a3c_visualize.py

# -*- coding: utf-8 -*-
import tensorflow as tf
import matplotlib.pyplot as plt

from game_ac_network import GameACFFNetwork, GameACLSTMNetwork
from a3c_training_thread import A3CTrainingThread
from rmsprop_applier import RMSPropApplier

import options
options = options.options

# use CPU for weight visualize tool
device = "/cpu:0"

if options.use_lstm:
  global_network = GameACLSTMNetwork(options.action_size, -1, device)
else:
  global_network = GameACFFNetwork(options.action_size, device)

training_threads = []

learning_rate_input = tf.placeholder("float")

grad_applier = RMSPropApplier(learning_rate = learning_rate_input,
                              decay = options.rmsp_alpha,
                              momentum = 0.0,
                              epsilon = options.rmsp_epsilon,
                              clip_norm = options.grad_norm_clip,
                              device = device)

sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)

saver = tf.train.Saver()
checkpoint = tf.train.get_checkpoint_state(options.checkpoint_dir)
if checkpoint and checkpoint.model_checkpoint_path:
  saver.restore(sess, checkpoint.model_checkpoint_path)
  print("checkpoint loaded:", checkpoint.model_checkpoint_path)
else:
  print("Could not find old checkpoint")
  
W_conv1 = sess.run(global_network.W_conv1)

# show graph of W_conv1
fig, axes = plt.subplots(4, 16, figsize=(12, 6),
             subplot_kw={'xticks': [], 'yticks': []})
fig.subplots_adjust(hspace=0.1, wspace=0.1)

for ax,i in zip(axes.flat, range(4*16)):
  inch = i//16
  outch = i%16
  img = W_conv1[:,:,inch,outch]
  ax.imshow(img, cmap=plt.cm.gray, interpolation='nearest')
  ax.set_title(str(inch) + "," + str(outch))

plt.show()