possible classes the keep_prob tensor is used to control the dropout rateand we initialize it as placeholder rather than an immutable variable because we want to use the same tensor both for training (when dropout is set to and testing (when dropout is set to the parameters that the network will update in the training process are the weight and bias valuesso for these we need to set an initial value rather than an empty placeholder these values are essentially where the network does its learningas they are used in the activation functions of the neuronsrepresenting the strength of the connections between units since the values are optimized during trainingwe could set them to zero for now but the initial value actually has significant impact on the final accuracy of the model we'll use random values from truncated normal distribution for the weights we want them to be close to zeroso they can adjust in either positive or negative directionand slightly differentso they generate different errors this will ensure that the model learns something useful add these linesmain py weights ' 'tf variable(tf truncated_normal([n_inputn_hidden ]stddev= ))' 'tf variable(tf truncated_normal([n_hidden n_hidden ]stddev= ))' 'tf variable(tf truncated_normal([n_hidden n_hidden ]stddev= ))'out'tf variable(tf truncated_normal([n_hidden n_output]