我是一名电气工程专业的学生，我试图根据Boiller内电阻器的功率，Boiller中水的温度以及使用python 3.5和tensorflow通过boiller的水流来建模工业设备。 问题是我是python和tensorflow的初学者，我编写了这个有效的代码，但是训练开始很快并且迅速开始减速，并且在训练的中间开始在步骤之间花费很长时间。 我只需要一些关于优化的帮助，当然，欢迎任何提示！ 非常感谢你！ 这是代码：
import numpy as np
import tensorflow as tf
input_vec_size = 3
step_size = 0.05
batch_size = 3
test_size = 16
train_end = 1905
eval_end = 290
predict_end = 1396
n_cores = 4
def read_my_file_format(filename_queue):
line_reader = tf.TextLineReader(skip_header_lines=1)
_, csv_row = line_reader.read(filename_queue)
record_defaults = [[0.0], [0.0], [0.0], [0.0]]
time, power_in, temperature, flow = \
tf.decode_csv(csv_row, record_defaults=record_defaults)
features = tf.pack([
power_in,
temperature
])
return features, flow
def input_pipeline(directory, batch_size, n_cores, buffer_size, num_epochs=None):
filename_queue = tf.train.string_input_producer(
tf.train.match_filenames_once(directory),
shuffle=True)
features, flow = read_my_file_format(filename_queue)
x, y = tf.train.batch(
[features, flow], batch_size=batch_size, allow_smaller_final_batch=True)
def init_weights(shape):
return tf.Variable(tf.random_normal(shape, stddev=0.001))
def init_bias(shape): #inicializa bias
initial = tf.constant(0.001, shape=shape) #variancia 0.1
return tf.Variable(initial)
def model(X, w_h, w_h2, w_o, B, B2, B3, p_keep_input, p_keep_hidden):
X = tf.nn.dropout(X, p_keep_input)
h = tf.nn.relu(tf.matmul(X, w_h)+B)
h = tf.nn.dropout(h, p_keep_hidden)
h2 = tf.nn.relu(tf.matmul(h, w_h2)+B2)
h2 = tf.nn.dropout(h2, p_keep_hidden)
return tf.matmul(h2, w_o)+B3
X = tf.placeholder("float", [None, input_vec_size])
Y = tf.placeholder("float", [None, 1])
p_keep_hidden = tf.placeholder("float")
p_keep_input = tf.placeholder("float")
w_h = init_weights([input_vec_size, fclayer_size])
w_h2= init_weights([fclayer_size, fclayer_size])
w_o= init_weights([fclayer_size, 1])
B = init_bias([fclayer_size])
B2 = init_bias([fclayer_size])
B3 = init_bias([1])
py_x = model(X, w_h, w_h2, w_o, B, B2, B3, p_keep_input, p_keep_hidden)
predict_op = py_x[0]
cost = tf.reduce_mean(tf.square(predict_op - Y))
train_op = tf.train.MomentumOptimizer(step_size, 0.5).minimize(cost)
saver = tf.train.Saver()
directory = "./train/*.csv"
x, y = input_pipeline(directory, batch_size, n_cores, buffer_size, num_epochs=None)
directory_eval = "./eval/*.csv"
xe, ye = input_pipeline(directory_eval, test_size, n_cores, buffer_size, num_epochs=None)
directory_predict = "./predict/*.csv"
xp, yp = input_pipeline(directory_predict, test_size, n_cores, buffer_size, num_epochs=None)
with tf.Session() as sess:
tf.initialize_all_variables().run()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
print("==================================TREINAMENTO=================================")
for iteraction in range(int(train_end/batch_size)):
trX, trY = sess.run([x,y])
for i in range(0, batch_size):
features, features_past, features_past2 = sess.run(tf.unpack(trX[i])), sess.run(tf.unpack(trX[i-1])), sess.run(tf.unpack(trX[i-2]))
power_in_i = features[0] - 4
temperature_i = features[1]
temperature_i1 = features_past[1]
temperature_i2 = features_past2[1]
trX_now = tf.pack([power_in_i, (temperature_i-temperature_i1), (temperature_i-temperature_i2)])
trX_now = sess.run(trX_now)
X_Batch, Y_Batch = trX_now.reshape([-1, input_vec_size]), trY[i].reshape([-1, 1])
sess.run(train_op, feed_dict={X: X_Batch,
Y: Y_Batch, p_keep_input: 0.95, p_keep_hidden: 0.7})
if(i%batch_size == 0):
predict_train = sess.run(tf.reshape(predict_op, [-1, 1]), feed_dict={X: X_Batch, p_keep_input: 1.0, p_keep_hidden: 1.0})
train_cost = sess.run(cost, feed_dict={py_x: predict_train, Y: Y_Batch})
print("Train Batch:", iteraction,"Sample:", batch_size*iteraction, "X:", X_Batch, "Y:", Y_Batch, "y_:",
predict_train, "Cost:", train_cost)
saver.save(sess, "./model.ckpt")
print('Variaveis salvas com sucesso')
coord.request_stop()
coord.join(threads)
sess.close()
print('=============================Fim do Treinamento=============================')
with tf.Session() as sess:
tf.initialize_all_variables().run()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
print("==============================VALIDAÇAO==================================")
saver.restore(sess, "./model.ckpt")
print("Model restored.")
