def save_model(self, save_path):
with open(save_path, 'w') as f:
data = L.get_all_param_values(self.network)
pkl.dump(data, f)
for item in self.trackers:
data = L.get_all_param_values(item)
pkl.dump(data, f)
def get_param_values(self): ''' returns a list of the trainable parameters *values*, as np.ndarray's. ''' return ( get_all_param_values(self.l_embed_query, trainable=True) + get_all_param_values(self.l_embed_context, trainable=True) )
def train(data_train, data_val, train_fn, val_fn, network, max_epochs=100, patience=20, save_run=True, eval_fn=None):
"""Generic train strategy for neural networks
(batch training, train/val sets, patience)
Trains a neural network according to some data (list of inputs, targets)
and a train function and an eval function on that data"""
print("training...")
run = []
best_model = None
if patience <= 0:
patience = max_epochs
patience_val = 0
best_val = None
for epoch in range(max_epochs):
start_time = time.time()
train_err, val_err = train_iteration(data_train, data_val,
train_fn, val_fn)
run.append(layers.get_all_param_values(network))
if np.isnan(val_err) or np.isnan(train_err):
print("Train error or validation error is NaN, "
"stopping now.")
break
# Calculating patience
if best_val == None or val_err < best_val:
best_val = val_err
patience_val = 0
best_model = layers.get_all_param_values(network)
else:
patience_val += 1
if patience_val > patience:
print("No improvements after {} iterations, "
"stopping now".format(patience))
break
# Then we print the results for this epoch:
print("Epoch {} of {} took {:.3f}s".format(
epoch + 1, max_epochs, time.time() - start_time))
print(" training loss:\t\t{:.6f}".format(train_err))
print(" validation loss:\t\t{:.6f}".format(val_err))
try:
print(" validation accuracy:\t\t{:.2f} %".format(
val_acc / val_batches * 100))
except:
if eval_fn != None:
acc = eval_fn(*data_val)
print(" validation accuracy:\t\t{:.2f} %".format(acc))
return best_model, run
def parameter_analysis(layer):
all_params = ll.get_all_param_values(layer, trainable=True)
param_names = [p.name for p in ll.get_all_params(layer, trainable=True)]
print_gradinfo(param_names, {'nneg':[np.count_nonzero(p < 0) / np.product(p.shape) for p in all_params],
'norm':[np.linalg.norm(p) for p in all_params],
'shape':[p.shape for p in all_params]})
"""
def multi_driver_training(options):
""" Train model using all drivers' data in the given datapath."""
print("Loading data...")
dataset = load_all(options)
print("Building model and compiling functions...")
net = models.cnn_1(options, output_size=len(dataset['label_map']))
# net = models.softmax_only(output_size=len(dataset['label_map']))
print("Starting training...")
start_time = time.time()
try:
train_loop(dataset['train_data'], dataset['val_data'], net, options)
print('Training Complete...')
except KeyboardInterrupt:
print('Keyboard Interrupt...')
end_time = time.time()
print('--------------------')
print(' Saving model, check logs for results.\n'
' Time taken: {0}\n'
.format(end_time - start_time))
utils.save_model(model=get_all_param_values(net), options=options)
return net
def load_model_predict(PATH_simresult, test_set_X):
# Load sim results
print 'loading', PATH_simresult, '\n'
with open(PATH_simresult, "rb") as f:
temp = pickle.load(f)
network = temp[-1]
best_network_params = get_all_param_values(network)
# extract input var
print 'extract input var \n'
X = get_all_layers(network)[0].input_var
# build test function
print 'build test function and reinit network \n'
test_fn = build_test_func(test_set_X,
network, X)
reinitiate_set_params(network,
weights=best_network_params)
print 'test set shape', test_set_X.shape, 'type:', type(test_set_X), '\n'
print 'make prediction \n'
# predictedy = test_fn(test_set_X)
# batched implementation
batch_size = 128
n_test_batches = test_set_X.shape[0] // batch_size + 1
test_set_x_size = test_set_X.shape[0]
predictedy = [test_fn(
test_set_X[index * batch_size: min((index + 1) * batch_size, test_set_x_size)])
for index in range(n_test_batches)]
predictedy = np.vstack(predictedy)
return predictedy
def summary(self, light=False):
""" Print a summary of the network architecture """
layer_list = get_all_layers(self.output_layer)
def filter_function(layer):
""" We only display the layers in the list below"""
return np.any([isinstance(layer, layer_type) for layer_type in
[InputLayer, Conv2DLayer, Pool2DLayer, Deconv2DLayer, ConcatLayer]])
layer_list = filter(filter_function, layer_list)
output_shape_list = map(get_output_shape, layer_list)
layer_name_function = lambda s: str(s).split('.')[3].split('Layer')[0]
if not light:
print('-' * 75)
print 'Warning : all the layers are not displayed \n'
print ' {:<15} {:<20} {:<20}'.format('Layer', 'Output shape', 'W shape')
for i, (layer, output_shape) in enumerate(zip(layer_list, output_shape_list)):
if hasattr(layer, 'W'):
input_shape = layer.W.get_value().shape
else:
input_shape = ''
