def main(num_epochs=NUM_EPOCHS):
print("Loading data...")
dataset = load_data()
print("Building model and compiling functions...")
output_layer = build_model(
input_height=dataset['input_height'],
input_width=dataset['input_width'],
output_dim=dataset['output_dim'],
)
iter_funcs = create_iter_functions(
dataset,
output_layer,
X_tensor_type=T.tensor4,
)
print("Starting training...")
now = time.time()
try:
for epoch in train(iter_funcs, dataset):
print("Epoch {} of {} took {:.3f}s".format(
epoch['number'], num_epochs, time.time() - now))
now = time.time()
print(" training loss:\t\t{:.6f}".format(epoch['train_loss']))
print(" validation loss:\t\t{:.6f}".format(epoch['valid_loss']))
print(" validation accuracy:\t\t{:.2f} %%".format(
epoch['valid_accuracy'] * 100))
if epoch['number'] >= num_epochs:
break
except KeyboardInterrupt:
pass
# Can we do something with final output model? Like show a couple of moves...
#test_batch = dataset['X_test']
# Test cases -- for Deuce. Can it keep good hands, break pairs, etc?
# NOTE: These cases, and entire training... do *not* include number of draw rounds left. Add that!
test_cases = ['As,Ad,4d,3s,2c', 'As,Ks,Qs,Js,Ts', '3h,3s,3d,5c,6d', '3h,4s,3d,5c,6d', '2h,3s,4d,6c,5s',
'8s,Ad,Kd,8c,Jd', '8s,Ad,2d,7c,Jd', '2d,7d,8d,9d,4d', '7c,8c,Tc,Js,Qh', '2c,8s,5h,8d,2s',
'[8s,9c,8c,Kd,7h]', '[Qh,3h,6c,5s,4s]', '[Jh,Td,9s,Ks,5s]', '[6c,4d,Ts,Jc,6s]',
'[4h,8h,2c,7d,3h]', '[2c,Ac,Tc,6d,3d]', '[Ad,3c,Tc,4d,5d]', '3d,Jc,7d,Ac,6s',
'7h,Kc,5s,2d,Tc', '5c,6h,Jc,7h,2d', 'Ts,As,3s,2d,4h']
print('looking at some test cases: %s' % test_cases)
# Fill in test cases to get to batch size?
for i in range(BATCH_SIZE - len(test_cases)):
test_cases.append(test_cases[1])
test_batch = np.array([cards_input_from_string(case) for case in test_cases], np.int32)
predict_model(output_layer=output_layer, test_batch=test_batch)
print('again, the test cases: \n%s' % test_cases)
#print(predict(output_layer[x=test_batch]))
return output_layer
def main(num_epochs=NUM_EPOCHS, out_file=None):
print("Loading data...")
dataset = load_data()
print("Building model and compiling functions...")
sys.stdout.flush() # Helps keep track of output live in re-directed out
output_layer = build_model(
input_height=dataset['input_height'],
input_width=dataset['input_width'],
output_dim=dataset['output_dim'],
)
# Optionally, load in previous model parameters, from pickle!
# NOTE: Network shape must match *exactly*
if os.path.isfile(out_file):
print('Existing model in file %s. Attempt to load it!' % out_file)
all_param_values_from_file = np.load(out_file)
print('Loaded values %d' % len(all_param_values_from_file))
lasagne.layers.set_all_param_values(output_layer, all_param_values_from_file)
print('Successfully initialized model with previous saved params!')
else:
print('No existing model file (or skipping intialization) %s' % out_file)
#iter_funcs = create_iter_functions(
# dataset,
# output_layer,
# X_tensor_type=T.tensor4,
# )
# Define iter functions differently. Since we now want to predict the entire vector.
iter_funcs = create_iter_functions_full_output(
dataset,
output_layer,
X_tensor_type=T.tensor4,
)
print("Starting training...")
now = time.time()
try:
for epoch in train(iter_funcs, dataset, epoch_switch_adapt=EPOCH_SWITCH_ADAPT):
sys.stdout.flush() # Helps keep track of output live in re-directed out
print("Epoch {} of {} took {:.3f}s".format(
epoch['number'], num_epochs, time.time() - now))
now = time.time()
print(" training loss:\t\t{:.6f}".format(epoch['train_loss']))
print(" validation loss:\t\t{:.6f}".format(epoch['valid_loss']))
print(" validation accuracy:\t\t{:.2f} %%".format(
epoch['valid_accuracy'] * 100))
# Save model, after every epoch.
save_model(out_file=out_file, output_layer=output_layer)
if epoch['number'] >= num_epochs:
break
except KeyboardInterrupt:
