Deep Learning - Revision history

Hdridder: /* Forward propagation */

2018-12-04T20:51:13Z

Forward propagation

← Older revision		Revision as of 20:51, 4 December 2018
Line 9:		Line 9:

	==Forward propagation==		==Forward propagation==
	Hidden ~~node give~~ a value to the input data based on their weight (Input value * weight).		Hidden nodes assign a value to the input data based on their weight (Input value * weight).

	Example:		Example:
	The amount of children has weight 2 on a hidden node~~, if~~ there are 3 children 6 will be added to the hidden node.		The amount of children has weight 2 on a hidden node. If there are 3 children, 6 will be added to the hidden node.
	Another attribute on the input layer will add a value to the hidden node too.		Another attribute on the input layer will add a value to the hidden node too.
	On another node in the hidden layer the amount of children may have the weight -1, to this node -3 will be added.		On another node in the hidden layer the amount of children may have the weight -1, to this node -3 will be added.

Hdridder: /* Forward propagation */

2018-12-04T20:48:53Z

Forward propagation

← Older revision		Revision as of 20:48, 4 December 2018
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	'node_1' : np_array([-1,1]), # The first attribute has weight -1 the other 1		'node_1' : np_array([-1,1]), # The first attribute has weight -1 the other 1
	'output' : np_array([2,-1]), # Weights for the hidden nodes (node_0 and node_1)}		'output' : np_array([2,-1]), # Weights for the hidden nodes (node_0 and node_1)}
	node_0_value = (input_data * weights['node_0'])~~.sum~~		node_0_value = np.sum(input_data * weights['node_0'])
	node_1_value = (input_data * weights['node_1']).sum		node_1_value = np.sum(input_data * weights['node_1'])
			layer1_value = np.array([node_0_value,node_1_value])
			output = np.sum(layer1_value * weights['output'])
	</syntaxhighlight>		</syntaxhighlight>

			===Rectified Linear Activation Function===
			A ReLU can be used to modify the node's value before it is passed to the next layer. The node will only fire (have a non-zero output value) or fire with more force if the criteria for that are matched. E.g. the output must be positive.

Hdridder: Created page with "From a [https://www.datacamp.com datacamp] course. =Concepts= ==Layers== # Input layer # Hidden layer(s); These find interactions between inputs. There can be several layers..."

2018-12-01T15:21:19Z

Created page with "From a [https://www.datacamp.com datacamp] course. =Concepts= ==Layers== # Input layer # Hidden layer(s); These find interactions between inputs. There can be several layers..."

New page

From a [https://www.datacamp.com datacamp] course.

=Concepts=

==Layers==
# Input layer
# Hidden layer(s); These find interactions between inputs. There can be several layers taking input from another layer. The more nodes on a layer the more interactions can be analyzed.
# Output layer

==Forward propagation==
Hidden node give a value to the input data based on their weight (Input value * weight).

Example:
The amount of children has weight 2 on a hidden node, if there are 3 children 6 will be added to the hidden node.
Another attribute on the input layer will add a value to the hidden node too.
On another node in the hidden layer the amount of children may have the weight -1, to this node -3 will be added.
The values in the nodes in a layer have a weight on the next layer nodes too. Again value * weight is put in the next layer node.
A node assigns a weight to each input node. The result of each input value is input * weight. All the results add up to the value of the node.

<syntaxhighlight lang=python>
import numpy as np
input_data = np_array([2,3]) # There are 2 input attributes
weights = {'node_0' : np_array([1,1]), # Both input attributes have weight 1
'node_1' : np_array([-1,1]), # The first attribute has weight -1 the other 1
'output' : np_array([2,-1]), # Weights for the hidden nodes (node_0 and node_1)}
node_0_value = (input_data * weights['node_0']).sum
node_1_value = (input_data * weights['node_1']).sum
</syntaxhighlight>