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 _experimental_predict_loop(model, iterator, verbose=0, steps=None):
"""Predict loop for predicting with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
verbose: Integer, Verbosity mode 0 or 1.
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).
"""
current_strategy = model._distribution_strategy
K.get_session().run(current_strategy.initialize())
# TODO(priyag, sourabhbajaj): This should likely not be hardcoded here.
K.set_learning_phase(0)
def _per_device_predict_function(model):
model._make_predict_function()
return (model.predict_function.inputs,
model.predict_function.outputs,
model.predict_function.updates_op,
model.predict_function.session_kwargs)
def step_fn(ctx, *inputs):
"""Clones the model and calls make_predict_function."""
# TODO(priyag, sourabhbajaj): The model gets cloned every time
# fit/test/predict is called. We should look into caching this keyed on
# input shapes.
clone_model_on_replicas(
model,
current_strategy,
make_callback_model=False,
inputs=inputs,
mode=_Mode.PREDICT)
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_replica(
_per_device_predict_function, args=(model._grouped_model_predict,))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args)
combined_fn = K.function(
all_inputs, all_outputs,
updates=all_updates,
name='distributed_predict_function',
**all_session_args)
for label, output in zip(model.output_names, combined_fn.outputs):
ctx.set_last_step_output(label, output)
return combined_fn.updates_op
# Add initial dummy values for outputs.
initial_loop_values = {}
batch_dimension = distributed_training_utils.get_batch_dimension(iterator)
for name, tensor in zip(model.output_names, model.outputs):
# TODO(priyag): This is a workaround as we do not know the batch dimension
# of the model's output at this point.
shape = tensor_shape.TensorShape(tensor.shape.dims)
shape.dims = [batch_dimension] + shape.dims[1:]
initial_loop_values[name] = array_ops.zeros(shape, tensor.dtype)
with current_strategy.scope():
# TODO(priyag, sourabhbajaj): Support steps_per_run if/when we add outfeed.
ctx = current_strategy.run_steps_on_dataset(
step_fn, iterator, iterations=1,
initial_loop_values=initial_loop_values)
predict_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
with current_strategy.scope():
distributed_model = current_strategy.unwrap(model._grouped_model_predict)[0]
distributed_training_utils.set_weights(
current_strategy, distributed_model, orig_model_weights)
assert steps is not None
# 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 _ in model.outputs]
for step in range(steps):
_, batch_outs = K.get_session().run([predict_op, output_tensors])
# TODO(priyag): maybe need to unwrap the outputs first for MirroredStrategy.
for i, label in enumerate(model.output_names):
unconcatenated_outs[i].extend(batch_outs[label])
if verbose >= 1:
progbar.update(step + 1)
K.get_session().run(current_strategy.finalize())
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))
]
def _experimental_test_loop(model, iterator, verbose=0, steps=None,
initialize_finalize_strategy=True):
"""Test loop for evaluating with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
initialize_finalize_strategy: Should the strategy initialize and finalize
functions be called.
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 outputs.
"""
current_strategy = model._distribution_strategy
if initialize_finalize_strategy:
K.get_session().run(current_strategy.initialize())
def _per_device_eval_function(model):
model._make_eval_function()
return (model._eval_function.inputs, model._eval_function.outputs,
model._eval_function.updates_op,
model._eval_function.session_kwargs)
# TODO(priyag, sourabhbajaj): This should likely not be hardcoded here.
K.set_learning_phase(0)
def step_fn(ctx, inputs, targets):
"""Clones the model and calls make_eval_function."""
# TODO(priyag, sourabhbajaj): The model gets cloned every time
# fit/test/predict is called. We should look into caching this keyed on
# input shapes.
clone_model_on_replicas(
model,
current_strategy,
make_callback_model=False,
inputs=inputs,
targets=targets,
mode=_Mode.TEST)
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_replica(
_per_device_eval_function, args=(model._grouped_model_test,))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args)
combined_fn = K.function(
all_inputs, all_outputs,
updates=all_updates,
name='distributed_test_function',
**all_session_args)
for label, output in zip(model.metrics_names, combined_fn.outputs):
if label == 'loss':
aggregation = distribute_lib.get_loss_reduction()
else:
# We aggregate all other metrics using mean for now. This is temporary
# workaround until new metrics are in place.
aggregation = variable_scope.VariableAggregation.MEAN
ctx.set_last_step_output(label, output, aggregation)
return combined_fn.updates_op
# Add initial dummy values for loss and other metric tensors.
initial_loop_values = {}
initial_loop_values['loss'] = constant_op.constant(1e7)
for name, tensor in zip(model.metrics_names[1:], model.metrics_tensors):
initial_loop_values[name] = array_ops.zeros(tensor.shape, tensor.dtype)
with current_strategy.scope():
# TODO(priyag): Use steps_per_run when we use new metrics as they will
# allow handling metric computation at each step using variables.
ctx = current_strategy.run_steps_on_dataset(
step_fn, iterator, iterations=1,
initial_loop_values=initial_loop_values)
test_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
with current_strategy.scope():
distributed_model = current_strategy.unwrap(model._grouped_model_test)[0]
distributed_training_utils.set_weights(
current_strategy, distributed_model, orig_model_weights)
assert steps is not None
outs = [0.] * len(model.metrics_names)
for step in range(steps):
_, batch_outs = K.get_session().run([test_op, output_tensors])
for i, label in enumerate(model.metrics_names):
outs[i] += batch_outs[label]
if verbose >= 1:
progbar.update(step + 1)
for i in range(len(outs)):
outs[i] /= (steps)
if initialize_finalize_strategy:
K.get_session().run(current_strategy.finalize())
if len(outs) == 1:
return outs[0]
return outs
def _fun_generator(self, x, generator, state):
self._model.optimizer.clear()
self._update_weights(x)
callbacks = state['callbacks']
if not state['in_epoch']:
callbacks.on_epoch_begin(state['epoch'])
state['in_epoch'] = True
cost_sum = 0
iterator = range(len(generator))
if state['verbose'] == 1 and len(generator) > 1:
# iterator = trange(len(generator))
progress_bar = Progbar(len(generator))
else:
# iterator = range(len(generator))
progress_bar = False
state['epoch_logs'] = {}
epoch_logs = state['epoch_logs']
for batch_index in iterator:
inputs, outputs, sample_weights = generator[batch_index]
if isinstance(inputs, list):
isize = inputs[0].shape[0]
else:
isize = inputs.shape[0]
batch_logs = {'batch': batch_index, 'size': isize}
callbacks.on_batch_begin(batch_index, batch_logs)
outs = self._model.train_on_batch(inputs, outputs, sample_weights)
if not isinstance(outs, list):
outs = [outs]
for lbl, v in zip(self._model.metrics_names, outs):
batch_logs[lbl] = v
epoch_logs[lbl] = epoch_logs.get(lbl, 0.0) + v
callbacks.on_batch_end(batch_index, batch_logs)
batch_cost = batch_logs['loss']
if progress_bar:
progress_bar.update(batch_index + 1)
# iterator.set_postfix(cost=batch_cost)
cost_sum += batch_cost
generator.on_epoch_end()
epoch_logs = state['epoch_logs']
if state['verbose'] > 0:
print(
'itr:', state['epoch'], ', '.join([
'{0}: {1:.4e}'.format(k, v) for k, v in epoch_logs.items()
]))
elif state['verbose'] < 0:
print('itr:', state['epoch'],
', loss: {0:.6e}'.format(epoch_logs['loss']))
# average the metrics
for lbl in self._model.metrics_names:
epoch_logs[lbl] = epoch_logs.get(lbl) / len(iterator)
cost = cost_sum / len(iterator)
gradients = self._model.optimizer.get_gradient_values()
x_grad = np.empty(x.shape)
x_offset = 0
for grad in gradients:
w_size = grad.size
x_grad[x_offset:x_offset + w_size] = grad.reshape(-1)
x_offset += w_size
assert x_offset == self._weights_size
self._cost = cost
self._gradients = x_grad
return cost, x_grad
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 experimental_tpu_test_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Test loop for evaluating with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
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 outputs.
"""
mode = ModeKeys.TEST
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset,
current_strategy)
steps = training_utils.infer_steps_for_dataset(dataset, steps,
steps_name='steps')
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=0)
scope.__enter__()
out_labels = model.metrics_names
def _test_step_fn(inputs):
"""A fn that returns output of single test step."""
inputs, targets = inputs
(distribution_strategy_context.get_replica_context().merge_call(
_build_model, args=(model, mode, inputs, targets)))
(_, outputs, updates, _) = (
_per_device_execution_function(
distributed_training_utils.get_distributed_model(model, mode),
mode))
with ops.control_dependencies([updates]):
return outputs
test_input_data = iterator.get_next()
per_replica_outputs = current_strategy.experimental_run_v2(
_test_step_fn, args=(test_input_data,))
output_tensors = {}
for label, output in zip(out_labels, per_replica_outputs):
if label == 'loss':
reduce_op = ds_reduce_util.ReduceOp.SUM
else:
# We reduce all other metrics using mean for now. This is temporary
# workaround until new metrics are in place.
reduce_op = ds_reduce_util.ReduceOp.MEAN
output_tensors[label] = current_strategy.reduce(reduce_op, output)
test_op = control_flow_ops.group(list(output_tensors.values()))
if verbose >= 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(model, mode)
distributed_training_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=ModeKeys.TEST)
callbacks._call_begin_hook(mode)
outs = [0.] * len(model.metrics_names)
if steps is not None:
target_steps = steps
else:
raise ValueError('Number of steps could not be infered from the data, '
'please pass the steps argument.')
current_step = 0
while current_step < target_steps:
batch_logs = {'batch': current_step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', current_step, batch_logs)
try:
_, batch_outs = K.batch_get_value([test_op, output_tensors])
except errors.OutOfRangeError:
warning_msg = 'Make sure that your dataset can generate at least '
'`steps` batches (in this case, {} batches).'.format(steps)
logging.warning('Your dataset iterator ran out of data; '
'interrupting evaluation. ' + warning_msg)
target_steps = current_step
break
for i, label in enumerate(model.metrics_names):
if i == 0:
# Loss is stateless metrics.
outs[i] += batch_outs[label]
else:
# For all stateful metrics, the aggregation is handled by mirrored vars.
outs[i] = batch_outs[label]
batch_logs = cbks.make_logs(model, batch_logs, outs, mode)
callbacks._call_batch_hook(mode, 'end', current_step, batch_logs)
if verbose == 1:
progbar.update(current_step + 1)
current_step += 1
if verbose >= 1:
# Progress bar finishes at the end.
progbar.update(target_steps)
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(outs) >= 0:
outs[0] /= (target_steps)
if len(outs) == 1:
return outs[0]
return 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`."""
if not context.executing_eagerly():
model._make_test_function()
if hasattr(model, 'metrics'):
for m in model.stateful_metric_functions:
m.reset_states()
steps_done = 0
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)
enqueuer.start(workers=workers, max_queue_size=max_queue_size)
output_generator = enqueuer.get()
else:
if is_sequence:
output_generator = iter_sequence_infinite(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 = [float(all_outs[-1][0])] # index 0 = 'loss'
averages.extend([
np.average([out[i]
for out in all_outs], weights=batch_sizes)
for i in range(1, len(outs))
])
return averages
def experimental_tpu_predict_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Predict loop for predicting with TPU tf.distribute.Strategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
mode = ModeKeys.PREDICT
dataset_fully_shaped = dist_utils.is_dataset_shape_fully_defined(dataset)
padding_handler = None
if not dataset_fully_shaped:
# TODO(hongjunchoi): Investigate whether operations from
# PartialBatchPaddingHandler are unnecessarily pruned out
# during graph optimization.
padding_handler = padding_util.PartialBatchPaddingHandler(
model._feed_output_shapes)
batch_size, _, prefetch_buffer = input_lib._get_dataset_attributes(
dataset)
padding_handler.padded_batch_size = batch_size
padding_handler.padding_mask = dataset.reduce(
padding_handler.padding_mask, padding_handler.update_mask)
dataset = dataset.map(padding_handler.pad_batch)
dataset = dataset.apply(batching.unbatch())
# Upon this point, it is guaranteed that the dataset does not
# have partial batches. Thus, we set `drop_remainder=True` to
# get static shape information about the elements in the dataset.
dataset = dataset.batch(batch_size, drop_remainder=True)
if prefetch_buffer is not None:
dataset = dataset.prefetch(prefetch_buffer)
current_strategy = model._distribution_strategy
iterator = dist_utils.get_iterator(dataset, current_strategy)
scope = dist_utils.distributed_scope(strategy=current_strategy,
learning_phase=0)
scope.__enter__()
def _predict_step_fn(inputs):
"""A fn that returns output of single prediction step."""
(distribution_strategy_context.get_replica_context().merge_call(
_build_model, args=(model, mode, inputs)))
(_, outputs, updates, _) = _per_replica_execution_function(
dist_utils.get_distributed_model(model, mode), mode)
with ops.control_dependencies([updates]):
return outputs
# TODO(hongjunchoi): When numpy array is passed as an input to `predict()`
# use numpy arrays directly to avoid cumulating unnecessary input pipeline
# ops.
predict_input_data = iterator.get_next()
per_replica_outputs = current_strategy.experimental_run_v2(
_predict_step_fn, args=(predict_input_data, ))
output_tensors = dist_utils.flatten_per_replica_values(
current_strategy, per_replica_outputs)
if verbose >= 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
dist_utils._copy_weights_to_distributed_model(model, mode)
dist_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=mode)
callbacks._call_begin_hook(mode)
# 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.
num_model_outputs = len(model.output_names)
unconcatenated_outs = [[] for _ in range(num_model_outputs)]
if steps is not None:
target_steps = steps
else:
raise ValueError(
'Number of steps could not be inferred from the data, '
'please pass the steps argument.')
current_step = 0
while current_step < target_steps:
batch_logs = {'batch': current_step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', current_step, batch_logs)
try:
predict_ops = control_flow_ops.group(output_tensors)
_, batch_outs = K.batch_get_value([predict_ops, output_tensors])
except errors.OutOfRangeError:
warning_msg = 'Make sure that your dataset can generate at least '
'`steps` batches (in this case, {} batches).'.format(steps)
logging.warning('Your dataset iterator ran out of data; '
'interrupting evaluation. ' + warning_msg)
break
# TODO(priyag): maybe need to unwrap the outputs first for MirroredStrategy.
for i in range(num_model_outputs):
output_start_index = i * current_strategy.num_replicas_in_sync
output_end_index = (output_start_index +
current_strategy.num_replicas_in_sync)
single_model_output = batch_outs[
output_start_index:output_end_index]
unconcatenated_outs[i].extend(single_model_output)
batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
callbacks._call_batch_hook(mode, 'end', current_step, batch_logs)
if verbose == 1:
progbar.update(current_step + 1)
current_step += 1
if verbose >= 1:
# Progress bar finishes at the end.
progbar.update(current_step)
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(unconcatenated_outs) == 1:
prediction_result = np.concatenate(unconcatenated_outs[0], axis=0)
else:
prediction_result = [
np.concatenate(out, axis=0) for out in unconcatenated_outs
]
if padding_handler:
prediction_result = padding_handler.apply_mask(prediction_result)
return prediction_result
def test_loop(model, inputs, targets, verbose=0, steps=None):
"""evaluate method to validate a model that uses DistributionStrategy.
Arguments:
model: Keras Model instance.
inputs: List of input arrays.
targets: List of target arrays.
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.
"""
current_strategy = model._distribution_strategy
def _per_device_test_function(model):
model._make_test_function()
return (model.test_function.inputs,
model.test_function.outputs,
model.test_function.updates_op,
model.test_function.session_kwargs)
with current_strategy.scope():
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_tower(
_per_device_test_function, model._grouped_model)
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args, with_loss_tensor=True)
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
dataset_targets = distributed_training_utils.flatten_perdevice_values(
current_strategy, targets)
distributed_test_function = K.Function(
all_inputs, all_outputs,
updates=all_updates,
name='distributed_test_function',
**all_session_args)
# We need to set sample_weights to None since there are sample weight
# placeholders that are created with default values.
sample_weights = [None for _ in range(len(model.outputs) *
current_strategy.num_towers)]
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = dataset_inputs + dataset_targets + sample_weights + [0]
else:
ins = dataset_inputs + dataset_targets
outs = []
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
with current_strategy.scope():
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(
current_strategy, distributed_model, orig_model_weights)
if steps is not None:
for step in range(steps):
batch_outs = distributed_test_function(ins)
batch_outs = _aggregate_metrics_across_towers(
len(current_strategy._devices), model.metrics_names, batch_outs)
if isinstance(batch_outs, list):
if step == 0:
for _ in enumerate(batch_outs):
outs.append(0.)
for i, batch_out in enumerate(batch_outs):
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)):
outs[i] /= steps
if len(outs) == 1:
return outs[0]
return outs
def test_loop(model, inputs, targets, verbose=0, steps=None):
"""evaluate method to validate a model that uses DistributionStrategy.
Arguments:
model: Keras Model instance.
inputs: List of input arrays.
targets: List of target arrays.
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.
"""
current_strategy = model._distribution_strategy
def _per_device_test_function(model):
model._make_test_function()
return (model.test_function.inputs, model.test_function.outputs,
model.test_function.updates_op,
model.test_function.session_kwargs)
with current_strategy.scope():
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_tower(
_per_device_test_function, model._grouped_model)
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy,
grouped_inputs,
grouped_outputs,
grouped_updates,
grouped_session_args,
with_loss_tensor=True)
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
dataset_targets = distributed_training_utils.flatten_perdevice_values(
current_strategy, targets)
distributed_test_function = K.Function(all_inputs,
all_outputs,
updates=all_updates,
name='distributed_test_function',
**all_session_args)
# We need to set sample_weights to None since there are sample weight
# placeholders that are created with default values.
sample_weights = [
None for _ in range(len(model.outputs) * current_strategy.num_towers)
]
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = dataset_inputs + dataset_targets + sample_weights + [0]
else:
ins = dataset_inputs + dataset_targets
outs = []
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
with current_strategy.scope():
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(current_strategy,
distributed_model,
orig_model_weights)
if steps is not None:
for step in range(steps):
batch_outs = distributed_test_function(ins)
batch_outs = _aggregate_metrics_across_towers(
len(current_strategy._devices), model.metrics_names,
batch_outs)
if isinstance(batch_outs, list):
if step == 0:
for _ in enumerate(batch_outs):
outs.append(0.)
for i, batch_out in enumerate(batch_outs):
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)):
outs[i] /= steps
if len(outs) == 1:
return outs[0]
return outs
def read_CVA_waveform(CVA_list, save_dir):
progbar = Progbar(len(CVA_list))
for q, list in enumerate(CVA_list):
f = open(list, 'r')
lines = f.readlines()
count = 0
t = []
e = []
n = []
z = []
filename = str(list[-24:-4]).replace('/', '')
if os.path.exists(f'{save_dir}{filename}.mseed'):
progbar.add(1)
continue
try:
for line in lines:
if count == 0:
station_code = line.strip()[14:20]
if len(station_code) > 4:
station_code = seisnn.io.change_station_code(
station_code)
network = 'TSMIP'
else:
network = 'CWBSN'
elif count == 2:
start_time = obspy.UTCDateTime(line.strip()[12:-1])
elif count == 3:
duration = float(line.strip()[20:28])
elif count == 4:
samplerate = int(line.strip()[17:20])
elif count == 1:
pass
elif count == 5:
pass
elif count == 6:
pass
elif count == 7:
pass
elif count == 8:
pass
elif count == 9:
pass
elif count == 10:
pass
else:
try:
tt = float(line[0:10])
except ValueError:
tt = 0
try:
te = float(line[10:20])
except ValueError:
te = 0
try:
tn = float(line[20:30])
except ValueError:
tn = 0
try:
tz = float(line[30:40])
except ValueError:
tz = 0
t.append(tt)
e.append(te)
n.append(tn)
z.append(tz)
count = count + 1
traceE = obspy.core.trace.Trace(np.array(e))
traceN = obspy.core.trace.Trace(np.array(n))
traceZ = obspy.core.trace.Trace(np.array(z))
for i, trace in enumerate([traceE, traceN, traceZ]):
try:
trace.stats.network = network
trace.stats.station = station_code
trace.stats.starttime = start_time
trace.stats.sampling_rate = samplerate
trace.stats.npts = len(t)
trace.stats.delta = t[1] - t[0]
except IndexError:
print(list)
if i == 0:
trace.stats.channel = 'EHE'
if i == 1:
trace.stats.channel = 'EHN'
if i == 2:
trace.stats.channel = 'EHZ'
st = obspy.core.stream.Stream([traceE, traceN, traceZ])
progbar.add(1)
st.write(f'{save_dir}{filename}.mseed', format='MSEED')
f.close()
except ValueError:
print(list)
or list of arrays of predictions
(if the model has multiple outputs).
"""
model._make_predict_function()
f = model.predict_function
# Callbacks batch end.
batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
callbacks._call_batch_hook(mode, 'end', batch_index, batch_logs)
progbar.on_batch_end(batch_index, batch_logs)
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
class TrainIntervalLogger(Callback):
""" This callback type logs the process every interval steps. Initialize it with interval=1
to log on every step (may slow down performance) """
def __init__(self, interval=10000):
self.interval = interval
self.step = 0
""" The reset step also predefined several necessary attributes e.g., self.metrics """
self.reset()
def reset(self):
""" Mainly used to clear up results of the previous logging interval
when we start a new logging interval """
self.interval_start = timeit.default_timer()
self.progbar = Progbar(target=self.interval)
self.metrics = []
""" Infos to be printed out """
self.infos = []
self.info_names = None
self.episode_rewards = []
def on_train_begin(self, logs):
self.train_start = timeit.default_timer()
""" self.metrics_names attribute is defined here"""
self.metrics_names = self.model.metrics_names
""" The base class keras.callback does have a property named params
which we will set later, in the core body """
print('Training for {} steps ...'.format(self.params['nb_steps']))
def on_train_end(self, logs):
duration = timeit.default_timer() - self.train_start
print('Finished. Took {:.3f} seconds'.format(duration))
def on_step_begin(self, step, logs):
""" Check if another interval has passed and printout the metric if it did
logs here is the accumulated logs """
if self.step % self.interval == 0:
if len(self.episode_rewards) > 0:
""" self.metrics should have already contained recorded metrics for
all steps in the current interval """
metrics = np.array(self.metrics)
assert metrics.shape == (self.interval,
len(self.metrics_names))
formatted_metrics = ''
if not np.isnan(metrics).all():
means = np.nanmean(self.metrics, axis=0)
assert means.shape == (len(self.metrics_names), )
for name, mean in zip(self.metrics_names, means):
formatted_metrics += ' - {}: {:,3f}'.format(name, mean)
formatted_infos = ''
if len(self.infos) > 0:
infos = np.array(self.infos)
if not np.isnan(infos).all():
means = np.nanmean(self.infos, axis=0)
assert means.shape == (len(self.info_names), )
for name, mean in zip(self.info_names, means):
formatted_infos += ' - {}: {:,.3f}'.format(
name, mean)
print(
'{} episodes - avg episode reward: {:,.3f} [min={:,.3f}, max={:,.3f}]{}{}'
.format(len(self.episode_rewards),
np.mean(self.episode_rewards),
np.min(self.episode_rewards),
np.max(self.episode_rewards), formatted_metrics,
formatted_infos))
print('')
self.reset()
print('Interval {} ({} steps performed)'.format(
self.step // self.interval + 1, self.step))
def on_step_end(self, step, logs):
""" Update the progression bar
logs here is the instant log of the step"""
if self.info_names is None:
self.info_names = logs['info'].keys()
values = [('reward', logs['reward'])]
self.progbar.update(self.step % self.interval + 1, values=values)
self.step += 1
self.metrics.append(logs['metrics'])
if len(self.info_names) > 0:
self.infos.append([logs['info'][k] for k in self.info_names])
def on_episode_end(self, episode, logs):
self.episode_rewards.append(logs['episode_reward'])
def experimental_tpu_predict_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Predict loop for predicting with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
mode = ModeKeys.PREDICT
steps = training_utils.infer_steps_for_dataset(dataset, steps,
steps_name='steps')
dataset_fully_shaped = (distributed_training_utils.
is_dataset_shape_fully_defined(dataset))
padding_handler = None
if not dataset_fully_shaped:
# TODO(hongjunchoi): Investigate whether operations from
# PartialBatchPaddingHandler are unnecessarily pruned out
# during graph optimization.
padding_handler = padding_util.PartialBatchPaddingHandler(
model._feed_output_shapes)
batch_size, _, prefetch_buffer = input_lib._get_dataset_attributes(dataset)
padding_handler.padded_batch_size = batch_size
padding_handler.padding_mask = dataset.reduce(padding_handler.padding_mask,
padding_handler.update_mask)
dataset = dataset.map(padding_handler.pad_batch)
dataset = dataset.apply(batching.unbatch())
# Upon this point, it is guaranteed that the dataset does not
# have partial batches. Thus, we set `drop_remainder=True` to
# get static shape information about the elements in the dataset.
dataset = dataset.batch(batch_size, drop_remainder=True)
if prefetch_buffer is not None:
dataset = dataset.prefetch(prefetch_buffer)
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset, current_strategy)
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=0)
scope.__enter__()
def _predict_step_fn(inputs):
"""A fn that returns output of single prediction step."""
(distribution_strategy_context.get_replica_context().merge_call(
_build_model, args=(model, mode, inputs)))
(_, outputs, updates, _) = (
_per_device_execution_function(
distributed_training_utils.get_distributed_model(model, mode),
mode))
with ops.control_dependencies([updates]):
return outputs
# TODO(hongjunchoi): When numpy array is passed as an input to `predict()`
# use numpy arrays directly to avoid cumulating unnecessary input pipeline
# ops.
predict_input_data = iterator.get_next()
per_replica_outputs = current_strategy.experimental_run_v2(
_predict_step_fn, args=(predict_input_data,))
output_tensors = distributed_training_utils.flatten_perdevice_values(
current_strategy, per_replica_outputs)
if verbose >= 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(model, mode)
distributed_training_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=mode)
callbacks._call_begin_hook(mode)
# 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.
num_model_outputs = len(model.output_names)
unconcatenated_outs = [[] for _ in range(num_model_outputs)]
if steps is not None:
target_steps = steps
else:
raise ValueError('Number of steps could not be infered from the data, '
'please pass the steps argument.')
current_step = 0
while current_step < target_steps:
batch_logs = {'batch': current_step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', current_step, batch_logs)
try:
predict_ops = control_flow_ops.group(output_tensors)
_, batch_outs = K.batch_get_value([predict_ops, output_tensors])
except errors.OutOfRangeError:
warning_msg = 'Make sure that your dataset can generate at least '
'`steps` batches (in this case, {} batches).'.format(steps)
logging.warning('Your dataset iterator ran out of data; '
'interrupting evaluation. ' + warning_msg)
break
# TODO(priyag): maybe need to unwrap the outputs first for MirroredStrategy.
for i in range(num_model_outputs):
output_start_index = i * current_strategy.num_replicas_in_sync
output_end_index = (
output_start_index + current_strategy.num_replicas_in_sync)
single_model_output = batch_outs[output_start_index:output_end_index]
unconcatenated_outs[i].extend(single_model_output)
batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
callbacks._call_batch_hook(mode, 'end', current_step, batch_logs)
if verbose == 1:
progbar.update(current_step + 1)
current_step += 1
if verbose >= 1:
# Progress bar finishes at the end.
progbar.update(current_step)
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(unconcatenated_outs) == 1:
prediction_result = np.concatenate(unconcatenated_outs[0], axis=0)
else:
prediction_result = [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
if padding_handler:
prediction_result = padding_handler.apply_mask(prediction_result)
return prediction_result
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
class TrainIntervalLogger(Callback):
def __init__(self, interval=10000):
self.interval = interval
self.step = 0
self.reset()
def reset(self):
""" Reset statistics """
self.interval_start = timeit.default_timer()
self.progbar = Progbar(target=self.interval)
self.metrics = []
self.infos = []
self.info_names = None
self.episode_rewards = []
def on_train_begin(self, logs):
""" Initialize training statistics at beginning of training """
self.train_start = timeit.default_timer()
self.metrics_names = self.model.metrics_names
print('Training for {} steps ...'.format(self.params['nb_steps']))
def on_train_end(self, logs):
""" Print training duration at end of training """
duration = timeit.default_timer() - self.train_start
print('done, took {:.3f} seconds'.format(duration))
def on_step_begin(self, step, logs):
""" Print metrics if interval is over """
if self.step % self.interval == 0:
if len(self.episode_rewards) > 0:
metrics = np.array(self.metrics)
assert metrics.shape == (self.interval,
len(self.metrics_names))
formatted_metrics = ''
if not np.isnan(metrics).all(): # not all values are means
means = np.nanmean(self.metrics, axis=0)
assert means.shape == (len(self.metrics_names), )
for name, mean in zip(self.metrics_names, means):
formatted_metrics += ' - {}: {:.3f}'.format(name, mean)
formatted_infos = ''
if len(self.infos) > 0:
infos = np.array(self.infos)
if not np.isnan(infos).all(): # not all values are means
means = np.nanmean(self.infos, axis=0)
assert means.shape == (len(self.info_names), )
for name, mean in zip(self.info_names, means):
formatted_infos += ' - {}: {:.3f}'.format(
name, mean)
print(
'{} episodes - episode_reward: {:.3f} [{:.3f}, {:.3f}]{}{}'
.format(len(self.episode_rewards),
np.mean(self.episode_rewards),
np.min(self.episode_rewards),
np.max(self.episode_rewards), formatted_metrics,
formatted_infos))
print('')
self.reset()
print('Interval {} ({} steps performed)'.format(
self.step // self.interval + 1, self.step))
def on_step_end(self, step, logs):
""" Update progression bar at the end of each step """
if self.info_names is None:
self.info_names = logs['info'].keys()
values = [('reward', logs['reward'])]
if KERAS_VERSION > '2.1.3':
self.progbar.update((self.step % self.interval) + 1, values=values)
else:
self.progbar.update((self.step % self.interval) + 1,
values=values,
force=True)
self.step += 1
self.metrics.append(logs['metrics'])
if len(self.info_names) > 0:
self.infos.append([logs['info'][k] for k in self.info_names])
def on_episode_end(self, episode, logs):
""" Update reward value at the end of each episode """
self.episode_rewards.append(logs['episode_reward'])
def predict_loop(model, inputs, 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`.
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).
"""
current_strategy = model._distribution_strategy
def _per_device_predict_function(model):
model._make_predict_function()
return (model.predict_function.inputs,
model.predict_function.outputs,
model.predict_function.updates_op,
model.predict_function.session_kwargs)
with current_strategy.scope():
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_tower(
_per_device_predict_function, model._grouped_model)
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args)
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
distributed_predict_function = K.Function(
all_inputs, all_outputs,
updates=all_updates,
name='distributed_predict_function',
**all_session_args)
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = dataset_inputs + [0]
else:
ins = dataset_inputs
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
with current_strategy.scope():
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(
current_strategy, distributed_model, orig_model_weights)
if steps is not None:
# 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 = distributed_predict_function(ins)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if step == 0:
for _ 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))
]
def test_loop(model, iterator, verbose=0, steps=None):
"""Test loop for evaluating with DistributionStrategy.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
verbose: Integer, Verbosity mode 0 or 1.
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 outputs.
"""
current_strategy = model._distribution_strategy
# TODO(priyag, sourabhbajaj): Remove this when the codepaths are merged.
if current_strategy.__class__.__name__ == 'TPUStrategy':
return _experimental_test_loop(model, iterator, verbose, steps)
if not model._grouped_model:
clone_model_on_towers(model, current_strategy)
def _per_device_test_function(model):
model._make_test_function()
return (model.test_function.inputs, model.test_function.outputs,
model.test_function.updates_op,
model.test_function.session_kwargs)
inputs, targets = _get_input_from_iterator(iterator, model)
with current_strategy.scope():
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_tower(
_per_device_test_function, model._grouped_model)
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy,
grouped_inputs,
grouped_outputs,
grouped_updates,
grouped_session_args,
with_loss_tensor=True)
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
dataset_targets = distributed_training_utils.flatten_perdevice_values(
current_strategy, targets)
distributed_test_function = K.Function(all_inputs,
all_outputs,
updates=all_updates,
name='distributed_test_function',
**all_session_args)
# We need to set sample_weights to None since there are sample weight
# placeholders that are created with default values.
sample_weights = [
None for _ in range(len(model.outputs) * current_strategy.num_towers)
]
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = dataset_inputs + dataset_targets + sample_weights + [0]
else:
ins = dataset_inputs + dataset_targets
outs = []
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
with current_strategy.scope():
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(current_strategy,
distributed_model,
orig_model_weights)
assert steps is not None
for step in range(steps):
batch_outs = distributed_test_function(ins)
batch_outs = _aggregate_metrics_across_towers(
current_strategy.num_towers, model.metrics_names, batch_outs)
if isinstance(batch_outs, list):
if step == 0:
outs = [0.] * len(batch_outs)
for i, batch_out in enumerate(batch_outs):
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)):
outs[i] /= steps
if len(outs) == 1:
return outs[0]
return outs
def experimental_tpu_test_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Test loop for evaluating with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
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 outputs.
"""
mode = ModeKeys.TEST
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset,
current_strategy)
steps = training_utils.infer_steps_for_dataset(dataset, steps,
steps_name='steps')
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=0)
scope.__enter__()
out_labels = model.metrics_names
step_fn = _make_step_fn(model, ModeKeys.TEST, current_strategy, out_labels)
# Add initial dummy values for loss and other metric tensors.
initial_loop_values = {}
initial_loop_values['loss'] = constant_op.constant(1e7)
for name in model.metrics_names[1:]:
tensor = model._all_stateful_metrics_tensors[name]
initial_loop_values[name] = array_ops.zeros(tensor.shape, tensor.dtype)
# TODO(priyag): Use steps_per_run when we use new metrics as they will
# allow handling metric computation at each step using variables.
ctx = current_strategy.extended.experimental_run_steps_on_iterator(
step_fn, iterator, iterations=1,
initial_loop_values=initial_loop_values)
test_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(model, mode)
distributed_training_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=ModeKeys.TEST)
callbacks._call_begin_hook(mode)
outs = [0.] * len(model.metrics_names)
if steps is not None:
target_steps = steps
else:
target_steps = np.inf
current_step = 0
while current_step < target_steps:
batch_logs = {'batch': current_step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', current_step, batch_logs)
try:
_, batch_outs = K.batch_get_value([test_op, output_tensors])
except errors.OutOfRangeError:
if steps is not None:
warning_msg = 'Make sure that your dataset can generate at least '
'`steps` batches (in this case, {} batches).'.format(steps)
else:
warning_msg = 'Number of steps ran: {} steps'.format(current_step)
logging.warning('Your dataset iterator ran out of data; '
'interrupting evaluation. ' + warning_msg)
target_steps = current_step
break
for i, label in enumerate(model.metrics_names):
if i == 0:
# Loss is stateless metrics.
outs[i] += batch_outs[label]
else:
# For all stateful metrics, the aggregation is handled by mirrored vars.
outs[i] = batch_outs[label]
batch_logs = cbks.make_logs(model, batch_logs, outs, mode)
callbacks._call_batch_hook(mode, 'end', current_step, batch_logs)
if verbose >= 1:
progbar.update(current_step + 1)
current_step += 1
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(outs) >= 0:
outs[0] /= (target_steps)
if len(outs) == 1:
return outs[0]
return outs
def experimental_tpu_test_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Test loop for evaluating with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
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 outputs.
"""
mode = ModeKeys.TEST
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset, current_strategy)
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=0)
scope.__enter__()
def _per_device_eval_function(model):
model._make_eval_function()
return (model._eval_function.inputs, model._eval_function.outputs,
model._eval_function.updates_op,
model._eval_function.session_kwargs)
def step_fn(ctx, inputs):
"""Clones the model and calls make_eval_function."""
inputs, targets = inputs
if model._compile_distribution:
distributed_training_utils.clone_model_on_replicas(
model, current_strategy, mode=mode, inputs=inputs, targets=targets)
else:
distributed_training_utils._build_distributed_network(
model, current_strategy, mode, inputs, targets)
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.extended.call_for_each_replica(
_per_device_eval_function,
args=(distributed_training_utils.get_distributed_model(
model, ModeKeys.TEST),))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args)
combined_fn = K.function(
all_inputs, all_outputs,
updates=all_updates,
name='distributed_test_function',
**all_session_args)
for label, output in zip(model.metrics_names, combined_fn.outputs):
if label == 'loss':
reduce_op = ds_reduce_util.ReduceOp.SUM
else:
# We reduce all other metrics using mean for now. This is temporary
# workaround until new metrics are in place.
reduce_op = ds_reduce_util.ReduceOp.MEAN
ctx.set_last_step_output(label, output, reduce_op)
return combined_fn.updates_op
# Add initial dummy values for loss and other metric tensors.
initial_loop_values = {}
initial_loop_values['loss'] = constant_op.constant(1e7)
for name in model.metrics_names[1:]:
tensor = model._all_stateful_metrics_tensors[name]
initial_loop_values[name] = array_ops.zeros(tensor.shape, tensor.dtype)
# TODO(priyag): Use steps_per_run when we use new metrics as they will
# allow handling metric computation at each step using variables.
ctx = current_strategy.extended.experimental_run_steps_on_iterator(
step_fn, iterator, iterations=1,
initial_loop_values=initial_loop_values)
test_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(model, mode)
distributed_training_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=ModeKeys.TEST)
callbacks._call_begin_hook(mode)
assert steps is not None
outs = [0.] * len(model.metrics_names)
for step in range(steps):
batch_logs = {'batch': step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', step, batch_logs)
_, batch_outs = K.get_session().run([test_op, output_tensors])
for i, label in enumerate(model.metrics_names):
if i == 0:
# Loss is stateless metrics.
outs[i] += batch_outs[label]
else:
# For all stateful metrics, the aggregation is handled by mirrored vars.
outs[i] = batch_outs[label]
batch_logs = cbks.make_logs(model, batch_logs, outs, mode)
callbacks._call_batch_hook(mode, 'end', step, batch_logs)
if verbose >= 1:
progbar.update(step + 1)
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(outs) >= 0:
outs[0] /= (steps)
if len(outs) == 1:
return outs[0]
return outs
# load mnist data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# use all available 70k samples from both train and test sets
X_train = np.concatenate((X_train, X_test))
y_train = np.concatenate((y_train, y_test))
# convert to -1..1 range, reshape to (sample_i, 28, 28, 1)
X_train = (X_train.astype(np.float32) - 127.5) / 127.5
X_train = np.expand_dims(X_train, axis=3)
# write tensorboard summaries
sw = tf.compat.v1.summary.FileWriter(TENSORBOARD_DIR)
progress_bar = Progbar(target=ITERATIONS)
DG_losses = []
D_true_losses = []
D_fake_losses = []
for it in range(ITERATIONS):
if len(D_true_losses) > 0:
progress_bar.update(
it,
values=[ # avg of 5 most recent
('D_real_is_fake', np.mean(D_true_losses[-5:], axis=0)[1]),
('D_real_class', np.mean(D_true_losses[-5:], axis=0)[2]),
('D_fake_is_fake', np.mean(D_fake_losses[-5:], axis=0)[1]),
('D_fake_class', np.mean(D_fake_losses[-5:], axis=0)[2]),
def predict_loop(model, iterator, verbose=0, steps=None):
"""Predict loop for predicting with DistributionStrategy.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
verbose: Integer, Verbosity mode 0 or 1.
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).
"""
current_strategy = model._distribution_strategy
# TODO(priyag, sourabhbajaj): Remove this when the codepaths are merged.
if current_strategy.__class__.__name__ == 'TPUStrategy':
return _experimental_predict_loop(model, iterator, verbose, steps)
if not model._grouped_model:
clone_model_on_towers(model, current_strategy)
def _per_device_predict_function(model):
model._make_predict_function()
return (model.predict_function.inputs, model.predict_function.outputs,
model.predict_function.updates_op,
model.predict_function.session_kwargs)
inputs, _ = _get_input_from_iterator(iterator, model)
with current_strategy.scope():
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_tower(
_per_device_predict_function, model._grouped_model)
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs,
grouped_updates, grouped_session_args)
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
distributed_predict_function = K.Function(
all_inputs,
all_outputs,
updates=all_updates,
name='distributed_predict_function',
**all_session_args)
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = dataset_inputs + [0]
else:
ins = dataset_inputs
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
with current_strategy.scope():
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(current_strategy,
distributed_model,
orig_model_weights)
if steps is not None:
# 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 = distributed_predict_function(ins)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if step == 0:
for _ in batch_outs:
unconcatenated_outs.append([])
# TODO(anjalisridhar): Should combine the outputs from multiple towers
# correctly here.
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))
]
def evaluate_generator(model,
generator,
steps=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
verbose=0):
"""See docstring for `Model.evaluate_generator`."""
if not context.executing_eagerly():
model._make_test_function()
if hasattr(model, '_compile_metrics'):
for m in model.metrics:
m.reset_states()
steps_done = 0
all_outs = []
batch_sizes = []
is_sequence = isinstance(generator, data_utils.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('Please specify the `steps` argument.')
enqueuer = None
try:
if workers > 0:
if is_sequence:
enqueuer = data_utils.OrderedEnqueuer(
generator, use_multiprocessing=use_multiprocessing)
else:
enqueuer = data_utils.GeneratorEnqueuer(
generator, use_multiprocessing=use_multiprocessing)
enqueuer.start(workers=workers, max_queue_size=max_queue_size)
output_generator = enqueuer.get()
else:
if is_sequence:
output_generator = data_utils.iter_sequence_infinite(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 = int(x[0].shape[0])
elif isinstance(x, dict):
batch_size = int(list(x.values())[0].shape[0])
else:
batch_size = int(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)
except (errors.OutOfRangeError, StopIteration):
logging.warning(
'Your dataset iterator ran out of data interrupting testing. '
'Make sure that your dataset can generate at least `steps` '
'batches (in this case, %d batches). You may need to use the '
'repeat() function when building your dataset.', steps)
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 = [float(all_outs[-1][0])] # index 0 = 'loss'
averages.extend([
np.average([out[i] for out in all_outs], weights=batch_sizes)
for i in range(1, len(outs))
])
return averages
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.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 = []
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 predict_generator(model,
generator,
steps=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
verbose=0):
"""See docstring for `Model.predict_generator`."""
if not context.executing_eagerly():
model._make_predict_function()
steps_done = 0
all_outs = []
is_sequence = isinstance(generator, data_utils.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('Please specify the `steps` argument.')
enqueuer = None
try:
if workers > 0:
if is_sequence:
enqueuer = data_utils.OrderedEnqueuer(
generator, use_multiprocessing=use_multiprocessing)
else:
enqueuer = data_utils.GeneratorEnqueuer(
generator, use_multiprocessing=use_multiprocessing)
enqueuer.start(workers=workers, max_queue_size=max_queue_size)
output_generator = enqueuer.get()
else:
if is_sequence:
output_generator = data_utils.iter_sequence_infinite(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)
except (errors.OutOfRangeError, StopIteration):
logging.warning(
'Your dataset iterator ran out of data interrupting testing. '
'Make sure that your dataset can generate at least `steps` '
'batches (in this case, %d batches). You may need to use the '
'repeat() function when building your dataset.', steps)
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, iterator, verbose=0, steps=None):
"""Test loop for evaluating with DistributionStrategy.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
verbose: Integer, Verbosity mode 0 or 1.
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 outputs.
"""
current_strategy = model._distribution_strategy
# TODO(priyag, sourabhbajaj): Remove this when the codepaths are merged.
if current_strategy.__class__.__name__ == 'TPUStrategy':
return _experimental_test_loop(model, iterator, verbose, steps)
if not model._grouped_model:
clone_model_on_replicas(model, current_strategy)
def _per_device_eval_function(model):
model._make_eval_function()
return (model._eval_function.inputs, model._eval_function.outputs,
model._eval_function.updates_op,
model._eval_function.session_kwargs)
inputs, targets, sample_weights = _get_input_from_iterator(iterator, model)
with current_strategy.scope():
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_replica(
_per_device_eval_function, args=(model._grouped_model,))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args, with_loss_tensor=True)
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
dataset_targets = distributed_training_utils.flatten_perdevice_values(
current_strategy, targets)
distributed_test_function = K.function(
all_inputs, all_outputs,
updates=all_updates,
name='distributed_test_function',
**all_session_args)
# We need to set sample_weights to None since there are sample weight
# placeholders that are created with default values.
sample_weights = [None for _ in range(
len(model.outputs) * current_strategy.num_replicas_in_sync)]
if not isinstance(K.learning_phase(), int):
ins = dataset_inputs + dataset_targets + sample_weights + [0]
else:
ins = dataset_inputs + dataset_targets
for m in model.stateful_metric_functions:
m.reset_states()
outs = []
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(
current_strategy, distributed_model, orig_model_weights)
assert steps is not None
for step in range(steps):
batch_outs = distributed_test_function(ins)
if isinstance(batch_outs, list):
if step == 0:
outs = [0.] * len(batch_outs)
outs[0] += batch_outs[0] # index 0 = 'loss'
outs[1:] = batch_outs[1:]
else:
if step == 0:
outs.append(0.)
outs[0] += batch_outs # index 0 = 'loss'
if verbose >= 1:
progbar.update(step + 1)
outs[0] /= steps # index 0 = 'loss'
if len(outs) == 1:
return outs[0]
return outs
def predict_loop(model, iterator, verbose=0, steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
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).
"""
current_strategy = model._distribution_strategy
def _per_device_predict_function(model):
model._make_predict_function()
return (model.predict_function.inputs, model.predict_function.outputs,
model.predict_function.updates_op,
model.predict_function.session_kwargs)
inputs, _ = _get_input_from_iterator(iterator, model)
with current_strategy.scope():
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_tower(
_per_device_predict_function, model._grouped_model)
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs,
grouped_updates, grouped_session_args)
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
distributed_predict_function = K.Function(
all_inputs,
all_outputs,
updates=all_updates,
name='distributed_predict_function',
**all_session_args)
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = dataset_inputs + [0]
else:
ins = dataset_inputs
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
with current_strategy.scope():
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(current_strategy,
distributed_model,
orig_model_weights)
if steps is not None:
# 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 = distributed_predict_function(ins)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if step == 0:
for _ 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))
]
def predict_loop(model, iterator, verbose=0, steps=None):
"""Predict loop for predicting with DistributionStrategy.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
verbose: Integer, Verbosity mode 0 or 1.
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).
"""
current_strategy = model._distribution_strategy
# TODO(priyag, sourabhbajaj): Remove this when the codepaths are merged.
if current_strategy.__class__.__name__ == 'TPUStrategy':
return _experimental_predict_loop(model, iterator, verbose, steps)
if not model._grouped_model:
clone_model_on_replicas(model, current_strategy)
def _per_device_predict_function(model):
model._make_predict_function()
return (model.predict_function.inputs,
model.predict_function.outputs,
model.predict_function.updates_op,
model.predict_function.session_kwargs)
inputs, _, _ = _get_input_from_iterator(iterator, model)
with current_strategy.scope():
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_replica(
_per_device_predict_function, args=(model._grouped_model,))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args)
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
distributed_predict_function = K.function(
all_inputs, all_outputs,
updates=all_updates,
name='distributed_predict_function',
**all_session_args)
if not isinstance(K.learning_phase(), int):
ins = dataset_inputs + [0]
else:
ins = dataset_inputs
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(
current_strategy, distributed_model, orig_model_weights)
num_replicas = current_strategy.num_replicas_in_sync
# 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 = []
assert steps is not None
for step in range(steps):
batch_outs = distributed_predict_function(ins)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if step == 0:
# batch_outs gives you the number of model outputs. In the distributed
# case this will be number of model_outputs * num_replicas.
for _ in range(len(model.outputs)):
unconcatenated_outs.append([])
for i in range(len(model.outputs)):
nested_outs = batch_outs[i * num_replicas:
i * num_replicas + num_replicas]
outs = nest.flatten(nested_outs)
unconcatenated_outs[i].extend(outs)
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))
]
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 experimental_predict_loop(model, iterator, verbose=0, steps=None):
"""Predict loop for predicting with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
verbose: Integer, Verbosity mode 0 or 1.
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).
"""
current_strategy = model._distribution_strategy
K.get_session().run(current_strategy.initialize())
# TODO(priyag, sourabhbajaj): This should likely not be hardcoded here.
K.set_learning_phase(0)
def _per_device_predict_function(model):
model._make_predict_function()
return (model.predict_function.inputs, model.predict_function.outputs,
model.predict_function.updates_op,
model.predict_function.session_kwargs)
def step_fn(ctx, inputs):
"""Clones the model and calls make_predict_function."""
# TODO(priyag, sourabhbajaj): The model gets cloned every time
# fit/test/predict is called. We should look into caching this keyed on
# input shapes.
clone_model_on_replicas(model,
current_strategy,
make_callback_model=False,
inputs=inputs,
mode=_Mode.PREDICT)
(grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args
) = current_strategy.extended.call_for_each_replica(
_per_device_predict_function,
args=(model._grouped_model_predict, ))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs,
grouped_updates, grouped_session_args)
combined_fn = K.function(all_inputs,
all_outputs,
updates=all_updates,
name='distributed_predict_function',
**all_session_args)
for label, output in zip(model.output_names, combined_fn.outputs):
ctx.set_last_step_output(label, output)
return combined_fn.updates_op
# Add initial dummy values for outputs.
initial_loop_values = {}
batch_dimension = distributed_training_utils.get_batch_dimension(iterator)
for name, tensor in zip(model.output_names, model.outputs):
# TODO(priyag): This is a workaround as we do not know the batch dimension
# of the model's output at this point.
shape = tensor_shape.TensorShape(tensor.shape.dims)
shape.dims = [batch_dimension] + shape.dims[1:]
initial_loop_values[name] = array_ops.zeros(shape, tensor.dtype)
with current_strategy.scope():
# TODO(priyag, sourabhbajaj): Support steps_per_run if/when we add outfeed.
ctx = current_strategy.extended.experimental_run_steps_on_iterator(
step_fn,
iterator,
iterations=1,
initial_loop_values=initial_loop_values)
predict_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
with current_strategy.scope():
distributed_model = current_strategy.unwrap(
model._grouped_model_predict)[0]
distributed_training_utils.set_weights(current_strategy,
distributed_model,
orig_model_weights)
assert steps is not None
# 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 _ in model.outputs]
for step in range(steps):
_, batch_outs = K.get_session().run([predict_op, output_tensors])
# TODO(priyag): maybe need to unwrap the outputs first for MirroredStrategy.
for i, label in enumerate(model.output_names):
unconcatenated_outs[i].extend(batch_outs[label])
if verbose >= 1:
progbar.update(step + 1)
K.get_session().run(current_strategy.finalize())
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))
]
def experimental_tpu_test_loop(model,
iterator,
verbose=0,
steps=None):
"""Test loop for evaluating with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
verbose: Integer, Verbosity mode 0 or 1.
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 outputs.
"""
current_strategy = model._distribution_strategy
scope = current_strategy.scope()
scope.__enter__()
def _per_device_eval_function(model):
model._make_eval_function()
return (model._eval_function.inputs, model._eval_function.outputs,
model._eval_function.updates_op,
model._eval_function.session_kwargs)
# TODO(priyag, sourabhbajaj): This should likely not be hardcoded here.
K.set_learning_phase(0)
def step_fn(ctx, inputs):
"""Clones the model and calls make_eval_function."""
inputs, targets = inputs
if model._compile_distribution:
distributed_training_utils. clone_model_on_replicas(
model, current_strategy,
make_callback_model=False, inputs=inputs,
targets=targets, mode=distributed_training_utils.ModeKeys.TEST)
else:
distributed_training_utils._build_distributed_network(
model, current_strategy, inputs, targets,
mode=distributed_training_utils.ModeKeys.TEST)
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.extended.call_for_each_replica(
_per_device_eval_function, args=(model._distributed_model_test,))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args)
combined_fn = K.function(
all_inputs, all_outputs,
updates=all_updates,
name='distributed_test_function',
**all_session_args)
for label, output in zip(model.metrics_names, combined_fn.outputs):
if label == 'loss':
reduce_op = distribute_lib.get_loss_reduction()
else:
# We reduce all other metrics using mean for now. This is temporary
# workaround until new metrics are in place.
reduce_op = ds_reduce_util.ReduceOp.MEAN
ctx.set_last_step_output(label, output, reduce_op)
return combined_fn.updates_op
# Add initial dummy values for loss and other metric tensors.
initial_loop_values = {}
initial_loop_values['loss'] = constant_op.constant(1e7)
for name in model.metrics_names[1:]:
tensor = model._all_stateful_metrics_tensors[name]
initial_loop_values[name] = array_ops.zeros(tensor.shape, tensor.dtype)
with current_strategy.scope():
# TODO(priyag): Use steps_per_run when we use new metrics as they will
# allow handling metric computation at each step using variables.
ctx = current_strategy.extended.experimental_run_steps_on_iterator(
step_fn, iterator, iterations=1,
initial_loop_values=initial_loop_values)
test_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
with current_strategy.scope():
distributed_training_utils._copy_weights_to_distributed_model(
model, model._distributed_model_test)
with current_strategy.scope():
distributed_training_utils._reset_metrics(
model, model._distributed_model_test)
assert steps is not None
outs = [0.] * len(model.metrics_names)
for step in range(steps):
_, batch_outs = K.get_session().run([test_op, output_tensors])
for i, label in enumerate(model.metrics_names):
if i == 0:
# Loss is stateless metrics.
outs[i] += batch_outs[label]
else:
# For all stateful metrics, the aggregation is handled by mirrored vars.
outs[i] = batch_outs[label]
if verbose >= 1:
progbar.update(step + 1)
scope.__exit__(None, None, None)
if len(outs) >= 0:
outs[0] /= (steps)
if len(outs) == 1:
return outs[0]
return outs
def predict_generator(model,
generator,
steps=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
verbose=0):
"""See docstring for `Model.predict_generator`."""
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 train(self,
idx_train,
idx_val=None,
epochs=200,
early_stopping=None,
verbose=0,
save_best=True,
weight_path=None,
as_model=False,
monitor='val_acc',
early_stop_metric='val_loss',
callbacks=None,
**kwargs):
"""Train the model for the input `idx_train` of nodes or `sequence`.
Note:
----------
You must compile your model before training/testing/predicting. Use `model.build()`.
Parameters:
----------
idx_train: Numpy array-like, `list`, Integer scalar or `graphgallery.Sequence`
The index of nodes (or sequence) that will be used during training.
idx_val: Numpy array-like, `list`, Integer scalar or
`graphgallery.Sequence`, optional
The index of nodes (or sequence) that will be used for validation.
(default :obj: `None`, i.e., do not use validation during training)
epochs: Positive integer
The number of epochs of training.(default :obj: `200`)
early_stopping: Positive integer or None
The number of early stopping patience during training. (default :obj: `None`,
i.e., do not use early stopping during training)
verbose: int in {0, 1, 2, 3, 4}
'verbose=0': not verbose;
'verbose=1': Progbar (one line, detailed);
'verbose=2': Progbar (one line, omitted);
'verbose=3': Progbar (multi line, detailed);
'verbose=4': Progbar (multi line, omitted);
(default :obj: 0)
save_best: bool
Whether to save the best weights (accuracy of loss depend on `monitor`)
of training or validation (depend on `validation` is `False` or `True`).
(default :bool: `True`)
weight_path: String or None
The path of saved weights/model. (default :obj: `None`, i.e.,
`./log/{self.name}_weights`)
as_model: bool
Whether to save the whole model or weights only, if `True`, the `self.custom_objects`
must be speficied if you are using custom `layer` or `loss` and so on.
monitor: String
One of (val_loss, val_acc, loss, acc), it determines which metric will be
used for `save_best`. (default :obj: `val_acc`)
early_stop_metric: String
One of (val_loss, val_acc, loss, acc), it determines which metric will be
used for early stopping. (default :obj: `val_loss`)
callbacks: tensorflow.keras.callbacks. (default :obj: `None`)
kwargs: other keyword Parameters.
Return:
----------
A `tf.keras.callbacks.History` object. Its `History.history` attribute is
a record of training loss values and metrics values
at successive epochs, as well as validation loss values
and validation metrics values (if applicable).
"""
raise_if_kwargs(kwargs)
if not (isinstance(verbose, int) and 0 <= verbose <= 4):
raise ValueError("'verbose=0': not verbose"
"'verbose=1': Progbar(one line, detailed), "
"'verbose=2': Progbar(one line, omitted), "
"'verbose=3': Progbar(multi line, detailed), "
"'verbose=4': Progbar(multi line, omitted), "
f"but got {verbose}")
model = self.model
# Check if model has been built
if model is None:
raise RuntimeError(
'You must compile your model before training/testing/predicting. Use `model.build()`.'
)
if isinstance(idx_train, Sequence):
train_data = idx_train
else:
idx_train = asintarr(idx_train)
train_data = self.train_sequence(idx_train)
self.idx_train = idx_train
validation = idx_val is not None
if validation:
if isinstance(idx_val, Sequence):
val_data = idx_val
else:
idx_val = asintarr(idx_val)
val_data = self.test_sequence(idx_val)
self.idx_val = idx_val
else:
monitor = 'acc' if monitor[:3] == 'val' else monitor
if not isinstance(callbacks, callbacks_module.CallbackList):
callbacks = callbacks_module.CallbackList(callbacks)
history = History()
callbacks.append(history)
if early_stopping:
es_callback = EarlyStopping(monitor=early_stop_metric,
patience=early_stopping,
mode='auto',
verbose=kwargs.pop('es_verbose', 1))
callbacks.append(es_callback)
if save_best:
if not weight_path:
weight_path = self.weight_path
else:
self.weight_path = weight_path
makedirs_from_path(weight_path)
if not weight_path.endswith(POSTFIX):
weight_path = weight_path + POSTFIX
mc_callback = ModelCheckpoint(weight_path,
monitor=monitor,
save_best_only=True,
save_weights_only=not as_model,
verbose=0)
callbacks.append(mc_callback)
callbacks.set_model(model)
model.stop_training = False
callbacks.on_train_begin()
if verbose:
stateful_metrics = {"acc", 'loss', 'val_acc', 'val_loss', 'time'}
if verbose <= 2:
progbar = Progbar(target=epochs,
verbose=verbose,
stateful_metrics=stateful_metrics)
print("Training...")
begin_time = time.perf_counter()
for epoch in range(epochs):
if verbose > 2:
progbar = Progbar(target=len(train_data),
verbose=verbose - 2,
stateful_metrics=stateful_metrics)
callbacks.on_epoch_begin(epoch)
callbacks.on_train_batch_begin(0)
loss, accuracy = self.train_step(train_data)
training_logs = {'loss': loss, 'acc': accuracy}
if validation:
val_loss, val_accuracy = self.test_step(val_data)
training_logs.update({
'val_loss': val_loss,
'val_acc': val_accuracy
})
val_data.on_epoch_end()
callbacks.on_train_batch_end(len(train_data), training_logs)
callbacks.on_epoch_end(epoch, training_logs)
train_data.on_epoch_end()
if verbose:
time_passed = time.perf_counter() - begin_time
training_logs.update({'time': time_passed})
if verbose > 2:
print(f"Epoch {epoch+1}/{epochs}")
progbar.update(len(train_data), training_logs.items())
else:
progbar.update(epoch + 1, training_logs.items())
if model.stop_training:
break
callbacks.on_train_end()
if save_best:
self.load(weight_path, as_model=as_model)
self.remove_weights()
return history
def experimental_tpu_test_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Test loop for evaluating with TPU tf.distribute.Strategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
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 outputs.
"""
mode = ModeKeys.TEST
current_strategy = model._distribution_strategy
iterator = dist_utils.get_iterator(dataset, current_strategy)
scope = dist_utils.distributed_scope(strategy=current_strategy,
learning_phase=0)
scope.__enter__()
out_labels = model.metrics_names
def _test_step_fn(inputs):
"""A fn that returns output of single test step."""
if isinstance(inputs, (tuple, list)) and len(inputs) == 2:
inputs, targets = inputs
else:
targets = None
(distribution_strategy_context.get_replica_context().merge_call(
_build_model, args=(model, mode, inputs, targets)))
(_, outputs, updates, _) = _per_replica_execution_function(
dist_utils.get_distributed_model(model, mode), mode)
with ops.control_dependencies([updates]):
return outputs
test_input_data = iterator.get_next()
per_replica_outputs = current_strategy.experimental_run_v2(
_test_step_fn, args=(test_input_data, ))
output_tensors = {}
for label, output in zip(out_labels, per_replica_outputs):
if label == 'loss':
reduce_op = ds_reduce_util.ReduceOp.SUM
else:
# We reduce all other metrics using mean for now. This is temporary
# workaround until new metrics are in place.
reduce_op = ds_reduce_util.ReduceOp.MEAN
output_tensors[label] = current_strategy.reduce(reduce_op,
output,
axis=None)
test_op = control_flow_ops.group(list(output_tensors.values()))
if verbose >= 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
dist_utils._copy_weights_to_distributed_model(model, mode)
dist_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=ModeKeys.TEST)
callbacks._call_begin_hook(mode)
outs = [0.] * len(model.metrics_names)
if steps is not None:
target_steps = steps
else:
raise ValueError(
'Number of steps could not be inferred from the data, '
'please pass the steps argument.')
current_step = 0
while current_step < target_steps:
batch_logs = {'batch': current_step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', current_step, batch_logs)
try:
_, batch_outs = K.batch_get_value([test_op, output_tensors])
except errors.OutOfRangeError:
warning_msg = 'Make sure that your dataset can generate at least '
'`steps` batches (in this case, {} batches).'.format(steps)
logging.warning('Your dataset iterator ran out of data; '
'interrupting evaluation. ' + warning_msg)
target_steps = current_step
break
for i, label in enumerate(model.metrics_names):
if i == 0:
# Loss is stateless metrics.
outs[i] += batch_outs[label]
else:
# For all stateful metrics, the aggregation is handled by mirrored vars.
outs[i] = batch_outs[label]
batch_logs = cbks.make_logs(model, batch_logs, outs, mode)
callbacks._call_batch_hook(mode, 'end', current_step, batch_logs)
if verbose == 1:
progbar.update(current_step + 1)
current_step += 1
if verbose >= 1:
# Progress bar finishes at the end.
progbar.update(target_steps)
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(outs) >= 0:
outs[0] /= (target_steps)
if len(outs) == 1:
return outs[0]
return outs
def experimental_tpu_test_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Test loop for evaluating with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
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 outputs.
"""
mode = ModeKeys.TEST
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset,
current_strategy)
steps = training_utils.infer_steps_for_dataset(dataset,
steps,
steps_name='steps')
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=0)
scope.__enter__()
def _per_device_eval_function(model):
model._make_eval_function()
return (model._eval_function.inputs, model._eval_function.outputs,
model._eval_function.updates_op,
model._eval_function.session_kwargs)
def step_fn(ctx, inputs):
"""Clones the model and calls make_eval_function."""
inputs, targets = inputs
if model._compile_distribution:
distributed_training_utils.clone_model_on_replicas(
model,
current_strategy,
mode=mode,
inputs=inputs,
targets=targets)
else:
distributed_training_utils._build_distributed_network(
model, current_strategy, mode, inputs, targets)
(grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args
) = current_strategy.extended.call_for_each_replica(
_per_device_eval_function,
args=(distributed_training_utils.get_distributed_model(
model, ModeKeys.TEST), ))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs,
grouped_updates, grouped_session_args)
combined_fn = K.function(all_inputs,
all_outputs,
updates=all_updates,
name='distributed_test_function',
**all_session_args)
for label, output in zip(model.metrics_names, combined_fn.outputs):
if label == 'loss':
reduce_op = ds_reduce_util.ReduceOp.SUM
else:
# We reduce all other metrics using mean for now. This is temporary
# workaround until new metrics are in place.
reduce_op = ds_reduce_util.ReduceOp.MEAN
ctx.set_last_step_output(label, output, reduce_op)
return combined_fn.updates_op
# Add initial dummy values for loss and other metric tensors.
initial_loop_values = {}
initial_loop_values['loss'] = constant_op.constant(1e7)
for name in model.metrics_names[1:]:
tensor = model._all_stateful_metrics_tensors[name]
initial_loop_values[name] = array_ops.zeros(tensor.shape, tensor.dtype)
# TODO(priyag): Use steps_per_run when we use new metrics as they will
# allow handling metric computation at each step using variables.
ctx = current_strategy.extended.experimental_run_steps_on_iterator(
step_fn,
iterator,
iterations=1,
initial_loop_values=initial_loop_values)
test_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(
model, mode)
distributed_training_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=ModeKeys.TEST)
callbacks._call_begin_hook(mode)
outs = [0.] * len(model.metrics_names)
if steps is not None:
target_steps = steps
else:
target_steps = np.inf
current_step = 0
while current_step < target_steps:
batch_logs = {'batch': current_step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', current_step, batch_logs)
try:
_, batch_outs = K.get_session().run([test_op, output_tensors])
except errors.OutOfRangeError:
if steps is not None:
warning_msg = 'Make sure that your dataset can generate at least '
'`steps` batches (in this case, {} batches).'.format(steps)
else:
warning_msg = 'Number of steps ran: {} steps'.format(
current_step)
logging.warning('Your dataset iterator ran out of data; '
'interrupting evaluation. ' + warning_msg)
target_steps = current_step
break
for i, label in enumerate(model.metrics_names):
if i == 0:
# Loss is stateless metrics.
outs[i] += batch_outs[label]
else:
# For all stateful metrics, the aggregation is handled by mirrored vars.
outs[i] = batch_outs[label]
batch_logs = cbks.make_logs(model, batch_logs, outs, mode)
callbacks._call_batch_hook(mode, 'end', current_step, batch_logs)
if verbose >= 1:
progbar.update(current_step + 1)
current_step += 1
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(outs) >= 0:
outs[0] /= (target_steps)
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 experimental_tpu_predict_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Predict loop for predicting with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
mode = ModeKeys.PREDICT
steps = training_utils.infer_steps_for_dataset(dataset,
steps,
steps_name='steps')
dataset_fully_shaped = (
distributed_training_utils.is_dataset_shape_fully_defined(dataset))
padding_handler = None
if not dataset_fully_shaped:
# TODO(hongjunchoi): Investigate whether operations from
# PartialBatchPaddingHandler are unnecessarily pruned out
# during graph optimization.
padding_handler = padding_util.PartialBatchPaddingHandler(
model._feed_output_shapes)
batch_size, _, prefetch_buffer = input_lib._get_dataset_attributes(
dataset)
padding_handler.padded_batch_size = batch_size
padding_handler.padding_mask = dataset.reduce(
padding_handler.padding_mask, padding_handler.update_mask)
dataset = dataset.map(padding_handler.pad_batch)
dataset = dataset.apply(batching.unbatch())
# Upon this point, it is guaranteed that the dataset does not
# have partial batches. Thus, we set `drop_remainder=True` to
# get static shape information about the elements in the dataset.
dataset = dataset.batch(batch_size, drop_remainder=True)
if prefetch_buffer is not None:
dataset = dataset.prefetch(prefetch_buffer)
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset,
current_strategy)
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=0)
scope.__enter__()
def _per_device_predict_function(model):
model._make_predict_function()
return (model.predict_function.inputs, model.predict_function.outputs,
model.predict_function.updates_op,
model.predict_function.session_kwargs)
def step_fn(ctx, inputs):
"""Clones the model and calls make_predict_function."""
if model._compile_distribution:
distributed_training_utils.clone_model_on_replicas(
model, current_strategy, mode, inputs=inputs)
else:
distributed_training_utils._build_distributed_network(
model, current_strategy, mode, inputs)
(grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args
) = current_strategy.extended.call_for_each_replica(
_per_device_predict_function,
args=(distributed_training_utils.get_distributed_model(
model, ModeKeys.PREDICT), ))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs,
grouped_updates, grouped_session_args)
combined_fn = K.function(all_inputs,
all_outputs,
updates=all_updates,
name='distributed_predict_function',
**all_session_args)
for label, output in zip(model.output_names, combined_fn.outputs):
ctx.set_last_step_output(label, output)
return combined_fn.updates_op
# Add initial dummy values for outputs.
initial_loop_values = {}
batch_dimension = distributed_training_utils.get_batch_dimension(iterator)
for name, tensor in zip(model.output_names, model.outputs):
# TODO(priyag): This is a workaround as we do not know the batch dimension
# of the model's output at this point.
shape = tensor_shape.TensorShape(tensor.shape.dims)
shape.dims = [batch_dimension] + shape.dims[1:]
initial_loop_values[name] = array_ops.zeros(shape, tensor.dtype)
# TODO(priyag, sourabhbajaj): Support steps_per_run if/when we add outfeed.
ctx = current_strategy.extended.experimental_run_steps_on_iterator(
step_fn,
iterator,
iterations=1,
initial_loop_values=initial_loop_values)
predict_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(
model, mode)
distributed_training_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=mode)
callbacks._call_begin_hook(mode)
# 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 _ in model.outputs]
if steps is not None:
target_steps = steps
else:
target_steps = np.inf
current_step = 0
while current_step < target_steps:
batch_logs = {'batch': current_step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', current_step, batch_logs)
try:
_, batch_outs = K.get_session().run([predict_op, output_tensors])
except errors.OutOfRangeError:
if steps is not None:
warning_msg = 'Make sure that your dataset can generate at least '
'`steps` batches (in this case, {} batches).'.format(steps)
else:
warning_msg = 'Number of steps ran: {} steps'.format(
current_step)
logging.warning('Your dataset iterator ran out of data; '
'interrupting evaluation. ' + warning_msg)
break
# TODO(priyag): maybe need to unwrap the outputs first for MirroredStrategy.
for i, label in enumerate(model.output_names):
unconcatenated_outs[i].extend(batch_outs[label])
batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
callbacks._call_batch_hook(mode, 'end', current_step, batch_logs)
if verbose >= 1:
progbar.update(current_step + 1)
current_step += 1
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(unconcatenated_outs) == 1:
prediction_result = np.concatenate(unconcatenated_outs[0], axis=0)
else:
prediction_result = [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
if padding_handler:
prediction_result = padding_handler.apply_mask(prediction_result)
return prediction_result
def _experimental_test_loop(model, iterator, verbose=0, steps=None):
"""Test loop for evaluating with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
verbose: Integer, Verbosity mode 0 or 1.
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 outputs.
"""
current_strategy = model._distribution_strategy
K.get_session().run(current_strategy.initialize())
def _per_device_test_function(model):
model._make_test_function()
return (model.test_function.inputs, model.test_function.outputs,
model.test_function.updates_op,
model.test_function.session_kwargs)
# TODO(priyag, sourabhbajaj): This should likely not be hardcoded here.
K.set_learning_phase(0)
def step_fn(ctx, inputs, targets):
"""Clones the model and calls make_test_function."""
# TODO(priyag, sourabhbajaj): The model gets cloned every time
# fit/test/predict is called. We should look into caching this keyed on
# input shapes.
clone_model_on_towers(model,
current_strategy,
make_callback_model=False,
inputs=inputs,
targets=targets)
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_tower(
_per_device_test_function, model._grouped_model)
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs,
grouped_updates, grouped_session_args)
combined_fn = K.Function(all_inputs,
all_outputs,
updates=all_updates,
name='distributed_test_function',
**all_session_args)
for label, output in zip(model.metrics_names, combined_fn.outputs):
if label == 'loss':
aggregation = distribute_lib.get_loss_reduction()
else:
# We aggregate all other metrics using mean for now. This is temporary
# workaround until new metrics are in place.
aggregation = variable_scope.VariableAggregation.MEAN
ctx.set_last_step_output(label, output, aggregation)
return combined_fn.updates_op
# Add initial dummy values for loss and other metric tensors.
initial_loop_values = {}
initial_loop_values['loss'] = constant_op.constant(1e7)
for name, tensor in zip(model.metrics_names[1:], model.metrics_tensors):
initial_loop_values[name] = array_ops.zeros(tensor.shape, tensor.dtype)
with current_strategy.scope():
# TODO(priyag): Use steps_per_run when we use new metrics as they will
# allow handling metric computation at each step using variables.
ctx = current_strategy.run_steps_on_dataset(
step_fn,
iterator,
iterations=1,
initial_loop_values=initial_loop_values)
test_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
with current_strategy.scope():
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(current_strategy,
distributed_model,
orig_model_weights)
assert steps is not None
outs = [0.] * len(model.metrics_names)
for step in range(steps):
_, batch_outs = K.get_session().run([test_op, output_tensors])
for i, label in enumerate(model.metrics_names):
outs[i] += batch_outs[label]
if verbose >= 1:
progbar.update(step + 1)
for i in range(len(outs)):
outs[i] /= (steps)
K.get_session().run(current_strategy.finalize())
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 predict_generator(model,
generator,
steps=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
verbose=0):
"""See docstring for `Model.predict_generator`."""
if not context.executing_eagerly():
model._make_predict_function()
steps_done = 0
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)
enqueuer.start(workers=workers, max_queue_size=max_queue_size)
output_generator = enqueuer.get()
else:
if is_sequence:
output_generator = iter_sequence_infinite(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 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.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 = []
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:
outs.extend([0.] * len(batch_outs))
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 experimental_tpu_predict_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Predict loop for predicting with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
mode = ModeKeys.PREDICT
dataset_fully_shaped = (distributed_training_utils.
is_dataset_shape_fully_defined(dataset))
padding_handler = None
if not dataset_fully_shaped:
# TODO(hongjunchoi): Investigate whether operations from
# PartialBatchPaddingHandler are unnecessarily pruned out
# during graph optimization.
padding_handler = padding_util.PartialBatchPaddingHandler(
model._feed_output_shapes)
batch_size, _, prefetch_buffer = input_lib._get_dataset_attributes(dataset)
padding_handler.padded_batch_size = batch_size
padding_handler.padding_mask = dataset.reduce(padding_handler.padding_mask,
padding_handler.update_mask)
dataset = dataset.map(padding_handler.pad_batch)
dataset = dataset.apply(batching.unbatch())
# Upon this point, it is guaranteed that the dataset does not
# have partial batches. Thus, we set `drop_remainder=True` to
# get static shape information about the elements in the dataset.
dataset = dataset.batch(batch_size, drop_remainder=True)
if prefetch_buffer is not None:
dataset = dataset.prefetch(prefetch_buffer)
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset, current_strategy)
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=0)
scope.__enter__()
def _per_device_predict_function(model):
model._make_predict_function()
return (model.predict_function.inputs,
model.predict_function.outputs,
model.predict_function.updates_op,
model.predict_function.session_kwargs)
def step_fn(ctx, inputs):
"""Clones the model and calls make_predict_function."""
if model._compile_distribution:
distributed_training_utils.clone_model_on_replicas(
model, current_strategy, mode, inputs=inputs)
else:
distributed_training_utils._build_distributed_network(
model, current_strategy, mode, inputs)
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.extended.call_for_each_replica(
_per_device_predict_function,
args=(distributed_training_utils.get_distributed_model(
model, ModeKeys.PREDICT),))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args)
combined_fn = K.function(
all_inputs, all_outputs,
updates=all_updates,
name='distributed_predict_function',
**all_session_args)
for label, output in zip(model.output_names, combined_fn.outputs):
ctx.set_last_step_output(label, output)
return combined_fn.updates_op
# Add initial dummy values for outputs.
initial_loop_values = {}
batch_dimension = distributed_training_utils.get_batch_dimension(iterator)
for name, tensor in zip(model.output_names, model.outputs):
# TODO(priyag): This is a workaround as we do not know the batch dimension
# of the model's output at this point.
shape = tensor_shape.TensorShape(tensor.shape.dims)
shape.dims = [batch_dimension] + shape.dims[1:]
initial_loop_values[name] = array_ops.zeros(shape, tensor.dtype)
# TODO(priyag, sourabhbajaj): Support steps_per_run if/when we add outfeed.
ctx = current_strategy.extended.experimental_run_steps_on_iterator(
step_fn, iterator, iterations=1,
initial_loop_values=initial_loop_values)
predict_op = ctx.run_op
output_tensors = ctx.last_step_outputs
if verbose == 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(model, mode)
distributed_training_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=mode)
callbacks._call_begin_hook(mode)
assert steps is not None
# 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 _ in model.outputs]
for step in range(steps):
batch_logs = {'batch': step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', step, batch_logs)
_, batch_outs = K.get_session().run([predict_op, output_tensors])
# TODO(priyag): maybe need to unwrap the outputs first for MirroredStrategy.
for i, label in enumerate(model.output_names):
unconcatenated_outs[i].extend(batch_outs[label])
batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
callbacks._call_batch_hook(mode, 'end', step, batch_logs)
if verbose >= 1:
progbar.update(step + 1)
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(unconcatenated_outs) == 1:
prediction_result = np.concatenate(unconcatenated_outs[0], axis=0)
else:
prediction_result = [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
if padding_handler:
prediction_result = padding_handler.apply_mask(prediction_result)
return prediction_result
def train(self,
train_data,
val_data=None,
epochs=200,
early_stopping=None,
verbose=1,
save_best=True,
ckpt_path=None,
as_model=False,
monitor='val_accuracy',
early_stop_metric='val_loss',
callbacks=None,
**kwargs):
"""Train the model for the input `train_data` of nodes or `sequence`.
Note:
----------
You must compile your model before training/testing/predicting. Use `model.build()`.
Parameters:
----------
train_data: Numpy array-like, `list`, Integer scalar or `graphgallery.Sequence`
The index of nodes (or sequence) that will be used during training.
val_data: Numpy array-like, `list`, Integer scalar or
`graphgallery.Sequence`, optional
The index of nodes (or sequence) that will be used for validation.
(default :obj: `None`, i.e., do not use validation during training)
epochs: Positive integer
The number of epochs of training.(default :obj: `200`)
early_stopping: Positive integer or None
The number of early stopping patience during training. (default :obj: `None`,
i.e., do not use early stopping during training)
verbose: int in {0, 1, 2, 3, 4}
'verbose=0': not verbose;
'verbose=1': Progbar (one line, detailed);
'verbose=2': Progbar (one line, omitted);
'verbose=3': Progbar (multi line, detailed);
'verbose=4': Progbar (multi line, omitted);
(default :obj: 1)
save_best: bool
Whether to save the best weights (accuracy of loss depend on `monitor`)
of training or validation (depend on `validation` is `False` or `True`).
(default :bool: `True`)
ckpt_path: String or None
The path of saved weights/model. (default :obj: `None`, i.e.,
`./log/{self.name}_weights`)
as_model: bool
Whether to save the whole model or weights only, if `True`, the `self.custom_objects`
must be speficied if you are using custom `layer` or `loss` and so on.
monitor: String
One of (val_loss, val_acc, loss, acc), it determines which metric will be
used for `save_best`. (default :obj: `val_acc`)
early_stop_metric: String
One of (val_loss, val_acc, loss, acc), it determines which metric will be
used for early stopping. (default :obj: `val_loss`)
callbacks: tensorflow.keras.callbacks. (default :obj: `None`)
kwargs: other keyword Parameters.
Return:
----------
A `tf.keras.callbacks.History` object. Its `History.history` attribute is
a record of training loss values and metrics values
at successive epochs, as well as validation loss values
and validation metrics values (if applicable).
"""
raise_if_kwargs(kwargs)
if not (isinstance(verbose, int) and 0 <= verbose <= 4):
raise ValueError("'verbose=0': not verbose"
"'verbose=1': Progbar(one line, detailed), "
"'verbose=2': Progbar(one line, omitted), "
"'verbose=3': Progbar(multi line, detailed), "
"'verbose=4': Progbar(multi line, omitted), "
f"but got {verbose}")
model = self.model
# Check if model has been built
if model is None:
raise RuntimeError(
'You must compile your model before training/testing/predicting. Use `model.build()`.'
)
metrics_names = getattr(model, "metrics_names", None)
# FIXME: This would be return '[]' for tensorflow>=2.2.0
# See <https://github.com/tensorflow/tensorflow/issues/37990>
if not metrics_names:
raise RuntimeError(
f"Please specify the attribute 'metrics_names' for the model.")
if not isinstance(train_data, Sequence):
train_data = self.train_sequence(train_data)
self.train_data = train_data
validation = val_data is not None
if validation:
if not isinstance(val_data, Sequence):
val_data = self.test_sequence(val_data)
self.val_data = val_data
metrics_names = metrics_names + [
"val_" + metric for metric in metrics_names
]
if not isinstance(callbacks, callbacks_module.CallbackList):
callbacks = callbacks_module.CallbackList(callbacks)
history = History()
callbacks.append(history)
if early_stopping:
es_callback = EarlyStopping(monitor=early_stop_metric,
patience=early_stopping,
mode='auto',
verbose=kwargs.pop('es_verbose', 1))
callbacks.append(es_callback)
if save_best:
if not ckpt_path:
ckpt_path = self.ckpt_path
else:
self.ckpt_path = ckpt_path
makedirs_from_filepath(ckpt_path)
if not ckpt_path.endswith(gg.file_ext()):
ckpt_path = ckpt_path + gg.file_ext()
assert monitor in metrics_names, f"'{monitor}' are not included in the metrics names."
mc_callback = ModelCheckpoint(ckpt_path,
monitor=monitor,
save_best_only=True,
save_weights_only=not as_model,
verbose=0)
callbacks.append(mc_callback)
callbacks.set_model(model)
model.stop_training = False
metrics_names = metrics_names + ["Duration"]
if verbose:
stateful_metrics = set(metrics_names)
if verbose <= 2:
progbar = Progbar(target=epochs,
verbose=verbose,
stateful_metrics=stateful_metrics)
print("Training...")
logs = BunchDict()
begin_time = time.perf_counter()
callbacks.on_train_begin()
try:
for epoch in range(epochs):
if verbose > 2:
progbar = Progbar(target=len(train_data),
verbose=verbose - 2,
stateful_metrics=stateful_metrics)
callbacks.on_epoch_begin(epoch)
callbacks.on_train_batch_begin(0)
train_logs = self.train_step(train_data)
train_data.on_epoch_end()
logs.update(train_logs)
if validation:
valid_logs = self.test_step(val_data)
logs.update({("val_" + k): v
for k, v in valid_logs.items()})
val_data.on_epoch_end()
callbacks.on_train_batch_end(len(train_data), logs)
callbacks.on_epoch_end(epoch, logs)
time_passed = time.perf_counter() - begin_time
logs["Duration"] = time_passed
if verbose > 2:
print(f"Epoch {epoch+1}/{epochs}")
progbar.update(len(train_data), logs.items())
elif verbose:
progbar.update(epoch + 1, logs.items())
if model.stop_training:
print(f"Early Stopping in Epoch {epoch}", file=sys.stderr)
break
callbacks.on_train_end()
self.load(ckpt_path, as_model=as_model)
finally:
# to avoid unexpected termination of the model
self.remove_weights()
return history
