【发布时间】:2017-02-24 16:21:27
【问题描述】:
我对 tensorflow 很陌生,我无法弄清楚这一点。
我有这个while循环:
def process_tree_tf(n_child, reprs, weights, bias, embed_dim, activation = tf.nn.relu):
n_child, reprs = n_child, reprs
parent_idxs = generate_parents_numpy(n_child)
loop_idx = reprs.shape[0] - 1
loop_vars = loop_idx, reprs, parent_idxs, weights, embed_dim
def loop_condition(loop_ind, *_):
return tf.greater(0, loop_idx)
def loop_body(loop_ind, reprs, parent_idxs, weights, embed_dim):
x = reprs[loop_ind]
x_expanded = tf.expand_dims(x, axis=-1)
w = weights
out = tf.squeeze(tf.add(tf.matmul(x_expanded,w,transpose_a=True), bias))
activated = activation(out)
par_idx = parent_idxs[loop_ind]
reprs = update_parent(reprs, par_idx, embed_dim, activated)
reprs = tf.Print(reprs, [reprs]) #This doesn't work
loop_ind = loop_ind-1
return loop_ind, reprs, parent_idxs, weights, embed_dim
return tf.while_loop(loop_condition, loop_body, loop_vars)
我是这样评价的:
embed_dim = 2
hidden_dim = 2
n_nodes = 4
batch = 2
reprs = np.ones((n_nodes, embed_dim+hidden_dim))
n_child = np.array([1, 1, 1, 0])
weights = np.ones((embed_dim+hidden_dim, hidden_dim))
bias = np.ones(hidden_dim)
with tf.Session() as sess:
_, r, *_ = process_tree_tf(n_child, reprs, weights, bias, embed_dim, activation=tf.nn.relu)
print(r.eval())
我想在 while 循环中检查 reprs 的值,但 tf.Print 似乎不起作用,print 只是告诉我这是一个张量并给了我它的形状。
我该怎么做?
非常感谢!
【问题讨论】:
标签: python-3.x tensorflow