tf.get_collection()
tf.get_collection()
狼啸风云
修改于 2022-09-02 20:42:03
修改于 2022-09-02 20:42:03
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。
进一步地：
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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原始发表：2020-10-04 ，如有侵权请联系 cloudcommunity@tencent.com 删除
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