merge_summary = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter('D:/mypath',sess.graph)
# 执行训练迭代
for it in range(iterations):
for n in range(batches_count):
summary,_=sess.run([merge_summary,train_step],feed_dict={x: input_images[n*batch_size:(n+1)*batch_size], y_: input_labels[n*batch_size:(n+1)*batch_size], keep_prob: 0.45})
summary_writer.add_summary(summary, i)
if remainder > 0:
start_index = batches_count * batch_size;
summary,_=sess.run([merge_summary,train_step],feed_dict={x: input_images[start_index:input_count-1], y_: input_labels[start_index:input_count-1], keep_prob: 0.45})
summary_writer.add_summary(summary, i)
# 每完成五次迭代，判断准确度是否已达到100%，达到则退出迭代循环
iterate_accuracy = 0
if it%5 == 0:
iterate_accuracy = accuracy.eval(feed_dict={x: val_images, y_: val_labels, keep_prob: 1.0})
print ('第 %d 次训练迭代: 准确率 %0.5f%%' % (it, iterate_accuracy*100))
if iterate_accuracy >= 0.95:
count=count+1
if count>4:
break;
报错：InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_2' with dtype float and shape [?,1280]
[[Node: Placeholder_2 = Placeholderdtype=DT_FLOAT, shape=[?,1280], _device="/job:localhost/replica:0/task:0/device:CPU:0"]]
感觉是这个句子错了：
summary,_=sess.run([merge_summary,train_step],feed_dict={x: input_images[n*batch_size:(n+1)*batch_size], y_: input_labels[n*batch_size:(n+1)*batch_size], keep_prob: 0.45})
summary_writer.add_summary(summary, i)
