def test_loop(model,
inputs,
targets,
sample_weights=None,
batch_size=None,
verbose=0,
steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model: Keras Model instance.
inputs: List of input arrays.
targets: List of target arrays.
sample_weights: Optional list of sample weight arrays.
batch_size: integer batch size or `None`.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
Returns:
Scalar loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.
"""
model._make_test_function()
f = model.test_function
sample_weights = sample_weights or []
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = inputs + targets + sample_weights + [0]
else:
ins = inputs + targets + sample_weights
if hasattr(model, 'metrics'):
for m in model.metrics:
if isinstance(m, Layer):
m.reset_states()
stateful_metric_indices = [
i for i, name in enumerate(model.metrics_names)
if str(name) in model.stateful_metric_names
]
else:
stateful_metric_indices = []
num_samples = training_utils.check_num_samples(ins, batch_size, steps,
'steps')
outs = []
if verbose == 1:
if steps is not None:
progbar = Progbar(target=steps)
else:
progbar = Progbar(target=num_samples)
# To prevent a slowdown, we find beforehand the arrays that need conversion.
feed = model._feed_inputs + model._feed_targets + model._feed_sample_weights
indices_for_conversion_to_dense = []
for i in range(len(feed)):
if issparse is not None and issparse(
ins[i]) and not K.is_sparse(feed[i]):
indices_for_conversion_to_dense.append(i)
if steps is not None:
for step in range(steps):
batch_outs = f(ins)
if isinstance(batch_outs, list):
if step == 0:
for _ in enumerate(batch_outs):
outs.append(0.)
for i, batch_out in enumerate(batch_outs):
if i in stateful_metric_indices:
outs[i] = batch_out
else:
outs[i] += batch_out
else:
if step == 0:
outs.append(0.)
outs[0] += batch_outs
if verbose == 1:
progbar.update(step + 1)
for i in range(len(outs)):
if i not in stateful_metric_indices:
outs[i] /= steps
else:
batches = make_batches(num_samples, batch_size)
index_array = np.arange(num_samples)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
if isinstance(ins[-1], int):
# Do not slice the training phase flag.
ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
else:
ins_batch = slice_arrays(ins, batch_ids)
for i in indices_for_conversion_to_dense:
ins_batch[i] = ins_batch[i].toarray()
batch_outs = f(ins_batch)
if isinstance(batch_outs, list):
if batch_index == 0:
for batch_out in enumerate(batch_outs):
outs.append(0.)
for i, batch_out in enumerate(batch_outs):
if i in stateful_metric_indices:
outs[i] = batch_out
else:
outs[i] += batch_out * len(batch_ids)
else:
if batch_index == 0:
outs.append(0.)
outs[0] += batch_outs * len(batch_ids)
if verbose == 1:
progbar.update(batch_end)
for i in range(len(outs)):
if i not in stateful_metric_indices:
outs[i] /= num_samples
if len(outs) == 1:
return outs[0]
return outs
class ProgbarLogger(Callback):
"""Callback that prints metrics to stdout.
Arguments:
count_mode: One of "steps" or "samples".
Whether the progress bar should
count samples seen or steps (batches) seen.
stateful_metrics: Iterable of string names of metrics that
should *not* be averaged over an epoch.
Metrics in this list will be logged as-is.
All others will be averaged over time (e.g. loss, etc).
Raises:
ValueError: In case of invalid `count_mode`.
"""
def __init__(self, count_mode='samples', stateful_metrics=None):
super(ProgbarLogger, self).__init__()
if count_mode == 'samples':
self.use_steps = False
elif count_mode == 'steps':
self.use_steps = True
else:
raise ValueError('Unknown `count_mode`: ' + str(count_mode))
self.stateful_metrics = set(stateful_metrics or [])
def on_train_begin(self, logs=None):
self.verbose = self.params['verbose']
self.epochs = self.params['epochs']
def on_epoch_begin(self, epoch, logs=None):
if self.verbose:
print('Epoch %d/%d' % (epoch + 1, self.epochs))
if self.use_steps:
target = self.params['steps']
else:
target = self.params['samples']
self.target = target
self.progbar = Progbar(target=self.target,
verbose=self.verbose,
stateful_metrics=self.stateful_metrics)
self.seen = 0
def on_batch_begin(self, batch, logs=None):
if self.seen < self.target:
self.log_values = []
def on_batch_end(self, batch, logs=None):
logs = logs or {}
batch_size = logs.get('size', 0)
if self.use_steps:
self.seen += 1
else:
self.seen += batch_size
for k in self.params['metrics']:
if k in logs:
self.log_values.append((k, logs[k]))
# Skip progbar update for the last batch;
# will be handled by on_epoch_end.
if self.verbose and self.seen < self.target:
self.progbar.update(self.seen, self.log_values)
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
for k in self.params['metrics']:
if k in logs:
self.log_values.append((k, logs[k]))
if self.verbose:
self.progbar.update(self.seen, self.log_values)
def predict_loop(model, inputs, batch_size=32, verbose=0, steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model: Keras Model instance.
inputs: list of tensors to be fed to `f`.
batch_size: integer batch size.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
model._make_predict_function()
f = model.predict_function
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = inputs + [0]
else:
ins = inputs
num_samples = training_utils.check_num_samples(inputs, batch_size, steps,
'steps')
if verbose == 1:
if steps is not None:
progbar = Progbar(target=steps)
else:
progbar = Progbar(target=num_samples)
indices_for_conversion_to_dense = []
for i in range(len(model._feed_inputs)):
if (issparse is not None and issparse(inputs[i])
and not K.is_sparse(model._feed_inputs[i])):
indices_for_conversion_to_dense.append(i)
if steps is not None:
# Step-based predictions.
# Since we do not know how many samples
# we will see, we cannot pre-allocate
# the returned Numpy arrays.
# Instead, we store one array per batch seen
# and concatenate them upon returning.
unconcatenated_outs = []
for step in range(steps):
batch_outs = f(ins)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if step == 0:
for batch_out in batch_outs:
unconcatenated_outs.append([])
for i, batch_out in enumerate(batch_outs):
unconcatenated_outs[i].append(batch_out)
if verbose == 1:
progbar.update(step + 1)
if len(unconcatenated_outs) == 1:
return np.concatenate(unconcatenated_outs[0], axis=0)
return [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
else:
# Sample-based predictions.
outs = []
batches = make_batches(num_samples, batch_size)
index_array = np.arange(num_samples)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
if ins and isinstance(ins[-1], int):
# Do not slice the training phase flag.
ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
else:
ins_batch = slice_arrays(ins, batch_ids)
for i in indices_for_conversion_to_dense:
ins_batch[i] = ins_batch[i].toarray()
batch_outs = f(ins_batch)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if batch_index == 0:
# Pre-allocate the results arrays.
for batch_out in batch_outs:
shape = (num_samples, ) + batch_out.shape[1:]
outs.append(np.zeros(shape, dtype=batch_out.dtype))
for i, batch_out in enumerate(batch_outs):
outs[i][batch_start:batch_end] = batch_out
if verbose == 1:
progbar.update(batch_end)
if len(outs) == 1:
return outs[0]
return outs
def predict_loop(model, ins, batch_size=32, verbose=0, steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model:
ins: list of tensors to be fed to `f`.
batch_size: integer batch size.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
K.set_learning_phase(False)
num_samples = model._check_num_samples(ins, batch_size, steps, 'steps')
if verbose == 1:
if steps is not None:
progbar = Progbar(target=steps)
else:
progbar = Progbar(target=num_samples)
outs = []
batches = _make_batches(num_samples, batch_size)
index_array = np.arange(num_samples)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
if ins and isinstance(ins[-1], float):
# Do not slice the training phase flag.
ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
else:
ins_batch = _slice_arrays(ins, batch_ids)
ins_batch_converted = []
for ib in ins_batch:
ins_batch_converted.append(
ops.convert_to_tensor(ib, dtype=K.floatx()))
eager_model_inputs = []
for i in range(len(model.inputs)):
eager_model_inputs.append(ins_batch_converted[i])
batch_outs = model(eager_model_inputs)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if batch_index == 0:
# Pre-allocate the results arrays.
for batch_out in batch_outs:
dims = batch_out.shape[1:].dims
dims_list = [d.value for d in dims]
shape = (num_samples, ) + tuple(dims_list)
outs.append(
np.zeros(shape, dtype=batch_out.dtype.as_numpy_dtype))
for i, batch_out in enumerate(batch_outs):
outs[i][batch_start:batch_end] = batch_out
if verbose == 1:
progbar.update(batch_end)
if len(outs) == 1:
return outs[0]
return outs
class ProgbarLogger(Callback):
"""Callback that prints metrics to stdout.
Arguments:
count_mode: One of "steps" or "samples".
Whether the progress bar should
count samples seen or steps (batches) seen.
stateful_metrics: Iterable of string names of metrics that
should *not* be averaged over an epoch.
