所以我通过以下代码让我的keras模型与tf.Dataset一起工作:
# 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)
但是当我尝试将validation_data参数传递给模型时.适合它告诉我,我不能用它与发电机.有没有办法在使用tf.Dataset时使用验证
例如在tensorflow中,我可以执行以下操作:
# 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) …