print '{:<3} {:<15} {:<20} {:<20}'.format(i + 1, layer_name_function(layer), output_shape, input_shape)
if isinstance(layer, Pool2DLayer) | isinstance(layer, Deconv2DLayer):
print('')
print '\nNumber of Convolutional layers : {}'.format(
len(filter(lambda x: isinstance(x, Conv2DLayer) | isinstance(x, Deconv2DLayer), layer_list)))
print 'Number of parameters : {}'.format(np.sum(map(np.size, get_all_param_values(self.output_layer))))
print('-' * 75)
def insert_weights(self, regr):
'''
In order the following operations are done:
- Update mask main part: activate another node
- Copy the 'new_node' weights in the main part of the net
- Copy the regr weights in the 'new_node' part
- Recompile the net
Structure of parameters:
- W1: (num_classes*num_filters1*num_nodes, num_inputs, filter_length1)
- b1: (num_classes*num_filters1*num_nodes, )
- W2: (num_classes, num_classes*num_filters1*num_nodes, filter_length2)
- b2: (num_classes,)
'''
# ------------------
# Update mask:
# ------------------
self.net.layers_['mask'].add_node()
actNode = self.net.layers_['mask'].active_nodes
self.active_nodes = actNode
# ------------------
# Get weights:
# ------------------
W1, b1, maskParam, W2, b2 = layers.get_all_param_values(self.net.layers_['conv2'])
newNode_W1, newNode_b1, newNode_W2, newNode_b2 = layers.get_all_param_values(self.net.layers_['conv2_newNode'])
reg_W1, reg_b1, reg_W2, reg_b2 = layers.get_all_param_values(regr.net.layers_['conv2'])
# boost_const = self.net.layers_['boosting_merge'].boosting_constant.get_value()
# --------------------
# Update main part:
# --------------------
if actNode>0:
nNodes = self.num_filters1 ### ReLU MOD
start = nNodes*(actNode-1)
stop = nNodes*actNode
slice_weights = slice(start,stop)
W1[slice_weights,:,:], b1[slice_weights] = newNode_W1, newNode_b1
# For the moment I don't touch b2... Not sure about this...
W2[:,slice_weights,:], b2 = newNode_W2, b2+newNode_b2
layers.set_all_param_values(self.net.layers_['conv2'], [W1, b1, maskParam, W2, b2])
# --------------------
# Insert new node:
# --------------------
newNode_W1, newNode_b1, newNode_W2, newNode_b2 = reg_W1, reg_b1, reg_W2, reg_b2
layers.set_all_param_values(self.net.layers_['conv2_newNode'], [newNode_W1, newNode_b1, newNode_W2, newNode_b2])
def train(data, train_fn, val_fn, network, max_epochs=4000, patience=100):
(train_words, train_clusters), (test_words, test_clusters) = data
run = []
best_model = None
if patience <= 0:
patience = max_epochs
patience_val = 0
best_val = None
for epoch in range(max_epochs):
data_train = sample_data.generate_abnet_batch(
train_words, train_clusters, epoch, features_getter,
input_features_getter, return_indexes=False)
data_val = sample_data.generate_abnet_batch(
test_words, test_clusters, epoch, features_getter,
input_features_getter, return_indexes=False)
start_time = time.time()
train_err, val_err = abnet2.train_iteration(
data_train, data_val, train_fn, val_fn)
if epoch % 20 == 0:
run.append(layers.get_all_param_values(network))
if np.isnan(val_err) or np.isnan(train_err):
print("Train error or validation error is NaN, "
"stopping now.")
break
# Calculating patience
if best_val == None or val_err < best_val:
best_val = val_err
patience_val = 0
best_model = layers.get_all_param_values(network)
else:
patience_val += 1
if patience_val > patience:
print("No improvements after {} iterations, "
"stopping now".format(patience))
break
# Then we print the results for this epoch:
print("Epoch {} of {} took {:.3f}s".format(
epoch + 1, max_epochs, time.time() - start_time))
print(" training loss:\t\t{:.6f}".format(train_err))
print(" validation loss:\t\t{:.6f}".format(val_err))
acc = nnet.eer(*data_val)
print(" score eer:\t\t{:.2f} %".format(acc))
auc = nnet.auc(*data_val)
print(" score auc:\t\t{:.2f} %".format(auc))
return best_model, run
def save_params(self, filename, quiet=False):
if not quiet:
print "Saving network weights to " + filename + "..."
self._prepare_for_save()
params = get_all_param_values(self.approximator.network)
pickle.dump(params, open(filename, "wb"))
if not quiet:
print "Saving finished."
def parameter_analysis(layer):
all_params = ll.get_all_param_values(layer, regularizable=True)
for param in all_params:
print(param.shape)
nneg_w = np.count_nonzero(param < 0) / np.product(param.shape)
normed_norm = np.linalg.norm(param) / np.product(param.shape)
print("Number of negative weights: %0.2f" % nneg_w)
print("Weight norm (normalized by size): %0.10f" % normed_norm)
def test_get_all_param_values(self):
from lasagne.layers import (InputLayer, DenseLayer,
get_all_param_values)
l1 = InputLayer((10, 20))
l2 = DenseLayer(l1, 30)
l3 = DenseLayer(l2, 40)
pvs = get_all_param_values(l3)
assert len(pvs) == 4
def save(self, filename, quiet=False):
if not quiet:
print "Saving qengine to " + filename + "..."
self._prepare_for_save()
network_params = get_all_param_values(self._evaluator.network)
params = [self.setup, network_params]
pickle.dump(params, open(filename, "wb"))
if not quiet:
print "Saving finished."
