Question

到目前为止，我已经编写了以下代码：

import pickle
import numpy as np
import pandas as pd
import tensorflow as tf

# load pickled objects (x and y)
x_input, y_actual = pickle.load(open('sample_input.pickle', 'rb'))
x_input = np.reshape(x_input, (50, 1))
y_actual = np.reshape(y_actual, (50, 1))

# parameters
batch_size = 50
hidden_size = 100

# create network graph
input_data = tf.placeholder(tf.float32, [batch_size, 1])
output_data = tf.placeholder(tf.float32, [batch_size, 1])

cell = tf.nn.rnn_cell.GRUCell(hidden_size)

initial_state = cell.zero_state(batch_size, tf.float32)

hidden_state = initial_state

output_of_cell, hidden_state = cell(inputs=input_data, state=hidden_state)

init_op = tf.initialize_all_variables()

softmax_w = tf.get_variable("softmax_w", [hidden_size, 1], )
softmax_b = tf.get_variable("softmax_b", [1])

logits = tf.matmul(output_of_cell, softmax_w) + softmax_b

probabilities = tf.nn.softmax(logits)

sess = tf.Session()
sess.run(init_op)

something = sess.run([probabilities, hidden_state], feed_dict={input_data:x_input, output_data:y_actual})

#cost = tf.nn.sigmoid_cross_entropy_with_logits(logits, output_data)


#sess.close()

但是我将s oftmax_w/b的错误视为未初始化的变量。

我没有得到如何使用这些W和b并执行列车操作。

如下所示：

## some cost function
## training operation minimizing cost function using gradient descent optimizer

Answer 1

tf.initialize_all_variables()从图表中获取“当前”变量集。由于您在致电softmax_w后创建了softmax_b和tf.initialize_all_variables()，因此它们不在tf.initialize_all_variables()咨询的列表中，因此在您运行{{1}时未初始化}。以下应该有效：

sess.run(init_op)

如何完成以下基于GRU的RNN写入的RNN？

1 个答案: