So I have got my keras model to work with a tf.Dataset through the following code:
# Initialize batch generators(returns tf.Dataset)
batch_train = build_features.get_train_batches(batch_size=batch_size)
# Create TensorFlow Iterator object
iterator = batch_train.make_one_shot_iterator()
dataset_inputs, dataset_labels = iterator.get_next()
# Create Model
logits = .....(some layers)
keras.models.Model(inputs=dataset_inputs, outputs=logits)
# Train network
model.compile(optimizer=train_opt, loss=model_loss, target_tensors=[dataset_labels])
model.fit(epochs=epochs, steps_per_epoch=num_batches, callbacks=callbacks, verbose=1)
however when I try to pass validation_data
parameter to the model. fit it tells me that I cannot use it with the generator. Is there a way to use validation while using tf.Dataset
for example in tensorflow I could do the following:
# initialize batch generators
batch_train = build_features.get_train_batches(batch_size=batch_size)
batch_valid = build_features.get_valid_batches(batch_size=batch_size)
# create TensorFlow Iterator object
iterator = tf.data.Iterator.from_structure(batch_train.output_types,
batch_train.output_shapes)
# create two initialization ops to switch between the datasets
init_op_train = iterator.make_initializer(batch_train)
init_op_valid = iterator.make_initializer(batch_valid)
then just use sess.run(init_op_train)
and sess.run(init_op_valid)
to switch between the datasets
I tried implementing a callback that does just that (switch to validation set, predict and back) but it tells me I can't use model.predict in a callback
can someone help me get validation working with Keras+Tf.Dataset
edit: incorporate answer into the code
so FINALLY what worked for me, thanks to the selected answer is:
# Initialize batch generators(returns tf.Dataset)
batch_train = # returns tf.Dataset
batch_valid = # returns tf.Dataset
# Create TensorFlow Iterator object and wrap it in a generator
itr_train = make_iterator(batch_train)
itr_valid = make_iterator(batch_train)
# Create Model
logits = # the keras model
keras.models.Model(inputs=dataset_inputs, outputs=logits)
# Train network
model.compile(optimizer=train_opt, loss=model_loss, target_tensors=[dataset_labels])
model.fit_generator(
generator=itr_train, validation_data=itr_valid, validation_steps=batch_size,
epochs=epochs, steps_per_epoch=num_batches, callbacks=cbs, verbose=1, workers=0)
def make_iterator(dataset):
iterator = dataset.make_one_shot_iterator()
next_val = iterator.get_next()
with K.get_session().as_default() as sess:
while True:
*inputs, labels = sess.run(next_val)
yield inputs, labels
This doesn't introduce any overhead
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