此函数有两个参数，key和scope。
Args:
1.key: The key for the collection. For example, the GraphKeys class contains many standard names for collections.2.scope: (Optional.) If supplied, the resulting list is filtered to include only items whose name attribute matches using re.match. Items without a name attribute are never returned if a scope is supplied and the choice or re.match means that a scope without special tokens filters by prefix.举个例子：
# 在'My-TensorFlow-tutorials-master/02 CIFAR10/cifar10.py'代码中 variables = tf.get_collection(tf.GraphKeys.VARIABLES) for i in variables: print(i) >>> <tf.Variable 'conv1/weights:0' shape=(3, 3, 3, 96) dtype=float32_ref> <tf.Variable 'conv1/biases:0' shape=(96,) dtype=float32_ref> <tf.Variable 'conv2/weights:0' shape=(3, 3, 96, 64) dtype=float32_ref> <tf.Variable 'conv2/biases:0' shape=(64,) dtype=float32_ref> <tf.Variable 'local3/weights:0' shape=(16384, 384) dtype=float32_ref> <tf.Variable 'local3/biases:0' shape=(384,) dtype=float32_ref> <tf.Variable 'local4/weights:0' shape=(384, 192) dtype=float32_ref> <tf.Variable 'local4/biases:0' shape=(192,) dtype=float32_ref> <tf.Variable 'softmax_linear/softmax_linear:0' shape=(192, 10) dtype=float32_ref> <tf.Variable 'softmax_linear/biases:0' shape=(10,) dtype=float32_ref>tf.get_collection会列出key里所有的值。
tf.GraphKeys的点后可以跟很多类， 比如VARIABLES类（包含所有variables）， 比如REGULARIZATION_LOSSES。
具体tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)的使用：
def easier_network(x, reg): """ A network based on tf.contrib.learn, with input `x`. """ with tf.variable_scope('EasyNet'): out = layers.flatten(x) out = layers.fully_connected(out, num_outputs=200, weights_initializer = layers.xavier_initializer(uniform=True), weights_regularizer = layers.l2_regularizer(scale=reg), activation_fn = tf.nn.tanh) out = layers.fully_connected(out, num_outputs=200, weights_initializer = layers.xavier_initializer(uniform=True), weights_regularizer = layers.l2_regularizer(scale=reg), activation_fn = tf.nn.tanh) out = layers.fully_connected(out, num_outputs=10, # Because there are ten digits! weights_initializer = layers.xavier_initializer(uniform=True), weights_regularizer = layers.l2_regularizer(scale=reg), activation_fn = None) return out def main(_): mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) x = tf.placeholder(tf.float32, [None, 784]) y_ = tf.placeholder(tf.float32, [None, 10]) # Make a network with regularization y_conv = easier_network(x, FLAGS.regu) weights = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'EasyNet') print("") for w in weights: shp = w.get_shape().as_list() print("- {} shape:{} size:{}".format(w.name, shp, np.prod(shp))) print("") reg_ws = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES, 'EasyNet') for w in reg_ws: shp = w.get_shape().as_list() print("- {} shape:{} size:{}".format(w.name, shp, np.prod(shp))) print("") # Make the loss function `loss_fn` with regularization. cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)) loss_fn = cross_entropy + tf.reduce_sum(reg_ws) train_step = tf.train.AdamOptimizer(1e-4).minimize(loss_fn) main() >>> - EasyNet/fully_connected/weights:0 shape:[784, 200] size:156800 - EasyNet/fully_connected/biases:0 shape:[200] size:200 - EasyNet/fully_connected_1/weights:0 shape:[200, 200] size:40000 - EasyNet/fully_connected_1/biases:0 shape:[200] size:200 - EasyNet/fully_connected_2/weights:0 shape:[200, 10] size:2000 - EasyNet/fully_connected_2/biases:0 shape:[10] size:10 - EasyNet/fully_connected/kernel/Regularizer/l2_regularizer:0 shape:[] size:1.0 - EasyNet/fully_connected_1/kernel/Regularizer/l2_regularizer:0 shape:[] size:1.0 - EasyNet/fully_connected_2/kernel/Regularizer/l2_regularizer:0 shape:[] size:1.0据:
for w in reg_ws: shp = ....
这段代码的输出可知， 在图上的所有regularization都会集中保存到tf.GraphKeys.REGULARIZATION_LOSSES去。
关于collection的详情请参见：http://blog.csdn.net/shenxiaolu1984/article/details/52815641
关于tf.GraphKeys.REGULARIZATION_LOSSES的详情参见：https://gxnotes.com/article/178205.html
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