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