def save_model(self, epoch=None):
if epoch is not None:
fname = self.conf.save_model.replace('%e', str(epoch).zfill(5))
else:
fname = self.conf.save_model.replace('%e', 'final')
if self.conf.verbosity > 1:
print "Saving model to", fname
np.savez(fname, *get_all_param_values(self.autoencoder))
def save_model(network, params, fname):
weights = get_all_param_values(network)
params['input_dim'] = weights[0].shape[0]
params['output_dim'] = weights[-1].shape[0]
params = {k: v for k, v in params.iteritems() if k in _standard_config}
p = _standard_config.copy()
p.update(params)
with open(fname, 'wb') as fout:
pickle.dump((p, weights), fout, -1)
def build_treatment_model(self, n_vars, **kwargs):
input_vars = TT.matrix()
instrument_vars = TT.matrix()
targets = TT.vector()
inputs = layers.InputLayer((None, n_vars), input_vars)
inputs = layers.DropoutLayer(inputs, p=0.2)
dense_layer = layers.DenseLayer(inputs, 2 * kwargs['dense_size'], nonlinearity=nonlinearities.rectify)
dense_layer = layers.batch_norm(dense_layer)
dense_layer= layers.DropoutLayer(dense_layer, p=0.2)
for _ in xrange(kwargs['n_dense_layers'] - 1):
dense_layer = layers.DenseLayer(dense_layer, kwargs['dense_size'], nonlinearity=nonlinearities.rectify)
dense_layer = layers.batch_norm(dense_layer)
self.treatment_output = layers.DenseLayer(dense_layer, 1, nonlinearity=nonlinearities.linear)
init_params = layers.get_all_param_values(self.treatment_output)
prediction = layers.get_output(self.treatment_output, deterministic=False)
test_prediction = layers.get_output(self.treatment_output, deterministic=True)
l2_cost = regularization.regularize_network_params(self.treatment_output, regularization.l2)
loss = gmm_loss(prediction, targets, instrument_vars) + 1e-4 * l2_cost
params = layers.get_all_params(self.treatment_output, trainable=True)
param_updates = updates.adadelta(loss, params)
self._train_fn = theano.function(
[
input_vars,
targets,
instrument_vars,
],
loss,
updates=param_updates
)
self._loss_fn = theano.function(
[
input_vars,
targets,
instrument_vars,
],
loss,
)
self._output_fn = theano.function(
[
input_vars,
],
test_prediction,
)
return init_params
def insert_weights(self, boostedPerceptron):
'''
In order the following operations are done:
- Update mask main part: activate another perceptron
- Copy the boostedPerceptron weights in the greedyLayer
Structure of parameters: (check lasagne doc)
- W1: (num_classes*num_filters1*num_nodes, num_inputs, filter_length1)
- b1: (num_classes*num_filters1*num_nodes, )
- W2: (num_classes, num_classes*num_filters1*num_nodes, filter_length2)
- b2: (num_classes,)
'''
# ------------------
# Update mask:
# ------------------
self.net.layers_['mask'].add_perceptron()
self.active_perceptrons = self.net.layers_['mask'].active_perceptrons
# ------------------
# Get weights:
# ------------------
all_net_params = layers.get_all_param_values(self.net.layers_['greedyConv_2'])
W1, b1, maskParam, W2, b2 = all_net_params[-5:]
perc_W1, perc_b1, perc_W2, perc_b2 = layers.get_all_param_values(boostedPerceptron.net.layers_['greedyConv_2'])[-4:]
# --------------------
# Update main part:
# --------------------
start = self.nodes_partition[self.active_perceptrons-1]
stop = self.nodes_partition[self.active_perceptrons]
slice_weights = slice(start,stop)
# !!! For the moment I don't touch b2... !!! #
b1[slice_weights] = perc_b1
if self.layer_type=="conv":
W1[slice_weights,:,:] = perc_W1
W2[:,slice_weights,:] = perc_W2
if self.layer_type=="trans_conv":
W1[:,slice_weights,:] = perc_W1
W2[slice_weights,:,:] = perc_W2
layers.set_all_param_values(self.net.layers_['greedyConv_2'], all_net_params[:-5] + [W1, b1, maskParam, W2, b2])
def build_instrument_model(self, n_vars, **kwargs):
targets = TT.vector()
instrument_vars = TT.matrix()
instruments = layers.InputLayer((None, n_vars), instrument_vars)
instruments = layers.DropoutLayer(instruments, p=0.2)
dense_layer = layers.DenseLayer(instruments, kwargs['dense_size'], nonlinearity=nonlinearities.tanh)
dense_layer = layers.DropoutLayer(dense_layer, p=0.2)
for _ in xrange(kwargs['n_dense_layers'] - 1):
dense_layer = layers.DenseLayer(dense_layer, kwargs['dense_size'], nonlinearity=nonlinearities.tanh)
dense_layer = layers.DropoutLayer(dense_layer, p=0.5)
self.instrument_output = layers.DenseLayer(dense_layer, 1, nonlinearity=nonlinearities.linear)
init_params = layers.get_all_param_values(self.instrument_output)
prediction = layers.get_output(self.instrument_output, deterministic=False)
test_prediction = layers.get_output(self.instrument_output, deterministic=True)
