Я пытаюсь использовать вызов keras для прогнозирования в конце пакета следующим образом:
from keras.layers import Dense
from keras.models import Sequential
from keras.callbacks import Callback
from keras import backend as K
import tensorflow as tf
import numpy as np
class CollectOutputAndTarget(Callback):
def __init__(self):
super(CollectOutputAndTarget, self).__init__()
self.targets = [] # collect y_true batches
self.inputs = [] # collect y_true batches
self.outputs = [] # collect y_pred batches
self.preds = []
# the shape of these 2 variables will change according to batch shape
# to handle the "last batch", specify `validate_shape=False`
self.var_y_true = tf.Variable(0., validate_shape=False)
self.var_input = tf.Variable(0., validate_shape=False)
self.var_y_pred = tf.Variable(0., validate_shape=False)
def on_batch_end(self, batch, logs=None):
# evaluate the variables and save them into lists
self.targets.append(K.eval(self.var_y_true))
batch_inp = K.eval(self.var_input)
self.inputs.append(batch_inp)
self.outputs.append(K.eval(self.var_y_pred))
current_pred = self.model.predict(batch_inp)
self.preds.append(current_pred)
# build a simple model
K.clear_session()
# have to compile first for model.targets and model.outputs to be prepared
model = Sequential([Dense(5, input_shape=(2,)), Dense(2)])
model.compile(loss='mse', optimizer='adam')
# initialize the variables and the `tf.assign` ops
cbk = CollectOutputAndTarget()
fetches = [tf.assign(cbk.var_y_true, model.targets[0], validate_shape=False),
tf.assign(cbk.var_input, model.inputs[0], validate_shape=False),
tf.assign(cbk.var_y_pred, model.outputs[0], validate_shape=False)]
model._function_kwargs = {'fetches': fetches} # use `model._function_kwargs` if using `Model` instead of `Sequential`
# fit the model and check results
X = np.arange(10).reshape((5, 2))
Y = X*2
model.fit(X, Y, epochs=1, batch_size=3, callbacks=[cbk], shuffle=False)
И получаю следующую ошибку:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-114-adfad08009ad> in <module>
3 Y = X*2
4
----> 5 model.fit(X, Y, epochs=1, batch_size=3, callbacks=[cbk], shuffle=False)
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
1037 initial_epoch=initial_epoch,
1038 steps_per_epoch=steps_per_epoch,
-> 1039 validation_steps=validation_steps)
1040
1041 def evaluate(self, x=None, y=None,
/usr/local/lib/python3.6/dist-packages/keras/engine/training_arrays.py in fit_loop(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)
202 batch_logs[l] = o
203
--> 204 callbacks.on_batch_end(batch_index, batch_logs)
205 if callback_model.stop_training:
206 break
/usr/local/lib/python3.6/dist-packages/keras/callbacks.py in on_batch_end(self, batch, logs)
113 t_before_callbacks = time.time()
114 for callback in self.callbacks:
--> 115 callback.on_batch_end(batch, logs)
116 self._delta_ts_batch_end.append(time.time() - t_before_callbacks)
117 delta_t_median = np.median(self._delta_ts_batch_end)
<ipython-input-111-65feb418f9ec> in on_batch_end(self, batch, logs)
19 self.inputs.append(batch_inp)
20 self.outputs.append(K.eval(self.var_y_pred))
---> 21 current_pred = self.model.predict(batch_inp)
22 self.preds.append(current_pred)
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in predict_on_batch(self, x)
1272 ins = x
1273 self._make_predict_function()
-> 1274 outputs = self.predict_function(ins)
1275 return unpack_singleton(outputs)
1276
/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py in __call__(self, inputs)
2713 return self._legacy_call(inputs)
2714
-> 2715 return self._call(inputs)
2716 else:
2717 if py_any(is_tensor(x) for x in inputs):
/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py in _call(self, inputs)
2673 fetched = self._callable_fn(*array_vals, run_metadata=self.run_metadata)
2674 else:
-> 2675 fetched = self._callable_fn(*array_vals)
2676 return fetched[:len(self.outputs)]
2677
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in __call__(self, *args, **kwargs)
1456 ret = tf_session.TF_SessionRunCallable(self._session._session,
1457 self._handle, args,
-> 1458 run_metadata_ptr)
1459 if run_metadata:
1460 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
InvalidArgumentError: You must feed a value for placeholder tensor 'dense_2_target' with dtype float and shape [?,?]
[[{{node dense_2_target}}]]
I Я могу получить выходные данные модели в конце пакета из переменной self.var_y_pred, которая присваивается model.outputs[0].
Однако, насколько я понимаю, этот прогноз делается до обратного распространения на текущем шаге. И моя цель - сделать прогноз текущей партии, используя версию модели, веса которой уже были обновлены с текущей тренировкой партии.
Как этого добиться?