experiments/STT-tensorflow
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Add input function for training and testing (#9617) (#9650)

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* Add input function for training and testing

Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn

* remove extra comma
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wuhaixutab 2017-05-04 12:23:13 +08:00 committed by Vijay Vasudevan
parent b0ab95c7af
commit d0042ed637
1 changed files with 13 additions and 3 deletions
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16
tensorflow/examples/tutorials/estimators/abalone.py
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@ -134,12 +134,22 @@ def main(unused_argv):
# Instantiate Estimator # Instantiate Estimator
nn = tf.contrib.learn.Estimator(model_fn=model_fn, params=model_params) nn = tf.contrib.learn.Estimator(model_fn=model_fn, params=model_params)
def get_train_inputs():
x = tf.constant(training_set.data)
y = tf.constant(training_set.target)
return x, y
# Fit # Fit
nn.fit(x=training_set.data, y=training_set.target, steps=5000) nn.fit(input_fn=get_train_inputs, steps=5000)
# Score accuracy # Score accuracy
ev = nn.evaluate(x=test_set.data, y=test_set.target, steps=1) def get_test_inputs():
x = tf.constant(test_set.data)
y = tf.constant(test_set.target)
return x, y
ev = nn.evaluate(input_fn=get_test_inputs, steps=1)
print("Loss: %s" % ev["loss"]) print("Loss: %s" % ev["loss"])
print("Root Mean Squared Error: %s" % ev["rmse"]) print("Root Mean Squared Error: %s" % ev["rmse"])