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我想通過張量流建立一個CNN + LSTM + CTC模型，但是我在訓練過程中總是得到NAN值，如何避免這種情況？另一方面，我發現劑量INPUT需要專門處理，我發現LOSS值保持不變大約30，再也不會減少，這種情況是否正常？（我之前用theano寫過這個模型，並面臨同樣的問題）感謝您的幫助！如何避免CTC培訓中的NAN價值？
#incoding:utf-8
import tensorflow as tf
import numpy as np
import cv2
import Get_Data
import random
import time
#load data
model_data_path = "checkpoints.data"
images_path = "/home/liuyi/test/images"
#images_path = "/home/night/test/images"
ans_name = "answer"
images_data, ans_data = Get_Data.get_data(images_path, ans_name)
np.save("/home/liuyi/test/tf_images.npy", images_data)
np.save("/home/liuyi/test/tf_labels.npy", ans_data)
print images_data.shape
print ans_data
#bulid model
#----define----
def conv2d(x, w, b, strides=1):
x = tf.nn.conv2d(x, w, (1, strides, strides, 1), "SAME")
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x)
def dropout(x, rate):
return tf.nn.dropout(x, rate)
def maxpool2d(x, poolsize=(2,2)):
px = poolsize[0]
py = poolsize[1]
return tf.nn.max_pool(x, ksize=(1, px, py, 1), strides=(1, px, py, 1),padding="SAME")
def flatten(x):
return tf.contrib.layers.flatten(x)
def full_con(x, w, b):
x = tf.matmul(x, w)
return tf.nn.bias_add(x, b)
def LSTM(x, n_input, hidden_units, out_dim, forget_bias = 1.0, layer_num = 1):
lstm = tf.nn.rnn_cell.LSTMCell(hidden_units, forget_bias=forget_bias, state_is_tuple=True,num_proj=out_dim)
lstms = tf.nn.rnn_cell.MultiRNNCell([lstm]*layer_num ,state_is_tuple=True)
x = tf.reshape(x, (int(x.get_shape()[0]), int(x.get_shape()[1]), n_input))
out, _ = tf.nn.dynamic_rnn(lstms, x, dtype="float")
out = tf.transpose(out, [1, 0, 2])
return out
#----define weight----
weights = {
'wc1': tf.Variable(tf.random_normal([5, 5, 1, 8])),
'wc2': tf.Variable(tf.random_normal([5, 5, 8, 16])),
'wc3': tf.Variable(tf.random_normal([5, 5, 16, 16])),
'wc4': tf.Variable(tf.random_normal([5, 5, 16, 16])),
'wc5': tf.Variable(tf.random_normal([5, 5, 16, 16])),
'wc6': tf.Variable(tf.random_normal([5, 5, 16, 16])),
'wf1': tf.Variable(tf.random_normal([3200, 1000])),
'wf2': tf.Variable(tf.random_normal([1000, 50])),
}
biases = {
'bc1': tf.Variable(tf.random_normal([8])),
'bc2': tf.Variable(tf.random_normal([16])),
'bc3': tf.Variable(tf.random_normal([16])),
'bc4': tf.Variable(tf.random_normal([16])),
'bc5': tf.Variable(tf.random_normal([16])),
'bc6': tf.Variable(tf.random_normal([16])),
'bf1': tf.Variable(tf.random_normal([1000])),
'bf2': tf.Variable(tf.random_normal([50])),
}
#----define model----
batch_size = 200
num_classes = 26+1+1
max_len = 21
sequence_length = np.full((batch_size),max_len,dtype=np.int32)#!
