def get_observe_forward_func(self, observe_nodes):
observe_nodes = to_list(observe_nodes)
inputs = self.in_nodes_ + [self.tr_phase_node_]
timer = Timer()
f_observe_forward = K.function_no_given(inputs, observe_nodes)
timer.show("Compiling f_observe_forward time:")
return f_observe_forward
def get_observe_forward_func(self, observe_nodes):
observe_nodes = to_list(observe_nodes)
inputs = self.in_nodes_ + [self.tr_phase_node_]
timer = Timer()
f_observe_forward = K.function_no_given(inputs, observe_nodes)
timer.show("Compiling f_observe_forward time:")
return f_observe_forward
def compile(self, md):
inputs = md.in_nodes_ + [md.tr_phase_node_]
self._f_pred = K.function_no_given(inputs, md.out_nodes_)
self._md_ = md
# memory usage
print "Callback",
self._md_._show_memory_usage(self._md_.effective_layers_, self._batch_size_)
def compile(self, md):
inputs = md.in_nodes_ + [md.tr_phase_node_]
self._f_pred = K.function_no_given(inputs, md.out_nodes_)
self._md_ = md
# memory usage
print "Callback",
self._md_._show_memory_usage(self._md_.effective_layers_,
self._batch_size_)
def _evaluate(self, x, y, eval_type):
# get metric losses node
loss_nodes = []
for metric in self._metrics_:
# if use default objective
if type(metric) is str:
assert len(self._md_.out_nodes_)==len(self._md_.gt_nodes_), "If you are using default objectives, " \
+ "out_node of out_layers must match ground truth!"
loss_node = sum([obj.get(metric)(pred_node, gt_node)
for pred_node, gt_node in zip(self._md_.out_nodes_, self._md_.gt_nodes_)])
# if user define their objective function
elif isfunction(metric):
loss_node = metric(self._md_)
else:
loss_node = metric
loss_nodes.append(loss_node)
# compile evaluation function
if not hasattr(self, '_f_evaluate'):
print 'compiling evaluation function ..'
inputs = self._md_.in_nodes_ + self._md_.gt_nodes_ + [self._md_.tr_phase_node_]
self._f_evaluate = K.function_no_given(inputs, loss_nodes)
print 'compile finished. '
# calculate metric values
t1 = time.time()
if self._generator_:
generator = self._generator_
else:
generator = self._DefaultGenerator(self._batch_size_)
n_all = 0.
cnt= 0.
metric_vals = np.zeros(len(self._metrics_))
batch_num = sum(1 for it in generator.generate(x, y))
for batch_x, batch_y in generator.generate(x, y):
batch_x = to_list(batch_x)
batch_y = to_list(batch_y)
curr_batch_size = batch_x[0].shape[0]
n_all += curr_batch_size
cnt += 1.
if self._transformer_:
(batch_x, batch_y) = self._transformer_.transform(batch_x, batch_y)
batch_x = format_data_list(batch_x)
batch_y = format_data_list(batch_y)
in_list = batch_x + batch_y + [0.]
batch_metric_vals = np.array(self._f_evaluate(*in_list))
metric_vals += batch_metric_vals * curr_batch_size
if self._verbose_==1: self._print_progress(batch_num, cnt)
metric_vals /= n_all
# timer
t2 = time.time()
# print results
self._print_time_results(eval_type, self._metrics_, metric_vals, t2-t1)
def get_observe_backward_func(self, observe_nodes):
if self.gt_nodes_ is None:
raise Exception("You must call set_gt_nodes method before call observe_backward method!")
observe_nodes = to_list(observe_nodes)
inputs = self.in_nodes_ + self.gt_nodes_ + [self.tr_phase_node_]
timer = Timer()
f_observe_backward = K.function_no_given(inputs, observe_nodes)
timer.show("Compiling f_observe_backward time:")
return f_observe_backward
def get_observe_backward_func(self, observe_nodes):
if self.gt_nodes_ is None:
raise Exception(
"You must call set_gt_nodes method before call observe_backward method!"
)
observe_nodes = to_list(observe_nodes)
inputs = self.in_nodes_ + self.gt_nodes_ + [self.tr_phase_node_]
timer = Timer()
f_observe_backward = K.function_no_given(inputs, observe_nodes)
timer.show("Compiling f_observe_backward time:")
return f_observe_backward
def get_optimization_func(self, target_dim_list, loss_func, optimizer,
clip):
"""Compile and return optimization function.
Args:
target_dim_list: list of integars. targets' dimension. e.g. target_dim_list=[2]
loss_func: string | function.
optimizer: object.
clip: None | real value.
Return:
optimization function.
"""
# set gt nodes
self.set_gt_nodes(target_dim_list)
# Default loss
if type(loss_func) is str:
assert len(
self.out_nodes_
) == 1, "If the number of out_layers > 1, you need define your own loss_func!"
loss_node = obj.get(loss_func)(self.out_nodes_[0],
self.gt_nodes_[0])
# User defined loss
else:
loss_node = loss_func(self)
# Compute gradient
gparams = K.grad(loss_node + self.reg_value_, self.params_)
# Clip gradient
if clip is not None:
gparams = [K.clip(gparam, -clip, clip) for gparam in gparams]
# Gradient based optimization
param_updates = optimizer.get_updates(self.params_, gparams)
# Get all updates
updates = param_updates + self.inner_updates_
# Compile model
inputs = self.in_nodes_ + self.gt_nodes_ + [K.common_tr_phase_node]
outputs = [loss_node]
f = K.function_no_given(inputs, outputs, updates)
return f
def predict(self, x, batch_size=128, tr_phase=0.):
"""Predict output using current model.
Args:
x: ndarray | list of ndarray.
batch_size: integar | None. Predict batch size.
tr_phase: 0. | 1. Test phase or train phase.
Returns:
ndarray | list of ndarray.
"""
# Compile predict model just once
if self._f_predict is None:
inputs = self.in_nodes_ + [self.tr_phase_node_]
timer = Timer()
self._f_predict = K.function_no_given(inputs, self.out_nodes_)
timer.show("Compiling f_predict time:")
return self.run_function(self._f_predict, x, batch_size, tr_phase)
def predict(self, x, batch_size=128, tr_phase=0.):
"""Predict output using current model.
Args:
x: ndarray | list of ndarray.
batch_size: integar | None. Predict batch size.
tr_phase: 0. | 1. Test phase or train phase.
Returns:
ndarray | list of ndarray.
"""
# Compile predict model just once
if self._f_predict is None:
inputs = self.in_nodes_ + [self.tr_phase_node_]
timer = Timer()
self._f_predict = K.function_no_given(inputs, self.out_nodes_)
timer.show("Compiling f_predict time:")
return self.run_function(self._f_predict, x, batch_size, tr_phase)
def get_optimization_func(self, target_dim_list, loss_func, optimizer, clip):
"""Compile and return optimization function.
Args:
target_dim_list: list of integars. targets' dimension. e.g. target_dim_list=[2]
loss_func: string | function.
optimizer: object.
clip: None | real value.
Return:
optimization function.
"""
# set gt nodes
self.set_gt_nodes(target_dim_list)
# Default loss
if type(loss_func) is str:
assert len(self.out_nodes_)==1, "If the number of out_layers > 1, you need define your own loss_func!"
loss_node = obj.get(loss_func)(self.out_nodes_[0], self.gt_nodes_[0])
# User defined loss
else:
loss_node = loss_func(self)
# Compute gradient
gparams = K.grad(loss_node + self.reg_value_, self.params_)
# Clip gradient
if clip is not None:
gparams = [K.clip(gparam, -clip, clip) for gparam in gparams]
# Gradient based optimization
param_updates = optimizer.get_updates(self.params_, gparams)
# Get all updates
updates = param_updates + self.inner_updates_
# Compile model
inputs = self.in_nodes_ + self.gt_nodes_ + [K.common_tr_phase_node]
outputs = [loss_node]
f = K.function_no_given(inputs, outputs, updates)
return f
def predict(self, x, batch_size=100):
# format data
x = to_list(x)
x = [K.format_data(e) for e in x]
# compile predict model
if not hasattr(self, '_f_predict'):
self._f_predict = K.function_no_given(self._in_nodes_,
self._tr_phase_node_,
self._out_nodes_)
# do predict
# put all data in GPU
if batch_size is None:
in_list = x + [0.]