pass
# Can we do something with final output model? Like show a couple of moves...
#test_batch = dataset['X_test']
# Test cases -- it keeps the two aces. But can it recognize a straight? A flush? Trips? Draw?? Two pair??
test_cases = ['As,Ad,4d,3s,2c', '6d,7d,8d,9d,Kh', 'Jh,Td,9s,8s,5s', 'As,Ks,Qs,Js,Ts', '3h,3s,3d,5c,6d',
'3h,4s,3d,5c,6d', '2h,3s,4d,6c,5s', '8s,Ad,Kd,8c,Jd', '8s,Ad,2d,7c,Jd', '2d,7d,8d,9d,4d',
'7c,8c,Tc,Js,Qh', '2c,8s,5h,8d,2s', '6s,5s,Kd,3c,4h', 'As,Ks,Qs,Js,Qc',
'[8s,9c,8c,Kd,7h]', '[Qh,3h,6c,5s,4s]', '[Jh,Td,9s,Ks,5s]', '[6c,4d,Ts,Jc,6s]',
'[4h,8h,2c,7d,3h]', '[2c,Ac,Tc,6d,3d]', '[Ad,3c,Tc,4d,5d]']
print('looking at some test cases: %s' % test_cases)
# Fill in test cases to get to batch size?
for i in range(BATCH_SIZE - len(test_cases)):
test_cases.append(test_cases[1])
test_batch = np.array([cards_input_from_string(case) for case in test_cases], np.int32)
predict_model(output_layer=output_layer, test_batch=test_batch)
print('again, the test cases: \n%s' % test_cases)
return output_layer
def main(num_epochs=NUM_EPOCHS):
print("Loading data...")
dataset = load_data()
print("Building model and compiling functions...")
output_layer = build_model(
input_height=dataset['input_height'],
input_width=dataset['input_width'],
output_dim=dataset['output_dim'],
)
iter_funcs = create_iter_functions(
dataset,
output_layer,
X_tensor_type=T.tensor4,
)
print("Starting training...")
now = time.time()
try:
for epoch in train(iter_funcs, dataset):
print("Epoch {} of {} took {:.3f}s".format(epoch['number'],
num_epochs,
time.time() - now))
now = time.time()
print(" training loss:\t\t{:.6f}".format(epoch['train_loss']))
print(" validation loss:\t\t{:.6f}".format(epoch['valid_loss']))
print(" validation accuracy:\t\t{:.2f} %%".format(
epoch['valid_accuracy'] * 100))
if epoch['number'] >= num_epochs:
break
except KeyboardInterrupt:
pass
# Can we do something with final output model? Like show a couple of moves...
#test_batch = dataset['X_test']
# Test cases -- for Deuce. Can it keep good hands, break pairs, etc?
# NOTE: These cases, and entire training... do *not* include number of draw rounds left. Add that!
test_cases = [
'As,Ad,4d,3s,2c', 'As,Ks,Qs,Js,Ts', '3h,3s,3d,5c,6d', '3h,4s,3d,5c,6d',
'2h,3s,4d,6c,5s', '8s,Ad,Kd,8c,Jd', '8s,Ad,2d,7c,Jd', '2d,7d,8d,9d,4d',
'7c,8c,Tc,Js,Qh', '2c,8s,5h,8d,2s', '[8s,9c,8c,Kd,7h]',
'[Qh,3h,6c,5s,4s]', '[Jh,Td,9s,Ks,5s]', '[6c,4d,Ts,Jc,6s]',
'[4h,8h,2c,7d,3h]', '[2c,Ac,Tc,6d,3d]', '[Ad,3c,Tc,4d,5d]',
'3d,Jc,7d,Ac,6s', '7h,Kc,5s,2d,Tc', '5c,6h,Jc,7h,2d', 'Ts,As,3s,2d,4h'
]
print('looking at some test cases: %s' % test_cases)
# Fill in test cases to get to batch size?
for i in range(BATCH_SIZE - len(test_cases)):
test_cases.append(test_cases[1])
test_batch = np.array(
[cards_input_from_string(case) for case in test_cases], np.int32)
predict_model(output_layer=output_layer, test_batch=test_batch)
print('again, the test cases: \n%s' % test_cases)
#print(predict(output_layer[x=test_batch]))
return output_layer
def main(num_epochs=NUM_EPOCHS):
print("Loading data...")
dataset = load_data()
print("Building model and compiling functions...")
output_layer = build_model(
input_height=dataset["input_height"], input_width=dataset["input_width"], output_dim=dataset["output_dim"]
)
iter_funcs = create_iter_functions(dataset, output_layer, X_tensor_type=T.tensor4)
print("Starting training...")