# flexible here, endog variable can be categorical, continuous, etc.
l2_cost = regularization.regularize_network_params(self.instrument_output, regularization.l2)
loss = objectives.squared_error(prediction.flatten(), targets.flatten()).mean() + 1e-4 * l2_cost
loss_total = objectives.squared_error(prediction.flatten(), targets.flatten()).mean()
params = layers.get_all_params(self.instrument_output, trainable=True)
param_updates = updates.adadelta(loss, params)
self._instrument_train_fn = theano.function(
[
targets,
instrument_vars,
],
loss,
updates=param_updates
)
self._instrument_loss_fn = theano.function(
[
targets,
instrument_vars,
],
loss_total
)
self._instrument_output_fn = theano.function([instrument_vars], test_prediction)
return init_params
def save(self, prefix):
import json
# save weights
np.savez(prefix + "_weights.npz", *get_all_param_values(self.output))
# save network config params
with open(prefix + "_config.json", "w") as f:
f.write(json.dumps(
{"emb_dim": self.emb_dim,
"rnn_dim": self.rnn_dim,
"hid_dim": self.hid_dim,
"vocab_size": self.vocab_size,
"context": self.context,
"cell": self.cell,
"add_dense": self.add_dense,
"depth": self.depth,
"cell_args": self.cell_args}))
def print_weight_distribution(net, layer_name=None):
n_layers = len(net.layers)
layers_names = [net.layers[i][1]['name'] for i in range(1,n_layers)]
mean, std, weights = {}, {}, {}
for name in layers_names:
if "conv" in name:
layer = net.layers_[name]
W, _ = get_all_param_values(layer)[-2:]
mean[name], std[name], weights[name] = W.mean(), W.std(), W
if layer_name:
# print "Mean: %g; \tstd: %g" %(mean[layer_name], std[layer_name])
return mean[layer_name], std[layer_name]
else:
for name in mean:
print "Layer %s: \tMean: %g; \tstd: %g" %(name, mean[name], std[name])
def per_driver_train_predict(options):
""" Predict output probabilities for each driver.
If `options.train` is true, this trains a model"""
# TODO: support for predict only
# TODO: support for pretrained weights
print("Loading data...")
dataset = load_all(options)
all_outputs = deque([])
for driver_index in range(len(dataset['label_map'])):
driver_id = dataset['label_map'][driver_index]
print('Working on Driver {0}'.format(driver_id))
net = _train_single(driver_index, dataset, options)
utils.save_model(model=get_all_param_values(net), options=options, driver_id=driver_id)
print('Training complete for driver {0}, making predictions...'.format(driver_id))
outputs = _predict_single(net, driver_id, options)
utils.save_outputs(outputs, options, driver_id=driver_id)
all_outputs.extend(outputs)
utils.save_outputs(all_outputs, options)
return
def convert(dataset_name):
dataset = Dataset(dataset_name)
# Create theano graph
input_var = T.tensor4('input')
net = build_model(input_var)
# Load caffe model
net_caffe = caffe.Net(dataset.model_path, dataset.pretrained_path, caffe.TEST)
# Set the parameters from caffe into lasagne
load_caffe_model(net, net_caffe)
# Save the parameters
p = join(dirname(__file__), 'pretrained', dataset.model_name + '.pkl')
output = open(p, 'wb')
params = get_all_param_values(net['prob'])
pickle.dump(params, output)
output.close()
def reinitiate_set_params(network,
weights = None):
# change weights of a trained network to a random set or a user defined value
# useful in case of big networks and cross validation
# instead of the long time of recompiling you can just
# re-init the network weights
if not weights:
old = get_all_param_values(network)
weights = []
for layer in old:
shape = layer.shape
if len(shape)<2:
shape = (shape[0], 1)
W= GlorotUniform()(shape)
if W.shape != layer.shape:
W = np.squeeze(W, axis= 1)
weights.append(W)
set_all_param_values(network, weights)
return network
def save_model(network, epoch, model_name, learning_rate = 0.0, directory = 'models'):
""" Saves networks parameters, epoch, learning_rate
"""
params = layers.get_all_param_values(network)
file_name = model_name + "-ep" + str(epoch) + ".pickle"
file_path = directory + '/' + file_name
print "==> Saving model to %s" % file_path
if (not os.path.exists(directory)):
os.makedirs(directory)
with open(file_path, 'w') as save_file:
pickle.dump(
obj = {
'params' : params,
'epoch' : epoch,
'learning_rate' : learning_rate,
},
file = save_file,
protocol = -1
)
def test_batch_size():
input_var01, input_var16 = T.tensor3s('input01', 'input16')
l_output01 = model(input_var01, batch_size=1)
l_output16 = model(input_var16, batch_size=16)
# Share the parameters for both models
params01 = get_all_param_values(l_output01)
set_all_param_values(l_output16, params01)
posterior_fn01 = theano.function([input_var01], get_output(l_output01))
posterior_fn16 = theano.function([input_var16], get_output(l_output16))
example_input = np.random.rand(16, 30, 8)
example_output16 = posterior_fn16(example_input)
example_output01 = np.zeros_like(example_output16)
for i in range(16):
example_output01[i] = posterior_fn01(example_input[i][np.newaxis, :, :])
assert example_output16.shape == (16, 30, 8)
assert np.allclose(example_output16, example_output01, atol=1e-3)
def __init__(self, rc):
# steal stuff from world/initialize
self.rc = rc
self.world = mj.MJCWorld(rc['model_file'])
self.model = self.world.get_model()
self.dX = self.model['nq'] + self.model['nv']
self.dU = self.model['nu']
# compute dO
# TODO: put this somewhere else, reorganize observation module
dO = 0
ndims = np.sum(rc['obs_dims'])
fields = rc['obs_fields']
if 'qpos' in fields:
dO += self.model['nq']
if 'qvel' in fields:
dO += self.model['nv']
if 'xipos' in fields:
dO += ndims*(self.model['nbody'] - 1)
if 'ximat' in fields:
dO += ndims*ndims*(self.model['nbody'] - 1)
if 'site_xpos' in fields:
dO += ndims*self.model['nsite']
if 'to_target' in fields:
dO += ndims
self.dO = dO
# TODO: start with synthetic data included/reorganize buffer storage
self.buf = ReplayBuffer(self.dO, self.dU)
# create nets and copy train net to target net
self.net = ActorCriticNet(self.dO, self.dU, rc['num_units'], rc['ctrl_limits'])
self.target_net = ActorCriticNet(self.dO, self.dU, rc['num_units'], rc['ctrl_limits'])
values = ll.get_all_param_values(self.net.critic)
ll.set_all_param_values(self.target_net.critic, values)
# compile all the theano functions
self._compile()
def train_model(data_logs, runtime_configuration):
print("********************************************************")
print("Setting up inputs & targets. This could take a while ...")
