在调用'slice_input_producer'时bao'c

运行以下代码,报错:

module 'tensorflow._api.v2.train' has no attribute 'slice_input_producer'

import tensorflow.compat.v1 as tf

tf.disable_v2_behavior()

import os

import numpy as np

import tensorflow as tf

#import input_data

#import model

N_CLASSES = 2 # 2个输出神经元,[1,0] 或者 [0,1]猫和狗的概率

IMG_W = 208 # 重新定义图片的大小,图片如果过大则训练比较慢

IMG_H = 208

BATCH_SIZE = 32 #每批数据的大小

CAPACITY = 256

MAX_STEP = 15000 # 训练的步数,应当 >= 10000

learning_rate = 0.0001 # 学习率,建议刚开始的 learning_rate <= 0.0001

def run_training():

# 数据集

train_dir = 'D:/Python/data/DogVsCat/kaggle/train' #My dir--20170727-csq

#logs_train_dir 存放训练模型的过程的数据,在tensorboard 中查看

logs_train_dir = 'D:/Python/data/DogVsCat/kaggle/'

# 获取图片和标签集

train, train_label = get_files(train_dir)

# 生成批次

train_batch, train_label_batch = get_batch(train,

train_label,

IMG_W,

IMG_H,

BATCH_SIZE,

CAPACITY)

# 进入模型

train_logits = inference(train_batch, BATCH_SIZE, N_CLASSES)

# 获取 loss

train_loss = losses(train_logits, train_label_batch)

# 训练

train_op = trainning(train_loss, learning_rate)

# 获取准确率

train__acc = evaluation(train_logits, train_label_batch)

# 合并 summary

summary_op = tf.summary.merge_all()

sess = tf.Session()

# 保存summary

train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph)

saver = tf.train.Saver()

sess.run(tf.global_variables_initializer())

coord = tf.train.Coordinator()

threads = tf.train.start_queue_runners(sess=sess, coord=coord)

try:

for step in np.arange(MAX_STEP):

if coord.should_stop():

break

_, tra_loss, tra_acc = sess.run([train_op, train_loss, train__acc])

if step % 50 == 0:

print('Step %d, train loss = %.2f, train accuracy = %.2f%%' %(step, tra_loss, tra_acc*100.0))

summary_str = sess.run(summary_op)

train_writer.add_summary(summary_str, step)

if step % 2000 == 0 or (step + 1) == MAX_STEP:

# 每隔2000步保存一下模型,模型保存在 checkpoint_path 中

checkpoint_path = os.path.join(logs_train_dir, 'model.ckpt')

saver.save(sess, checkpoint_path, global_step=step)

except tf.errors.OutOfRangeError:

print('Done training -- epoch limit reached')

finally:

coord.request_stop()

coord.join(threads)

sess.close()

# train

run_training()

是不是版本问题?我的版本是2.0以上的。

回答

是的,你的代码是 tf 2.0,api改动很大,以前的api可以在 tf.compat.v1 里面找到。

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