Metrics in this list will be logged as-is.
All others will be averaged over time (e.g. loss, etc).
Raises:
ValueError: In case of invalid `count_mode`.
"""
def __init__(self, count_mode='samples', stateful_metrics=None):
super(ProgbarLogger, self).__init__()
if count_mode == 'samples':
self.use_steps = False
elif count_mode == 'steps':
self.use_steps = True
else:
raise ValueError('Unknown `count_mode`: ' + str(count_mode))
self.stateful_metrics = set(stateful_metrics or [])
def on_train_begin(self, logs=None):
self.verbose = self.params['verbose']
self.epochs = self.params['epochs']
def on_epoch_begin(self, epoch, logs=None):
if self.verbose:
print('Epoch %d/%d' % (epoch + 1, self.epochs))
if self.use_steps:
target = self.params['steps']
else:
target = self.params['samples']
self.target = target
self.progbar = Progbar(
target=self.target,
verbose=self.verbose,
stateful_metrics=self.stateful_metrics)
self.seen = 0
def on_batch_begin(self, batch, logs=None):
if self.seen < self.target:
self.log_values = []
def on_batch_end(self, batch, logs=None):
logs = logs or {}
batch_size = logs.get('size', 0)
if self.use_steps:
self.seen += 1
else:
self.seen += batch_size
for k in self.params['metrics']:
if k in logs:
self.log_values.append((k, logs[k]))
# Skip progbar update for the last batch;
# will be handled by on_epoch_end.
if self.verbose and self.seen < self.target:
self.progbar.update(self.seen, self.log_values)
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
for k in self.params['metrics']:
if k in logs:
self.log_values.append((k, logs[k]))
if self.verbose:
self.progbar.update(self.seen, self.log_values)
def predict_generator(model,
generator,
steps=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
verbose=0):
"""See docstring for `Model.predict_generator`."""
model._make_predict_function()
steps_done = 0
wait_time = 0.01
all_outs = []
is_sequence = isinstance(generator, Sequence)
if not is_sequence and use_multiprocessing and workers > 1:
logging.warning(
UserWarning('Using a generator with `use_multiprocessing=True`'
' and multiple workers may duplicate your data.'
' Please consider using the`keras.utils.Sequence'
' class.'))
if steps is None:
if is_sequence:
steps = len(generator)
else:
raise ValueError('`steps=None` is only valid for a generator'
' based on the `keras.utils.Sequence` class.'
' Please specify `steps` or use the'
' `keras.utils.Sequence` class.')
enqueuer = None
try:
if workers > 0:
if is_sequence:
enqueuer = OrderedEnqueuer(
generator, use_multiprocessing=use_multiprocessing)
else:
enqueuer = GeneratorEnqueuer(
generator,
use_multiprocessing=use_multiprocessing,
wait_time=wait_time)
enqueuer.start(workers=workers, max_queue_size=max_queue_size)
output_generator = enqueuer.get()
else:
output_generator = generator
if verbose == 1:
progbar = Progbar(target=steps)
while steps_done < steps:
generator_output = next(output_generator)
if isinstance(generator_output, tuple):
# Compatibility with the generators
# used for training.
if len(generator_output) == 2:
x, _ = generator_output
elif len(generator_output) == 3:
x, _, _ = generator_output
else:
raise ValueError('Output of generator should be '
'a tuple `(x, y, sample_weight)` '
'or `(x, y)`. Found: ' + str(generator_output))
else:
# Assumes a generator that only
# yields inputs (not targets and sample weights).
x = generator_output
outs = model.predict_on_batch(x)
if not isinstance(outs, list):
outs = [outs]
if not all_outs:
for out in outs:
all_outs.append([])
for i, out in enumerate(outs):
all_outs[i].append(out)
steps_done += 1
if verbose == 1:
progbar.update(steps_done)
finally:
if enqueuer is not None:
enqueuer.stop()
if len(all_outs) == 1:
if steps_done == 1:
return all_outs[0][0]
else:
return np.concatenate(all_outs[0])
if steps_done == 1:
return [out[0] for out in all_outs]
else:
return [np.concatenate(out) for out in all_outs]
def test_loop(model, ins, batch_size=None, verbose=0, steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model: Model instance that is being evaluated in Eager mode.
ins: list of tensors to be fed to `f`.
batch_size: integer batch size or `None`.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
Returns:
Scalar loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.
