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記事と画像をつなぐためにRNNモデルを訓練したいと思います。入力と出力は2つの配列です。tensorflow rnn nan error
私は次のようにRNNのパラメータを定義します。
learning_rate = 0.001
training_iters = 100000
batch_size = 128
display_step = 10
# Network Parameters
n_input = 128
n_steps = 168 # timesteps
n_hidden = 512 # hidden layer num of features
output = 200
画像は、* 168 128であるとの記事が最終的な結果については200
cost = tf.reduce_mean(pow(pred-y,2)/2)
#cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
で、私はネットワークを訓練したいです画像を記事に変換する。しかし、モデルをトレーニングしようとすると、コストはNaNとして返されます。ここで
はコードです：
# coding=utf-8
from __future__ import print_function
from tensorflow.contrib import rnn
import scipy.io as scio
import tensorflow as tf
import numpy as np
import os
TextPath = 'F://matlab_code//readtxt//ImageTextVector.mat';
ImageDirPath = 'F://matlab_code//CVPR10-LLC//features//1';
Text = scio.loadmat(TextPath)
learning_rate = 0.001
training_iters = 100000
batch_size = 128
display_step = 10
# Network Parameters
n_input = 128 #
n_steps = 168 # timesteps
n_hidden = 512 # hidden layer num of features
output = 200 #
x = tf.placeholder("float", [None, n_steps, n_input])
y = tf.placeholder("float", [None, output])
weights = {
'out': tf.Variable(tf.random_normal([n_hidden, output]))
}
biases = {
'out': tf.Variable(tf.random_normal([output]))
}
def RNN(x, weights, biases):
lstm_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)
outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)
return tf.matmul(outputs[-1], weights['out']) + biases['out']
pred = RNN(x, weights, biases)
# Define loss and optimizer
cost = tf.reduce_mean(pow(pred-y,2)/2)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
init = tf.global_variables_initializer()
train_count=0;
with tf.Session() as sess:
sess.run(init)
step = 0
while step* batch_size < training_iters:
iter = step*batch_size
batch_x = []
batch_y = []
while iter < (step+1)*batch_size:
ImagePath = ImageDirPath + '//' + Text['X'][train_count][0][0] +'.mat'
if os.path.exists(ImagePath):
batch_xx=[]
batch_yy=[]
Image = scio.loadmat(ImagePath)
i=0
while i<21504 :
batch_xx.append(Image['fea'][i][0])
i=i+1
batch_yy = Text['X'][train_count][1][0]
batch_xx = np.array(batch_xx)
batch_x=np.hstack((batch_x,batch_xx))
batch_y=np.hstack((batch_y,batch_yy))
iter = iter+1
train_count=train_count+1
batch_x = batch_x.reshape((batch_size,n_steps, n_input))
batch_y = batch_y.reshape((batch_size,output))
# Run optimization op (backprop)
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
if step % display_step == 0:
# Calculate batch loss
loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})
print("Iter " + str(step* batch_size) + ", Minibatch Loss= " + \
"{:.6f}".format(loss))
step += 1
print("Optimization Finished!")