Documentation on how to use tf.estimator in TensorFlow

Question

I understand that we can write custom models and encapsulate it using tf.estimator. But I just can't seem to find any documentation with an example.

I know that you have to define your model inside a 'model_fn' but what exactly should I return from this function. Also am I supposed to put the the loss and the training step within the 'model_fn' or just the network. How should I modify the code give below to make it work with tf.estimator. Would really appreciate some help.

def test_model(features,labels):
    X = tf.placeholder(tf.float32,shape=(None,1),name="Data_Input")
    #Output
    Y = tf.placeholder(tf.float32,shape=(None,1),name="Target_Labels")
    W =  tf.Variable(tf.random_normal([0],stddev=stddev0)) 
    b = tf.Variable(tf.random_normal([0],stddev=stddev0))

    Ypredict = W*X + b
    return Ypredict

 estimator = tf.estimator.Estimator(model_fn = test_model)

fenkerbb · Accepted Answer · 2017-09-26 19:23:32Z

1

You should return a tf.estimator.EstimatorSpec object. Something to the effect of:

def model_fn(features, labels, mode, params):
    /*
    Your marvelous model
    */
    loss = tf.losses.softmax_cross_entropy(onehot_labels=labels_onehot, logits=logits)
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
    train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())  
    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)

There's more to it, so for a better walkthrough, see here.

answered Sep 26, 2017 at 19:23

fenkerbb

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