我正在训练一个双向 LSTM 网络,但是当我训练它时,我得到了如下信息:
"
Iter 3456, Minibatch Loss= 10.305597, Training Accuracy= 0.25000
Iter 3840, Minibatch Loss= 22.018646, Training Accuracy= 0.00000
Iter 4224, Minibatch Loss= 34.245750, Training Accuracy= 0.00000
Iter 4608, Minibatch Loss= 13.833059, Training Accuracy= 0.12500
Iter 4992, Minibatch Loss= 19.687658, Training Accuracy= 0.00000
"
即使迭代次数为 50 0000,损失和准确率也几乎相同。我的设置如下:
# Parameters
learning_rate = 0.0001
training_iters = 20000#120000
batch_size = tf.placeholder(dtype=tf.int32)#24,128
display_step = 16#10
test_num = 275#245
keep_prob = tf.placeholder("float") #probability for dropout
kp = 1.0
# Network Parameters
n_input = 240*160 #28 # MNIST data input (img shape: 28*28)
n_steps = 16 #28 # timesteps
n_hidden = 500 # hidden layer num of features
n_classes = 20
这是技术问题还是方案问题?