def fit_loop(model, inputs, targets, sample_weights=None, batch_size=None, epochs=100, verbose=1, callbacks=None, val_inputs=None, val_targets=None, val_sample_weights=None, shuffle=True, callback_metrics=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None): """Abstract fit function for arrays of data. 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 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_inputs: List of input arrays. val_targets: List of target arrays. val_sample_weights: Optional list of sample weight arrays. 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. Raises: ValueError: in case of invalid arguments. """ model._make_train_function() f = model.train_function sample_weights = sample_weights or [] val_sample_weights = val_sample_weights or [] if model.uses_learning_phase and not isinstance(K.learning_phase(), int): ins = inputs + targets + sample_weights + [1] if val_inputs: val_ins = val_inputs + val_targets + val_sample_weights + [1] else: ins = inputs + targets + sample_weights if val_inputs: val_ins = val_inputs + val_targets + val_sample_weights if not val_inputs: val_ins = [] do_validation = False if val_inputs: do_validation = True if verbose and inputs and hasattr(inputs[0], 'shape') and hasattr( val_inputs[0], 'shape'): print('Train on %d samples, validate on %d samples' % (inputs[0].shape[0], val_inputs[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.') out_labels = model.metrics_names if do_validation: callback_metrics = copy.copy(out_labels) + [ 'val_' + n for n in out_labels ] else: callback_metrics = copy.copy(out_labels) num_train_samples = training_utils.check_num_samples( ins, batch_size, steps_per_epoch, 'steps_per_epoch') if num_train_samples is not None: index_array = np.arange(num_train_samples) model.history = cbks.History() all_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' all_callbacks.append( cbks.ProgbarLogger(count_mode, stateful_metrics=model.stateful_metric_names)) all_callbacks += (callbacks or []) + [model.history] callbacks = cbks.CallbackList(all_callbacks) out_labels = out_labels or [] # it's possible to callback a different model than self # (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 is not None and 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.metrics: if isinstance(m, Layer): m.reset_states() # Update callbacks 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) try: outs = f(ins) except errors.OutOfRangeError: logging.warning( 'Your dataset iterator ran out of data; ' 'interrupting training. Make sure that your dataset ' 'can generate at least `steps_per_epoch * epochs` ' 'batches (in this case, %d batches).' % steps_per_epoch * epochs) break if not isinstance(outs, list): outs = [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_inputs, val_targets, sample_weights=val_sample_weights, batch_size=batch_size, steps=validation_steps, verbose=0) if not isinstance(val_outs, list): val_outs = [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 = training_utils.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], 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) 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) if not isinstance(outs, list): outs = [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_inputs, val_targets, sample_weights=val_sample_weights, batch_size=batch_size, verbose=0) if not isinstance(val_outs, list): val_outs = [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 fit_loop(model, inputs, targets, sample_weights=None, val_inputs=None, val_targets=None, val_sample_weights=None, batch_size=None, epochs=100, verbose=1, callbacks=None, shuffle=True, callback_metrics=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None): """Abstract fit function for eager execution. Arguments: model: Instance of the model that is being executed in Eager mode. inputs: List of input arrays. targets: List of target arrays. sample_weights: Optional list of sample weight arrays. val_inputs: Input data for validation. val_targets: Target data for validation. val_sample_weights: Sample weight data for validation. 