print("")
packaged_data = set_inputs_n_targets(data_logs, runtime_configuration)
neural_net = conv_net_X(runtime_configuration,\
num_output_units=len(packaged_data['class_labels']))
print("********************************************************")
print("Now training ...")
tick = time.time()
try:
train_using(packaged_data['train_with'], packaged_data['val_with'],\
neural_net, data_logs, runtime_configuration)
print('Finished training without interruption ...')
except KeyboardInterrupt:
print('Terminating ...')
tock = time.time()
saved_to = os.path.abspath(data_logs['record_stats_location'])
save_it(model=get_all_param_values(neural_net), data_logs=data_logs,\
runtime_configuration=runtime_configuration)
mins, secs = divmod(int(tock-tick), 60)
hrs, mins = divmod(mins, 60)
print('===============================')
print(' Network\'s "state" saved to '
' "{0}".\n'
'===============================\n'
'Training time: {1[0]:02d} hrs, {1[1]:02d} mins, {1[2]:02d} secs \n'
.format(saved_to, (hrs,mins,secs)))
return neural_net
def learn(self, render_training=False, render_test=False, learning_steps_per_epoch=10000, \
test_episodes_per_epoch=1, epochs=200, max_test_steps=2000):
print "Starting the training!"
train_results = []
test_results = []
time_start = time()
for epoch in range(epochs):
print "\nEpoch %d\n-------" % (epoch + 1)
eps = self.exploration_rate(epoch + 1, epochs)
print "Eps = %.2f" % eps
train_episodes_finished = 0
train_scores = []
print "Training..."
s1 = env.reset()
s1 = self.preprocess(s1)
score = 0
for learning_step in trange(learning_steps_per_epoch):
s2, reward, isterminal = self.perform_learning_step(
epoch, epochs, s1)
'''
a = self.get_best_action(s1)
(s2, reward, isterminal, _) = env.step(a) # TODO: Check a
s2 = self.preprocess(s2) if not isterminal else None
'''
score += reward
s1 = s2
if (render_training):
env.render()
if isterminal:
train_scores.append(score)
s1 = env.reset()
s1 = self.preprocess(s1)
train_episodes_finished += 1
score = 0
print "%d training episodes played." % train_episodes_finished
train_scores = np.array(train_scores)
print "Results: mean: %.1f±%.1f," % (train_scores.mean(), train_scores.std()), \
"min: %.1f," % train_scores.min(), "max: %.1f," % train_scores.max()
train_results.append((train_scores.mean(), train_scores.std()))
print("Saving training results...")
with open("train_results.txt", "w") as train_result_file:
train_result_file.write(str(train_results))
print "\nTesting..."
test_scores = []
for test_episode in trange(test_episodes_per_epoch):
s1 = env.reset()
s1 = self.preprocess(s1)
score = 0
isterminal = False
frame = 0
while not isterminal and frame < max_test_steps:
a = self.get_best_action(s1)
(s2, reward, isterminal, _) = env.step(a) # TODO: Check a
s2 = self.preprocess(s2) if not isterminal else None
score += reward
s1 = s2
if (render_test):
env.render()
frame += 1
test_scores.append(score)
test_scores = np.array(test_scores)
print "Results: mean: %.1f±%.1f," % (test_scores.mean(
), test_scores.std()), "min: %.1f" % test_scores.min(
), "max: %.1f" % test_scores.max()
test_results.append((test_scores.mean(), test_scores.std()))
print("Saving test results...")
with open("test_results.txt", "w") as test_result_file:
test_result_file.write(str(test_results))
print "Saving the network weigths..."
pickle.dump(get_all_param_values(self.dqn),
open('weights.dump', "w"))
print "Total elapsed time: %.2f minutes" % (
(time() - time_start) / 60.0)
env.render(close=True)
print "======================================"
print "Training finished. It's time to watch!"