x = tf.placeholder("float", [batch_size, 200, 60, 1], "images")
y_i = tf.placeholder(tf.int64, [None, 2], "y_i")
y_v = tf.placeholder(tf.int32, [None,], "y_v")
y_shape = tf.placeholder(tf.int64, [2,], "y_shape")
#--------CNN Layer--------
conv2do1 = conv2d(x, weights['wc1'], biases['bc1'])
conv2do2 = conv2d(conv2do1, weights['wc2'], biases['bc2'])
conv2do2 = maxpool2d(conv2do2)
#--------CNN Layer--------
conv2do3 = conv2d(conv2do2, weights['wc3'], biases['bc3'])
conv2do4 = conv2d(conv2do3, weights['wc4'], biases['bc4'])
conv2do4 = maxpool2d(conv2do4)
#--------CNN Layer--------
conv2do5 = conv2d(conv2do4, weights['wc5'], biases['bc5'])
conv2do6 = conv2d(conv2do5, weights['wc6'], biases['bc6'])
conv2do6 = maxpool2d(conv2do6)
#--------Flatten Layer--------
conv2do6 = flatten(conv2do6)
#--------Full Connection--------
fc1 = full_con(conv2do6, weights['wf1'], biases['bf1'])
fc2 = full_con(fc1, weights['wf2'], biases['bf2'])
#--------LSTM Layer--------
lstms = LSTM(fc2, n_input=1, hidden_units=32, out_dim=num_classes, layer_num=3)
#--------CTC Layer--------
ctc_o = tf.nn.ctc_loss(lstms, tf.SparseTensor(y_i, y_v, y_shape), sequence_length)
#----------------
loss = tf.reduce_mean(ctc_o)
ctc_p = tf.nn.ctc_greedy_decoder(lstms, sequence_length)[0][0]
o = ctc_p
train = tf.train.AdagradOptimizer(learning_rate=0.01).minimize(loss)
saver = tf.train.Saver(tf.all_variables())
#run model
epoch = 200
images_sum = 10000
train_rate = 0.8
slice_pos = 9800
train_images = images_data[:slice_pos]
train_labels = ans_data[:slice_pos]
test_images = images_data[slice_pos:]
test_labels = ans_data[slice_pos:]
random_list = np.arange(slice_pos)
batch_sum = int(slice_pos/batch_size)
test_batch = int(images_sum-slice_pos)/batch_size
init = tf.initialize_all_variables()
sess = tf.InteractiveSession()
sess.run(init)
file_name = "out"
for e in range(epoch):
random.shuffle(random_list)
for i in range(batch_sum):
begin_time = time.clock()
train_x = [train_images[m] for m in random_list[i*batch_size:(i+1)*batch_size]]
train_y = [train_labels[m] for m in random_list[i*batch_size:(i+1)*batch_size]]
train_yi, train_yv, train_ys = Get_Data.SparseDataFrom(train_y)
batch_loss = sess.run(loss, feed_dict={x: train_x, y_i: train_yi, y_v: train_yv, y_shape: train_ys})
sess.run(train, feed_dict={x: train_x, y_i: train_yi, y_v: train_yv, y_shape: train_ys})
end_time = time.clock()
print "epoch{0}/{1}: batch{2}/{3} loss={4} time={5}s".format(e+1, epoch, (i+1)*batch_size, slice_pos, batch_loss,(end_time-begin_time)*(batch_sum-i))
right_num = 0
loss_sum = 0
begin_time = time.clock()
for i in range(test_batch):
test_x = [test_images[i * batch_size:(i + 1) * batch_size]]
test_y = [test_labels[i * batch_size:(i + 1) * batch_size]]
test_yi, test_yv, test_ys = Get_Data.SparseDataFrom(test_y)
tmp_loss = sess.run(loss, feed_dict={x: test_x, y_i: test_yi, y_v: test_yv, y_shape: test_ys})
test_ans = sess.run(o, feed_dict={x: test_x, y_i: test_yi, y_v: test_yv, y_shape: test_ys})
test_ans = Get_Data.SparseDatatoDense(test_ans)
tmp_right_num = Get_Data.data_difference(test_ans, test_y)
loss_sum += tmp_loss
right_num += tmp_right_num
end_time = time.clock()
print "epoch{0}/{1}: loss={2} right_num = {3} time={4}s".format(e + 1, epoch, loss_sum, right_num, end_time - begin_time)
saver.save(sess, model_data_path)
sess.close()
Nan值通常由操作產生，例如零對數或數字除以非常接近零。考慮發佈您的代碼以獲得更詳細的答案。 –