for iteraction in range(int(eval_end/test_size)):
teX, teY = sess.run([xe, ye])
for i in range(0, test_size):
features, features_past, features_past2 = sess.run(tf.unpack(teX[i])), sess.run(tf.unpack(teX[i - 1])), sess.run(tf.unpack(teX[i-2]))
power_in_i = features[0] - 4
temperature_i = features[1]
temperature_i1 = features_past[1]
teX_now = tf.pack([power_in_i, (temperature_i - temperature_i1), (temperature_i-temperature_i2)])
teX_now = sess.run(teX_now)
X_Batch, Y_Batch = teX_now.reshape([-1, input_vec_size]), teY[i].reshape([-1, 1])
predict_eval = sess.run(tf.reshape(predict_op, [-1, 1]), feed_dict={X: X_Batch, p_keep_input: 1.0, p_keep_hidden: 1.0})
eval_cost = sess.run(cost, feed_dict={py_x: predict_eval, Y: Y_Batch})
print("Eval Batch:", iteraction,"Sample:", batch_size*iteraction, "X:", X.eval(feed_dict={X: X_Batch}), "Y:", Y_Batch, "y_:",
predict_eval, "Cost:", eval_cost)
coord.request_stop()
coord.join(threads)
sess.close()
print('=============================FIM DA VALIDAÇAO=============================')
with tf.Session() as sess:
tf.initialize_all_variables().run()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
print("==============================PREDIÇÃO==================================")
saver.restore(sess, "./model.ckpt")
print("Model restored.")
predict_batch_mean = 0
predict_mean = 0
for iteraction in range(int(predict_end / test_size)):
tpX, tpY = sess.run([xp, yp])
for i in range(0, test_size):
features, features_past, features_past2 = sess.run(tf.unpack(tpX[i])), sess.run(tf.unpack(tpX[i - 1])), sess.run(tf.unpack(tpX[i-2]))
power_in_i = features[0]- 4
temperature_i = features[1]
temperature_i1 = features_past[1]
tpX_now = tf.pack([power_in_i, (temperature_i - temperature_i1), (temperature_i-temperature_i2)])
tpX_now = sess.run(tpX_now)
X_Batch, Y_Batch = tpX_now.reshape([-1, input_vec_size]), tpY[i].reshape([-1, 1])
prediction = sess.run(tf.reshape(predict_op, [-1, 1]), feed_dict={X: X_Batch, p_keep_input: 1.0, p_keep_hidden: 1.0})
print("Predict Batch:", iteraction,"Sample:", batch_size*iteraction, "X:", X.eval(feed_dict={X: X_Batch}), "y_:",
prediction)
predict_batch_mean = (predict_batch_mean + prediction)/i
predict_mean = (predict_mean + predict_batch_mean)/iteraction
print("Predicted Flow:", predict_mean)
coord.request_stop()
coord.join(threads)
sess.close()
答案 0 :(得分：3)
我的快速猜测是你通过训练在每次迭代中创建了很多新节点：那些tf.packs和tf.reshapes只会让你的图表变得越来越大。
在训练循环之外构建一次图表，我敢打赌一切都会变得快乐。
答案 1 :(得分：1)
熊猫这次救了我，我已经爱上了它！ 经过一番学习，有工作代码。现在它很快，除了那个好的＆＃34;预测准确性。 下面是代码：
import numpy as np
import pandas as pd
import tensorflow as tf
#VARIAVEIS
input_vec_size = 6
layer1_size = 512
fclayer_size = 1024
step_size = 0.02
test_size = 16
train_end = 1905
eval_end = 290
predict_end = 1396
#READS TRAIN FILE
def read_data(directory):
data=pd.read_csv(directory, sep=',',header=None)
return data
#Batch Maker
def get_batch(data, i, data_size):
j = i + (input_vec_size - 1 - data_size)*(i//(data_size - input_vec_size + 1)) + input_vec_size - 1
# print(j, i//(data_size - 5))
features = [(data[1][j] - 4) / 16,
(data[2][j] - data[2][j - 1])*10,
(data[2][j] - data[2][j - 2])*10,
(data[2][j] - data[2][j - 3])*10,
(data[2][j] - data[2][j - 4])*10,
(data[2][j] - data[2][j - 5])*10]
features = np.reshape(features, [-1, input_vec_size])
flow = data[3][j]/1500
flow = np.reshape(flow, [-1,1])
return features, flow
#Inicializaçao de variaveis
def init_weights(shape):
return tf.Variable(tf.random_normal(shape, stddev=0.001))
def init_bias(shape): #inicializa bias
