# TensorFlow

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Created: January 26, 2016 / Updated: December 30, 2017 / Status: in progress / 7 min read (~1369 words)

### Context

TensorFlow has rapidly grown in popularity due to the fact that is developed/supported by Google. As more and more developers move to the platform, it becomes essential to learn how it works and have a general idea of the various concepts it makes use of. This is a short article about some of these concepts.

## Overview

• Computations are represented as graphs
• Graphs are executed in the context of Sessions

## Building a graph

• Start with ops that do not need any input (called source ops), such as Constant

## Session

• Graphs are executed within a session (context)
session = tf.session()
• Sessions are given one or many tensor to resolve
session.run([tensorA, tensorB])
• Once we're done with a session, it should be closed
session.close()

## Tensors

A tensor is simply a multidimensional array of data. A scalar is a 0-D tensor, a vector is a 1-D tensor, a matrix is a 2-D tensor and anything over 3-D is called an n-D tensor.

Rank: The number of dimensions of a tensor.

Rank Math entity Example
0 Scalar s = 483
1 Vector v = [1.1, 2.2, 3.3]
2 Matrix m = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
3 3-Tensor t = [[[2], [4], [6]], [[8], [10], [12]], [[14], [16], [18]]]

Shape: A vector describing the number of elements at each point within a dimension.

Rank Shape Dimension number Example
0 [] 0-D A 0-D tensor. A scalar.
1 [D0] 1-D A 1-D tensor with shape [5] = [1, 2, 3, 4, 5].
2 [D0, D1] 2-D A 2-D tensor with shape [3, 4] = [[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]].
3 [D0, D1, D2] 3-D A 3-D tensor with shape [1, 4, 3] = [[[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]]].
n [D0, D1, ..., Dn] n-D A tensor with shape [D0, D1, ... Dn].

Type: Type of the data contained within the tensor.

Data type Description
DT_FLOAT 32 bits floating point.
DT_DOUBLE 64 bits floating point.
DT_INT64 64 bits signed integer.
DT_INT32 32 bits signed integer.
DT_INT16 16 bits signed integer.
DT_INT8 8 bits signed integer.
DT_UINT8 8 bits unsigned integer.
DT_STRING Variable length byte arrays. Each element of a Tensor is a byte array.
DT_BOOL Boolean.
DT_COMPLEX64 Complex number made of two 32 bits floating points: real and imaginary parts.
DT_QINT32 32 bits signed integer used in quantized Ops.
DT_QINT8 8 bits signed integer used in quantized Ops.
DT_QUINT8 8 bits unsigned integer used in quantized Ops.

## Variables

• Variables must be initialized (tf.initialize_all_variables())
• Initialization is an operation, and thus must be executed within a session

## Fetches

• All the ops needed to produce the values of requested tensors are run once (not once per requested tensor)

## Feeds

• Temporarily replaces the output of an operation with a tensor value (act as a placeholder)
• The feed data is provided as an argument to a session.run() call
sess.run([output], feed_dict={input1:[7.], input2:[2.]})

## Operations/Functions of interest

### CNN

• tf.nn.conv2d(input, kernel, strides, padding): apply a convolution using kernel
• tf.nn.relu(input): rectifier linear unit, every negative value is set to 0, and positive values are kept the same
• tf.sigmoid(input): returns a value in the range [0.0, 1.0]
• tf.tanh(input): returns a value in the range [-1.0, 1.0]
• tf.nn.dropout(input, keep_prob): set the output to 0.0 based on a given probability. The output is multiplied by 1/keep_prob in order to keep the expected sum unchanged
• tf.nn.max_pool(input, kernel, strides, padding): take the maximum value found within a certain kernel size
• tf.nn.avg_pool(input, kernel, strides, padding): averages out all the values at each depth found within a kernel size
• tf.nn.local_response_normalization

### RNN

• tf.nn.rnn_cell.BasicRNNCell(num_neurons): declares a recurrent neural network cell
• tf.nn.dynamic_rnn(network, input): simulate the given RNN
• tf.nn.rnn_cell.LSTMCell(num_neurons): declares a long short-term memory neural network cell
• tf.nn.rnn_cell.GRUCell(num_neurons): declares a gated recurrent unit cell

## CNN

• Used mostly to process high density matrices where the data surrounding a value is generally highly correlated with it
• Apply the convolution operator to a 2d matrix using a given kernel/filter

## RNN

• Used to process sequential inputs (speech recognition, speech synthesis, connected handwriting recognition, time-series forecast, image caption generation, end-to-end translation)