N = 400 feats = 784 D = (rng.randn(N,feats),rng.randint(size=N,low=0,high=2)) training_steps = 10000 # Declare Theano symbolic variables x = T.matrix("x") y = T.vector('y') w = theano_test.shared(rng.randn(feats),name='w') b = theano_test.shared(0.,name='b') print 'Initial model:' print w.get_value(),b.get_value() # Construct Theano expression graph p_1 = 1 / (1 + T.exp(-T.dot(x, w) - b)) # Probability that target = 1 prediction = p_1 > 0.5 # The prediction thresholded xent = -y * T.log(p_1) - (1-y) * T.log(1-p_1) # Cross-entropy loss function cost = xent.mean() + 0.01 * (w ** 2).sum()# The cost to minimize gw, gb = T.grad(cost, [w, b]) # Compute the gradient of the cost # (we shall return to this in a # following section of this tutorial) # Compile train = theano_test.function( inputs=[x,y], outputs=[prediction, xent], updates=((w, w - 0.1 * gw), (b, b - 0.1 * gb))) predict = theano_test.function(inputs=[x], outputs=prediction) # Train for i in range(training_steps):
D = (rng.randn(N, feats).astype(theano_test.config.floatX), rng.randint(size=N,low=0, high=2).astype(theano_test.config.floatX)) training_steps = 10000 # Declare Theano symbolic variables x = T.matrix("x") y = T.vector("y") w = theano_test.shared(rng.randn(feats).astype(theano_test.config.floatX), name="w") b = theano_test.shared(numpy.asarray(0., dtype=theano_test.config.floatX), name="b") x.tag.test_value = D[0] y.tag.test_value = D[1] #print "Initial model:" #print w.get_value(), b.get_value() # 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 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]) # Compile expressions to functions train = theano_test.function( inputs=[x,y], outputs=[prediction, xent], updates={w:w-0.01*gw, b:b-0.01*gb}, name = "train") predict = theano_test.function(inputs=[x], outputs=prediction, name = "predict") if any([x.op.__class__.__name__ in ['Gemv', 'CGemv', 'Gemm', 'CGemm'] for x in
print f(4) x2 = T.dmatrix('x2') s = T.sum(1/(1+T.exp(-x2))) gs = T.grad(s,x2) dlogistic = function([x2],gs) print dlogistic([[0,1],[-1,-2]]) x3 = T.dvector('x3') y3 = x3**2 J,updates = theano_test.scan(lambda i,y,x:T.grad(y[i],x),sequences=T.arange(y3.shape[0]),non_sequences=[y3,x3]) f = function([x3],J,updates=updates) print f([4,4]) x4 = T.dvector('x4') y4 = x4**2 cost = y4.sum() gy4 = T.grad(cost,x4) H,updates2 = theano_test.scan(lambda i,gy,x4:T.grad(gy[i],x4),sequences=T.arange(gy4.shape[0]),non_sequences=[gy4,x4]) f2 = function([x4],H,updates=updates2) print f2([4,4]) W = T.dmatrix('W') V = T.dmatrix('V') xx = T.dvector('xx') yy = T.dot(xx,W) JV = T.Rop(yy,W,V) fwv = theano_test.function([W,V,xx],JV) print fwv([[1, 1], [1, 1]], [[2, 2], [2, 2]], [0,1])
import numpy import theano_test import theano_test.tensor as T rng = numpy.random N = 400 feats = 784 D = (rng.randn(N,feats).astype(theano_test.config.floatX),rng.randint(size=N,low=0,high=2).astype(theano_test.config.floatX)) traing_steps = 10000 x = T.dmatrix('x') y = T.vector('y') w = theano_test.shared(rng.rand(feats).astype(theano_test.config.floatX),name='w') b = theano_test.shared(numpy.asarray(0.,dtype=theano_test.config.floatX),name='b') x.tag.test_value = D[0] y.tag.test_value = D[1] p_1 = 1/(1+T.exp(-T.dot(x,w)-b)) prediction = p_1>0.5 xent = -y*T.log(p_1) - (1-y)*T.log(1-p_1) cost = xent.mean() + 0.01*(w**2).sum() gw,gb = T.grad(cost, [w,b]) train = theano_test.function(inputs=[x,y], outputs=[prediction, xent], updates=[[w, w-0.01*gw], [b, b-0.01*gb]], name = "train") predict = theano_test.function(inputs=[x], outputs=prediction, name = "predict") print theano_test.printing.pprint(prediction) print theano_test.printing.debugprint(prediction) print theano_test.printing.debugprint(predict)