def run_case(n, hyperparameter_file='hyperparameters.csv'):
"""
Runs the dataset and network defined on line n in the given hyperparameter file.
:param n: The line of the scenario.
:param hyperparameter_file: A CSV file containing references to data sets and hyperparameters.
:return:
"""
params = pd.read_csv(hyperparameter_file).loc[n - 1]
dataset = params['Dataset']
try:
dataset = eval(dataset)
except NameError:
pass
output_functions = params['Output_functions']
try:
output_functions = eval(output_functions)
except NameError:
pass
network = Network(eval(params['Network']),
dataset,
minibatch_size=int(params['Minibatch_size']),
steps=int(params['Steps']),
loss_function=params['Loss_function'],
output_functions=output_functions,
case_fraction=float(params['case_fraction']),
validation_fraction=float(params['validation_fraction']),
validation_interval=int(params['validation_interval']),
test_fraction=float(params['test_fraction']),
learning_rate=float(params['learning_rate']),
optimizer=params['optimizer'],
one_hot_encode_target=bool(
params['one_hot_encode_target']))
network.build()
network.train()
network.test()
# network.mapping_test(10, [0])
network.mapping_test(dataset, [0], [0])
network.visualize_weights(weight_layers=[0, 1], bias_layers=[0, 1])
return result
if __name__ == '__main__':
data = load_data('model/100x50-500/replays.txt')
network = Network([game.state_size(state_format)] + layers +
[game.num_possible_moves()],
data,
minibatch_size=500,
steps=5000,
loss_function='cross_entropy',
case_fraction=0.1,
validation_fraction=0,
validation_interval=1000,
test_fraction=0,
learning_rate=0.01,
optimizer='rmsprop',
accuracy_argmax=True,
output_functions=[tf.nn.softmax])
network.build()
#network.save('model/pre-trained/step-0')
# for i in range(4):
# network.train(plot_results=True)
# network.save(f'model/pre-trained/game_{i*50}')
network.train(plot_results=True)
network.save('model/manual/test4')
#network.test()