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Adjust getting started guide to use a training and eval data set.

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Change: 155089162
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Andrew Selle 2017-05-04 08:43:15 -08:00 committed by TensorFlower Gardener
parent 1e4899035e
commit 074ede9a23
1 changed files with 34 additions and 17 deletions
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tensorflow/docs_src/get_started/get_started.md
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@ -372,25 +372,36 @@ features = [tf.contrib.layers.real_valued_column("x", dimension=1)]
estimator = tf.contrib.learn.LinearRegressor(feature_columns=features)
# TensorFlow provides many helper methods to read and set up data sets.
# Here we use `numpy_input_fn`. We have to tell the function how many batches
# Here we use two data sets: one for training and one for evaluation
# We have to tell the function how many batches
# of data (num_epochs) we want and how big each batch should be.
x = np.array([1., 2., 3., 4.])
y = np.array([0., -1., -2., -3.])
input_fn = tf.contrib.learn.io.numpy_input_fn({"x":x}, y, batch_size=4,
x_train = np.array([1., 2., 3., 4.])
y_train = np.array([0., -1., -2., -3.])
x_eval = np.array([2., 5., 8., 1.])
y_eval = np.array([-1.01, -4.1, -7, 0.])
input_fn = tf.contrib.learn.io.numpy_input_fn({"x":x_train}, y_train,
batch_size=4,
num_epochs=1000)
eval_input_fn = tf.contrib.learn.io.numpy_input_fn(
{"x":x_eval}, y_eval, batch_size=4, num_epochs=1000)
# We can invoke 1000 training steps by invoking the `fit` method and passing the
# We can invoke 1000 training steps by invoking the method and passing the
# training data set.
estimator.fit(input_fn=input_fn, steps=1000)
# Here we evaluate how well our model did. In a real example, we would want
# to use a separate validation and testing data set to avoid overfitting.
print(estimator.evaluate(input_fn=input_fn))
# Here we evaluate how well our model did.
train_loss = estimator.evaluate(input_fn=input_fn)
eval_loss = estimator.evaluate(input_fn=eval_input_fn)
print("train loss: %r"% train_loss)
print("eval loss: %r"% eval_loss)
```
When run, it produces
```
{'global_step': 1000, 'loss': 1.9650059e-11}
train loss: {'global_step': 1000, 'loss': 4.3049088e-08}
eval loss: {'global_step': 1000, 'loss': 0.0025487561}
```
Notice how our eval data has a higher loss, but it is still close to zero.
That means we are learning properly.
### A custom model
@ -432,19 +443,25 @@ def model(features, labels, mode):
train_op=train)
estimator = tf.contrib.learn.Estimator(model_fn=model)
# define our data set
x = np.array([1., 2., 3., 4.])
y = np.array([0., -1., -2., -3.])
input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x}, y, 4, num_epochs=1000)
# define our data sets
x_train = np.array([1., 2., 3., 4.])
y_train = np.array([0., -1., -2., -3.])
x_eval = np.array([2., 5., 8., 1.])
y_eval = np.array([-1.01, -4.1, -7, 0.])
input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x_train}, y_train, 4, num_epochs=1000)
# train
estimator.fit(input_fn=input_fn, steps=1000)
# evaluate our model
print(estimator.evaluate(input_fn=input_fn, steps=10))
# Here we evaluate how well our model did.
train_loss = estimator.evaluate(input_fn=input_fn)
eval_loss = estimator.evaluate(input_fn=eval_input_fn)
print("train loss: %r"% train_loss)
print("eval loss: %r"% eval_loss)
```
When run, it produces
```python
{'loss': 5.9819476e-11, 'global_step': 1000}
```
train loss: {'global_step': 1000, 'loss': 4.9380226e-11}
eval loss: {'global_step': 1000, 'loss': 0.01010081}
```
Notice how the contents of the custom `model()` function are very similar