TensorFlow version: 1.x - Date: Octobre 2018
import tensorflow as tf
Bring in all of the public TensorFlow interface.
#Classes: tf.Graph() '''A TensorFlow computation, represented as a dataflow graph.''' tf.device() '''A TensorFlow class to define the device (cpu/gpu) to use ''' tf.Operation() '''Represents a graph node that performs computation on tensors.''' tf.Session()'''A class for running TensorFlow operations.''' #Functions: '''Returns x + y element-wise.''' a = tf.add(x, y) '''Return a tensor with the same shape and contents as input. (name: A name for the operation (optional))''' b = tf.identity(a, name=None) '''Returns the index with the largest value across axes of a tensor example: for a 2D tensor, axis=1 refers to rows''' c = tf.argmax(input, axis=None,name=None) '''Computes square root of x element-wise.''' d = tf.sqrt(x, name=None)
More functions are available on the following link : https://www.tensorflow.org/api_docs/python/tf
This module contains different class used to manipulate data. Different files format are supported. It also introduces special classes fitted to TensorFlow for encoding/decoding data.
# Load the training data into two NumPy arrays, for example using `np.load()`. with np.load("/var/data/training_data.npy") as data: features = data["features"] labels = data["labels"] # Assume that each row of `features` corresponds to the same row as `labels`. assert features.shape == labels.shape dataset = tf.data.Dataset.from_tensor_slices((features, labels))
If you choose tfrecord format for encoding your data, you can use the following way to read (parse) your file(s):
# It accepts one or more filenames. filenames = ["/var/data/file1.tfrecord", "/var/data/file2.tfrecord"] dataset = tf.data.TFRecordDataset(filenames) #Apply a transformation function to your data dataset = dataset.map(func)
If your data is contained in text files, you can use the following way to read (parse) it:
# It accepts one or more filenames. filenames = ["/var/data/file1.txt", "/var/data/file2.txt"] dataset = tf.data.TextLineDataset(filenames)
In order to iterate over the dataset, TensorFlow provides the Iterator class:
# The returned iterator will be in an uninitialized state, and you must run the iterator.initializer operation before using it: iterator = dataset.make_initializable_iterator() tf.Session().run(iterator.initializer) #Or use one_shot iterator that will be automatically initialized: iterator = dataset.make_one_shot_iterator()