目前在学习阶段,看到网上的资料使用的是tf1的代码,我使用的是tf2的,所以需要直接将tf1转换为tf2代码以适应tf2的开发方式 遇到的情景: 1. 使用Session 的情景: y = tf.constant(3, name='y_hat'); y_hat = tf.constant(5, name='y') init = tf.global_variables_initializer() loss = tf.Variable((y...
我们需要初始化全局变量 → 建立会话 → 执行计算,最终才能打印出张量的运算结果。 init_op = tf.global_variables_initializer()# 初始化全局变量withtf.Session()assess:# 启动会话sess.run(init_op)print(sess.run(c + c))# 执行计算 Eager Execution 带来的好处显而易见,其进一步降低了 TensorFlow 的入门...
self.optimizer = optiminzer.minimize(self.cost) init = tf.global_variables_initializer() self.sess = tf.Session() self.sess.run(init) # 权值初始化函数 def _initialize_weights(self): all_weights = dict() all_weights["w1"] = tf.Variable( xavier_init(self.n_input, self.n_hidden) ) a...
例如,将tf.Session()替换为tf.compat.v1.Session(),将tf.global_variables_initializer()替换为tf.compat.v1.global_variables_initializer()。 使用Eager Execution:TensorFlow 2默认启用Eager Execution,这意味着可以立即执行操作,无需构建静态计算图。因此,可以直接使用Python控制流语句(如if-else、for循环)。 更新...
init = tf.global_variables_initializer() 创建会话并运行神经网络: 代码语言:txt 复制 with tf.Session() as sess: sess.run(init) for i in range(num_epochs): sess.run(train_op, feed_dict={input_placeholder: input_data, output_placeholder: output_data}) if i % 100 == 0: current_los...
构建计算图# x = x + yadd_op=x.assign(x+y)# y = y / 2div_op=y.assign(y/2)# 3.打开会话、初始化会话、运行图withtf.Session()assess:# 4.初始化会话sess.run(tf.global_variables_initializer())# 5.运行图、执行50foriterationinrange(50):sess.run(add_op)sess.run(div_op)print(x....
projector.visualize_embeddings(self.output_path,config)#sess.run(tf.compat.v1.global_variables_initializer())#saver = tf.compat.v1.train.Saver()#saver.save(sess, os.path.join(self.output_path, 'w2x_metadata.ckpt'),STEP)#train_writer = tf.summary.create_file_writer('./logs/1/train')#...
(x,10)returnxinputs=tf.placeholder(shape=[10,32],dtype=tf.float32)outputs=model(inputs)print(tf.global_variables())# 输出当前计算图中的所有变量节点sess=tf.Session()sess.run(tf.global_variables_initializer())outputs_=sess.run(outputs,feed_dict={inputs:np.random.rand(10,32)})print(...
init = tf.global_variables_initializer() self.sess.run(init) def initialize_NN(self, layers): weights = [] biases = [] num_layers = len(layers) for l in range(0,num_layers-1): W = self.xavier_init ifname== "main": nu = 0.01/np.pi noise = 0.0 N_u = 100 N_f = 10000 ...
compat.v1.global_variables_initializer()) stock_values = {v.name.split(":")[0]: v.read_value() for v in sm.variables} stock_values = sess.run(stock_values) loaded_weights = set() skip_count = 0 weight_value_tuples = [] skipped_weight_value_tuples = [] if FLAGS.model_type ...