提供的训练数据和定义的模型之间的维度不对应. 在MNIST手写数字识别时,在 mnist = input_data.read_data_sets("MNIST_data/") 中,没有加关键字参数one_hot=True 应该为: mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)…
这里x_,y_是两个数字,当我运行时 with tf.Session() as sess: #定义session对象生成器 for step in range(201) : sess.run(train,feed_dict = {x: x_data,y: y_data}) 遇到了 Traceback (most recent call last): File "/home/wbt1995/PycharmProjects/tensorflow_demo/tensor_pingmian.py&quo…
tensorflow models api:ValueError: Tensor conversion requested dtype string for Tensor with dtype float32: 'Tensor("arg0:0", shape=(), dtype=float32, device=/device:CPU:0)' 这个原因是你的tf-record有问题哈.检查pipline里面的tfrecord.…
本人在写Django RESful API时,碰到一个难题,老出现,整合Keras,报如下错误:很纠结,探索找资料近一个星期,皇天不负有心人,解决了 Internal Server Error: /pic/analysis/ Traceback (most recent call last): File "D:\AI\Python35\lib\site-packages\tensorflow\python\client\session.py", line 1064, in _run a…
faster-rcnn错误信息 : tensorflow.python.framework.errors_impl.InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [21] rhs shape= [2] Traceback (most recent call last): File "C:\Users\Administrator\AppData\Local\Programs\Pyt…
Android里面常常会使用shape来定制一些View的背景 能够改动View的背景颜色.形状等属性 普通情况下.shape都是在xml文件中面写死了.今天遇到一个需求,View的形状是圆角的,可是颜色是在代码里面设置的 最開始的思路是先在代码里给View设置颜色.再在shape里面设置solid属性为透明色 <?xml version="1.0" encoding="utf-8"? > <shape xmlns:android="ht…
TensorFlow中的Session.Graph.operation.tensor…
在session范围内不要进行eval()或者convert_to_tensor()操作, 否则会造成OOM,或者报出错误:GraphDef cannot be larger than 2GB using input pipelines.…
问题描述 在将一个数组送入tensorflow训练时,报错如下: ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray) 数组元素为数组,每个数组元素的shape不一致,示例如下: cropImg[0].shape = (13, 13, 3) cropImg[1].shape = (14, 13, 3) cropImg[2].shape = (12, 13, 3…
import tensorflow as tf #Fetch input1 = tf.constant(1.0)input2 = tf.constant(3.0)input3 = tf.constant(5.0) add = tf.add(input2,input3)mul = tf.multiply(input1,add) with tf.Session() as sess: result = sess.run([mul,add]) print(result) ############输出  …