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add dlc url to ann2
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‎ann_class2/dropout_tensorflow.py‎

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# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFLow
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# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow
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# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow
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# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow
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import numpy as np
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import tensorflow as tf

‎ann_class2/dropout_theano.py‎

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# This code is not optimized for speed.
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# It's just to get something working, using the principles we know.
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# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFLow
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# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow
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# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow
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# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow
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import numpy as np

‎ann_class2/mlp.py‎

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# Simple multi-layer preceptron / neural network in Python and Numpy
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# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFLow
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# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow
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# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow
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# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow
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import numpy as np
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def forward(X, W1, b1, W2, b2):
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# Z = 1 / (1 + np.exp(-( X.dot(W1) + b1 )))
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Z = 1 / (1 + np.exp(-( X.dot(W1) + b1 )))
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# rectifier
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Z = X.dot(W1) + b1
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Z[Z < 0] = 0
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# Z = X.dot(W1) + b1
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# Z[Z < 0] = 0
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# print "Z:", Z
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A = Z.dot(W2) + b2
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return (Y - T).sum(axis=0)
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def derivative_w1(X, Z, T, Y, W2):
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# return X.T.dot( ( ( Y-T ).dot(W2.T) * ( Z*(1 - Z) ) ) ) # for sigmoid
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return X.T.dot( ( ( Y-T ).dot(W2.T) * np.sign(Z) ) ) # for relu
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return X.T.dot( ( ( Y-T ).dot(W2.T) * ( Z*(1 - Z) ) ) ) # for sigmoid
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# return X.T.dot( ( ( Y-T ).dot(W2.T) * np.sign(Z) ) ) # for relu
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def derivative_b1(Z, T, Y, W2):
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# return (( Y-T ).dot(W2.T) * ( Z*(1 - Z) )).sum(axis=0) # for sigmoid
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return (( Y-T ).dot(W2.T) * np.sign(Z)).sum(axis=0) # for relu
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return (( Y-T ).dot(W2.T) * ( Z*(1 - Z) )).sum(axis=0) # for sigmoid
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# return (( Y-T ).dot(W2.T) * np.sign(Z)).sum(axis=0) # for relu

‎ann_class2/momentum.py‎

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# Compare momentum with regular gradient descent
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# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFLow
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# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow
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# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow
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# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow
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# NOTE: MUST restrict initial values of W by dividing by #

‎ann_class2/rmsprop.py‎

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# Compare RMSprop vs. constant learning rate
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# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFLow
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# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow
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# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow
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# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow
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import numpy as np

‎ann_class2/sgd.py‎

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# (but not actually waiting for convergence) and what the cost looks like at
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# each iteration.
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#
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# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFLow
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# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow
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# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow
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# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow
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import numpy as np

‎ann_class2/tensorflow1.py‎

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# I compare this to theano1.py multiple times.
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# So you might want to check that out first.
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# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFLow
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# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow
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# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow
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# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow
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import numpy as np

‎ann_class2/tensorflow2.py‎

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# This code is not optimized for speed.
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# It's just to get something working, using the principles we know.
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# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFLow
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# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow
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# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow
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# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow
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import numpy as np

‎ann_class2/tf_with_save.py‎

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# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow
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# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow
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import json

‎ann_class2/theano1.py‎

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# Theano basics.
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# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFLow
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# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow
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# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow
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# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow
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import theano.tensor as T

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