Neural Network Programming with TensorFlow

Code execution

Let's run this code for h_size of 128, standard deviation of 0.1, and sgd_step of 0.01:

def run(h_size, stddev, sgd_step): ...def main():run(128,0.1,0.01)if __name__ == '__main__':main()

The preceding code outputs the following graph, which plots the steps versus the test and train accuracy:

Let's compare the change in SGD steps and its effect on training accuracy. The following code is very similar to the previous code example, but we will rerun it for multiple SGD steps to see how SGD steps affect accuracy levels.

def run(h_size, stddev, sgd_steps):    ....    test_accs = []    train_accs = []    time_taken_summary = [] for sgd_step in ...