X = tf.placeholder(tf.float32, shape=[None, n_inputs])

weights1_init = initializer([n_inputs, n_hidden1])
weights2_init = initializer([n_hidden1, n_hidden2])

weights1 = tf.Variable(weights1_init, dtype=tf.float32, name="weights1")
weights2 = tf.Variable(weights2_init, dtype=tf.float32, name="weights2")
weights3 = tf.transpose(weights2, name="weights3")  # tied weights
weights4 = tf.transpose(weights1, name="weights4")  # tied weights

biases1 = tf.Variable(tf.zeros(n_hidden1), name="biases1")
biases2 = tf.Variable(tf.zeros(n_hidden2), name="biases2")
biases3 = tf.Variable(tf.zeros(n_hidden3), name="biases3")
biases4 = tf.Variable(tf.zeros(n_outputs), name="biases4")

hidden1 = activation(tf.matmul(X, weights1) + biases1)
hidden2 = activation(tf.matmul(hidden1, weights2) + biases2)
hidden3 = activation(tf.matmul(hidden2, weights3) + biases3)
outputs = tf.matmul(hidden3, weights4) + biases4

reconstruction_loss = tf.reduce_mean(tf.square(outputs - X))
reg_loss = regularizer(weights1) + regularizer(weights2)
loss = reconstruction_loss + reg_loss

optimizer = tf.train.AdamOptimizer(learning_rate)
training_op = optimizer.minimize(loss)