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!!!