Train Tensorflow model with estimator (from_genera

Question

I am trying train an estimator with a generator, but I want to feed this estimator with a package of samples for each iteration. I show the code:

def _generator():
for i in range(100):
    feats  = np.random.rand(4,2)
    labels = np.random.rand(4,1)

    yield feats, labels


def input_func_gen():
    shapes = ((4,2),(4,1))
    dataset = tf.data.Dataset.from_generator(generator=_generator,
                                         output_types=(tf.float32, tf.float32),
                                         output_shapes=shapes)
dataset = dataset.batch(4)
# dataset = dataset.repeat(20)
iterator = dataset.make_one_shot_iterator()
features_tensors, labels = iterator.get_next()
features = {'x': features_tensors}
return features, labels


x_col = tf.feature_column.numeric_column(key='x', shape=(4,2))
es = tf.estimator.LinearRegressor(feature_columns=[x_col],model_dir=tf_data)
es = es.train(input_fn=input_func_gen,steps = None)

When I run this code, it raises this error:

    raise ValueError(err.message)
ValueError: Dimensions must be equal, but are 2 and 3 for 'linear/head/labels/assert_equal/Equal' (op: 'Equal') with input shapes: [2], [3].

How do I have to call to this structure??

thx!!!

Answer 1

The batch size is automatically computed and added to the tensors shapes by Tensorflow, so it doesn't have to be done manually. Your generator should also be defined to output single samples.

Assuming the 4 in position 0 of your shapes are for the batch size, then:

import tensorflow as tf
import numpy

def _generator():
    for i in range(100):
        feats  = numpy.random.rand(2)
        labels = numpy.random.rand(1)

        yield feats, labels


def input_func_gen():
    shapes = ((2),(1))
    dataset = tf.data.Dataset.from_generator(generator=_generator,
                                         output_types=(tf.float32, tf.float32),
                                         output_shapes=shapes)
    dataset = dataset.batch(4)
    # dataset = dataset.repeat(20)
    iterator = dataset.make_one_shot_iterator()
    features_tensors, labels = iterator.get_next()
    features = {'x': features_tensors}
    return features, labels


x_col = tf.feature_column.numeric_column(key='x', shape=(2))
es = tf.estimator.LinearRegressor(feature_columns=[x_col])
es = es.train(input_fn=input_func_gen,steps = None)