Typeerror: Unsupported Callable Using Dataset With Estimator Input_fn

I'm trying to convert the Iris tutorial (https://www.tensorflow.org/get_started/estimator) to read training data from .png files instead of .csv. It works using numpy_input_fn but

Solution 1:

I've noticed several mistakes in your code snippet:

• train method accepts the input function, so it should be input_fn, not input_fn().
• The features are expected as a dictionary, e.g. {'x': features}.
• DNNClassifier uses SparseSoftmaxCrossEntropyWithLogits loss function. Sparse means that it assumes ordinal class representation, instead of one-hot, so your conversion is unnecessary (this question explains the difference between cross-entropy losses in tf).

Try this code below:

import tensorflow as tf
from tensorflow.contrib.dataimport Dataset

NUM_CLASSES = 3

def model_fn():
    feature_columns = [tf.feature_column.numeric_column("x", shape=[4], dtype=tf.float32)]
    return tf.estimator.DNNClassifier([10, 20, 10], feature_columns, "tmp/iris_model", NUM_CLASSES)

def input_parser(img_path, label):
    file_contents = tf.read_file(img_path)
    image_decoded = tf.image.decode_png(file_contents, channels=1)
    image_decoded = tf.image.resize_images(image_decoded, [2, 2])
    image_decoded = tf.reshape(image_decoded, [4])
    label = tf.reshape(label, [1])
    return image_decoded, label

def input_fn():
    filenames = tf.constant(['input1.jpg', 'input2.jpg'])
    labels = tf.constant([0,1], dtype=tf.int32)
    data = Dataset.from_tensor_slices((filenames, labels))
    data = data.map(input_parser)
    data = data.batch(1)
    iterator = data.make_one_shot_iterator()
    features, labels = iterator.get_next()
    return {'x': features}, labels

model_fn().train(input_fn, steps=1)