def save_model(self, save_path):
data = L.get_all_param_values([self.e_net, self.q_net])
with open(save_path, 'w') as f:
pkl.dump(data, f)
def train_loop(output_layer,
iter_funcs,
dataset,
batch_size,
max_epochs,
patience=100,
learning_rate_start=theano.shared(float32(0.03)),
learning_rate_stop=theano.shared(float32(0.001)),
momentum_start=theano.shared(float32(0.9)),
momentum_stop=theano.shared(float32(0.999)),
verbose=True):
best_valid_loss = np.inf
best_valid_epoch = 0
best_train_loss = np.inf
best_weights = None
learning_rates = np.logspace(
np.log10(learning_rate_start.get_value()),
np.log10(learning_rate_stop.get_value()),
max_epochs)
momentums = np.linspace(
momentum_start.get_value(), momentum_stop.get_value(), max_epochs)
now = time.time()
history = []
if verbose:
printer = ProgressPrinter(color=True)
try:
for epoch in train(iter_funcs, dataset,
batch_size=batch_size):
epoch_number = epoch['number']
train_loss = epoch['train_loss']
valid_loss = epoch['valid_loss']
valid_acc = epoch['valid_accuracy']
info = OrderedDict([
('epoch', epoch_number),
('train_loss', train_loss),
('train_loss_best', train_loss <= best_train_loss),
('train_loss_worse', train_loss > history[-1]['train_loss']
if len(history) > 0 else False),
('valid_loss', valid_loss),
('valid_loss_best', valid_loss <= best_valid_loss),
('valid_loss_worse', valid_loss > history[-1]['valid_loss']
if len(history) > 0 else False),
('valid_accuracy', valid_acc),
('duration', time.time() - now)])
history.append(info)
now = time.time()
if verbose:
printer(history)
# early stopping
if epoch['valid_loss'] < best_valid_loss:
best_valid_loss = valid_loss
best_valid_epoch = epoch_number
best_weights = get_all_param_values(output_layer)
elif epoch['number'] >= max_epochs:
break
elif best_valid_epoch + patience < epoch_number:
if verbose:
print(" stopping early")
print(" best validation loss was {:.6f} at epoch {}."
.format(best_valid_loss, best_valid_epoch))
break
if epoch['number'] >= max_epochs:
if verbose:
print(' last epoch')
print(' best validation loss was {:.6f} at epoch {}.'
.format(best_valid_loss, best_valid_epoch))
break
# adjust learning rate and momentum
new_learning_rate = float32(learning_rates[epoch_number-1])
learning_rate_start.set_value(new_learning_rate)
new_momentum = float32(momentums[epoch_number-1])
momentum_start.set_value(new_momentum)
except KeyboardInterrupt:
pass
return best_valid_loss, best_valid_epoch, best_weights, history
def get_model_parameters(self, layer='output'): """return all the parameters of the network""" return layers.get_all_param_values(self.network[layer])
def weights(self):
return layers.get_all_param_values(self.outputs)
best_action_index = get_best_action(state)
game.make_action(actions[best_action_index], skiprate + 1)
r = game.get_total_reward()
test_rewards.append(r)
test_end = time()
test_time = test_end - test_start
print "Test results:"
test_rewards = np.array(test_rewards)
print "mean:", test_rewards.mean(), "std:", test_rewards.std(), "max:", test_rewards.max(), "min:", test_rewards.min()
print "t:", str(round(test_time, 2)) + "s"
if params_savefile:
print "Saving network weigths to:", params_savefile
pickle.dump(get_all_param_values(net), open(params_savefile, "w"))
print "========================="
print "Training finished! Time to watch!"
game.close()
game.set_window_visible(True)
game.set_mode(Mode.ASYNC_PLAYER)
game.init()
# Sleeping time between episodes, for convenience.
episode_sleep = 0.0
for i in range(episodes_to_watch):
game.new_episode()
while not game.is_episode_finished():
print(('[Epoch %03i][trn] cost %9.6f (cla %6.4f, reg %6.4f), |grad| = %.06f, acc = %7.5f %% (%.2fsec)') %
(it_count, epoch_cost[0], epoch_cost[1], epoch_cost[2], epoch_cost[3], epoch_cost[4] * 100,
time.time() - tic))
if np.isnan(epoch_cost[0]):
print("NaN in the loss function...let's stop here")
break
if (it_count % eval_freq) == 0:
v_c, v_a = [], []
for x_ in ds.test_iter():
tmp = funcs['acc_loss'](*x_)
v_a.append(tmp[0])
v_c.append(tmp[1])
test_cost = [np.mean(v_c), np.mean(v_a)]
print((' [tst] cost %9.6f, acc = %7.5f %%') % (test_cost[0], test_cost[1] * 100))
if epoch_cost[0] < best_trn:
kvs.store('best_train_params', [it_count, LL.get_all_param_values(ffn)])
best_trn = epoch_cost[0]
if test_cost[0] < best_tst:
kvs.store('best_test_params', [it_count, LL.get_all_param_values(ffn)])
best_tst = test_cost[0];\
print("...done training %f" % (time.time() - start_time))
rewrite = True
out_path = '../output/'
print "Saving output to: %s" % out_path
if not os.path.isdir(out_path) or rewrite==True:
try:
os.makedirs(out_path)
except:
def train(self, dataset, save_name='Best_model', num_epochs=100, batch_size=1, LR_start=1e-4, LR_decay=1,
compute_confusion=False, justTest=False, debug=False, roundParams=False,
withNoise=False, noiseType='white',ratio_dB=0, logger=logger_RNNtools):
X_train, y_train, valid_frames_train, X_val, y_val, valid_frames_val, X_test, y_test, valid_frames_test = dataset
confusion_matrices = []
# try to load performance metrics of stored model
best_val_acc, test_acc, old_train_info = self.loadPreviousResults(save_name) #stores old_train_info into self.network_train_info
logger.info("Initial best Val acc: %s", best_val_acc)
logger.info("Initial best test acc: %s\n", test_acc)
self.best_val_acc = best_val_acc
logger.info("Pass over Test Set")
test_cost, test_acc, test_topk_acc = self.run_epoch(X=X_test, y=y_test,
valid_frames=valid_frames_test)
logger.info("Test cost:\t\t{:.6f} ".format(test_cost))
logger.info("Test accuracy:\t\t{:.6f} %".format(test_acc))
logger.info("Test Top 3 accuracy:\t{:.6f} %".format(test_topk_acc))
self.network_train_info['nb_params'] = lasagne.layers.count_params(self.network_lout_batch)
if justTest:
if os.path.exists(save_name+".npz"):
self.saveFinalResults(logger, noiseType, ratio_dB, roundParams, save_name, test_acc, test_cost,
test_topk_acc, withNoise)
return 0
# else do nothing and train anyway
else:
self.network_train_info['test_cost'].append(test_cost)
self.network_train_info['test_acc'].append(test_acc)
self.network_train_info['test_topk_acc'].append(test_topk_acc)
logger.info("\n* Starting training...")
