Search

module 'tensorflow' has no attribute 'global_variables_initializer'. it is an AttributeError. 💻problem #init_op = tf.initialize_all_variables() init_op = tf.global_variables_initializer () --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-13-9cab3748941a> in <module>() 12 13 #init_op = tf.initialize_all_variables() ---> 14 init_op = tf.global_variables_initializer() 15 16 sess = tf.Session() AttributeError: module 'tensorflow' has no attribute 'global_variables_initializer' 💻Changes should be made #init_op = tf.initialize_all_variables() init = tf.compat.v1.global_variables_initializer ()

TensorFlow Train/test /evaluation flow

June 17, 2021

TensorFlow Train/test /evaluation flow step1: Choose a model First, we'll need to choose which machine learning algorithm we want to use to build our model. we can pick any standard machine learning algorithm but in this section, we will be using neural networks. then we can start with the training phase. step2: we train the algorithm by showing its training data and the expected output for the data and it has to figure out how to come up with the expected result. in other words, the algorithm learns how to transform the input to produce the correct output. for example, we can train it to do multiplication by showing it two numbers and the expected result and it will eventually work out that we want to multiply the two numbers. After we train the model, we will load up the second set of data, which has never seen before called the testing data set. This is the test phase, we will feed this testing data through the model and make sure it is able to predict the correct result ...