While any implementation of the Asynchronous Advantage Actor Critic algorithm must be complex, all implementations will have one thing in common — the presence of a global network and a working class.
- The Global Network: class contains all the necessary operations Tensorflow for autonomous creation of neural networks.
- Working class. This class is used to simulate the process of training an employee who has his own copy of the environment and a "personal" neural network.
The following implementation will require the following modules:
- Numpy
- Tensorflow
- Multiprocessing ol >
- Filter: this is a small matrix that is used to apply various effects to a given image.
- Padding: is the process of adding an extra row or column at the edges of an image to fully compute filter convolution values.
- Step: is the number of steps after which the filter is set to a pixel in the given direction.
The following lines of code indicate the basic functionality required to create the corresponding class.
WAN class:
|
The following lines contain various functions that describe the member functions of the class defined above.
Class initialization:
|
-" tf.placeholder () - Inserts a placeholder for a tensor that will always be fed. -" tf.reshape () - Reshapes the input tensor -" slim.conv2d () - Adds an n-dimensional convolutional network -" slim.fully_connected () - Adds a fully connected layer
Note the following definitions:
Recurrent construction networks:
|
-" tf.nn.rnn_cell.BasicLSTMCell () - Builds a basic LSTM Recurrent network cell -" tf.expand_dims () - Inserts a dimension of 1 at the dimension index axis of input’s shape -" tf.shape () - returns the shape of the tensor -" tf.nn.rnn_cell.LSTMStateTuple () - Creates a tuple to be used by the LSTM cells for state_size, zero_state and output state. -" tf.nn.dynamic_rnn () - Builds a Recurrent network according to the Recurrent network cell
Generate pricing and policy output layers:
|
Build a master network and deploy workers:
|
Performing parallel Tensorflow operations:
|
-" tf.Session () - A class to run the Tensorflow operations -" tf.train.Coordinator () - Returns a coordinator for the multiple threads -" tf.train.get_checkpoint_state () - Returns a valid checkpoint state from the "checkpoint" file -" saver.restore () - Is used to store and restore the models -" sess.run () - Outputs the tensors and metadata obtained from running a session
Updating WAN parameters:
Latest questions |