我试图根据层将权重和偏差添加到张量板。我尝试了以下方式：
tf.reset_default_graph()
X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
y = tf.placeholder(tf.float32, [None,n_outputs])
layers = [tf.contrib.rnn.LSTMCell(num_units=n_neurons,
activation=tf.nn.leaky_relu, use_peepholes = True)
for layer in range(n_layers)]
# for i, layer in enumerate(layers):
# tf.summary.histogram('layer{0}'.format(i), tf.convert_to_tensor(layer))
multi_layer_cell = tf.contrib.rnn.MultiRNNCell(layers)
for index,one_lstm_cell in enumerate(multi_layer_cell):
one_kernel, one_bias = one_lstm_cell.variables
# I think TensorBoard handles summaries with the same name fine.
tf.summary.histogram("Kernel", one_kernel)
tf.summary.histogram("Bias", one_bias)
rnn_outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)
stacked_rnn_outputs = tf.reshape(rnn_outputs, [-1, n_neurons])
stacked_outputs = tf.layers.dense(stacked_rnn_outputs, n_outputs)
outputs = tf.reshape(stacked_outputs, [-1, n_steps, n_outputs])
outputs = outputs[:,n_steps-1,:] # keep only last output of sequence
但是我收到以下错误：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-43-761df6e116a7> in <module>()
44
45 multi_layer_cell = tf.contrib.rnn.MultiRNNCell(layers)
---> 46 for index,one_lstm_cell in enumerate(multi_layer_cell):
47 one_kernel, one_bias = one_lstm_cell.variables
48 # I think TensorBoard handles summaries with the same name fine.
TypeError: 'MultiRNNCell' object is not iterable
我想知道我错过了什么，以便可以在张量板上添加用于可视化的变量。请帮助我。
最佳答案
MultiRNNCell确实不是可迭代的。对于您的情况，首先，在调用tf.nn.dynamic_rnn之前不会创建RNN变量，因此您应尝试在此之后检索它们。其次，使用use_peephole不仅具有内核变量和偏差变量。要检索它们，您可以从multi_layer_cell.variables或通过layers中存储的单元对象从每个图层自己的集合一起访问所有RNN变量：
multi_layer_cell = tf.contrib.rnn.MultiRNNCell(layers)
rnn_outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)
for index, one_lstm_cell in enumerate(layers):
one_kernel, one_bias, *one_peepholes = one_lstm_cell.variables
tf.summary.histogram("Kernel", one_kernel)
tf.summary.histogram("Bias", one_bias)