How to exactly add L1 regularisation to tensorflow error function
<p>Hey I am new to tensorflow and even after a lot of efforts could not add
L1 regularisation term to the error term</p>

<pre><code>x = tf.placeholder("float", [None, n_input])
# Weights and biases to hidden layer
ae_Wh1 = tf.Variable(tf.random_uniform((n_input, n_hidden1), -1.0 / math.sqrt(n_input), 1.0 / math.sqrt(n_input)))
ae_bh1 = tf.Variable(tf.zeros([n_hidden1]))
ae_h1 = tf.nn.tanh(tf.matmul(x,ae_Wh1) + ae_bh1)

ae_Wh2 = tf.Variable(tf.random_uniform((n_hidden1, n_hidden2), -1.0 / math.sqrt(n_hidden1), 1.0 / math.sqrt(n_hidden1)))
ae_bh2 = tf.Variable(tf.zeros([n_hidden2]))
ae_h2 = tf.nn.tanh(tf.matmul(ae_h1,ae_Wh2) + ae_bh2)

ae_Wh3 = tf.transpose(ae_Wh2)
ae_bh3 = tf.Variable(tf.zeros([n_hidden1]))
ae_h1_O = tf.nn.tanh(tf.matmul(ae_h2,ae_Wh3) + ae_bh3)

ae_Wh4 = tf.transpose(ae_Wh1)
ae_bh4 = tf.Variable(tf.zeros([n_input]))
ae_y_pred = tf.nn.tanh(tf.matmul(ae_h1_O,ae_Wh4) + ae_bh4)



ae_y_actual = tf.placeholder("float", [None,n_input])
meansq = tf.reduce_mean(tf.square(ae_y_actual - ae_y_pred))
train_step = tf.train.GradientDescentOptimizer(0.05).minimize(meansq)
</code></pre>

<p>after this I run the above graph using</p>

<pre><code>init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)

n_rounds = 100
batch_size = min(500, n_samp)
for i in range(100):
    sample = np.random.randint(n_samp, size=batch_size)
    batch_xs = input_data[sample][:]
    batch_ys = output_data_ae[sample][:]
    sess.run(train_step, feed_dict={x: batch_xs, ae_y_actual:batch_ys})
</code></pre>

<p><strong>Above</strong> is the code for a 4 layer <strong>autoencoder,</strong> <strong>"meansq" is my squared loss function. How can I add L1 reguarisation for the weight matrix (tensors) in the network?</strong></p>