tensorflow-datasets

For the application, such as pair text similarity , the input data is similar to: pair_1, pair_2 . In these problems, we usually have multiple input data. Previously, I implemented my models successfully: model.fit([pair_1, pair_2], labels, epochs=50) I decided to replace my input pipeline with tf.data API. To this end, I create a Dataset similar to: dataset = tf.data.Dataset.from_tensor_slices((pair_1, pair2, labels)) It compiles successfully but when start to train it throws the following exception: AttributeError: 'tuple' object has no attribute 'ndim' My Keras and Tensorflow version

I want to use feedable iterator design in tensorflow Dataset API, so I can switch to validation data after some training steps. But if I switched to validation data, it will end the whole session. The following code demonstrate what I want to do: import tensorflow as tf graph = tf.Graph() with graph.as_default(): training_ds = tf.data.Dataset.range(32).batch(4) validation_ds = tf.data.Dataset.range(8).batch(4) handle = tf.placeholder(tf.string, shape=[]) iterator = tf.data.Iterator.from_string_handle( handle, training_ds.output_types, training_ds.output_shapes) next_element = iterator.get_next

I am trying to use new features of TF, namely Data API, and I am not sure how prefetch works. In the code below def dataset_input_fn(...) dataset = tf.data.TFRecordDataset(filenames, compression_type="ZLIB") dataset = dataset.map(lambda x:parser(...)) dataset = dataset.map(lambda x,y: image_augmentation(...) , num_parallel_calls=num_threads ) dataset = dataset.shuffle(buffer_size) dataset = dataset.batch(batch_size) dataset = dataset.repeat(num_epochs) iterator = dataset.make_one_shot_iterator() does it matter between each lines above I put dataset=dataset.prefetch(batch_size) ? Or maybe it

I'm looking at source code from this Tensorflow article that talks about how to create a wide-and-deep learning model. https://www.tensorflow.org/versions/r1.3/tutorials/wide_and_deep Here is the link to the python source code: https://github.com/tensorflow/tensorflow/blob/r1.3/tensorflow/examples/learn/wide_n_deep_tutorial.py What the goal of it is, is to train a model that will predict if someone makes more or less than $50k a year given the data in the census information. As instructed, I'm running this command to execute: python wide_n_deep_tutorial.py --model_type=wide_n_deep The result

I'm trying to optimize the input pipeline for .h5 data with tf.data. But I encountered a TypeError: expected str, bytes or os.PathLike object, not Tensor . I did a research but can't find anything about converting a tensor of string to string. This simplified code is executable and return the same error: batch_size = 1000 conv_size = 3 nb_conv = 32 learning_rate = 0.0001 # define parser function def parse_function(fname): with h5py.File(fname, 'r') as f: #Error comes from here X = f['X'].reshape(batch_size, patch_size, patch_size, 1) y = f['y'].reshape(batch_size, patch_size, patch_size, 1)

I trained a resnet with tf.estimator, the model was saved during the training process. The saved files consist of .data , .index and .meta . I'd like to load this model back and get predictions for new images. The data was fed to the model during training using tf.data.Dataset . I have closely followed the resnet implementation given here . I would like to restore the model and feed inputs to the nodes using a feed_dict. First attempt #rebuild input pipeline images, labels = input_fn(data_dir, batch_size=32, num_epochs=1) #rebuild graph prediction= imagenet_model_fn(images,labels,{'batch_size'