LR = LR_start
self.best_cost = 100
for epoch in range(num_epochs):
self.curr_epoch += 1
epoch_time = time.time()
logger.info("\n\nCURRENT EPOCH: %s", self.curr_epoch)
logger.info("Pass over Training Set")
train_cost, train_acc, train_topk_acc = self.run_epoch(X=X_train, y=y_train, valid_frames=valid_frames_train, LR=LR)
logger.info("Pass over Validation Set")
val_cost, val_acc, val_topk_acc = self.run_epoch(X=X_val, y = y_val, valid_frames=valid_frames_val)
# Print epoch summary
logger.info("Epoch {} of {} took {:.3f}s.".format(
epoch + 1, num_epochs, time.time() - epoch_time))
logger.info("Learning Rate:\t\t{:.6f} %".format(LR))
logger.info("Training cost:\t{:.6f}".format(train_cost))
logger.info("Validation Top 3 accuracy:\t{:.6f} %".format(val_topk_acc))
logger.info("Validation cost:\t{:.6f} ".format(val_cost))
logger.info("Validation accuracy:\t\t{:.6f} %".format(val_acc))
logger.info("Validation Top 3 accuracy:\t{:.6f} %".format(val_topk_acc))
# better model, so save parameters
if val_acc > self.best_val_acc:
# only reset if significant improvement
if val_acc - self.best_val_acc > 0.2:
self.epochsNotImproved = 0
# store new parameters
self.best_cost = val_cost
self.best_val_acc = val_acc
self.best_epoch = self.curr_epoch
self.best_param = L.get_all_param_values(self.network_lout)
self.best_updates = [p.get_value() for p in self.updates.keys()]
logger.info("New best model found!")
if save_name is not None:
logger.info("Model saved as " + save_name)
self.save_model(save_name)
logger.info("Pass over Test Set")
test_cost, test_acc, test_topk_acc = self.run_epoch(X=X_test, y=y_test,
valid_frames=valid_frames_test)
logger.info("Test cost:\t\t{:.6f} ".format(test_cost))
logger.info("Test accuracy:\t\t{:.6f} %".format(test_acc))
logger.info("Test Top 3 accuracy:\t{:.6f} %".format(test_topk_acc))
# save the training info
self.network_train_info['train_cost'].append(train_cost)
self.network_train_info['val_cost'].append(val_cost)
self.network_train_info['val_acc'].append(val_acc)
self.network_train_info['val_topk_acc'].append(val_topk_acc)
self.network_train_info['test_cost'].append(test_cost)
self.network_train_info['test_acc'].append(test_acc)
self.network_train_info['test_topk_acc'].append(test_topk_acc)
saveToPkl(save_name + '_trainInfo.pkl', self.network_train_info)
logger.info("Train info written to:\t %s", save_name + '_trainInfo.pkl')
if compute_confusion:
confusion_matrices.append(self.create_confusion(X_val, y_val)[0])
logger.info(' Confusion matrix computed')
with open(save_name + '_conf.pkl', 'wb') as cPickle_file:
cPickle.dump(
[confusion_matrices],
cPickle_file,
protocol=cPickle.HIGHEST_PROTOCOL)
# update LR, see if we can stop training
LR = self.updateLR(LR, LR_decay, logger=logger_RNNtools)
if self.epochsNotImproved >= 3:
logging.warning("\n\nNo more improvements, stopping training...")
