我在 theano 中有以下功能:
def forward_prop_step(x_t, s_t1_prev, s_t2_prev):
# This is how we calculated the hidden state in a simple RNN. No longer!
# s_t = T.tanh(U[:,x_t] + W.dot(s_t1_prev))
# Word embedding layer
x_e = E[:,x_t]
# GRU Layer 1
z_t1 = T.nnet.hard_sigmoid(U[0].dot(x_e) + W[0].dot(s_t1_prev) + b[0])
r_t1 = T.nnet.hard_sigmoid(U[1].dot(x_e) + W[1].dot(s_t1_prev) + b[1])
c_t1 = T.tanh(U[2].dot(x_e) + W[2].dot(s_t1_prev * r_t1) + b[2])
s_t1 = (T.ones_like(z_t1) - z_t1) * c_t1 + z_t1 * s_t1_prev
# GRU Layer 2
z_t2 = T.nnet.hard_sigmoid(U[3].dot(s_t1) + W[3].dot(s_t2_prev) + b[3])
r_t2 = T.nnet.hard_sigmoid(U[4].dot(s_t1) + W[4].dot(s_t2_prev) + b[4])
c_t2 = T.tanh(U[5].dot(s_t1) + W[5].dot(s_t2_prev * r_t2) + b[5])
s_t2 = (T.ones_like(z_t2) - z_t2) * c_t2 + z_t2 * s_t2_prev
# Final output calculation
# Theano's softmax returns a matrix with one row, we only need the row
o_t = T.nnet.softmax(V.dot(s_t2) + c)[0]
return [o_t, s_t1, s_t2]
我使用 scan 调用这个函数:
[o, s, s2], updates = theano.scan(
forward_prop_step,
sequences=x,
truncate_gradient=self.bptt_truncate,
outputs_info=[None,
dict(initial=T.zeros(self.hidden_dim)),
dict(initial=T.zeros(self.hidden_dim))])
我试图在 tensorflow 中重写相同的函数:
def forward_prop_step(x_t, s_t1_prev, s_t2_prev):
# Word embedding layer
x_e = E[:, x_t]
# GRU Layer 1
z_t1 = tf.sigmoid(tf.reduce_sum(U[0] * x_e, axis=1) + tf.reduce_sum(W[0] * s_t1_prev, axis=1) + b[0])
r_t1 = tf.sigmoid(tf.reduce_sum(U[1] * x_e, axis=1) + tf.reduce_sum(W[1] * s_t1_prev, axis=1) + b[1])
c_t1 = tf.tanh(tf.reduce_sum(U[2] * x_e, axis=1) + tf.reduce_sum(W[2] * (s_t1_prev * r_t1), axis=1) + b[2])
s_t1 = (tf.ones_like(z_t1) - z_t1) * c_t1 + z_t1 * s_t1_prev
# GRU Layer 2
z_t2 = tf.sigmoid(tf.reduce_sum(U[3] * s_t1, axis=1) + tf.reduce_sum(W[3] * s_t2_prev, axis=1) + b[3])
r_t2 = tf.sigmoid(tf.reduce_sum(U[4] * s_t1, axis=1) + tf.reduce_sum(W[4] * s_t2_prev) + b[1])
c_t2 = tf.tanh(tf.reduce_sum(U[5] * s_t1, axis=1) + tf.reduce_sum(W[5] * (s_t2_prev * r_t2), axis=1) + b[5])
s_t2 = (tf.ones_like(z_t2) - z_t2) * c_t2 + z_t2 * s_t2_prev
# Final output calculation
o_t = tf.softmax(tf.reduce_sum(V * s_t2, axis=1) + c)[0]
return [o_t, s_t1, s_t2]
我使用 scan 调用了这个函数:
s = tf.zeros([self.hidden_dim, 0])
s2 = tf.zeros([self.hidden_dim, 0])
[o, s, s2] = tf.scan(
fn=forward_prop_step,
elems=[x, s, s2])
我没有使用初始化程序,而是在扫描之前初始化了 s 和 s2 变量。当我在 tensorflow 中运行我的代码时,出现以下错误:
类型错误:forward_prop_step() 正好需要 3 个参数(给定 2 个)
我确信唯一的问题不是上面的错误。如何通过引用 theano 代码来重写 tensorflow 中的扫描功能?
如果要将多个元素传递给tf.scan()
,则需要将它们包装在列表或元组中。以下是如何执行此操作的示例:
def f(x, ys):
(y1, y2) = ys
return x + y1 * y2
a = tf.constant([1, 2, 3, 4, 5])
b = tf.constant([2, 3, 2, 2, 1])
c = tf.scan(f, (a, b), initializer=0)
with tf.Session() as sess:
print(sess.run(c))
打印:
[ 2 8 14 22 27]
我希望这有帮助!
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