y_out = self._f_predict(*in_list)
# put batch data in GPU
else:
N = len(x[0])
batch_num = int(np.ceil(float(N) / batch_size))
n_out_nodes = len(self._out_nodes_)
y_out = [[] for e in self._out_nodes_]
for i1 in xrange(batch_num):
in_list = [
e[i1 * batch_size:min((i1 + 1) * batch_size, N)] for e in x
] + [0.]
batch_y_out = self._f_predict(*in_list)
for j1 in xrange(n_out_nodes):
y_out[j1].append(batch_y_out[j1])
# get y_out
y_out = [np.concatenate(e, axis=0) for e in y_out]
if len(y_out) == 1:
return y_out[0]
else:
return y_out
def fit(self,
x,
y,
batch_size=100,
n_epochs=10,
loss_func='categorical_crossentropy',
optimizer=SGD(lr=0.01, rho=0.9),
clip=None,
callbacks=[],
shuffle=True,
verbose=1):
x = to_list(x)
y = to_list(y)
# format
x = [K.format_data(e) for e in x]
y = [K.format_data(e) for e in y]
# shuffle data
if shuffle:
x, y = supports.shuffle(x, y)
# check data
self._check_data(y, loss_func)
# init gt_nodes
self._gt_nodes_ = [K.placeholder(e.ndim) for e in y]
# memory usage
print "Train",
self._show_memory_usage(self._layer_list_, batch_size)
# default objective
if type(loss_func) is str:
assert len(self._out_nodes_)==len(self._gt_nodes_), "If you are using default objectives, " \
+ "out_node of out_layers must match ground truth!"
loss_node = sum([
obj.get(loss_func)(pred_node,
gt_node) for pred_node, gt_node in zip(
self._out_nodes_, self._gt_nodes_)
])
# user defined objective
else:
loss_node = loss_func(self._out_nodes_, self._any_nodes_,
self._gt_nodes_)
#loss_node = loss_func( self )
# gradient
gparams = K.grad(loss_node + self._reg_value_, self._params_)
# todo clip gradient
if clip is not None:
gparams = [K.clip(gparam, -clip, clip) for gparam in gparams]
# gradient based opt
param_updates = optimizer.get_updates(self._params_, gparams)
# get all updates
updates = param_updates + self._inner_updates_
# compile for callback
if callbacks is not None:
callbacks = to_list(callbacks)
for callback in callbacks:
callback.compile(self)
# compile model
input_nodes = self._in_nodes_ + self._gt_nodes_
output_nodes = [loss_node]
f = K.function_no_given(input_nodes, self._tr_phase_node_,
output_nodes, updates)
# train
N = len(x[0])
batch_num = int(np.ceil(float(N) / batch_size))
n_abs_epoch = n_epochs + self._epoch_
# callback
print '\n0th epoch:'
for callback in callbacks:
if (self._epoch_ % callback.call_freq == 0):
callback.call()
while self._epoch_ < n_abs_epoch:
self._epoch_ += 1
# train
t1 = time.time()
loss_list = []
for i2 in xrange(batch_num):
batch_x = [
e[i2 * batch_size:min((i2 + 1) * batch_size, N)] for e in x
]
batch_y = [
e[i2 * batch_size:min((i2 + 1) * batch_size, N)] for e in y
]
in_list = batch_x + batch_y + [1.]
loss = f(*in_list)[0] # training phase
loss_list.append(loss)
if verbose == 1:
self._print_progress(self._epoch_, batch_num, i2)
if verbose == 2:
self._print_progress_loss(self._epoch_, batch_num, i2,
loss)
t2 = time.time()
self._tr_time_ += (t2 - t1)
if verbose != 0:
print '\n', ' tr_time: ', "%.2f" % (
t2 - t1), 's' # print an empty line
# callback
for callback in callbacks:
if (self._epoch_ % callback.call_freq == 0):
callback.call()
def _evaluate(self, x, y, eval_type):
# get metric losses node
loss_nodes = []
for metric in self._metrics_:
# if use default objective
if type(metric) is str:
assert len(self._md_.out_nodes_)==len(self._md_.gt_nodes_), "If you are using default objectives, " \
+ "out_node of out_layers must match ground truth!"
