5

我遵循教程https://www.tensorflow.org/tutorials/layers，我想用它来使用我自己的数据集。

def train_input_fn_custom(filenames_array, labels_array, batch_size):
    # Reads an image from a file, decodes it into a dense tensor, and resizes it to a fixed shape.
    def _parse_function(filename, label):
        image_string = tf.read_file(filename)
        image_decoded = tf.image.decode_png(image_string, channels=1)
        image_resized = tf.image.resize_images(image_decoded, [40, 40])
        return image_resized, label

    filenames = tf.constant(filenames_array)
    labels = tf.constant(labels_array)

    dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
    dataset = dataset.map(_parse_function)
    dataset = dataset.shuffle(1000).repeat().batch(batch_size)

    return dataset.make_one_shot_iterator().get_next()


def main(self):
    tf.logging.set_verbosity(tf.logging.INFO)

    # Get data
    filenames_train = ['blackcorner-data/1.png', 'blackcorner-data/2.png']
    labels_train = [0, 1]

    # Create the Estimator
    classifier = tf.estimator.Estimator(model_fn=cnn_model_fn, model_dir="/tmp/test_convnet_model")

    # Set up logging for predictions
    tensors_to_log = {"probabilities": "softmax_tensor"}
    logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log, every_n_iter=50)

    # Train the model
    cust_train_input_fn = train_input_fn_custom(
            filenames_array=filenames_train,
            labels_array=labels_train,
            batch_size=3)

    classifier.train(
            input_fn=cust_train_input_fn,
            steps=2000,
            hooks=[logging_hook])


if __name__ == "__main__":
    tf.app.run()

但我有这个错误：

    Traceback (most recent call last):
      File "/usr/lib/python3.6/inspect.py", line 1119, in getfullargspec
        sigcls=Signature)
      File "/usr/lib/python3.6/inspect.py", line 2186, in _signature_from_callable
        raise TypeError('{!r} is not a callable object'.format(obj))
    TypeError: (<tf.Tensor 'IteratorGetNext:0' shape=(?, 40, 40, ?) dtype=float32>, <tf.Tensor 'IteratorGetNext:1' shape=(?,) dtype=int32>) is not a callable object

    The above exception was the direct cause of the following exception:

    Traceback (most recent call last):
      File "cnn_mnist_for_stackoverflow.py", line 139, in <module>
        tf.app.run()
      File "/home/geo/Projet/ML/cnn_mnist/venv/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 126, in run
        _sys.exit(main(argv))
      File "cnn_mnist_for_stackoverflow.py", line 135, in main
        hooks=[logging_hook])
     ...
        raise TypeError('unsupported callable') from ex
    TypeError: unsupported callable

我不明白这个错误，我只知道它来自 train_input_fn_custom。张量流版本是1.6

如果有人有想法..谢谢！

4

1 回答 1

13

的input_fn参数classifier.train()必须是可调用对象（没有参数），例如函数或lambda. 在您的代码中，您传递的是调用的结果train_input_fn_custom()，而不是调用的可调用对象 train_input_fn_custom()。要解决此问题，请替换以下定义cust_train_input_fn：

# The `lambda:` creates a callable object with no arguments.
cust_train_input_fn = lambda: train_input_fn_custom(
    filenames_array=filenames_train, labels_array=labels_train, batch_size=3)
于 2018-03-06T21:47:12.613 回答