在張量流圖中創建計數器

Question

我想在圖中創建一個計數器。只要函數運行，它就會增加。在這裏，我現在正在嘗試。有任何想法嗎？

# init variable 
tf_i = tf.Variable(1, name='v', dtype=tf.int32, expected_shape=()) 

# init assign op 
tf_i_plus_one = tf.assign_add(tf_i, 1) 
sess.run(tf.global_variables_initializer()) 

# simple test fx 
def test(x): 
    v = tf.get_variable('v', shape=()) 
    return x + v 

# run test fx 
z = tf.placeholder(tf.float32, shape=(), name='z') 
out = test(z) 

print(sess.run(out, feed_dict={z: 4})) 
sess.run(tf_i_plus_one) 

print(sess.run(out, feed_dict={z: 4})) 

我現在得到：

Attempting to use uninitialized value v_1 

Answer 1

如果你想要的是一個功能的計數器，請嘗試以下操作：

def wrap_with_counter(fn, counter): 
    def wrapped_fn(*args, **kwargs): 
     # control_dependencies forces the assign op to be run even if we don't use the result 
     with tf.control_dependencies([tf.assign_add(counter, 1)]): 
      return fn(*args, **kwargs) 
    return wrapped_fn 

用法示例：

dense_counter = tf.get_variable(
    dtype=tf.int32, shape=(), name='dense_counter', 
    initializer=tf.zeros_initializer()) 
wrapped_dense = wrap_with_counter(tf.layers.dense, dense_counter) 


batch_size = 4 
n_features = 8 
# dummy input 
x = tf.random_normal(
    shape=(batch_size, n_features), dtype=tf.float32, name='x') 

out = wrapped_dense(x, 1) 

with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer()) 
    c0 = sess.run(dense_counter) 
    print(c0) # 0 
    sess.run(out) 
    c1 = sess.run(dense_counter) 
    print(c1) # 1