# tensorflow How to use TensorFlow Graph Collections? Collect variables from nested scopes

## Example

Below is a single hidden layer Multilayer Perceptron (MLP) using nested scoping of variables.

``````def weight_variable(shape):
return tf.get_variable(name="weights", shape=shape,
initializer=tf.zeros_initializer(dtype=tf.float32))

def bias_variable(shape):
return tf.get_variable(name="biases", shape=shape,
initializer=tf.zeros_initializer(dtype=tf.float32))

def fc_layer(input, in_dim, out_dim, layer_name):
with tf.variable_scope(layer_name):
W = weight_variable([in_dim, out_dim])
b = bias_variable([out_dim])
linear = tf.matmul(input, W) + b
output = tf.sigmoid(linear)

with tf.variable_scope("MLP"):
x = tf.placeholder(dtype=tf.float32, shape=[None, 1], name="x")
y = tf.placeholder(dtype=tf.float32, shape=[None, 1], name="y")
fc1 = fc_layer(x, 1, 8, "fc1")
fc2 = fc_layer(fc1, 8, 1, "fc2")

mse_loss = tf.reduce_mean(tf.reduce_sum(tf.square(fc2 - y), axis=1))
``````

The MLP uses the the top level scope name `MLP` and it has two layers with their respective scope names `fc1` and `fc2`. Each layer also has its own `weights` and `biases` variables.

The variables can be collected like so:

``````trainable_var_key = tf.GraphKeys.TRAINABLE_VARIABLES
all_vars = tf.get_collection(key=trainable_var_key, scope="MLP")
fc1_vars = tf.get_collection(key=trainable_var_key, scope="MLP/fc1")
fc2_vars = tf.get_collection(key=trainable_var_key, scope="MLP/fc2")
fc1_weight_vars = tf.get_collection(key=trainable_var_key, scope="MLP/fc1/weights")
fc1_bias_vars = tf.get_collection(key=trainable_var_key, scope="MLP/fc1/biases")
``````

The values of the variables can be collected using the `sess.run()` command. For example if we would like to collect the values of the `fc1_weight_vars` after training, we could do the following:

``````sess = tf.Session()
# add code to initialize variables
# add code to train the network
# add code to create test data x_test and y_test

fc1_weight_vals = sess.run(fc1, feed_dict={x: x_test, y: y_test})
print(fc1_weight_vals)  # This should be an ndarray with ndim=2 and shape=[1, 8]
`````` PDF - Download tensorflow for free