def _create_layer_for_test(self):
return BackWeightLayer(self._input_layer, self.OUTPUT_NODES, session=self.session)
