问Tensorflow Estimator API:摘要

EN

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)