"""
K.set_learning_phase(False)
num_samples = model._check_num_samples(ins, batch_size, steps, 'steps')
outs = []
if verbose == 1:
progbar = Progbar(target=num_samples)
batches = _make_batches(num_samples, batch_size)
index_array = np.arange(num_samples)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
if isinstance(ins[-1], float):
# Do not slice the training phase flag.
ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
else:
ins_batch = _slice_arrays(ins, batch_ids)
ins_batch_converted = []
for ib in ins_batch:
ins_batch_converted.append(
ops.convert_to_tensor(ib, dtype=K.floatx()))
eager_model_inputs = []
eager_model_outputs = []
for i in range(len(model.inputs)):
eager_model_inputs.append(ins_batch_converted[i])
for i in range(len(model.inputs), len(ins_batch_converted)):
eager_model_outputs.append(ins_batch_converted[i])
loss_outs, loss, loss_metrics = _model_loss(model, eager_model_inputs,
eager_model_outputs)
_, metrics_results = _eager_metrics_fn(model, loss_outs,
eager_model_outputs)
batch_outs = []
for _, v in zip(model.metrics_names,
[K.mean(loss)] + loss_metrics + metrics_results):
batch_outs.append(tensor_util.constant_value(v))
if isinstance(batch_outs, list):
if batch_index == 0:
for batch_out in enumerate(batch_outs):
outs.append(0.)
for i, batch_out in enumerate(batch_outs):
outs[i] += batch_out * len(batch_ids)
else:
if batch_index == 0:
outs.append(0.)
outs[0] += batch_outs * len(batch_ids)
if verbose == 1:
progbar.update(batch_end)
for i in range(len(outs)):
outs[i] /= num_samples
if len(outs) == 1:
return outs[0]
return outs
def predict_loop(model, inputs, batch_size=32, verbose=0, steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model: Keras Model instance.
inputs: list of tensors to be fed to `f`.
batch_size: integer batch size.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
model._make_predict_function()
f = model.predict_function
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = inputs + [0]
else:
ins = inputs
num_samples = training_utils.check_num_samples(
inputs, batch_size, steps, 'steps')
if verbose == 1:
if steps is not None:
progbar = Progbar(target=steps)
else:
progbar = Progbar(target=num_samples)
indices_for_conversion_to_dense = []
for i in range(len(model._feed_inputs)):
if (issparse is not None and issparse(inputs[i]) and
not K.is_sparse(model._feed_inputs[i])):
indices_for_conversion_to_dense.append(i)
if steps is not None:
# Step-based predictions.
# Since we do not know how many samples
# we will see, we cannot pre-allocate
# the returned Numpy arrays.
# Instead, we store one array per batch seen
# and concatenate them upon returning.
unconcatenated_outs = []
for step in range(steps):
batch_outs = f(ins)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if step == 0:
for batch_out in batch_outs:
unconcatenated_outs.append([])
for i, batch_out in enumerate(batch_outs):
unconcatenated_outs[i].append(batch_out)
if verbose == 1:
progbar.update(step + 1)
if len(unconcatenated_outs) == 1:
return np.concatenate(unconcatenated_outs[0], axis=0)
return [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
else:
# Sample-based predictions.
outs = []
batches = make_batches(num_samples, batch_size)
index_array = np.arange(num_samples)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
if ins and isinstance(ins[-1], int):
# Do not slice the training phase flag.
ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
else:
ins_batch = slice_arrays(ins, batch_ids)
for i in indices_for_conversion_to_dense:
ins_batch[i] = ins_batch[i].toarray()
batch_outs = f(ins_batch)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if batch_index == 0:
# Pre-allocate the results arrays.
for batch_out in batch_outs:
shape = (num_samples,) + batch_out.shape[1:]
outs.append(np.zeros(shape, dtype=batch_out.dtype))
for i, batch_out in enumerate(batch_outs):
outs[i][batch_start:batch_end] = batch_out
if verbose == 1:
progbar.update(batch_end)
if len(outs) == 1:
return outs[0]
return outs
def test_loop(model, inputs, targets,
sample_weights=None,
batch_size=None,
verbose=0,
steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model: Keras Model instance.
inputs: List of input arrays.
targets: List of target arrays.
sample_weights: Optional list of sample weight arrays.
batch_size: integer batch size or `None`.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
Returns:
Scalar loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.
