# Table of Contents¶

- Model
- Example 1
- Example 2
- Example 3

In [1]:

    import numpy
    import theano
    import theano.tensor as T
    import theano.printing as pr
    import theano.d3printing as d3p
    rng = numpy.random

    Couldn't import dot_parser, loading of dot files will not be possible.

In [2]:

    %load_ext autoreload
    %autoreload 2

# Model¶

In [3]:

    # Training data
    N = 400
    feats = 784
    D = (rng.randn(N, feats).astype(theano.config.floatX), rng.randint(size=N,low=0, high=2).astype(theano.config.floatX))
    training_steps = 10000

    # Declare Theano symbolic variables
    x = T.matrix("x")
    y = T.vector("y")
    w = theano.shared(rng.randn(feats).astype(theano.config.floatX), name="w")
    b = theano.shared(numpy.asarray(0., dtype=theano.config.floatX), name="b")
    x.tag.test_value = D[0]
    y.tag.test_value = D[1]

    # Construct Theano expression graph
    p_1 = 1 / (1 + T.exp(-T.dot(x, w)-b)) # Probability of having a one
    prediction = p_1 > 0.5 # The prediction that is done: 0 or 1

    # Compute gradients
    xent = -y*T.log(p_1) - (1-y)*T.log(1-p_1) # Cross-entropy
    cost = xent.mean() + 0.01*(w**2).sum() # The cost to optimize
    gw,gb = T.grad(cost, [w,b])

    # Training and prediction function
    train = theano.function(inputs=[x,y], outputs=[prediction, xent], updates=[[w, w-0.01*gw], [b, b-0.01*gb]], name = "train")
    predict = theano.function(inputs=[x], outputs=prediction, name = "predict")

# Example 1¶

In [4]:

    pr.pydotprint(p_1, outfile='p1.png', var_with_name_simple=True)
    d3p.d3write(p_1, 'p1.dot')

    The output file is available at p1.png

open

# Example 2¶

In [5]:

    pr.pydotprint(predict, outfile='predict.png', var_with_name_simple=True)
    d3p.d3write(predict, 'predict.dot')

    The output file is available at predict.png

open

# Example 3¶

In [6]:

    pr.pydotprint(train, outfile='train.png', var_with_name_simple=True)
    d3p.d3write(train, 'train.dot')

    The output file is available at train.png

open