https://youtu.be/-9H_eVZdtN8
Here it is the TensorFlow neuronal network to solve it:
tf_nn.py
- Code: Select all Expand view
- # Building a neuronal network with TensorFlow
import tensorflow as tf
def multilayer_perceptron( x, weights, biases ):
# Hidden layer with RELU activation
layer_1 = tf.add( tf.matmul( x, weights[ 'h1' ] ), biases[ 'b1' ] )
layer_1 = tf.nn.relu(layer_1)
# Output layer with linear activation
out_layer = tf.matmul( layer_1, weights[ 'out' ] ) + biases[ 'out' ]
return out_layer
session = tf.Session()
nInputs = 7 # Number of inputs to the neuronal network
nHiddenPerceptrons = 12
nTypes = 10 # Number of different types in the output
nLearningRate = 0.002
nTrainingEpochs = 500
# Input data
aInputs = [ [ 1, 1, 1, 0, 1, 1, 1 ], # zero 2
[ 1, 0, 0, 0, 0, 0, 1 ], # one -------
[ 1, 1, 0, 1, 1, 1, 0 ], # two 3 | | 1
[ 1, 1, 0, 1, 0, 1, 1 ], # three | 4 |
[ 1, 0, 1, 1, 0, 0, 1 ], # four -------
[ 0, 1, 1, 1, 0, 1, 1 ], # five | |
[ 0, 1, 1, 1, 1, 1, 1 ], # six 5 | | 7
[ 1, 1, 0, 0, 0, 0, 1 ], # seven -------
[ 1, 1, 1, 1, 1, 1, 1 ], # eight 6
[ 1, 1, 1, 1, 0, 1, 1 ] ] # nine
aOutputs = [ [ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0 ],
[ 0, 1, 0, 0, 0, 0, 0, 0, 0, 0 ],
[ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ],
[ 0, 0, 0, 1, 0, 0, 0, 0, 0, 0 ],
[ 0, 0, 0, 0, 1, 0, 0, 0, 0, 0 ],
[ 0, 0, 0, 0, 0, 1, 0, 0, 0, 0 ],
[ 0, 0, 0, 0, 0, 0, 1, 0, 0, 0 ],
[ 0, 0, 0, 0, 0, 0, 0, 1, 0, 0 ],
[ 0, 0, 0, 0, 0, 0, 0, 0, 1, 0 ],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 ] ]
input = tf.placeholder( "float", shape=( None, nInputs ) )
output = tf.placeholder( "float", shape=( None, nTypes ) )
# Store layers weight & bias
weights = { 'h1': tf.Variable(tf.random_normal( [ nInputs, nHiddenPerceptrons ] ) ),
'out': tf.Variable(tf.random_normal( [ nHiddenPerceptrons, nTypes ] ) ) }
biases = { 'b1': tf.Variable( tf.random_normal( [ nHiddenPerceptrons ] ) ),
'out': tf.Variable( tf.random_normal( [ nTypes ] ) ) }
# Create model
network = multilayer_perceptron( input, weights, biases )
loss = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits( logits=network, labels=output ) )
optimizer = tf.train.AdamOptimizer( learning_rate = nLearningRate ).minimize( loss )
init = tf.global_variables_initializer()
with tf.Session() as session:
session.run( init )
# Training cycle
for epoch in range( nTrainingEpochs ) :
avg_error = 0
for n in range( len( aInputs ) ) :
cost = session.run( [ optimizer, loss ], { input: [ aInputs[ n ] ], output: [ aOutputs[ n ] ] } )
# Compute average error
avg_error += cost[ 1 ] / len( aInputs )
print( "Epoch:", '%04d' % ( epoch + 1 ), "error=", "{:.9f}".format( avg_error ) )
print( "Optimization Finished" )
# Test model on train data
print( "Testing:" )
for n in range( len( aInputs ) ) :
print( tf.argmax( network, 1 ).eval( { input: [ aInputs[ n ] ] } )[ 0 ] )
From a cmd window type:
python tf_nn.py
and you will see how the neuronal network learns (the error decreases) and finally when we test it, the right values appear!
Epoch: 0491 error= 0.010902708
Epoch: 0492 error= 0.010839775
Epoch: 0493 error= 0.010745070
Epoch: 0494 error= 0.010681662
Epoch: 0495 error= 0.010590052
Epoch: 0496 error= 0.010513857
Epoch: 0497 error= 0.010433172
Epoch: 0498 error= 0.010375975
Epoch: 0499 error= 0.010283007
Epoch: 0500 error= 0.010227598
Optimization Finished
Testing:
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