Once we have defined the model graph, we want to train it using our input data. Then, we will have a well-tuned set of parameters that can be used for accurate predictions.
First, we specify the TensorFlow's Session object that encapsulates the environment in which Operation (summation, subtraction, and so on) objects are executed and Tensor (placeholders, variables, and so on) objects are evaluated:
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False))
sess.run(tf.global_variables_initializer())
A good explanation of the config parameter can be found at https://stackoverflow.com/questions/44873273/what-do-the-options-in-configproto-like-allow-soft-placement-and-log-device-plac. In summary, once we specify allow_soft_placement, the operations will be executed on the CPU only if there is no GPU registered...