initial = tf.constant(0.001, shape=shape) #variancia 0.1
return tf.Variable(initial)
#Definindo Modelo DNN
def model(X, w_h, w_h2, w_o, B, B2, p_keep_input, p_keep_hidden):
X = tf.nn.dropout(X, p_keep_input)
h = tf.nn.relu(tf.matmul(X, w_h)+B)
h = tf.nn.dropout(h, p_keep_hidden)
h2 = tf.nn.relu(tf.matmul(h, w_h2))
h2 = tf.nn.dropout(h2, p_keep_hidden)
return tf.matmul(h2, w_o)
#PLaceholders
X = tf.placeholder("float", [None, input_vec_size])
Y = tf.placeholder("float", [None, 1])
p_keep_hidden = tf.placeholder("float")
p_keep_input = tf.placeholder("float")
#Estados iniciais das variaveis
w_h = init_weights([input_vec_size, layer1_size])
w_h2= init_weights([layer1_size, fclayer_size])
w_o= init_weights([fclayer_size, 1])
B = init_bias([layer1_size])
B2 = init_bias([fclayer_size])
#Modelo
py_x = model(X, w_h, w_h2, w_o, B, B2, p_keep_input, p_keep_hidden)
#Operaçao de previsão
predict_op = tf.reshape(py_x[0], [-1,1])
#Funçao custo
cost = tf.reduce_mean(tf.square(predict_op - Y))
#Operação de treinamento
train_op = tf.train.AdadeltaOptimizer(step_size).minimize(cost)
#Utilizado para salvar as variaveis apos o treinamento
saver = tf.train.Saver()
with tf.Session() as sess:
tf.initialize_all_variables().run()
directory = '~/PycharmProjects/modelagemELT430/train/G2.csv'
data = read_data(directory)
for i in range(0, 10*(train_end - input_vec_size + 1)):
features, flow = get_batch(data, i, train_end)
# features = sess.run(features)
sess.run(train_op, feed_dict={X: features,
Y: flow, p_keep_input: 0.9, p_keep_hidden: 0.6})
predict_train = sess.run(predict_op,
feed_dict={X: features, p_keep_input: 1.0, p_keep_hidden: 1.0})
train_cost = sess.run(cost, feed_dict={py_x: predict_train, Y: flow})
print("Train Sample:", i, "X:", features, "Y:", flow*1500, "y_:",
predict_train*1500, "Cost:", train_cost)
saver.save(sess, "./model.ckpt")
print('Variaveis salvas com sucesso')
sess.close()
print('=============================Fim do Treinamento=============================')
with tf.Session() as sess:
tf.initialize_all_variables().run()
directory = '~/PycharmProjects/modelagemELT430/eval/G2E.csv'
data = read_data(directory)
print("==============================VALIDAÇAO==================================")
saver.restore(sess, "./model.ckpt")
print("Model restored.")
for i in range(0, eval_end - input_vec_size + 1):
features, flow = get_batch(data, i, eval_end)
predict_eval = sess.run(predict_op,
feed_dict={X: features, p_keep_input: 1.0, p_keep_hidden: 1.0})
eval_cost = sess.run(cost, feed_dict={py_x: predict_eval, Y: flow})
print("Eval Sample:", i, "X:", features, "Y:",flow*1500, "y_:",predict_eval*1500, "Cost:", eval_cost)
sess.close()
print('============================Fim da Validação=================================')
with tf.Session() as sess:
tf.initialize_all_variables().run()
directory = '~/PycharmProjects/modelagemELT430/predict/G2P.csv'
data = read_data(directory)
print("==============================Predição==================================")
saver.restore(sess, "./model.ckpt")
print("Model restored.")
for i in range(0, predict_end - input_vec_size + 1):
features, flow = get_batch(data, i, predict_end)
predict = sess.run(predict_op,
feed_dict={X: features, p_keep_input: 1.0, p_keep_hidden: 1.0})
eval_cost = sess.run(cost, feed_dict={py_x: predict, Y: flow})
print("Predict Sample:", i, "X:", features, "y_:",predict*1500)
sess.close()
print('============================Fim da Prediçao=================================')