When following the tensorflow image classification tutorial, at first it caches the bottleneck of each image:
I have rewritten the training using tensorflow's Estimator. This really simplified all the code. However I want to cache the bottleneck features here.
Here is my model_fn. I want to cache the results of the dense layer so I can make changes to the actual training without having to compute the bottlenecks each time.
How can I accomplish that?
def model_fn(features, labels, mode, params): is_training = mode == tf.estimator.ModeKeys.TRAIN num_classes = len(params['label_vocab']) module = hub.Module(params['module_spec'], trainable=is_training and params['train_module']) bottleneck_tensor = module(features['image']) with tf.name_scope('final_retrain_ops'): logits = tf.layers.dense(bottleneck_tensor, units=num_classes, trainable=is_training) # save this? def train_op_fn(loss): optimizer = tf.train.AdamOptimizer() return optimizer.minimize(loss, global_step=tf.train.get_global_step()) head = tf.contrib.estimator.multi_class_head(n_classes=num_classes, label_vocabulary=params['label_vocab']) return head.create_estimator_spec( features, mode, logits, labels, train_op_fn=train_op_fn ) 
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