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...
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...
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_loss ...
kernel=tf.compat.v1.get_variable(shape=[out.shape[-1],units],regularizer=tf.keras.regularizers.L2(),initializer=tf.compat.v1.initializers.glorot_normal,name="kernel")bias=tf.compat.v1.get_variable(shape=[units,],initializer=tf.compat.v1.initializers...
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...
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')#...
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....
Temporal Fusion Transformers for Tensorflow 2.x. Contribute to greatwhiz/tft_tf2 development by creating an account on GitHub.
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clear_devices, initializer_nodes, variable_names_whitelist="", variable_names_blacklist="", input_meta_graph=None, input_saved_model_dir=None, saved_model_tags=tag_constants.SERVING, checkpoint_version=saver_pb2.SaverDef.V2): """Converts all variables in a graph and checkpoint into constants...