在張量流中訪問隱式張量

Question

下面的代碼創建了一些隱式的張量。我不知道如何可以查看這些張量的值：

<tf.Variable 'rnn/basic_lstm_cell/kernel:0' shape=(43, 160) dtype=float32_ref> 
<tf.Variable 'rnn/basic_lstm_cell/bias:0' shape=(160,) dtype=float32_ref> 
<tf.Variable 'rnn/basic_lstm_cell/kernel/Adagrad:0' shape=(43, 160) dtype=float32_ref> 
<tf.Variable 'rnn/basic_lstm_cell/bias/Adagrad:0' shape=(160,) dtype=float32_ref> 
<tf.Variable 'softmax/W/Adagrad:0' shape=(40, 10) dtype=float32_ref> 
<tf.Variable 'softmax/b/Adagrad:0' shape=(10,) dtype=float32_ref> 

這裏是代碼本身。

import tensorflow as tf 
import numpy as np 

VECTOR_SIZE = 3 
SEQUENCE_LENGTH = 5 
BATCH_SIZE = 7 
STATE_SIZE = 40 
NUM_CLASSES = 10 
LEARNING_RATE = 0.1 



x = tf.placeholder(tf.float32, [BATCH_SIZE, SEQUENCE_LENGTH, VECTOR_SIZE], 
        name='input_placeholder') 
y = tf.placeholder(tf.int32, [BATCH_SIZE, SEQUENCE_LENGTH], 
        name='labels_placeholder') 
init_state = tf.zeros([BATCH_SIZE, STATE_SIZE]) 

rnn_inputs = tf.unstack(x, axis = 1) 
y_as_list = tf.unstack(y, axis=1) 

cell = tf.contrib.rnn.BasicLSTMCell(STATE_SIZE, state_is_tuple = True) 
rnn_outputs, final_state = tf.contrib.rnn.static_rnn(cell, rnn_inputs, 
          initial_state=(init_state,init_state)) 

with tf.variable_scope('softmax'): 
    W = tf.get_variable('W', [STATE_SIZE, NUM_CLASSES]) 
    b = tf.get_variable('b', [NUM_CLASSES], initializer=tf.constant_initializer(0.0)) 
logits = [tf.matmul(rnn_output, W) + b for rnn_output in rnn_outputs] 

predictions = [tf.nn.softmax(logit) for logit in logits] 
losses = [tf.nn.sparse_softmax_cross_entropy_with_logits(labels=label, logits=logit) for \ 
      logit, label in zip(logits, y_as_list)] 
total_loss = tf.reduce_mean(losses) 
train_step = tf.train.AdagradOptimizer(LEARNING_RATE).minimize(total_loss) 

X = np.ones([BATCH_SIZE, SEQUENCE_LENGTH, VECTOR_SIZE]) 
Y = np.ones([BATCH_SIZE, SEQUENCE_LENGTH]) 

saver = tf.train.Saver() 

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

batch_total_loss = sess.run([total_loss, train_step], 
             feed_dict = {x:X,y:Y}) 


save_path = saver.save(sess, "/tmp/model.ckpt") 

for el in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES): 
    print(el) 

Answer 1

使用sess.run()：

for el in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES): 
    print(el) # this will print the tensor's name, shape, data type 
    print(sess.run(el)) # this will print the tensor's current value