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设置GPU使用方式为按需分配 gpus=tf.config.experimental.list_physical_devices('GPU') if gpus: try:#设置GPU显存占用为按需分配 for gpu in gpus: tf.config.experimental.set_memory_growth(gpu,True) logical_gpus=tf.confi...

代码 # coding:utf-8 # zhong import tensorflow as tf # Import MINST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) learni...

python代码 import tensorflow as tf import numpy as np from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('/tmp/data/', one_hot=True) xtrain, ytrain = mnist....

import tensorflow as tf hello = tf.constant('hello, chenjun!') sess = tf.Session() print(sess.run(hello)) >>>b'hello, chenjun!'

# 1. 使用占位符placeholder import tensorflow as tf a = tf.placeholder(tf.int16) b = tf.placeholder(tf.int16) add = tf.add(a,b) mul = tf.multiply(a,b) with tf.Session() as sess: print('addition with vari...

tensorflow版 # tensorflow 实现线性回归 import tensorflow as tf import numpy as np import matplotlib.pyplot as plt # plt.ion() rng = np.random # 超参数 learning_rate = 0.01 traing_epochs = 1000 display_step = ...

在分类的时候，一般采用交叉熵损失函数，然而今天用tensorflow做分类的时候，发现采用tensorflow的自用函数，和自己写的，计算出来的结果不一样。 而且采用自己实现时，我出现了loss为nan的情况 # tensorflow自带 loss = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels=y, logit...