# Construct cost
cost_sparse = n_beta * tf.reduce_sum(KL_Div(n_rho, rho_hat))
cost_J = tf.reduce_mean(tf.nn.l2_loss(pred['out'] - x))
cost_reg = n_lambda * (tf.nn.l2_loss(weights['hidden']) + tf.nn.l2_loss(weights['out']))
cost = cost_J + cost_reg + cost_sparse
optimizer = tf.train.GradientDescentOptimizer(learning_rate=n_learning_rate).minimize(cost)
# Initializing the variables
init = tf.initialize_all_variables()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
saver = tf.train.Saver()
# Training cycle
for epoch in range(n_num_epochs):
batch_xs=gid.getPatches(n_epoch_size)
# Fit training using batch data
sess.run(optimizer, feed_dict={x: batch_xs})
print("Optimization Finished!")
saver.save(sess, 'my-SAE')
outWeights = sess.run(weights['hidden'])
vis.display_network(outWeights)
cost_reg = n_lambda * (tf.nn.l2_loss(weights['hidden']) + tf.nn.l2_loss(weights['out']))
cost = cost_J + cost_reg + cost_sparse
optimizer = tf.train.Optimizer(learning_rate=n_learning_rate).minimize(cost)
# Initializing the variables
init = tf.initialize_all_variables()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
saver = tf.train.Saver()
outWeights = sess.run(weights['hidden'])
vis.display_network(outWeights, filename='pretrainweights')
# Training cycle
for epoch in range(n_num_epochs):
batch_xs=gid.getPatches(n_epoch_size)
# Fit training using batch data
sess.run(optimizer, feed_dict={x: batch_xs})
print("Optimization Finished!")
saver.save(sess, 'my-SAE')
outWeights = sess.run(weights['hidden'])
vis.display_network(outWeights)
vis.display_network(outWeights.T, filename="weightsT")
