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Transcript

Neural Networks Background - Neural Networks can be : - Biological models - Artificial models - Desire to produce artificial systems capable of sophisticated computations similar to the human brain. Biological analogy and some main ideas • The brain is composed of a mass of interconnected neurons – each neuron is connected to many other neurons • Neurons transmit signals to each other • Whether a signal is transmitted is an all-or-nothing event (the electrical potential in the cell body of the neuron is thresholded) • Whether a signal is sent, depends on the strength of the bond (synapse) between two neurons How Does the Brain Work ? (1) NEURON - The cell that performs information processing in the brain. - Fundamental functional unit of all nervous system tissue. How Does the Brain Work ? (2) Each consists of : SOMA, DENDRITES, AXON, and SYNAPSE. Brain vs. Digital Computers (1) - Computers require hundreds of cycles to simulate a firing of a neuron. - The brain can fire all the neurons in a single step. Parallelism - Serial computers require billions of cycles to perform some tasks but the brain takes less than a second. e.g. Face Recognition Definition of Neural Network A Neural Network is a system composed of many simple processing elements operating in parallel which can acquire, store, and utilize experiential knowledge. Artificial Neural Network? Neurons vs. Units (1) • Each element of NN is a node called unit. • Units are connected by links. • Each link has a numeric weight. Neurons vs. units (2) Real neuron is far away from our simplified model - unit Chemistry, biochemistry, quantumness. Computing Elements A typical unit: Planning in building a Neural Network Decisions must be taken on the following: - The number of units to use. - The type of units required. - Connection between the units. How NN learns a task. Issues to be discussed - Initializing the weights. - Use of a learning algorithm. - Set of training examples. - Encode the examples as inputs. - Convert output into meaningful results. Neural Network Example A very simple, two-layer, feed-forward network with two inputs, two hidden nodes, and one output node. Simple Computations in this network - There are 2 types of components: Linear and Nonlinear. - Linear: Input function - calculate weighted sum of all inputs. - Non-linear: Activation function - transform sum into activation level. Calculations Input function: Activation function g: A Computing Unit. Now in more detail but for a particular model only A unit Activation Functions - Use different functions to obtain different models. - 3 most common choices : 1) Step function 2) Sign function 3) Sigmoid function - An output of 1 represents firing of a neuron down the axon. Step Function Perceptrons 3 Activation Functions Standard structure of an artificial neural network • Input units – represents the input as a fixed-length vector of numbers (user defined) • Hidden units – calculate thresholded weighted sums of the inputs – represent intermediate calculations that the network learns • Output units – represent the output as a fixed length vector of numbers Representations • Logic rules – If color = red ^ shape = square then + • Decision trees – tree • Nearest neighbor – training examples • Probabilities – table of probabilities • Neural networks – inputs in [0, 1] Can be used for all of them Many variants exist Notation Notation (cont.) Operation of individual units • Outputi = f(Wi,j * Inputj + Wi,k * Inputk + Wi,l * Inputl) – where f(x) is a threshold (activation) function – f(x) = 1 / (1 + e-Output) • “sigmoid” – f(x) = step function Artificial Neural Networks Perceptron Learning Theorem • Recap: A perceptron (threshold unit) can learn anything that it can represent (i.e. anything separable with a hyperplane) 26 The Exclusive OR problem A Perceptron cannot represent Exclusive OR since it is not linearly separable. 27 28 Properties of architecture • No connections within a layer • No direct connections between input and output layers • Fully connected between layers • Often more than 3 layers • Number of output units need not equal number of input units • Number of hidden units per layer can be more or less than input or output units Each unit is a perceptron m yi f ( wij x j bi ) j 1 Often include bias as an extra weight 29 Conceptually: Forward Activity Backward Error 30 Backpropagation learning algorithm ‘BP’ Solution to credit assignment problem in MLP. Rumelhart, Hinton and Williams (1986) (though actually invented earlier in a PhD thesis relating to economics) BP has two phases: Forward pass phase: computes ‘functional signal’, feed forward propagation of input pattern signals through network Backward pass phase: computes ‘error signal’, propagates the error backwards through network starting at output units (where the error is the difference between actual and desired output values) 31 Forward Propagation of Activity • Step 1: Initialize weights at random, choose a learning rate η • Until network is trained: • For each training example i.e. input pattern and target output(s): • Step 2: Do forward pass through net (with fixed weights) to produce output(s) – i.e., in Forward Direction, layer by layer: • • • • • Inputs applied Multiplied by weights Summed ‘Squashed’ by sigmoid activation function Output passed to each neuron in next layer – Repeat above until network output(s) produced 32 Step 3. Back-propagation of error • Compute error (delta or local gradient) for each output unit δ k • Layer-by-layer, compute error (delta or local gradient) for each hidden unit δ j by backpropagating errors (as shown previously) Step 4: Next, update all the weights Δwij By gradient descent, and go back to Step 2 The overall MLP learning algorithm, involving forward pass and backpropagation of error (until the network training completion), is known as the Generalised Delta Rule (GDR), or more commonly, the Back Propagation (BP) algorithm 33 ‘Back-prop’ algorithm summary (with Maths!) 34 ‘Back-prop’ algorithm summary (with NO Maths!) 35 MLP/BP: A worked example 36 Worked example: Forward Pass 37 Worked example: Forward Pass 38 Worked example: Backward Pass 39 Worked example: Update Weights Using Generalized Delta Rule (BP) 40 Similarly for the all weights wij: 41 Verification that it works 42 Training • This was a single iteration of back-prop • Training requires many iterations with many training examples or epochs (one epoch is entire presentation of complete training set) • It can be slow ! • Note that computation in MLP is local (with respect to each neuron) • Parallel computation implementation is also possible 43 Training and testing data • How many examples ? – The more the merrier ! • Disjoint training and testing data sets – learn from training data but evaluate performance (generalization ability) on unseen test data • Aim: minimize error on test data 44 More resources • Binary Logic Unit in an example – http://www.cs.usyd.edu.au/~irena/ai01/nn/5.html • MultiLayer Perceptron Learning Algorithm – http://www.cs.usyd.edu.au/~irena/ai01/nn/8.html 45