TensorFlow Quick Reference Table – Cheat Sheet.
TensorFlow is very popular deep learning library, with its complexity can be overwhelming especially for new users. Here is a short summary of often used functions, if you want to download it in pdf it is available here:
TensorFlow Cheat Sheet – TensorFlow.uk
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Import TensorFlow: 

import tensorflow as tf  
Basic math operations: 

tf.add()  sum 
tf.subtract()  substraction 
tf.multiply()  multiplication 
tf.div()  division 
tf.mod()  module 
tf.abs()  absolute value 
tf.negative()  negative value 
tf.sign()  return sign 
tf.reciprocal()  reciprocal 
tf.square()  square 
tf.round()  nearest intiger 
tf.sqrt()  square root 
tf.pow()  power 
tf.exp()  exponent 
tf.log()  logarithm 
tf.maximum()  maximum 
tf.minimum()  minimum 
tf.cos()  cosine 
tf.sin()  sine 
Basic operations on tensors: 

tf.string_to_number()  converts string to numeric type 
tf.cast()  casts to new type 
tf.shape()  returns shape of tensor 
tf.reshape()  reshapes tensor 
tf.diag()  creates tensor with given diagonal values 
tf.zeros()  creates tensor with all elements set to zero 
tf.fill()  creates tensor with all elements set given value 
tf.concat()  concatenates tensors 
tf.slice()  extracts slice from tensor 
tf.transpose()  transpose the argument 
tf.matmul()  matrices multiplication 
tf.matrix_determinant()  determinant of matrices 
tf.matrix_inverse()  computes inverse of matrices 
Control Flow: 

tf.while_loop()  repeat body while condition true 
tf.case()  case operator 
tf.count_up_to()  incriments ref untill limit 
tf.tuple()  groups tensors together 
Logical/Comparison Operators: 

tf.equal()  returns truth value elementwise 
tf.not_equal()  returns truth value of X!=Y 
tf.less()  returns truth value of X<Y 
tf.less_equal()  returns truth value of X<=Y 
tf.greater()  returns truth value of X>Y 
tf.greater_equal()  returns truth value of X>=Y 
tf.is_nan()  returns which elements are NaN 
tf.logical_and()  returns truth value of ‘AND’ for given tensors 
tf.logical_or()  returns truth value of ‘OR’ for given tensors 
tf.logical_not()  returns truth value of ‘NOT’ for given tensors 
tf.logical_xor()  returns truth value of ‘XOR’ for given tensors 
Working with Images: 

tf.image.decode_image()  converts image to tensor type uint8 
tf.image.resize_images()  resize images 
tf.image.resize_image_with_crop_or_pad()  resize image by cropping or padding 
tf.image.flip_up_down()  flip image horizontally 
tf.image.rot90()  rotate image 90 degrees counterclockwise 
tf.image.rgb_to_grayscale()  converts image from RGB to grayscale 
tf.image.per_image_standardization()  scales image to zero mean and unit norm 
Neural Networks: 

tf.nn.relu()  rectified linear activation function 
tf.nn.softmax()  softmax activation function 
tf.nn.sigmoid()  sigmoid activation function 
tf.nn.tanh()  hyperbolic tangent activation function 
tf.nn.dropout  dropout 
tf.nn.bias_add  adds bias to value 
tf.nn.all_candidate_sampler()  set of all classes 
tf.nn.weighted_moments()  returns mean and variance 
tf.nn.softmax_cross_entropy_with_logits()  softmax cross entropy 
tf.nn.sigmoid_cross_entropy_with_logits()  sigmoid cross entropy 
tf.nn.l2_normalize()  normalization using L2 Norm 
tf.nn.l2_loss()  L2 loss 
tf.nn.dynamic_rnn()  RNN specified by given cell 
tf.nn.conv2d()  2D convolutions given 4D input 
tf.nn.conv1d()  1D convolution given 3D input 
tf.nn.batch_normalization()  batch normalization 
tf.nn.xw_plus_b()  computes matmul(x,weights)+biases 
High level Machine Learning: 

tf.contrib.keras  Keras API as high level API for TensorFlow 
tf.contrib.layers.one_hot_column()  one hot encoding 
tf.contrib.learn.LogisticRegressor()  logistic regression 
tf.contrib.learn.DNNClassifier()  DNN classifier 
tf.contrib.learn.DynamicRnnEstimator()  Rnn Estimator 
tf.contrib.learn.KMeansClustering()  KMeans Clusstering 
tf.contrib.learn.LinearClassifier()  linear classifier 
tf.contrib.learn.LinearRegressor()  linear regressor 
tf.contrib.learn.extract_pandas_data()  extract data from Pandas dataframe 
tf.contrib.metrics.accuracy()  accuracy 
tf.contrib.metrics.auc_using_histogram()  AUC 
tf.contrib.metrics.confusion_matrix()  confusion matrix 
tf.contrib.metrics.streaming_mean_absolute_error()  mean absolute error 
tf.contrib.rnn.BasicLSTMCell()  basic lstm cell 
tf.contrib.rnn.BasicRNNCell()  basic rnn cell 
Placeholders and Variables: 

tf.placeholder()  defines placeholder 
tf.Variable(tf.random_normal([3, 4], stddev=0.1)  defines variable 
tf.Variable(tf.zeros([50]), name=’x’)  defines variable 
tf.global_variables_initializer()  initialize global variables 
tf.local_variables_initializer()  initialize local variables 
with tf.device(“/cpu:0”):  pin variable to CPU 
v = tf.Variable()  
with tf.device(“/gpu:0”):  pin variable to GPU 
v = tf.Variable()  
sess = tf.Session()  run session 
sess.run()  
sess.close()  
with tf.Session() as session:  run session(2) 
session.run()  
saver=tf.train.Saver()  Saving and restoring variables. 
saver.save(sess,’file_name’)  
saver.restore(sess,’file_name’)  
Working with Data: 

tf.decode_csv()  converts csv to tensors 
tf.read_file()  reads file 
tf.write_file()  writes to file 
tf.train.batch()  creates batches of tensors 
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If you want to look for more information, check some free online courses available at coursera.org, edx.org or udemy.com.
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