now = time.time()
try:
for epoch in train(iter_funcs, dataset):
print("Epoch {} of {} took {:.3f}s".format(epoch["number"], num_epochs, time.time() - now))
now = time.time()
print(" training loss:\t\t{:.6f}".format(epoch["train_loss"]))
print(" validation loss:\t\t{:.6f}".format(epoch["valid_loss"]))
print(" validation accuracy:\t\t{:.2f} %%".format(epoch["valid_accuracy"] * 100))
if epoch["number"] >= num_epochs:
break
except KeyboardInterrupt:
pass
# Can we do something with final output model? Like show a couple of moves...
# test_batch = dataset['X_test']
# Test cases -- it keeps the two aces. But can it recognize a straight? A flush? Trips? Draw?? Two pair??
test_cases = [
"As,Ad,4d,3s,2c",
"As,Ks,Qs,Js,Ts",
"3h,3s,3d,5c,6d",
"3h,4s,3d,5c,6d",
"2h,3s,4d,6c,5s",
"8s,Ad,Kd,8c,Jd",
"8s,Ad,2d,7c,Jd",
"2d,7d,8d,9d,4d",
"7c,8c,Tc,Js,Qh",
"2c,8s,5h,8d,2s",
"[8s,9c,8c,Kd,7h]",
"[Qh,3h,6c,5s,4s]",
"[Jh,Td,9s,Ks,5s]",
"[6c,4d,Ts,Jc,6s]",
"[4h,8h,2c,7d,3h]",
"[2c,Ac,Tc,6d,3d]",
"[Ad,3c,Tc,4d,5d]",
]
print("looking at some test cases: %s" % test_cases)
# Fill in test cases to get to batch size?
for i in range(BATCH_SIZE - len(test_cases)):
test_cases.append(test_cases[1])
test_batch = np.array([cards_input_from_string(case) for case in test_cases], np.int32)
predict_model(output_layer=output_layer, test_batch=test_batch)
print("again, the test cases: \n%s" % test_cases)
# print(predict(output_layer[x=test_batch]))
return output_layer
def main(num_epochs=NUM_EPOCHS, out_file=None):
print("Loading data...")
dataset = load_data()
print("Building model and compiling functions...")
sys.stdout.flush() # Helps keep track of output live in re-directed out
output_layer = build_model(
input_height=dataset['input_height'],
input_width=dataset['input_width'],
output_dim=dataset['output_dim'],
)
# Optionally, load in previous model parameters, from pickle!
# NOTE: Network shape must match *exactly*
if os.path.isfile(out_file):
print('Existing model in file %s. Attempt to load it!' % out_file)
all_param_values_from_file = np.load(out_file)
print('Loaded values %d' % len(all_param_values_from_file))
lasagne.layers.set_all_param_values(output_layer,
all_param_values_from_file)
print('Successfully initialized model with previous saved params!')
else:
print('No existing model file (or skipping intialization) %s' %
out_file)
#iter_funcs = create_iter_functions(
# dataset,
# output_layer,
# X_tensor_type=T.tensor4,
# )
# Define iter functions differently. Since we now want to predict the entire vector.
iter_funcs = create_iter_functions_full_output(
dataset,
output_layer,
X_tensor_type=T.tensor4,
)
print("Starting training...")
now = time.time()
try:
for epoch in train(iter_funcs,
dataset,
epoch_switch_adapt=EPOCH_SWITCH_ADAPT):
sys.stdout.flush(
) # Helps keep track of output live in re-directed out
print("Epoch {} of {} took {:.3f}s".format(epoch['number'],
num_epochs,
time.time() - now))
now = time.time()
print(" training loss:\t\t{:.6f}".format(epoch['train_loss']))
print(" validation loss:\t\t{:.6f}".format(epoch['valid_loss']))
print(" validation accuracy:\t\t{:.2f} %%".format(
epoch['valid_accuracy'] * 100))
# Save model, after every epoch.
save_model(out_file=out_file, output_layer=output_layer)
if epoch['number'] >= num_epochs:
break
except KeyboardInterrupt:
pass
# Can we do something with final output model? Like show a couple of moves...
#test_batch = dataset['X_test']
# Test cases -- it keeps the two aces. But can it recognize a straight? A flush? Trips? Draw?? Two pair??
test_cases = [
'As,Ad,4d,3s,2c', '6d,7d,8d,9d,Kh', 'Jh,Td,9s,8s,5s', 'As,Ks,Qs,Js,Ts',
'3h,3s,3d,5c,6d', '3h,4s,3d,5c,6d', '2h,3s,4d,6c,5s', '8s,Ad,Kd,8c,Jd',
'8s,Ad,2d,7c,Jd', '2d,7d,8d,9d,4d', '7c,8c,Tc,Js,Qh', '2c,8s,5h,8d,2s',
'6s,5s,Kd,3c,4h', 'As,Ks,Qs,Js,Qc', '[8s,9c,8c,Kd,7h]',
'[Qh,3h,6c,5s,4s]', '[Jh,Td,9s,Ks,5s]', '[6c,4d,Ts,Jc,6s]',
'[4h,8h,2c,7d,3h]', '[2c,Ac,Tc,6d,3d]', '[Ad,3c,Tc,4d,5d]'
]
print('looking at some test cases: %s' % test_cases)
# Fill in test cases to get to batch size?
for i in range(BATCH_SIZE - len(test_cases)):
test_cases.append(test_cases[1])
test_batch = np.array(
[cards_input_from_string(case) for case in test_cases], np.int32)
predict_model(output_layer=output_layer, test_batch=test_batch)
print('again, the test cases: \n%s' % test_cases)
return output_layer