loss_node = sum([
obj.get(metric)(pred_node, gt_node) for pred_node, gt_node
in zip(self._md_.out_nodes_, self._md_.gt_nodes_)
])
# if user define their objective function
elif isfunction(metric):
loss_node = metric(self._md_)
else:
loss_node = metric
loss_nodes.append(loss_node)
# compile evaluation function
if not hasattr(self, '_f_evaluate'):
print 'compiling evaluation function ..'
inputs = self._md_.in_nodes_ + self._md_.gt_nodes_ + [
self._md_.tr_phase_node_
]
self._f_evaluate = K.function_no_given(inputs, loss_nodes)
print 'compile finished. '
# calculate metric values
t1 = time.time()
if self._generator_:
generator = self._generator_
else:
generator = self._DefaultGenerator(self._batch_size_)
n_all = 0.
cnt = 0.
metric_vals = np.zeros(len(self._metrics_))
batch_num = sum(1 for it in generator.generate(x, y))
for batch_x, batch_y in generator.generate(x, y):
batch_x = to_list(batch_x)
batch_y = to_list(batch_y)
curr_batch_size = batch_x[0].shape[0]
n_all += curr_batch_size
cnt += 1.
if self._transformer_:
(batch_x,
batch_y) = self._transformer_.transform(batch_x, batch_y)
batch_x = format_data_list(batch_x)
batch_y = format_data_list(batch_y)
in_list = batch_x + batch_y + [0.]
batch_metric_vals = np.array(self._f_evaluate(*in_list))
metric_vals += batch_metric_vals * curr_batch_size
if self._verbose_ == 1: self._print_progress(batch_num, cnt)
metric_vals /= n_all
# timer
t2 = time.time()
# print results
self._print_time_results(eval_type, self._metrics_, metric_vals,
t2 - t1)
def compile(self, md):
self._md = md
self._f = K.function_no_given(md.in_nodes, md.tr_phase_node,
md._layer_seq[3].output)
def _evaluate(self, x, y, eval_type):
# init gt_nodes
#gt_nodes = [ K.placeholder( e.ndim ) for e in y ]
# get metric losses node
loss_nodes = []
for metric in self._metrics_:
# if use default objective
if type(metric) is str:
assert len(self._md_.out_nodes_)==len(self._md_.gt_nodes_), "If you are using default objectives, " \
+ "out_node of out_layers must match ground truth!"
loss_node = sum([
obj.get(metric)(pred_node, gt_node) for pred_node, gt_node
in zip(self._md_.out_nodes_, self._md_.gt_nodes_)
])
# if user define their objective function
elif isfunction(metric):
loss_node = metric(self._md_.out_nodes_, self._md_.any_nodes_,
self._md_.gt_nodes_)
#loss_node = metric( self._md_ )
# if node
else:
loss_node = metric
loss_nodes.append(loss_node)
# compile evaluation function
if not hasattr(self, '_f_evaluate'):
print 'compiling evaluation function ..'
#input_nodes = self._md_.in_nodes_ + gt_nodes
input_nodes = self._md_.in_nodes_ + self._md_.gt_nodes_
self._f_evaluate = K.function_no_given(input_nodes,
self._md_.tr_phase_node_,
loss_nodes)
print 'compile finished. '
# calculate metric values
t1 = time.time()
if self._batch_size_ is None:
in_list = x + y + [0.]
metric_vals = np.array(self._f_evaluate(*in_list))
else:
N = len(x[0])
batch_num = int(np.ceil(float(N) / self._batch_size_))
# metric_container = [ [] for e in y ]
metric_vals = np.zeros(len(self._metrics_))
# evaluate for each batch
for i1 in xrange(batch_num):
curr_batch_size = min(
(i1 + 1) * self._batch_size_, N) - i1 * self._batch_size_
in_list = [ e[i1*self._batch_size_ : min( (i1+1)*self._batch_size_, N ) ] for e in x ] \
+ [ e[i1*self._batch_size_ : min( (i1+1)*self._batch_size_, N ) ] for e in y ] + [0.]
batch_metric_vals = np.array(self._f_evaluate(*in_list))
metric_vals += batch_metric_vals * curr_batch_size
if self._verbose_ == 1: self._print_progress(batch_num, i1)
metric_vals /= N
# timer
t2 = time.time()
# print results
self._print_time_results(eval_type, self._metrics_, metric_vals,
t2 - t1)
