def main():
FLAGS = lambda: None
FLAGS.pretrain_batch_size = 2
FLAGS.finetune_batch_size = None
FLAGS.finetuning_epochs_epochs = 200
FLAGS.pretraining_epochs = 20000
optimizer = input('Select Optimizer [0:GradientDescent, 1:Adam]:')
if optimizer == '0':
FLAGS.pre_layer_learning_rate = (0.01,0.01)#GD[0.01,0.01]
FLAGS.supervised_learning_rate = 0.5#GD 0.5
FLAGS.optim_method = tf.train.GradientDescentOptimizer
FLAGS.exp_dir = 'experiment/GradientDescent/pretrain={0}_finetune={1}'.format(
FLAGS.pre_layer_learning_rate,FLAGS.supervised_learning_rate)
elif optimizer == '1':
FLAGS.supervised_learning_rate = 0.001#GD 0.5
FLAGS.pre_layer_learning_rate = (0.001,0.001)#GD[0.01,0.01]
FLAGS.optim_method = tf.train.AdamOptimizer
FLAGS.exp_dir = 'experiment/Adam/pretrain={0}_finetune={1}'.format(
FLAGS.pre_layer_learning_rate,FLAGS.supervised_learning_rate)
FLAGS.flush_secs = 120
FLAGS.summary_dir = FLAGS.exp_dir + '/summaries'
FLAGS._ckpt_dir = FLAGS.exp_dir + '/modelAe'
FLAGS._confusion_dir = FLAGS.exp_dir + '/confusionMatrix'
FLAGS.tacticName =['F23','EV','HK','PD','PT','RB','SP','WS','WV','WW']
#tacticNumKP=[3,3,3,3,5,3,2,3,5,2]
#NUM_CLASS = len(tacticName)
#C5k k=3,2,5
FLAGS.C5k_CLASS = [[0,1,2,3,5,7],[6,9],[4,8]]
FLAGS.k = [3,2,5]
FLAGS.playerMap = [[[1,1,1,0,0],[1,1,0,1,0],[1,1,0,0,1],[1,0,1,1,0],[1,0,1,0,1],
[1,0,0,1,1],[0,1,1,1,0],[0,1,1,0,1],[0,1,0,1,1],[0,0,1,1,1]],
[[1,1,0,0,0],[1,0,1,0,0],[1,0,0,1,0],[1,0,0,0,1],[0,1,1,0,0],
[0,1,0,1,0],[0,1,0,0,1],[0,0,1,1,0],[0,0,1,0,1],[0,0,0,1,1]],
[[1,1,1,1,1]]];
#instNet_shape = [1040,130,10,1] #[1040,10,1]
instNet_shape = np.array([[1040,130,10,len(FLAGS.C5k_CLASS[0])],
[1040,130,10,len(FLAGS.C5k_CLASS[1])],
[1040,130,10,len(FLAGS.C5k_CLASS[2])]],
np.int32)
num_inst = np.array([10,10,1],np.int32) # 5 choose 3 key players, 5 choose 2 key players, 5 choose 3 key players
fold_str = input('Select Fold to run (0~5)[0:all fold]:') # 0 stay for all fold
if fold_str == '0':
queue = range(5)
else:
queue = [int(fold_str) - 1]
for fold in queue:
miList = miNet.main_unsupervised(instNet_shape,fold,FLAGS)
miNet.main_supervised(miList,num_inst,fold,FLAGS)
def main(optimizer, num_hidden_layer, fld=1):
FLAGS = lambda: None
FLAGS.pretrain_batch_size = 2
FLAGS.finetune_batch_size = None
FLAGS.finetuning_epochs_epochs = 200
if num_hidden_layer is None:
num_hidden_layer = input('how many hidden layer?')