"""
model._make_test_function()
f = model.test_function
sample_weights = sample_weights or []
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = inputs + targets + sample_weights + [0]
else:
ins = inputs + targets + sample_weights
if hasattr(model, 'metrics'):
for m in model.metrics:
if isinstance(m, Layer):
m.reset_states()
stateful_metric_indices = [
i for i, name in enumerate(model.metrics_names)
if str(name) in model.stateful_metric_names
]
else:
stateful_metric_indices = []
num_samples = training_utils.check_num_samples(
ins, batch_size, steps, 'steps')
outs = []
if verbose == 1:
if steps is not None:
progbar = Progbar(target=steps)
else:
progbar = Progbar(target=num_samples)
# To prevent a slowdown, we find beforehand the arrays that need conversion.
feed = model._feed_inputs + model._feed_targets + model._feed_sample_weights
indices_for_conversion_to_dense = []
for i in range(len(feed)):
if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]):
indices_for_conversion_to_dense.append(i)
if steps is not None:
for step in range(steps):
batch_outs = f(ins)
if isinstance(batch_outs, list):
if step == 0:
for _ in enumerate(batch_outs):
outs.append(0.)
for i, batch_out in enumerate(batch_outs):
if i in stateful_metric_indices:
outs[i] = batch_out
else:
outs[i] += batch_out
else:
if step == 0:
outs.append(0.)
outs[0] += batch_outs
if verbose == 1:
progbar.update(step + 1)
for i in range(len(outs)):
if i not in stateful_metric_indices:
outs[i] /= steps
else:
batches = make_batches(num_samples, batch_size)
index_array = np.arange(num_samples)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
if isinstance(ins[-1], int):
# Do not slice the training phase flag.
ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
else:
ins_batch = slice_arrays(ins, batch_ids)
for i in indices_for_conversion_to_dense:
ins_batch[i] = ins_batch[i].toarray()
batch_outs = f(ins_batch)
if isinstance(batch_outs, list):
if batch_index == 0:
for batch_out in enumerate(batch_outs):
outs.append(0.)
for i, batch_out in enumerate(batch_outs):
if i in stateful_metric_indices:
outs[i] = batch_out
else:
outs[i] += batch_out * len(batch_ids)
else:
if batch_index == 0:
outs.append(0.)
outs[0] += batch_outs * len(batch_ids)
if verbose == 1:
progbar.update(batch_end)
for i in range(len(outs)):
if i not in stateful_metric_indices:
outs[i] /= num_samples
if len(outs) == 1:
return outs[0]
return outs
def test_loop(model, inputs, targets,
sample_weights=None,
batch_size=None,
verbose=0,
steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model: Model instance that is being evaluated in Eager mode.
inputs: List of input arrays.
targets: List of target arrays.
sample_weights: Optional list of sample weight arrays.
batch_size: integer batch size or `None`.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
Returns:
Scalar loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.
"""
with backend.learning_phase_scope(0):
feed_data = inputs + targets
if sample_weights:
feed_data += sample_weights
num_samples = training_utils.check_num_samples(
feed_data, batch_size=batch_size, steps=steps, steps_name='steps')
outs = []
if verbose == 1:
progbar = Progbar(target=num_samples)
batches = make_batches(num_samples, batch_size)
index_array = np.arange(num_samples)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
inputs_batch = slice_arrays(inputs, batch_ids)
targets_batch = slice_arrays(targets, batch_ids)
if sample_weights:
sample_weights_batch = slice_arrays(sample_weights, batch_ids)
else:
sample_weights_batch = None
inputs_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
for val in inputs_batch]
targets_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
for val in targets_batch]
if sample_weights:
sample_weights_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
if val is not None else None
for val in sample_weights_batch]
loss_outs, loss, loss_metrics = _model_loss(
model,
inputs_batch,
targets_batch,
sample_weights=sample_weights_batch,
training=False)
_, metrics_results = _eager_metrics_fn(model, loss_outs, targets_batch)
batch_outs = []
for _, v in zip(model.metrics_names,
[backend.mean(loss)] + loss_metrics + metrics_results):
batch_outs.append(tensor_util.constant_value(v))
if isinstance(batch_outs, list):
if batch_index == 0:
for batch_out in enumerate(batch_outs):
outs.append(0.)
for i, batch_out in enumerate(batch_outs):
outs[i] += batch_out * len(batch_ids)
else:
if batch_index == 0:
outs.append(0.)
outs[0] += batch_outs * len(batch_ids)
if verbose == 1:
progbar.update(batch_end)
for i in range(len(outs)):
outs[i] /= num_samples
if len(outs) == 1:
return outs[0]
return outs
def predict_loop(model, inputs,
batch_size=32,
verbose=0,
steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model:
inputs: List of input arrays.
batch_size: integer batch size.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
with backend.learning_phase_scope(0):
num_samples = training_utils.check_num_samples(
inputs, batch_size, steps, 'steps')
if verbose == 1:
if steps is not None:
progbar = Progbar(target=steps)
else:
progbar = Progbar(target=num_samples)
outs = []
batches = make_batches(num_samples, batch_size)
index_array = np.arange(num_samples)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
inputs_batch = slice_arrays(inputs, batch_ids)
inputs_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
for val in inputs_batch]
if len(inputs_batch) == 1:
if model._expects_training_arg:
batch_outs = model.call(inputs_batch[0], training=False)
else:
batch_outs = model.call(inputs_batch[0])
else:
if model._expects_training_arg:
batch_outs = model.call(inputs_batch, training=False)
else:
batch_outs = model.call(inputs_batch)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if batch_index == 0:
# Pre-allocate the results arrays.
for batch_out in batch_outs:
dims = batch_out.shape[1:].dims
dims_list = [d.value for d in dims]
shape = (num_samples,) + tuple(dims_list)
outs.append(np.zeros(shape, dtype=batch_out.dtype.as_numpy_dtype))
for i, batch_out in enumerate(batch_outs):
outs[i][batch_start:batch_end] = batch_out
if verbose == 1:
progbar.update(batch_end)
if len(outs) == 1:
return outs[0]
return outs
def predict_loop(model, ins, batch_size=32, verbose=0, steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model:
ins: list of tensors to be fed to `f`.
batch_size: integer batch size.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
K.set_learning_phase(False)
num_samples = model._check_num_samples(ins, batch_size, steps, 'steps')
if verbose == 1:
if steps is not None:
progbar = Progbar(target=steps)
else:
progbar = Progbar(target=num_samples)
outs = []
batches = make_batches(num_samples, batch_size)
index_array = np.arange(num_samples)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
if ins and isinstance(ins[-1], float):
# Do not slice the training phase flag.
ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
else:
ins_batch = slice_arrays(ins, batch_ids)
ins_batch_converted = []
for ib in ins_batch:
ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx()))
eager_model_inputs = []
for i in range(len(model.inputs)):
eager_model_inputs.append(ins_batch_converted[i])
if len(eager_model_inputs) == 1:
batch_outs = model.call(eager_model_inputs[0])
else:
batch_outs = model.call(eager_model_inputs)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if batch_index == 0:
# Pre-allocate the results arrays.
for batch_out in batch_outs:
dims = batch_out.shape[1:].dims
dims_list = [d.value for d in dims]
shape = (num_samples,) + tuple(dims_list)
outs.append(np.zeros(shape, dtype=batch_out.dtype.as_numpy_dtype))
for i, batch_out in enumerate(batch_outs):
outs[i][batch_start:batch_end] = batch_out
if verbose == 1:
progbar.update(batch_end)
if len(outs) == 1:
return outs[0]
return outs
def test_loop(model, ins, batch_size=None, verbose=0, steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model: Model instance that is being evaluated in Eager mode.
ins: list of tensors to be fed to `f`.
batch_size: integer batch size or `None`.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
Returns:
Scalar loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.
"""
K.set_learning_phase(False)
num_samples = model._check_num_samples(ins, batch_size, steps, 'steps')
outs = []
if verbose == 1:
progbar = Progbar(target=num_samples)
batches = make_batches(num_samples, batch_size)
index_array = np.arange(num_samples)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
if isinstance(ins[-1], float):
# Do not slice the training phase flag.
ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
else:
ins_batch = slice_arrays(ins, batch_ids)
ins_batch_converted = []
for ib in ins_batch:
ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx()))
eager_model_inputs = []
eager_model_outputs = []
for i in range(len(model.inputs)):
eager_model_inputs.append(ins_batch_converted[i])
for i in range(len(model.inputs), len(ins_batch_converted)):
eager_model_outputs.append(ins_batch_converted[i])
loss_outs, loss, loss_metrics = _model_loss(model, eager_model_inputs,
eager_model_outputs)
_, metrics_results = _eager_metrics_fn(model, loss_outs,
eager_model_outputs)
batch_outs = []
for _, v in zip(model.metrics_names,
[K.mean(loss)] + loss_metrics + metrics_results):
batch_outs.append(tensor_util.constant_value(v))
if isinstance(batch_outs, list):
if batch_index == 0:
for batch_out in enumerate(batch_outs):
outs.append(0.)
for i, batch_out in enumerate(batch_outs):
outs[i] += batch_out * len(batch_ids)
else:
if batch_index == 0:
outs.append(0.)
outs[0] += batch_outs * len(batch_ids)
if verbose == 1:
progbar.update(batch_end)
for i in range(len(outs)):
outs[i] /= num_samples
if len(outs) == 1:
return outs[0]
return outs
def run_training():
data_sets = input_data.read_data_sets(FLAGS.input_data_dir)
max_steps = math.ceil(CONFIG.epoch * data_sets.train.num_examples /
CONFIG.batch_size)
with tf.Graph().as_default():
images_placeholder, labels_placeholder = placeholder_inputs(
CONFIG.batch_size)
logits = mnist.inference(images_placeholder, CONFIG.size_hidden_1,
CONFIG.size_hidden_2)
# Add to the Graph the Ops for loss calculation.
loss = mnist.loss(logits, labels_placeholder)
# Add to the Graph the Ops that calculate and apply gradients.
train_op = mnist.training(loss, CONFIG.learning_rate)
# Add the Op to compare the logits to the labels during evaluation.
eval_correct = mnist.evaluation(logits, labels_placeholder)
# Build the summary Tensor based on the TF collection of Summaries.
summary = tf.summary.merge_all()
# Add the variable initializer Op.
init = tf.global_variables_initializer()
# Create a saver for writing training checkpoints.
saver = tf.train.Saver()
# Create a session for running Ops on the Graph.
sess = tf.Session()
if FLAGS.c:
saver.restore(sess, os.path.join(FLAGS.log_dir, 'model.ckpt'))
# Instantiate a SummaryWriter to output summaries and the Graph.
summary_writer = tf.summary.FileWriter(FLAGS.log_dir, sess.graph)
# And then after everything is built:
# Run the Op to initialize the variables.
sess.run(init)
progbar = Progbar(target=CONFIG.eval_every_n_steps)
for step in xrange(max_steps):
start_time = time.time()
feed_dict = fill_feed_dict(data_sets.train, images_placeholder,
labels_placeholder)
_, loss_value = sess.run([train_op, loss], feed_dict=feed_dict)
progbar.update((step % CONFIG.eval_every_n_steps) + 1,
[("Loss", loss_value)],
force=True)
duration = time.time() - start_time
# Save a checkpoint and evaluate the model periodically.
if (step + 1) % CONFIG.eval_every_n_steps == 0 or (step +
1) == max_steps:
print("Total : ", int(
(step + 1) / CONFIG.eval_every_n_steps), "/",
int(math.ceil(max_steps / CONFIG.eval_every_n_steps)))
summary_str = sess.run(summary, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
summary_writer.flush()
checkpoint_file = os.path.join(FLAGS.log_dir, 'model.ckpt')
saver.save(sess, checkpoint_file, global_step=step)
# Evaluate against the training set.
print('Training Data Eval:')
do_eval(sess, eval_correct, images_placeholder,
labels_placeholder, data_sets.train)
# Evaluate against the validation set.
print('Validation Data Eval:')
do_eval(sess, eval_correct, images_placeholder,
labels_placeholder, data_sets.validation)
# Evaluate against the test set.
print('Test Data Eval:')
do_eval(sess, eval_correct, images_placeholder,
labels_placeholder, data_sets.test)
progbar = Progbar(target=CONFIG.eval_every_n_steps)
def predict_generator(model,
generator,
steps=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
verbose=0):
"""See docstring for `Model.predict_generator`."""
model._make_predict_function()
steps_done = 0
wait_time = 0.01
all_outs = []
is_sequence = isinstance(generator, Sequence)
if not is_sequence and use_multiprocessing and workers > 1:
logging.warning(
UserWarning('Using a generator with `use_multiprocessing=True`'
' and multiple workers may duplicate your data.'
' Please consider using the`keras.utils.Sequence'
' class.'))
if steps is None:
if is_sequence:
steps = len(generator)
else:
raise ValueError('`steps=None` is only valid for a generator'
' based on the `keras.utils.Sequence` class.'
' Please specify `steps` or use the'
' `keras.utils.Sequence` class.')
enqueuer = None
try:
if workers > 0:
if is_sequence:
enqueuer = OrderedEnqueuer(
generator, use_multiprocessing=use_multiprocessing)
else:
enqueuer = GeneratorEnqueuer(
generator,
use_multiprocessing=use_multiprocessing,
wait_time=wait_time)
enqueuer.start(workers=workers, max_queue_size=max_queue_size)
output_generator = enqueuer.get()
else:
if is_sequence:
output_generator = iter(generator)
else:
output_generator = generator
if verbose == 1:
progbar = Progbar(target=steps)
while steps_done < steps:
generator_output = next(output_generator)
if isinstance(generator_output, tuple):
# Compatibility with the generators
# used for training.
if len(generator_output) == 2:
x, _ = generator_output
elif len(generator_output) == 3:
x, _, _ = generator_output
else:
raise ValueError('Output of generator should be '
'a tuple `(x, y, sample_weight)` '
'or `(x, y)`. Found: ' +
str(generator_output))
else:
# Assumes a generator that only
# yields inputs (not targets and sample weights).
x = generator_output
outs = model.predict_on_batch(x)
if not isinstance(outs, list):
outs = [outs]
if not all_outs:
for out in outs:
all_outs.append([])
for i, out in enumerate(outs):
all_outs[i].append(out)
steps_done += 1
if verbose == 1:
progbar.update(steps_done)
finally:
if enqueuer is not None:
enqueuer.stop()
if len(all_outs) == 1:
if steps_done == 1:
return all_outs[0][0]
else:
return np.concatenate(all_outs[0])
if steps_done == 1:
return [out[0] for out in all_outs]
else:
return [np.concatenate(out) for out in all_outs]
def evaluate_generator(model,
generator,
steps=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
verbose=0):
"""See docstring for `Model.evaluate_generator`."""
stateful_metric_indices = []
if hasattr(model, 'metrics'):
for m in model.stateful_metric_functions:
m.reset_states()
stateful_metric_indices = [
i for i, name in enumerate(model.metrics_names)
if str(name) in model.stateful_metric_names
]
else:
stateful_metric_indices = []
steps_done = 0
wait_time = 0.01
all_outs = []
batch_sizes = []
is_sequence = isinstance(generator, Sequence)
if not is_sequence and use_multiprocessing and workers > 1:
logging.warning(
UserWarning('Using a generator with `use_multiprocessing=True`'
' and multiple workers may duplicate your data.'
' Please consider using the`keras.utils.Sequence'
' class.'))
if steps is None:
if is_sequence:
steps = len(generator)
else:
raise ValueError('`steps=None` is only valid for a generator'
' based on the `keras.utils.Sequence` class.'
' Please specify `steps` or use the'
' `keras.utils.Sequence` class.')
enqueuer = None
try:
if workers > 0:
if is_sequence:
enqueuer = OrderedEnqueuer(
generator, use_multiprocessing=use_multiprocessing)
else:
enqueuer = GeneratorEnqueuer(
generator,
use_multiprocessing=use_multiprocessing,
wait_time=wait_time)
enqueuer.start(workers=workers, max_queue_size=max_queue_size)
output_generator = enqueuer.get()
else:
if is_sequence:
output_generator = iter(generator)
else:
output_generator = generator
if verbose == 1:
progbar = Progbar(target=steps)
while steps_done < steps:
generator_output = next(output_generator)
if not hasattr(generator_output, '__len__'):
raise ValueError('Output of generator should be a tuple '
'(x, y, sample_weight) '
'or (x, y). Found: ' + str(generator_output))
if len(generator_output) == 2:
x, y = generator_output
sample_weight = None
elif len(generator_output) == 3:
x, y, sample_weight = generator_output
else:
raise ValueError('Output of generator should be a tuple '
'(x, y, sample_weight) '
'or (x, y). Found: ' + str(generator_output))
outs = model.test_on_batch(x, y, sample_weight=sample_weight)
if isinstance(x, list):
batch_size = x[0].shape[0]
elif isinstance(x, dict):
batch_size = list(x.values())[0].shape[0]
else:
batch_size = x.shape[0]
if batch_size == 0:
raise ValueError('Received an empty batch. '
'Batches should at least contain one item.')
all_outs.append(outs)
steps_done += 1
batch_sizes.append(batch_size)
if verbose == 1:
progbar.update(steps_done)
finally:
if enqueuer is not None:
enqueuer.stop()
if not isinstance(outs, list):
return np.average(np.asarray(all_outs), weights=batch_sizes)
else:
averages = []
for i in range(len(outs)):
if i not in stateful_metric_indices:
averages.append(
np.average([out[i] for out in all_outs],
weights=batch_sizes))
else:
averages.append(float(all_outs[-1][i]))
return averages