Question

我正在通过https://medium.com/@erikhallstrm/hello-world-rnn-83cd7105b767学习RNN。我将损失函数更改为均方误差，发现它没有收敛。输出卡在0.5。不知何故，我觉得错误在里面

midlosses = [tf.squeeze(logits)-tf.squeeze(labels)  for logits, labels in zip(logits_series,labels_series)]

但是我没有。我不熟悉数据类型。这可能是一个愚蠢的问题。如果我不清楚的话，完整的代码如下：

from __future__ import print_function, division
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

num_epochs = 100
total_series_length = 50000
truncated_backprop_length = 15
state_size = 4
num_classes = 1
echo_step = 3
batch_size = 5
num_batches = total_series_length//batch_size//truncated_backprop_length

def generateData():
    x = np.array(np.random.choice(2, total_series_length, p=[0.5, 0.5]))
    y = np.roll(x, echo_step)
    y[0:echo_step] = 0

    x = x.reshape((batch_size, -1))  # The first index changing slowest, subseries as rows
    y = y.reshape((batch_size, -1))

    return (x, y)

tf.reset_default_graph()
batchX_placeholder = tf.placeholder(tf.float32, [batch_size, truncated_backprop_length])
batchY_placeholder = tf.placeholder(tf.float32, [batch_size, truncated_backprop_length])

init_state = tf.placeholder(tf.float32, [batch_size, state_size])

W = tf.Variable(np.random.rand(state_size+1, state_size), dtype=tf.float32)
b = tf.Variable(np.zeros((1,state_size)), dtype=tf.float32)

W2 = tf.Variable(np.random.rand(state_size, num_classes),dtype=tf.float32)
b2 = tf.Variable(np.zeros((1,num_classes)), dtype=tf.float32)

# Unpack columns
inputs_series = tf.unstack(batchX_placeholder, axis=1)
labels_series = tf.unstack(batchY_placeholder, axis=1)

# Forward pass
current_state = init_state
states_series = []
for current_input in inputs_series:
    current_input = tf.reshape(current_input, [batch_size, 1])
    input_and_state_concatenated = tf.concat([current_input, current_state],axis=1)  # Increasing number of columns

    next_state = tf.tanh(tf.matmul(input_and_state_concatenated, W) + b)  # Broadcasted addition
    states_series.append(next_state)
    current_state = next_state

logits_series = [tf.matmul(state, W2) + b2 for state in states_series] 
#Loss function HERE
midlosses = [tf.squeeze(logits)-tf.squeeze(labels)  for logits, labels in zip(logits_series,labels_series)]
losses = tf.square(midlosses)
total_loss = tf.reduce_mean(losses)
train_step = tf.train.AdagradOptimizer(0.3).minimize(total_loss)
with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())
    loss_list = []

    for epoch_idx in range(num_epochs):
        x,y = generateData()
        _current_state = np.zeros((batch_size, state_size))

        print("New data, epoch", epoch_idx)

        for batch_idx in range(num_batches):
            start_idx = batch_idx * truncated_backprop_length
            end_idx = start_idx + truncated_backprop_length

            batchX = x[:,start_idx:end_idx]
            batchY = y[:,start_idx:end_idx]

            _total_loss, _train_step, _current_state,_logits_series,_midlosses = sess.run(
                [total_loss, train_step, current_state,logits_series,midlosses],
                feed_dict={
                    batchX_placeholder:batchX,
                    batchY_placeholder:batchY,
                    init_state:_current_state
                }) 
            loss_list.append(_total_loss)
            if batch_idx%100 == 0:
                print("Step",batch_idx, "Loss", _total_loss)

Answer 1

只需替换

logits_series = [tf.matmul(state, W2) + b2 for state in states_series]

通过

logits_series = [tf.squeeze(tf.matmul(state, W2) + b2) for state in states_series] #Broadcasted addition

问题可以解决。

RNN使用均方误差不收敛

1 个答案: