],[1,1,1])withtf.Session()assess: print(sess.run(x)) 更好的文字理解可以参考点击打开链接...最近在处理遥感图像的时候要用到tensorflow的tf.strided_slice函数,下面是我的理解: 附检验代码: import tensorflowastfdata = [[[1, 1 模块TensorFlow中没有Session ...
pythonCopy codeimporttensorflowastf# 创建一个计算图a=tf.constant(1)b=tf.constant(2)c=tf.add(a,b)# 创建一个会话并配置设备日志withtf.Session(config=tf.ConfigProto(log_device_placement=True))assess:result=sess.run(c)print(result)# 输出 3 上面的代码与之前的示例非常相似,唯一的区别是在tf.Sessi...
>>> with tf.Session() as sess: ...printsess.run(a) ... ... 2020-06-1609:54:51.947755: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFl ow binary wasnotcompiled to use: AVX2 FMA 2020-06-1609:54:52.243377: I tensorflow/stream_ex...
Notice that we need to return tensorflow functions of the input. Making a TF function with gradients: As explained in the sources mentioned above, there is a hack to define gradients of a function using tf.RegisterGradient [doc] and tf.Graph.gradient_override_map [doc]. Copying the...
importtensorflow as tf#假设只有三个类,分别编号0,1,2,labels就可以直接输入下面的向量,不用转换与logits一致的维度labels = [0,1,2] logits= [[2,0.5,1], [0.1,1,3], [3.1,4,2]] logits_scaled=tf.nn.softmax(logits) result= tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, ...
# creating TensorFlow session and loading the model graph = tf.Graph() sess = tf.InteractiveSession(graph=graph) with tf.gfile.FastGFile(model_fn, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) t_input = tf.placeholder(np.float32, name='input') # defin...
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with tf.Session() as sess: softmax=sess.run(y) c_e = sess.run(cross_entropy) c_e2 = sess.run(cross_entropy2) print("step1:softmax result=") print(softmax) print("step2:cross_entropy result=") print(c_e) print("Function(softmax_cross_entropy_with_logits) result=") ...
sess.run(loss_sparse)) 代码中的labels_sparse和labels表示的是一个意思,不过是两种表达方式,输出的结果是一样的: 这两个api的区别就在于labels的表达方式,正因为表达方式不同,也决定了 tf.nn.sparse_softmax_cross_entropy_with_logits不能处理一个样本被分为多个类别的情况。
pool 3 -> filter size: 2×2, output: 6×6 fc 1 -> hidden nodes: 200, output: 1×100 out -> 1×2 import string, os, sys import numpy as np import matplotlib.pyplot as plt import scipy.io import random import tensorflow as tf ...