some tests and bugs fixed to the new scan interface

theano/sandbox/my_test_scan.py

+import numpy
+import theano
+import theano.sandbox.scan
+
+
+
+# generator network, only one output , type scalar ; no sequence or 
+# non sequence arguments
+def test_1():
+  def f_pow2(x_tm1):
+    return (2*x_tm1, {})
+
+  s = theano.tensor.dvector()
+  n_steps = theano.tensor.dscalar()
+  Y = theano.sandbox.scan.scan(f_pow2, [],s, [],n_steps = n_steps)
+
+  f1 = theano.function([s,n_steps], Y)
+  assert( numpy.any(f1([1],3)== [2,4,8])  )
+
+# simple rnn, one input, one state, weights for each; input/state are 
+# vectors, weights are scalars
+def test_2():
+    def f_rnn(u_t,x_tm1,W_in, W):
+        return (u_t*W_in+x_tm1*W, {})
+
+    u    = theano.tensor.dvector()
+    x0   = theano.tensor.dvector()
+    W_in = theano.tensor.dscalar()
+    W    = theano.tensor.dscalar()
+
+    Y = theano.sandbox.scan.scan(f_rnn, u,x0,[W_in,W])
+
+    f2 = theano.function([u,x0,W_in,W], Y)
+    
+    assert(numpy.any(f2([1,2,3,4],[1],.1,1)== numpy.array([1.1,1.3,1.6,2.])))
+
+# simple rnn, one input, one state, weights for each; input/state are 
+# vectors, weights are scalars; using shared variables
+def test_3():
+
+    u    = theano.tensor.dvector()
+    x0   = theano.tensor.dvector()
+    W_in = theano.shared(.1, name = 'w_in')
+    W    = theano.shared(1., name ='w')
+
+    def f_rnn_shared(u_t,x_tm1):
+        return (u_t*W_in+x_tm1*W, {})
+
+    Y = theano.sandbox.scan.scan(f_rnn_shared, u,x0,[])
+
+    f3 = theano.function([u,x0], Y)
+    
+    assert(numpy.any(f3([1,2,3,4],[1])== numpy.array([1.1,1.3,1.6,2.])))
+
+
+# some rnn with multiple outputs and multiple inputs; other dimension 
+# instead of scalars/vectors
+def test_4():
+
+    W_in2 = theano.shared(numpy.array([1.,2.]), name='win2')
+    W     = theano.shared(numpy.array([[2.,1.],[1.,1.]]), name='w')
+    W_out = theano.shared(numpy.array([.5,1.]), name = 'wout')
+    W_in1 = theano.tensor.dmatrix('win')
+    u1    = theano.tensor.dmatrix('u1')
+    u2    = theano.tensor.dvector('u2')
+    x0    = theano.tensor.dmatrix('x0')
+    y0    = theano.tensor.dvector('y0')
+
+## Why dot doesn;t work with scalars !??
+## Why  *  doesn't support SharedVariable and TensorVariable
+
+    def f_rnn_cmpl(u1_t, u2_t, x_tm1, y_tm1, W_in1):
+        return ({}, [theano.dot(u1_t,W_in1) + u2_t* W_in2 + \
+                    theano.dot(x_tm1, W), theano.dot(x_tm1, W_out)])
+
+    Y = theano.sandbox.scan.scan(f_rnn_cmpl,[u1,u2],[x0,y0],W_in1)
+
+    f4 = theano.function([u1,u2,x0,y0,W_in1], Y)
+
+    (x,y) =  f4( numpy.array([[1,2],[1,2],[1,2]]), \
+              numpy.array([1,2,3]),             \
+              numpy.array([[0,0]]),             \
+              numpy.array([1]),                 \
+              numpy.array([[1,1],[1,1]]))
+
+    assert( numpy.all(x == numpy.array([[4.,5.],[18.,16.],[58.,43.]])))
+    assert( numpy.all(y == numpy.array([0.,7.,25.])))
+
+
+# basic ESN using updates 
+def test_5(): 
+    W_in = theano.shared(numpy.array([1.,1.]), name='win')
+    W    = theano.shared(numpy.array([[.1,0.],[.0,.1]]),name='w')
+    W_out= theano.shared(numpy.array([.5,1.]), name='wout')
+
+    u  = theano.tensor.dvector('u')
+    x  = theano.shared(numpy.array([0.,0.]),'x')
+    y0 = theano.tensor.dvector('y0')
+
+    def f_ESN(u_t):
+        return ( theano.dot(x,W_out), \
+         { x: W_in*u_t + theano.dot(x,W) } )
+
+    Y = theano.sandbox.scan.scan(f_ESN,u,y0,[],outputs_taps={0:[]})
+
+    f5 = theano.function([u,y0],Y)
+    assert( f5( numpy.array([1,2,3]), numpy.array([0])) == \
+             numpy.array([0.,1.4,3.15]))
+
+# basic ESN using updates ; moving backwards
+def test_6(): 
+    W_in = theano.shared(numpy.array([1.,1.]), name='win')
+    W    = theano.shared(numpy.array([[.1,0.],[.0,.1]]),name='w')
+    W_out= theano.shared(numpy.array([.5,1.]), name='wout')
+
+    u  = theano.tensor.dvector('u')
+    x  = theano.shared(numpy.array([0.,0.]),'x')
+    y0 = theano.tensor.dvector('y0')
+
+    def f_ESN(u_t):
+        return ( theano.dot(x,W_out), \
+         { x: W_in*u_t + theano.dot(x,W) } )
+
+    Y = theano.sandbox.scan.scan(f_ESN,u,y0,[],outputs_taps={0:[]}, \
+                                 go_backwards = True)
+
+    f6 = theano.function([u,y0],Y)
+    assert( f6( numpy.array([1,2,3]), numpy.array([0])) == \
+             numpy.array([0., 4.5, 3.45]))
+
+
+'''
+ TO TEST: 
+    - test taps (for sequences and outputs )
+    - test gradient (one output)
+    - test gradient (multiple outputs)
+    - test gradient (go_bacwards) 
+    - test gradient (multiple outputs / some uncomputable )
+    - test gradient (truncate_gradient)
+    - test gradient (force_gradient) 
+    - test inplace map
+'''
+
+
+if __name__=='__main__':
+
+    test_1()
+    test_2()
+    test_3()
+    test_4()
+    test_5()
+    test_6()
+
+
+
+