logger.info("Pass over Test Set")
test_cost, test_acc, test_topk_acc = self.run_epoch(X=X_test, y=y_test,
valid_frames=valid_frames_test)
logger.info("Test cost:\t\t{:.6f} ".format(test_cost))
logger.info("Test accuracy:\t\t{:.6f} %".format(test_acc))
logger.info("Test Top 3 accuracy:\t{:.6f} %".format(test_topk_acc))
self.network_train_info['test_cost'][-1]=test_cost
self.network_train_info['test_acc'][-1] = test_acc
self.network_train_info['test_topk_acc'][-1] = test_topk_acc
self.saveFinalResults(logger, noiseType, ratio_dB, roundParams, save_name, test_acc, test_cost,
test_topk_acc, withNoise)
break
"datatype: {}".format(Recurrent_output_value.shape, type(Recurrent_output_value)))
print("network output"
"Shape: {}"
"datatype: {}".format(network_output_value.shape, type(network_output_value)))
print("Pointwise Cost: {}"
"Cost: {}".format(cost_values_pointwise.shape, cost_value))
cost_vector = []
for epoch in range(NUM_EPOCHS):
#pdb.set_trace()
shuffle_order = np.random.permutation(x.shape[0])
x = x[shuffle_order, :]
y = y[shuffle_order, :]
y_merged = y.reshape([-1])
mask = mask[shuffle_order, :]
mask_merged = mask.reshape([-1])
cost = train(x,y_merged,mask,mask_merged)
print("Epoch: {}"
"\tcost = {}".format(epoch,cost) )
cost_vector.append(cost)
if epoch % 10 == 0:
np.savez('CLM_model.npz', *get_all_param_values(l_dense, trainable=True))
# plt.plot(np.arange(NUM_EPOCHS),cost_vector)
# plt.show()
def save_all_params(self, agent, key=None):
"""saves agent params into the database under given name. overwrites by default"""
key = key or self.default_params_key
all_params = get_all_param_values(
list(agent.agent_states) + agent.policy + agent.action_layers)
self.redis.set(key, self.dumps(all_params))
def save_model(filename, model): save(filename, LL.get_all_param_values(model))
grad, params, learning_rate=0.05
) #u can change learning rate, 0.05 was being used in tutorial
f_train = t.function([x_sym, y_sym], [loss, acc], updates=updates)
f_predict = t.function([x_sym], pred)
batch_size = 100 #u can change batch size too according to ur number of images
max_epoch = 5 #u can change this number too,it is number of cycles
n_batches = len(
x_train
) // batch_size #try to make this value integer otherwise the remaining images won't be trained
train_batches = batch_gen(x_train, y_train, batch_size)
for epoch in range(max_epoch):
train_loss = 0
train_acc = 0
for _ in range(n_batches):
x, y = next(train_batches)
loss, acc = f_train(x, y)
train_loss += loss
train_acc += acc
train_loss /= n_batches
train_acc /= n_batches
print(epoch, train_loss, train_acc)
np.savez(
'c:/users/Microsoft/desktop/trained_parameters' + str(epoch) + '.npz',
*L.get_all_param_values(l_output))
#np.savez('c:/users/Microsoft/desktop/trained_parameters.npz', *L.get_all_param_values(l_output)) #this will save trained parameters in .npz file to use for testing
#f = open('c:/users/microsoft/desktop/trained_parameters1.pkl', 'wb')
#pickle.dump(L.get_all_param_values(l_output), f, protocol=pickle.HIGHEST_PROTOCOL)#this will save trained parameters in .pkl file.u can use either one of the files
#f.close()
def train():
"""
Training model.
"""
# Compile training and testing functions
[model, train_fn, val_fn, predict_fn] = get_model()
# Load training data
print('Loading training data...')
X, y = load_train_data()
#print('Pre-processing images...')
#X = preprocess(X)
# split to training and test
X_train, y_train, X_test, y_test = split_data(X, y, split_ratio=0.2)
nb_epoch = 200
batch_size = 32
calc_crps = 0 # calculate CRPS every n-th iteration (set to 0 if CRPS estimation is not needed) NOT IMPLEMENTED YET
print('-' * 50)
print('Training...')
print('-' * 50)
min_val_err = sys.float_info.max
patience = 0
for i in range(nb_epoch):
print('-' * 50)
print('Iteration {0}/{1}'.format(i + 1, nb_epoch))
print('-' * 50)
print('Augmenting images - rotations')
X_train_aug = rotation_augmentation(X_train, 15)
print('Augmenting images - shifts')
X_train_aug = shift_augmentation(X_train_aug, 0.1, 0.1)
# In each epoch, we do a full pass over the training data:
print('Fitting model...')
train_err = 0
train_batches = 0
for batch in iterate_minibatches(X_train_aug,
y_train,
batch_size,
shuffle=True):
inputs, targets = batch
train_err += train_fn(inputs, targets)
train_batches += 1
# And a full pass over the validation data:
val_err = 0
val_batches = 0
for batch in iterate_minibatches(X_test,
y_test,
batch_size,
shuffle=False):
inputs, targets = batch
val_err += val_fn(inputs, targets)
val_batches += 1
print('Saving weights...')
# save weights so they can be loaded later
# np.savez('weights.npz', *get_all_param_values(model))
# for best (lowest) val losses, save weights
if val_err < min_val_err:
patience = 0
min_val_err = val_err
np.savez('weights_best.npz', *get_all_param_values(model))
else:
patience += 1
print('error on validation set: ' + str(val_err))
print('patience variable is: ' + str(patience))
print('\n')
# save best (lowest) val losses in file (to be later used for generating submission)
with open('val_loss.txt', mode='a') as f:
f.write(str(val_err))
f.write('\n')
if (patience == 8):
break