Tensorflow is a machine learning framework that is provided by Google. It is an open−source framework used in conjunction with Python to implement algorithms, deep learning applications and much more. It is used in research and for production purposes.
This is because it uses NumPy and multi−dimensional arrays. These multi-dimensional arrays are also known as ‘tensors’. The framework supports working with deep neural network. It is highly scalable, and comes with many popular datasets. It uses GPU computation and automates the management of resources. It comes with multitude of machine learning libraries, and is well-supported and documented. The framework has the ability to run deep neural network models, train them, and create applications that predict relevant characteristics of the respective datasets.
The ‘tensorflow’ package can be installed on Windows using the below line of code −
pip install tensorflow
Tensor is a data structure used in TensorFlow. It helps connect edges in a flow diagram. This flow diagram is known as the ‘Data flow graph’. Tensors are nothing but multidimensional array or a list. They can be identified using three main attributes −
The ‘IMDB’ dataset contains reviews of over 50 thousand movies. This dataset is generally used with operations associated with Natural Language Processing.
We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.
Following is the code −
import matplotlib.pyplot as plt import os import re import shutil import string import tensorflow as tf from tensorflow.keras import layers from tensorflow.keras import losses from tensorflow.keras import preprocessing from tensorflow.keras.layers.experimental.preprocessing import TextVectorization print("The tensorflow version is ") print(tf.__version__) url = "https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz" dataset = tf.keras.utils.get_file("aclImdb_v1.tar.gz", url, untar=True, cache_dir='.', cache_subdir='') print("The dataset is being downloaded") dataset_dir = os.path.join(os.path.dirname(dataset), 'aclImdb') print("The directories in the downloaded folder are ") os.listdir(dataset_dir) train_dir = os.path.join(dataset_dir, 'train') os.listdir(train_dir) print("The sample of data : ") sample_file = os.path.join(train_dir, 'pos/1181_9.txt') with open(sample_file) as f: print(f.read()) remove_dir = os.path.join(train_dir, 'unsup') shutil.rmtree(remove_dir) batch_size = 32 seed = 42 print("The batch size is") print(batch_size) raw_train_ds = tf.keras.preprocessing.text_dataset_from_directory( 'aclImdb/train', batch_size=batch_size, validation_split=0.2, subset='training', seed=seed) for text_batch, label_batch in raw_train_ds.take(1): for i in range(3): print("Review", text_batch.numpy()[i]) print("Label", label_batch.numpy()[i]) print("Label 0 corresponds to", raw_train_ds.class_names) print("Label 1 corresponds to", raw_train_ds.class_names) raw_val_ds = tf.keras.preprocessing.text_dataset_from_directory( 'aclImdb/train', batch_size=batch_size, validation_split=0.2, subset='validation', seed=seed) raw_test_ds = tf.keras.preprocessing.text_dataset_from_directory( 'aclImdb/test', batch_size=batch_size)
The tensorflow version is 2.4.0 The dataset is being downloaded The directories in the downloaded folder are The sample of data : Rachel Griffiths writes and directs this award winning short film. A heartwarming story about coping with grief and cherishing the memory of those we've loved and lost. Although, only 15 minutes long, Griffiths manages to capture so much emotion and truth onto film in the short space of time. Bud Tingwell gives a touching performance as Will, a widower struggling to cope with his wife's death. Will is confronted by the harsh reality of loneliness and helplessness as he proceeds to take care of Ruth's pet cow, Tulip. The film displays the grief and responsibility one feels for those they have loved and lost. Good cinematography, great direction, and superbly acted. It will bring tears to all those who have lost a loved one, and survived. The batch size is 32 Found 25000 files belonging to 2 classes. Using 20000 files for training. Review b'"Pandemonium" is a horror movie spoof that comes off more stupid than funny. Believe me when I tell you, I love comedies. Especially comedy spoofs. "Airplane", "The Naked Gun" trilogy, "Blazing Saddles", "High Anxiety", and "Spaceballs" are some of my favorite comedies that spoof a particular genre. "Pandemonium" is not up there with those films. Most of the scenes in this movie had me sitting there in stunned silence because the movie wasn\'t all that funny. There are a few laughs in the film, but when you watch a comedy, you expect to laugh a lot more than a few times and that\'s all this film has going for it. Geez, "Scream" had more laughs than this film and that was more of a horror film. How bizarre is that? *1/2 (out of four)' Label 0 Review b"David Mamet is a very interesting and a very un-equal director. His first movie 'House of Games' was the one I liked best, and it set a series of films with characters whose perspective of life changes as they get into complicated situations, and so does the perspective of the viewer. So is 'Homicide' which from the title tries to set the mind of the viewer to the usual crime drama. The principal characters are two cops, one Jewish and one Irish who deal with a racially charged area. The murder of an old Jewish shop owner who proves to be an ancient veteran of the Israeli Independence war triggers the Jewish identity in the mind and heart of the Jewish detective. This is were the flaws of the film are the more obvious. The process of awakening is theatrical and hard to believe, the group of Jewish militants is operatic, and the way the detective eventually walks to the final violent confrontation is pathetic. The end of the film itself is Mamet-like smart, but disappoints from a human emotional perspective. Joe Mantegna and William Macy give strong performances, but the flaws of the story are too evident to be easily compensated." Label 0 Review b'Great documentary about the lives of NY firefighters during the worst terrorist attack of all time.. That reason alone is why this should be a must see collectors item.. What shocked me was not only the attacks, but the"High Fat Diet" and physical appearance of some of these firefighters. I think a lot of Doctors would agree with me that,in the physical shape they were in, some of these firefighters would NOT of made it to the 79th floor carrying over 60 lbs of gear. Having said that i now have a greater respect for firefighters and i realize becoming a firefighter is a life altering job. The French have a history of making great documentary\'s and that is what this is, a Great Documentary.....' Label 1 Label 0 corresponds to neg Label 1 corresponds to pos Found 25000 files belonging to 2 classes. Using 5000 files for validation. Found 25000 files belonging to 2 classes.
The required packages are imported and aliased.
The ImdB data is loaded and stored in a location for Colab to access.
A sample of the original data is displayed on the console.
The original data is split into training and test dataset.
The training data is used to build a model.
The given data is tried to be classified into a negative review or a positive value.