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 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 default value of `None`. Returns: `History` object. Raises: ValueError: In case of invalid argument values. """ if not batch_size: raise ValueError( 'With eager execution, `batch_size` should be specified.') if steps_per_epoch or validation_steps: raise ValueError('With eager execution, `steps_per_epoch` and ' '`validation_steps` are not valid arguments ' '(set `batch_size` instead).') # Required for Eager mode with backend.learning_phase_scope(1): do_validation = False if val_inputs: do_validation = True if (verbose and inputs and hasattr(inputs[0], 'shape') and hasattr(val_inputs[0], 'shape')): print('Train on %d samples, validate on %d samples' % (inputs[0].shape[0], val_inputs[0].shape[0])) if validation_steps: 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.') do_validation = True out_labels = model.metrics_names if do_validation: callback_metrics = copy.copy(out_labels) + [ 'val_' + n for n in out_labels ] else: callback_metrics = copy.copy(out_labels) if sample_weights: feed_data = inputs + targets + sample_weights else: feed_data = inputs + targets num_train_samples = training_utils.check_num_samples( feed_data, 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()] + (callbacks or []) + [model.history] if verbose: if steps_per_epoch is not None: count_mode = 'steps' else: count_mode = 'samples' callbacks += [cbks.ProgbarLogger(count_mode)] callbacks = cbks.CallbackList(callbacks) # it's possible to callback a different model than self # (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: if not val_inputs: cbk.validation_data = [] elif val_sample_weights: cbk.validation_data = val_inputs + val_targets + val_sample_weights else: cbk.validation_data = val_inputs + val_targets for epoch in range(initial_epoch, epochs): callbacks.on_epoch_begin(epoch) epoch_logs = {} if shuffle == 'batch': index_array = model._batch_shuffle(index_array, batch_size) elif shuffle: np.random.shuffle(index_array) batches = generic_utils.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: inputs_batch = slice_arrays(inputs, batch_ids, contiguous=not shuffle) targets_batch = slice_arrays(targets, batch_ids, contiguous=not shuffle) if sample_weights: sample_weights_batch = slice_arrays( sample_weights, batch_ids, contiguous=not shuffle) else: sample_weights_batch = None 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) inputs_batch = [ ops.convert_to_tensor(val, dtype=backend.floatx()) for val in inputs_batch ] targets_batch = [ ops.convert_to_tensor(val, dtype=backend.floatx()) for val in targets_batch ] if sample_weights: sample_weights_batch = [ ops.convert_to_tensor(val, dtype=backend.floatx()) if val is not None else None for val in sample_weights_batch ] outs, loss, loss_metrics = _process_single_batch( model, inputs_batch, targets_batch, sample_weights=sample_weights_batch, training=True) if not isinstance(outs, list): outs = [outs] for l, o in zip(out_labels, outs): batch_logs[l] = o # Required for Eager mode metrics_results = _eager_metrics_fn(model, outs, targets_batch) batch_logs['loss'] = tensor_util.constant_value( backend.mean(loss)) for k, v in zip(model.metrics_names, [backend.mean(loss)] + loss_metrics + metrics_results): batch_logs[k] = tensor_util.constant_value(v) 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_inputs, val_targets, sample_weights=val_sample_weights, batch_size=batch_size, verbose=0) if not isinstance(val_outs, list): val_outs = [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 fit_loop(model, inputs, targets, sample_weights=None, class_weight=None, val_inputs=None, val_targets=None, val_sample_weights=None, batch_size=None, epochs=1, verbose=1, callbacks=None, shuffle=True, callback_metrics=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None): """Fit function for eager execution. Arguments: model: Instance of the model that is being executed in Eager mode. inputs: List of input arrays. targets: List of target arrays. sample_weights: Optional list of sample weight arrays. class_weight: Optional class-weight array to weight the importance of samples in `inputs` based on the class they belong to, as conveyed by `targets`. val_inputs: Input data for validation. val_targets: Target data for validation. val_sample_weights: Sample weight data for validation. 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 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 default value of `None`. Returns: `History` object. Raises: ValueError: In case of invalid argument values. """ # Required for eager execution with backend.learning_phase_scope(1): do_validation = False if val_inputs: do_validation = True if (steps_per_epoch is None and verbose and inputs and hasattr(inputs[0], 'shape') and hasattr(val_inputs[0], 'shape')): print('Train on %d samples, validate on %d samples' % (inputs[0].shape[0], val_inputs[0].shape[0])) num_train_samples = None out_labels = None if steps_per_epoch is None or model._is_compiled: out_labels = model.metrics_names if do_validation: callback_metrics = copy.copy(out_labels) + [ 'val_' + n for n in out_labels ] else: callback_metrics = copy.copy(out_labels) if steps_per_epoch is None: if sample_weights: feed_data = inputs + targets + sample_weights else: feed_data = inputs + targets num_train_samples = training_utils.check_num_samples( feed_data, 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()] + (callbacks or []) + [model.history] if verbose: if steps_per_epoch is not None: count_mode = 'steps' else: count_mode = 'samples' callbacks += [cbks.ProgbarLogger(count_mode)] callbacks = cbks.CallbackList(callbacks) # it's possible to callback a different model than self # (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: if not val_inputs: cbk.validation_data = [] elif isinstance(val_inputs, iterator_ops.EagerIterator): cbk.validation_data = val_inputs elif val_sample_weights: cbk.validation_data = val_inputs + val_targets + val_sample_weights else: cbk.validation_data = val_inputs + val_targets for epoch in range(initial_epoch, epochs): callbacks.on_epoch_begin(epoch) epoch_logs = {} if steps_per_epoch is not None: iterator_fit_loop(model, inputs, class_weight, steps_per_epoch=steps_per_epoch, callback_model=callback_model, out_labels=out_labels, epoch_logs=epoch_logs, val_inputs=val_inputs, val_targets=val_targets, val_sample_weights=val_sample_weights, epochs=epochs, verbose=verbose, callbacks=callbacks, callback_metrics=callback_metrics, validation_steps=validation_steps, do_validation=do_validation) else: batch_fit_loop(model, inputs, targets, epoch_logs=epoch_logs, index_array=index_array, out_labels=out_labels, callback_model=callback_model, batch_size=batch_size, sample_weights=sample_weights, val_inputs=val_inputs, val_targets=val_targets, val_sample_weights=val_sample_weights, callbacks=callbacks, shuffle=shuffle, num_train_samples=num_train_samples, do_validation=do_validation) callbacks.on_epoch_end(epoch, epoch_logs) if callback_model.stop_training: break callbacks.on_train_end() return model.history
def fit_loop(model, ins, out_labels=None, batch_size=None, epochs=100, verbose=1, callbacks=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)`. Assume that f returns a list, labeled by out_labels. Arguments: model: Instance of the model that is being executed in Eager mode. 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_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 default value of `None`. Returns: `History` object. Raises: ValueError: In case of invalid argument values. """ # Required for Eager mode K.set_learning_phase(True) do_validation = False if 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: 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.') do_validation = True num_train_samples = model._check_num_samples(ins, batch_size, steps_per_epoch, 'steps_per_epoch') if num_train_samples is not None: index_array = np.arange(num_train_samples) model.history = cbks.History() callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history] if verbose: if steps_per_epoch is not None: count_mode = 'steps' else: count_mode = 'samples' callbacks += [cbks.ProgbarLogger(count_mode)] callbacks = cbks.CallbackList(callbacks) out_labels = out_labels or [] # it's possible to callback a different model than self # (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 for epoch in range(initial_epoch, epochs): callbacks.on_epoch_begin(epoch) epoch_logs = {} if shuffle == 'batch': index_array = model._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) ins_batch_converted = [] for ib in ins_batch: ins_batch_converted.append( ops.convert_to_tensor(ib, dtype=K.floatx())) eager_model_inputs = [] eager_model_outputs = [] for i in range(len(model.inputs)): eager_model_inputs.append(ins_batch_converted[i]) for i in range(len(model.inputs), len(ins_batch_converted)): eager_model_outputs.append(ins_batch_converted[i]) outs, loss, loss_metrics = _process_single_batch( eager_model_inputs, eager_model_outputs, model) if not isinstance(outs, list): outs = [outs] for l, o in zip(out_labels, outs): batch_logs[l] = o # Required for Eager mode metrics_names, metrics_results = _eager_metrics_fn( model, outs, eager_model_outputs) batch_logs['loss'] = tensor_util.constant_value(K.mean(loss)) # TODO(anjalisridhar): Move this to compile to avoid duplicate code. # In graph mode we set the metric names in compile. However in # Eager mode we calculate the metrics for each batch in fit_loop. # We could calculate the metric names and functions in compile. # This would avoid setting the callback parameters separately. # We need to do this for the first iteration alone for m in metrics_names: if m not in callback_metrics: callback_metrics.append(m) 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 [], }) for k, v in zip(model.metrics_names, [K.mean(loss)] + loss_metrics + metrics_results): batch_logs[k] = tensor_util.constant_value(v) 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_ins, batch_size=batch_size, verbose=0) if not isinstance(val_outs, list): val_outs = [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 fit_generator(model, generator, steps_per_epoch=None, epochs=1, verbose=1, callbacks=None, validation_data=None, validation_steps=None, class_weight=None, max_queue_size=10, workers=1, use_multiprocessing=False, shuffle=True, initial_epoch=0): """See docstring for `Model.fit_generator`.""" wait_time = 0.01 # in seconds epoch = initial_epoch do_validation = bool(validation_data) model._make_train_function() if do_validation: model._make_test_function() 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_per_epoch is None: if is_sequence: steps_per_epoch = len(generator) else: raise ValueError('`steps_per_epoch=None` is only valid for a' ' generator based on the `keras.utils.Sequence`' ' class. Please specify `steps_per_epoch` or use' ' the `keras.utils.Sequence` class.') # python 2 has 'next', 3 has '__next__' # avoid any explicit version checks val_gen = (hasattr(validation_data, 'next') or hasattr(validation_data, '__next__') or isinstance(validation_data, Sequence)) if (val_gen and not isinstance(validation_data, Sequence) and not validation_steps): raise ValueError('`validation_steps=None` is only valid for a' ' generator based on the `keras.utils.Sequence`' ' class. Please specify `validation_steps` or use' ' the `keras.utils.Sequence` class.') # Prepare display labels. out_labels = model.metrics_names callback_metrics = out_labels + ['val_%s' % n for n in out_labels] # prepare callbacks model.history = cbks.History() callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history] if verbose: callbacks += [cbks.ProgbarLogger(count_mode='steps')] callbacks = cbks.CallbackList(callbacks) # it's possible to callback a different model than self: 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({ 'epochs': epochs, 'steps': steps_per_epoch, 'verbose': verbose, 'do_validation': do_validation, 'metrics': callback_metrics, }) callbacks.on_train_begin() enqueuer = None val_enqueuer = None try: if do_validation and not val_gen: # Prepare data for validation if len(validation_data) == 2: val_x, val_y = validation_data # pylint: disable=unpacking-non-sequence val_sample_weight = None elif len(validation_data) == 3: val_x, val_y, val_sample_weight = validation_data # pylint: disable=unpacking-non-sequence else: raise ValueError('`validation_data` should be a tuple ' '`(val_x, val_y, val_sample_weight)` ' 'or `(val_x, val_y)`. Found: ' + str(validation_data)) val_x, val_y, val_sample_weights = model._standardize_user_data( val_x, val_y, val_sample_weight) val_data = val_x + val_y + val_sample_weights if model.uses_learning_phase and not isinstance( K.learning_phase(), int): val_data += [0.] for cbk in callbacks: cbk.validation_data = val_data if workers > 0: if is_sequence: enqueuer = OrderedEnqueuer( generator, use_multiprocessing=use_multiprocessing, shuffle=shuffle) 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 callback_model.stop_training = False # Construct epoch logs. epoch_logs = {} while epoch < epochs: callbacks.on_epoch_begin(epoch) steps_done = 0 batch_index = 0 while steps_done < steps_per_epoch: 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)) # build batch logs batch_logs = {} 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] batch_logs['batch'] = batch_index batch_logs['size'] = batch_size callbacks.on_batch_begin(batch_index, batch_logs) outs = model.train_on_batch(x, y, sample_weight=sample_weight, class_weight=class_weight) if not isinstance(outs, list): outs = [outs] for l, o in zip(out_labels, outs): batch_logs[l] = o callbacks.on_batch_end(batch_index, batch_logs) batch_index += 1 steps_done += 1 # Epoch finished. if steps_done >= steps_per_epoch and do_validation: if val_gen: val_outs = evaluate_generator( model, validation_data, validation_steps, workers=workers, use_multiprocessing=use_multiprocessing, max_queue_size=max_queue_size) else: # No need for try/except because # data has already been validated. val_outs = model.evaluate( val_x, val_y, batch_size=batch_size, sample_weight=val_sample_weights, verbose=0) if not isinstance(val_outs, list): val_outs = [val_outs] # Same labels assumed. for l, o in zip(out_labels, val_outs): epoch_logs['val_' + l] = o if callback_model.stop_training: break callbacks.on_epoch_end(epoch, epoch_logs) epoch += 1 if callback_model.stop_training: break finally: try: if enqueuer is not None: enqueuer.stop() finally: if val_enqueuer is not None: val_enqueuer.stop() callbacks.on_train_end() return model.history