I can't achieve to make summaries work with the Estimator API of Tensorflow.
The Estimator class is very useful for many reasons: I have already implemented my own classes which are really similar but I am trying to switch to this one.
Here is the code sample:
import tensorflow as tf
import tensorflow.contrib.layers as layers
import tensorflow.contrib.learn as learn
import numpy as np
# To reproduce the error: docker run --rm -w /algo -v $(pwd):/algo tensorflow/tensorflow bash -c "python sample.py"
def model_fn(x, y, mode):
logits = layers.fully_connected(x, 12, scope="dense-1")
logits = layers.fully_connected(logits, 56, scope="dense-2")
logits = layers.fully_connected(logits, 4, scope="dense-3")
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y), name="xentropy")
return {"predictions":logits}, loss, tf.train.AdamOptimizer(0.001).minimize(loss)
def input_fun():
""" To be completed for a 4 classes classification problem """
feature = tf.constant(np.random.rand(100,10))
labels = tf.constant(np.random.random_integers(0,3, size=(100,)))
return feature, labels
estimator = learn.Estimator(model_fn=model_fn, )
trainingConfig = tf.contrib.learn.RunConfig(save_checkpoints_secs=60)
estimator = learn.Estimator(model_fn=model_fn, model_dir="./tmp", config=trainingConfig)
# Works
estimator.fit(input_fn=input_fun, steps=2)
# The following code does not work
# Can't initialize saver
# saver = tf.train.Saver(max_to_keep=10) # Error: No variables to save
# The following fails because I am missing a saver... :(
hooks=[
tf.train.LoggingTensorHook(["xentropy"], every_n_iter=100),
tf.train.CheckpointSaverHook("./tmp", save_steps=1000, checkpoint_basename='model.ckpt'),
tf.train.StepCounterHook(every_n_steps=100, output_dir="./tmp"),
tf.train.SummarySaverHook(save_steps=100, output_dir="./tmp"),
]
estimator.fit(input_fn=input_fun, steps=2, monitors=hooks)
As you can see, I can create an Estimator and use it but I can achieve to add hooks to the fitting process.
The logging hooks works just fine but the others require both tensors and a saver which I can't provide.
The tensors are defined in the model function, thus I can't pass them to the SummaryHook and the Saver can't be initialized because there is no tensor to save...
Is there a solution to my problem? (I am guessing yes but there is a lack of documentation of this part in the tensorflow documentation)
- How can I initialized my saver? Or should I use other objects such as Scaffold?
- How can I pass summaries to the SummaryHook since they are defined in my model function?
Thanks in advance.
PS: I have seen the DNNClassifier API but I want to use the estimator API for Convolutional Nets and others. I need to create summaries for any estimator.
