Question

在我的代码中，我正在尝试练习使用tr.train.batch函数。在sess.run([optimizer])行中，它不返回任何内容，它只是被冻结。你能找到我的错误吗？

tensors = tf.convert_to_tensor(x_train, dtype=tf.float32)
tensors = tf.reshape(tensors, shape=x_train.shape)
batch = tf.train.batch([tensors], batch_size=BATCH_SIZE, enqueue_many=True)

# Weights and biases to hidden layer
Wh = tf.Variable(tf.random_normal([COLUMN-2, UNITS_OF_HIDDEN_LAYER],  mean=0.0, stddev=0.05))
bh = tf.Variable(tf.zeros([UNITS_OF_HIDDEN_LAYER]))
h = tf.nn.tanh(tf.matmul(batch, Wh) + bh)
# Weights and biases to output layer
Wo = tf.transpose(Wh) # tied weights
bo = tf.Variable(tf.zeros([COLUMN-2]))
y = tf.nn.tanh(tf.matmul(h, Wo) + bo)

# Objective functions
mean_sqr = tf.reduce_mean(tf.pow(batch - y, 2))
optimizer = tf.train.AdamOptimizer(LEARNING_RATE).minimize(mean_sqr)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

for j in range(TRAINING_EPOCHS):
    sess.run([optimizer])
    print("optimizer: ")

Answer 1

tf.train.start_queue_runners是一个队列，因此您需要使用with tf.Session() as sess: sess.run(init_op) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) try: # Training loop for j in range(TRAINING_EPOCHS): if coord.should_stop(): break sess.run([optimizer]) print("optimizer: ") except Exception, e: # When done, ask the threads to stop. coord.request_stop(e) finally: coord.request_stop() # Wait for threads to finish. coord.join(threads)在会话中启动队列。您可以在tensorflow的threading and Queues guide中了解这一点。

进行以下更改：

Array ( [0] => Array ( [0] => 2011-11-12 [1] => 1963-02-29 [3] => 2029-05-14 [9] => 1812-08-12 [11] => 1537-05-17  [16] => 2005-17-04  [30] => 3000-42-99  ) )

tensorflow autoencoder无法在训练中学习

1 个答案: