"""Part of the training engine related to plain array data (e.g. Numpy).
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from scipy.sparse import issparse
from .training_utils import batch_shuffle
from .training_utils import make_batches
from .training_utils import check_num_samples
from .. import backend as K
from .. import callbacks as cbks
from ..utils.generic_utils import Progbar
from ..utils.generic_utils import slice_arrays
from ..utils.generic_utils import to_list
from ..utils.generic_utils import unpack_singleton
def fit_loop(model, f, ins,
out_labels=None,
batch_size=None,
epochs=100,
verbose=1,
callbacks=None,
val_f=None,
val_ins=None,
shuffle=True,
callback_metrics=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None):
"""Abstract fit function for `f(ins)`.
Assumes that f returns a list, labeled by out_labels.
# Arguments
model: Keras model instance.
f: Keras function returning a list of tensors
ins: List of tensors to be fed to `f`
out_labels: List of strings, display names of
the outputs of `f`
batch_size: Integer batch size or None if unknown.
epochs: Number of times to iterate over the data
verbose: Verbosity mode, 0, 1 or 2
callbacks: List of callbacks to be called during training
val_f: Keras function to call for validation
val_ins: List of tensors to be fed to `val_f`
shuffle: Whether to shuffle the data at the beginning of each epoch
callback_metrics: List of strings, the display names of the metrics
passed to the callbacks. They should be the
concatenation of list the display names of the outputs of
`f` and the list of display names of the outputs of `f_val`.
initial_epoch: Epoch at which to start training
(useful for resuming a previous training run)
steps_per_epoch: Total number of steps (batches of samples)
before declaring one epoch finished and starting the
next epoch. Ignored with the default value of `None`.
validation_steps: Number of steps to run validation for
(only if doing validation from data tensors).
Ignored with the default value of `None`.
# Returns
`History` object.
"""
do_validation = False
if val_f and val_ins:
do_validation = True
if (verbose and ins and
hasattr(ins[0], 'shape') and hasattr(val_ins[0], 'shape')):
print('Train on %d samples, validate on %d samples' %
(ins[0].shape[0], val_ins[0].shape[0]))
if validation_steps:
do_validation = True
if steps_per_epoch is None:
raise ValueError('Can only use `validation_steps` '
'when doing step-wise '
'training, i.e. `steps_per_epoch` '
'must be set.')
elif do_validation:
if steps_per_epoch:
raise ValueError('Must specify `validation_steps` '
'to perform validation '
'when doing step-wise training.')
num_train_samples = check_num_samples(ins,
batch_size=batch_size,
steps=steps_per_epoch,
steps_name='steps_per_epoch')
if num_train_samples is not None:
index_array = np.arange(num_train_samples)
model.history = cbks.History()
_callbacks = [cbks.BaseLogger(
stateful_metrics=model.stateful_metric_names)]
if verbose:
if steps_per_epoch is not None:
count_mode = 'steps'
else:
count_mode = 'samples'
_callbacks.append(
cbks.ProgbarLogger(
count_mode,
stateful_metrics=model.stateful_metric_names))
_callbacks += (callbacks or []) + [model.history]
callbacks = cbks.CallbackList(_callbacks)
out_labels = out_labels or []
# it's possible to callback a different model than itself
# (used by Sequential models)
if hasattr(model, 'callback_model') and model.callback_model:
callback_model = model.callback_model
else:
callback_model = model
callbacks.set_model(callback_model)
callbacks.set_params({
'batch_size': batch_size,
'epochs': epochs,
'steps': steps_per_epoch,
'samples': num_train_samples,
'verbose': verbose,
'do_validation': do_validation,
'metrics': callback_metrics or [],
})
callbacks.on_train_begin()
callback_model.stop_training = False
for cbk in callbacks:
cbk.validation_data = val_ins
# 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(ins[i]) and not K.is_sparse(feed[i]):
indices_for_conversion_to_dense.append(i)
for epoch in range(initial_epoch, epochs):
# Reset stateful metrics
for m in model.stateful_metric_functions:
m.reset_states()
callbacks.on_epoch_begin(epoch)
epoch_logs = {}
if steps_per_epoch is not None:
for step_index in range(steps_per_epoch):
batch_logs = {}
batch_logs['batch'] = step_index
batch_logs['size'] = 1
callbacks.on_batch_begin(step_index, batch_logs)
outs = f(ins)
outs = to_list(outs)
for l, o in zip(out_labels, outs):
batch_logs[l] = o
callbacks.on_batch_end(step_index, batch_logs)
if callback_model.stop_training:
break
if do_validation:
val_outs = test_loop(model, val_f, val_ins,
steps=validation_steps,
verbose=0)
val_outs = to_list(val_outs)
# Same labels assumed.
for l, o in zip(out_labels, val_outs):
epoch_logs['val_' + l] = o
else:
if shuffle == 'batch':
index_array = batch_shuffle(index_array, batch_size)
elif shuffle:
np.random.shuffle(index_array)
batches = make_batches(num_train_samples, batch_size)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
try:
if isinstance(ins[-1], float):
# Do not slice the training phase flag.
ins_batch = slice_arrays(
ins[:-1], batch_ids) + [ins[-1]]
else:
ins_batch = slice_arrays(ins, batch_ids)
except TypeError:
raise TypeError('TypeError while preparing batch. '
'If using HDF5 input data, '
'pass shuffle="batch".')
batch_logs = {}
batch_logs['batch'] = batch_index
batch_logs['size'] = len(batch_ids)
callbacks.on_batch_begin(batch_index, batch_logs)
for i in indices_for_conversion_to_dense:
ins_batch[i] = ins_batch[i].toarray()
outs = f(ins_batch)
outs = to_list(outs)
for l, o in zip(out_labels, outs):
batch_logs[l] = o
callbacks.on_batch_end(batch_index, batch_logs)
if callback_model.stop_training:
break
if batch_index == len(batches) - 1: # Last batch.
if do_validation:
val_outs = test_loop(model, val_f, val_ins,
batch_size=batch_size,
verbose=0)
val_outs = to_list(val_outs)
# Same labels assumed.
for l, o in zip(out_labels, val_outs):
epoch_logs['val_' + l] = o
callbacks.on_epoch_end(epoch, epoch_logs)
if callback_model.stop_training:
break
callbacks.on_train_end()
return model.history
def predict_loop(model, f, ins, batch_size=32, verbose=0, steps=None):
"""Abstract method to loop over some data in batches.
# Arguments
model: Keras model instance.
f: Keras function returning a list of tensors.
ins: list of tensors to be fed to `f`.
batch_size: integer batch size.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring `predict_loop` finished.
Ignored with the default value of `None`.
# Returns
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
num_samples = check_num_samples(ins,
batch_size=batch_size,
steps=steps,
steps_name='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(ins[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)
batch_outs = to_list(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], float):
# Do not slice the training phase flag.
ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
else:
ins_batch = slice_arrays(ins, batch_ids)
for i in indices_for_conversion_to_dense:
ins_batch[i] = ins_batch[i].toarray()
batch_outs = f(ins_batch)
batch_outs = to_list(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)
return unpack_singleton(outs)
def test_loop(model, f, ins, batch_size=None, verbose=0, steps=None):
"""Abstract method to loop over some data in batches.
# Arguments
model: Keras model instance.
f: Keras function returning a list of tensors.
ins: list of tensors to be fed to `f`.
batch_size: integer batch size or `None`.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
# Returns
Scalar loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.
"""
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 = check_num_samples(ins,
batch_size=batch_size,
steps=steps,
steps_name='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(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] = float(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], float):
# Do not slice the training phase flag.
ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
else:
ins_batch = slice_arrays(ins, batch_ids)
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
return unpack_singleton(outs)
