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62 lines
1.8 KiB
62 lines
1.8 KiB
import numpy
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import theano
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import theano.tensor as T
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rng = numpy.random
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N = 400 # training sample size
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feats = 784 # number of input variables
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# generate a dataset: D = (input_values, target_class)
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D = (rng.randn(N, feats), rng.randint(size=N, low=0, high=2))
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training_steps = 10000
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# Declare Theano symbolic variables
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x = T.dmatrix("x")
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y = T.dvector("y")
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# initialize the weight vector w randomly
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#
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# this and the following bias variable b
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# are shared so they keep their values
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# between training iterations (updates)
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w = theano.shared(rng.randn(feats), name="w")
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# initialize the bias term
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b = theano.shared(0., name="b")
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print("Initial model:")
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print(w.get_value())
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print(b.get_value())
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# Construct Theano expression graph
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p_1 = 1 / (1 + T.exp(-T.dot(x, w) - b)) # Probability that target = 1
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prediction = p_1 > 0.5 # The prediction thresholded
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xent = -y * T.log(p_1) - (1-y) * T.log(1-p_1) # Cross-entropy loss function
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cost = xent.mean() + 0.01 * (w ** 2).sum()# The cost to minimize
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gw, gb = T.grad(cost, [w, b]) # Compute the gradient of the cost
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# w.r.t weight vector w and
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# bias term b
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# (we shall return to this in a
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# following section of this tutorial)
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# Compile
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train = theano.function(
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inputs=[x,y],
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outputs=[prediction, xent],
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updates=((w, w - 0.1 * gw), (b, b - 0.1 * gb)))
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predict = theano.function(inputs=[x], outputs=prediction)
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# Train
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for i in range(training_steps):
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pred, err = train(D[0], D[1])
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print("Final model:")
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print(w.get_value())
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print(b.get_value())
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print("target values for D:")
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print(D[1])
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print("prediction on D:")
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print(predict(D[0]))
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