num_hidden_layer = int(num_hidden_layer)
if num_hidden_layer < 1:
print("number of hidden layer can't be less than 1")
return
else:
num_hidden_layer = int(num_hidden_layer)
num_input = 1040
num_output = 10
pretrain_shape = createPretrainShape(num_input, num_output,
num_hidden_layer)
print(pretrain_shape)
#optimizer = input('Select Optimizer [0:GradientDescent, 1:Adam]:')
FLAGS.pre_layer_learning_rate = []
if optimizer == '0':
FLAGS.pretraining_epochs = 2000
for h in range(num_hidden_layer + 1):
FLAGS.pre_layer_learning_rate.extend([0.01]) #GD[0.01,0.01]
FLAGS.supervised_learning_rate = 0.5 #GD 0.5
FLAGS.optim_method = tf.train.GradientDescentOptimizer
FLAGS.exp_dir = 'experiment/GradientDescent/numHiddenLayer{0}'.format(
num_hidden_layer)
elif optimizer == '1':
FLAGS.pretraining_epochs = 600
FLAGS.supervised_learning_rate = 0.001 #GD 0.5
for h in range(num_hidden_layer + 1):
FLAGS.pre_layer_learning_rate.extend([0.001]) #GD[0.01,0.01]
FLAGS.optim_method = tf.train.AdamOptimizer
FLAGS.exp_dir = 'experiment//Adam/numHiddenLayer{0}'.format(
num_hidden_layer)
FLAGS.flush_secs = 120
FLAGS.summary_dir = FLAGS.exp_dir + '/summaries'
FLAGS._ckpt_dir = FLAGS.exp_dir + '/model'
FLAGS._confusion_dir = FLAGS.exp_dir + '/confusionMatrix'
FLAGS._result_txt = FLAGS.exp_dir + '/final_result.txt'
FLAGS.tacticName = [
'F23', 'EV', 'HK', 'PD', 'PT', 'RB', 'SP', 'WS', 'WV', 'WW'
]
#tacticNumKP=[3,3,3,3,5,3,2,3,5,2]
#NUM_CLASS = len(tacticName)
#C5k k=3,2,5
FLAGS.C5k_CLASS = [[0, 1, 2, 3, 5, 7], [6, 9], [4, 8]]
FLAGS.k = [3, 2, 5]
FLAGS.playerMap = [[[1, 1, 1, 0, 0], [1, 1, 0, 1, 0], [1, 1, 0, 0, 1],
[1, 0, 1, 1, 0], [1, 0, 1, 0, 1], [1, 0, 0, 1, 1],
[0, 1, 1, 1, 0], [0, 1, 1, 0, 1], [0, 1, 0, 1, 1],
[0, 0, 1, 1, 1]],
[[1, 1, 0, 0, 0], [1, 0, 1, 0, 0], [1, 0, 0, 1, 0],
[1, 0, 0, 0, 1], [0, 1, 1, 0, 0], [0, 1, 0, 1, 0],
[0, 1, 0, 0, 1], [0, 0, 1, 1, 0], [0, 0, 1, 0, 1],
[0, 0, 0, 1, 1]], [[1, 1, 1, 1, 1]]]
#instNet_shape = [1040,130,10,1] #[1040,10,1]
instNet_shape = np.array([
np.append(pretrain_shape, len(FLAGS.C5k_CLASS[0])),
np.append(pretrain_shape, len(FLAGS.C5k_CLASS[1])),
np.append(pretrain_shape, len(FLAGS.C5k_CLASS[2]))
], np.int32)
print(instNet_shape)
num_inst = np.array(
[10, 10, 1], np.int32
) # 5 choose 3 key players, 5 choose 2 key players, 5 choose 3 key players
if fld is None:
fold_str = input(
'Select Fold to run (0~5)[0:all fold]:') # 0 stay for all fold
if fold_str == '0':
queue = range(5)
else:
queue = [int(fold_str) - 1]
else:
queue = [fld - 1]
for fold in queue:
miList = miNet.main_unsupervised(instNet_shape, fold, FLAGS)
miNet.main_supervised(miList, num_inst, fold, FLAGS)
#==============================================================================
pretrain_shape = net_param.createPretrainShape(
FLAGS.lstm_hidden_dim * len(FLAGS.nk_pooling.split('_')),
FLAGS.miNet_last_hidden_dim, FLAGS.miNet_num_hidden_layer)
instNet_shape = np.array([
np.append(pretrain_shape, len(datasets.C5k_CLASS[0])),
np.append(pretrain_shape, len(datasets.C5k_CLASS[1])),
np.append(pretrain_shape, len(datasets.C5k_CLASS[2]))
], np.int32)
print(instNet_shape)
num_inst = np.array([len(nk) for nk in datasets.np_nchoosek], np.int32)
miNet_common_acfun = FLAGS.miNet_common_acfun
acfunList = []
for h in range(FLAGS.miNet_num_hidden_layer):
acfunList.append(utils.get_activation_fn(miNet_common_acfun))
#acfunList.append(utils.get_activation_fn('linear')) #log_sigmoid
acfunList.append(None)
batch_norm = np.zeros(FLAGS.miNet_num_hidden_layer + 1, dtype=bool)
if FLAGS.batch_norm_layer >= 0 and FLAGS.batch_norm_layer <= FLAGS.miNet_num_hidden_layer:
batch_norm[FLAGS.batch_norm_layer] = True
miList = miNet.main_unsupervised(instNet_shape, acfunList, batch_norm,
datasets, FLAGS, sess)
miNet.main_supervised(miList, num_inst, nchoosek_inputs, datasets, FLAGS)
sess.close()