Question

因此，我正在运行此代码，以使用tf在张量流中获取单个样本梯度。仅一次gradients（）。当我只有一组变量时，它完全可以正常工作：

Include(...).ThenInclude(...)

当我获得sample_gradient时，我得到了：

def f(x, W):
return tf.matmul(x, W)

graph1 = tf.Graph()
with graph1.as_default():
W = tf.get_variable('wd1', shape=(4,2), initializer=tf.contrib.layers.xavier_initializer())

X = tf.placeholder(tf.float32, [None, 4], name="X")
a = tf.placeholder(tf.float32, [None, 2], name="a")
split_x = tf.unstack(X, num = 5, axis=0)
q = [tf.expand_dims(split_x[i], 0) for i in range(10)]
split_a = tf.unstack(a, num = 5, axis=0)
weight_copies = [tf.identity(W) for x in split_x]



output = tf.stack([f(x, w) for (x,w) in zip(q, weight_copies)])
cost_function = tf.nn.softmax_cross_entropy_with_logits(logits = output, labels = split_a)
grad_sample = tf.gradients(cost_function, weight_copies)


init = tf.global_variables_initializer()
with tf.Session(graph=graph1) as sess:

init.run()

x_sample, a_sample = fetch_data()

sample_gradient = sess.run(grad_sample, feed_dict= {X: x_sample, a: a_sample})

这是正确的。但是，如果我只是向其添加偏差或任何其他权重集并希望执行相同操作，则会收到错误消息：

[array([[ 0.18143043, -0.18143043],
    [ 0.52393097, -0.52393097],
    [ 0.00645372, -0.00645372],
    [ 0.5445979 , -0.5445979 ]], dtype=float32),
 array([[ 0.14291364, -0.14291364],
    [ 0.20688654, -0.20688656],
    [ 0.10543547, -0.10543548],
    [ 0.22011465, -0.22011466]], dtype=float32),
 array([[-0.7126652 ,  0.7126652 ],
    [-0.6779973 ,  0.6779973 ],
    [-0.166183  ,  0.166183  ],
    [-0.26456204,  0.26456204]], dtype=float32),
 array([[ 0.18173395, -0.1817339 ],
    [ 0.07825114, -0.07825112],
    [ 0.01917152, -0.01917152],
    [ 0.00939495, -0.00939495]], dtype=float32),
 array([[-0.44272456,  0.44272456],
    [-0.373744  ,  0.373744  ],
    [-0.6325189 ,  0.6325189 ],
    [-0.3546079 ,  0.3546079 ]], dtype=float32),
 array([[0.22570643, 0.33411032],
    [0.18552302, 0.27462733],
    [0.10510764, 0.15558948],
    [0.26456347, 0.3916299 ]], dtype=float32),
 array([[0.01360785, 0.00632451],
    [0.42770416, 0.1987838 ],
    [0.58186275, 0.27043203],
    [0.6803998 , 0.31622902]], dtype=float32),
 array([[0.20075822, 0.44605315],
    [0.10817435, 0.24034637],
    [0.12729922, 0.2828388 ],
    [0.08586845, 0.19078615]], dtype=float32),
 array([[0.20855448, 0.5140207 ],
    [0.19167873, 0.4724273 ],
    [0.13740788, 0.3386669 ],
    [0.06688789, 0.16485746]], dtype=float32),
 array([[0.25728938, 0.41753346],
    [0.33400562, 0.5420298 ],
    [0.27250844, 0.44223118],
    [0.2579242 , 0.41856363]], dtype=float32)]

TypeError：获取参数None的类型无效

我确定问题是在tf.gradients（）函数行中发生的，但是我不知道如何更改函数的xs参数，因此它不会返回None。

有人知道如何解决此问题吗？

仅供参考：

def f(x, W):
return tf.add(tf.matmul(x, W[0]),W[1])

graph1 = tf.Graph()
with graph1.as_default():
W = [tf.get_variable('wd1', shape=(4,2), initializer=tf.contrib.layers.xavier_initializer()),
tf.get_variable('bd1', shape=(2), initializer=tf.contrib.layers.xavier_initializer())]

X = tf.placeholder(tf.float32, [None, 4], name="X")
a = tf.placeholder(tf.float32, [None, 2], name="a")

logits = f(X, W)

split_x = tf.unstack(X, num = 5, axis=0)
q = [tf.expand_dims(split_x[i], 0) for i in range(5)]
split_a = tf.unstack(a, num = 5, axis=0)

weight_copies = [[tf.identity(v) for v in W] for x in split_x]
qqq = list(zip(*weight_copies))
output = tf.stack([f(x, w) for (x,w) in zip(q, weight_copies)])
cost_function = tf.nn.softmax_cross_entropy_with_logits(logits = output, labels = split_a)
grad_sample = tf.gradients(cost_function, qqq)

init = tf.global_variables_initializer()
with tf.Session(graph=graph1) as sess:

init.run()

x_sample, a_sample = fetch_data()

sample_gradient = sess.run(grad_sample, feed_dict= {X: x_sample, a: a_sample})

仅使用tf.gradients一次的单一样本渐变（TypeError：提取参数None具有无效的类型<class'nonetype'=“”>）

0 个答案: