pep8

theano/tests/test_tutorial.py

@@ -1137,213 +1137,208 @@ class T_graphstructures(unittest.TestCase):
         assert e.owner.inputs[1].owner.inputs[0] is y
         assert e.owner.inputs[1].owner.inputs[1] is z
 
+
 class T_scan(unittest.TestCase):
     ## All tests here belong to
     ## http://deeplearning.net/software/theano/tutorial/loop.html
     ## Theano/doc/tutorial/loop.txt
     ## Any change you do here also add it to the tutorial !
-    
+
     def test_elemwise(self):
-        
           # defining the tensor variables
           X = T.matrix("X")
           W = T.matrix("W")
           b_sym = T.vector("b_sym")
-  
+
           results, updates = theano.scan(lambda v:T.tanh(T.dot(v,W)+b_sym), \
                     sequences=X)
           compute_elementwise = theano.function(inputs = [X, W, b_sym], \
                     outputs=[results])
-  
+
           # test values
           x = numpy.eye(2)
           w = numpy.ones((2,2))
           b = numpy.ones((2))
           b[1] = 2
-  
+
           print "Scan results:", compute_elementwise(x, w, b)[0]
-  
+
           # comparison with numpy
           print "Numpy results:", numpy.tanh(x.dot(w) + b)
-    
+
     def test_sequence(self):
-        
-          # define tensor variables
-          X = T.vector("X")
-          W = T.matrix("W")
-          b_sym = T.vector("b_sym")
-          U = T.matrix("U")
-          Y = T.matrix("Y")
-          V = T.matrix("V")
-          P = T.matrix("P")
-  
-          results, updates = theano.scan(lambda \
+        # define tensor variables
+        X = T.vector("X")
+        W = T.matrix("W")
+        b_sym = T.vector("b_sym")
+        U = T.matrix("U")
+        Y = T.matrix("Y")
+        V = T.matrix("V")
+        P = T.matrix("P")
+
+        results, updates = theano.scan(lambda \
                         y,p,x_tm1:T.tanh(T.dot(x_tm1,W) + \
                         T.dot(y,U)+T.dot(p,V)), \
-                    sequences=[Y,P[::-1]], outputs_info=[X])
-            
-          compute_seq = theano.function(inputs = [X, W, Y, U, P, V], \
-                    outputs=[results])
-  
-          # test values
-          x = numpy.zeros((2))
-          x[1] = 1
-          w = numpy.ones((2,2))
-          y = numpy.ones((5,2))
-          y[0,:] = -3
-          u = numpy.ones((2,2))
-          p = numpy.ones((5,2))
-          p[0,:] = 3
-          v = numpy.ones((2,2))
-  
-          print "Scan results", compute_seq(x,w,y,u,p,v)[0]
-  
-          # comparison with numpy
-          x_res = numpy.zeros((5,2))
-          x_res[0] = numpy.tanh(x.dot(w) + y[0].dot(u) + p[4].dot(v))
-          for i in range(1,5):
+                                       sequences=[Y,P[::-1]], outputs_info=[X])
+
+        compute_seq = theano.function(inputs = [X, W, Y, U, P, V], \
+                                      outputs=[results])
+
+        # test values
+        x = numpy.zeros((2))
+        x[1] = 1
+        w = numpy.ones((2,2))
+        y = numpy.ones((5,2))
+        y[0,:] = -3
+        u = numpy.ones((2,2))
+        p = numpy.ones((5,2))
+        p[0,:] = 3
+        v = numpy.ones((2,2))
+
+        print "Scan results", compute_seq(x,w,y,u,p,v)[0]
+
+        # comparison with numpy
+        x_res = numpy.zeros((5,2))
+        x_res[0] = numpy.tanh(x.dot(w) + y[0].dot(u) + p[4].dot(v))
+        for i in range(1,5):
             x_res[i] = numpy.tanh(x_res[i-1].dot(w) \
-                        + y[i].dot(u) + p[4-i].dot(v))
-        
-          print "Numpy results:", x_res
-    
+                                + y[i].dot(u) + p[4-i].dot(v))
+
+        print "Numpy results:", x_res
+
     def test_norm(self):
-          # define tensor variable
-          X = T.matrix("X")
-          results, updates = theano.scan(lambda x_i:T.sqrt((x_i**2).sum()), \
-                            sequences=[X])
-          compute_norm_lines = theano.function(inputs = [X], outputs=[results])
-          
-          results, updates = theano.scan(lambda x_i:T.sqrt((x_i**2).sum()), \
-                            sequences=[X.T])
-          compute_norm_cols = theano.function(inputs = [X], outputs=[results])
-  
-          # test value
-          x = numpy.diag(numpy.arange(1,6),1)
-          print "Scan results:", compute_norm_lines(x)[0], \
+        # define tensor variable
+        X = T.matrix("X")
+        results, updates = theano.scan(lambda x_i:T.sqrt((x_i**2).sum()), \
+                                       sequences=[X])
+        compute_norm_lines = theano.function(inputs = [X], outputs=[results])
+
+        results, updates = theano.scan(lambda x_i:T.sqrt((x_i**2).sum()), \
+                                       sequences=[X.T])
+        compute_norm_cols = theano.function(inputs = [X], outputs=[results])
+
+        # test value
+        x = numpy.diag(numpy.arange(1,6),1)
+        print "Scan results:", compute_norm_lines(x)[0], \
                             compute_norm_cols(x)[0]
-  
-          # comparison with numpy
-          print "Numpy results:", numpy.sqrt((x**2).sum(1)), \
+
+        # comparison with numpy
+        print "Numpy results:", numpy.sqrt((x**2).sum(1)), \
                             numpy.sqrt((x**2).sum(0))
-    
+
     def test_trace(self):
-        
-          # define tensor variable
-          X = T.matrix("X")
-          results, updates = theano.scan(lambda i, j, t_f:T.cast(X[i,j] + \
+        # define tensor variable
+        X = T.matrix("X")
+        results, updates = theano.scan(lambda i, j, t_f:T.cast(X[i,j] + \
                                 t_f, theano.config.floatX), \
                             sequences=[T.arange(X.shape[0]), \
                                 T.arange(X.shape[1])], \
                             outputs_info=numpy.asarray(0., \
                                 dtype=theano.config.floatX))
-            
-          result = results[-1]
-          compute_trace = theano.function(inputs = [X], outputs=[result])
-  
-          # test value
-          x = numpy.eye(5)
-          x[0] = numpy.arange(5)
-          print "Scan results:", compute_trace(x)[0]
-  
-          # comparison with numpy
-          print "Numpy results:", numpy.diagonal(x).sum()
 
-    def test_taps(self):
-    
-          # define tensor variables
-          X = T.matrix("X")
-          W = T.matrix("W")
-          b_sym = T.vector("b_sym")
-          U = T.matrix("U")
-          V = T.matrix("V")
-          n_sym = T.iscalar("n_sym")
+        result = results[-1]
+        compute_trace = theano.function(inputs = [X], outputs=[result])
 
-          results, updates = theano.scan(lambda x_tm2,x_tm1:T.dot(x_tm2,U) \
+        # test value
+        x = numpy.eye(5)
+        x[0] = numpy.arange(5)
+        print "Scan results:", compute_trace(x)[0]
+
+        # comparison with numpy
+        print "Numpy results:", numpy.diagonal(x).sum()
+
+    def test_taps(self):
+        # define tensor variables
+        X = T.matrix("X")
+        W = T.matrix("W")
+        b_sym = T.vector("b_sym")
+        U = T.matrix("U")
+        V = T.matrix("V")
+        n_sym = T.iscalar("n_sym")
+
+        results, updates = theano.scan(lambda x_tm2,x_tm1:T.dot(x_tm2,U) \
                         + T.dot(x_tm1,V) + T.tanh(T.dot(x_tm1,W) + b_sym), \
                     n_steps=n_sym, \
                     outputs_info=[dict(initial = X, taps = [-2,-1])])
-        
-          compute_seq2 = theano.function(inputs = [X, U, V, W, b_sym, \
-                    n_sym], outputs=[results])
-  
-          # test values
-          x = numpy.zeros((2,2)) 
-          # the initial value must be able to return x[-2]
-          x[1,1] = 1
-          w = 0.5*numpy.ones((2,2))
-          u = 0.5*(numpy.ones((2,2))-numpy.eye(2))
-          v = 0.5*numpy.ones((2,2))
-          n = 10
-          b = numpy.ones((2))
-  
-          print "Scan results:", compute_seq2(x,u,v,w,b,n)
-  
-          # comparison with numpy
-          x_res = numpy.zeros((10,2))
-          x_res[0] = x[0].dot(u) + x[1].dot(v) + numpy.tanh(x[1].dot(w) + b)
-          x_res[1] = x[1].dot(u) + x_res[0].dot(v) \
+
+        compute_seq2 = theano.function(inputs = [X, U, V, W, b_sym, \
+                                                 n_sym], outputs=[results])
+
+        # test values
+        x = numpy.zeros((2,2))
+        # the initial value must be able to return x[-2]
+        x[1,1] = 1
+        w = 0.5*numpy.ones((2,2))
+        u = 0.5*(numpy.ones((2,2))-numpy.eye(2))
+        v = 0.5*numpy.ones((2,2))
+        n = 10
+        b = numpy.ones((2))
+
+        print "Scan results:", compute_seq2(x,u,v,w,b,n)
+
+        # comparison with numpy
+        x_res = numpy.zeros((10,2))
+        x_res[0] = x[0].dot(u) + x[1].dot(v) + numpy.tanh(x[1].dot(w) + b)
+        x_res[1] = x[1].dot(u) + x_res[0].dot(v) \
                         + numpy.tanh(x_res[0].dot(w) + b)
-          x_res[2] = x_res[0].dot(u) + x_res[1].dot(v) \
-                    + numpy.tanh(x_res[1].dot(w) + b)
-          for i in range(2,10):
+        x_res[2] = x_res[0].dot(u) + x_res[1].dot(v) \
+                   + numpy.tanh(x_res[1].dot(w) + b)
+        for i in range(2,10):
             x_res[i] = (x_res[i-2].dot(u) + x_res[i-1].dot(v) \
-                    + numpy.tanh(x_res[i-1].dot(w) + b))
-          
-          print "Numpy results:", x_res
+                        + numpy.tanh(x_res[i-1].dot(w) + b))
+
+        print "Numpy results:", x_res
 
     def test_jacobian(self):
-    
-          # define tensor variables
-          v = T.vector()
-          A = T.matrix()
-          y = T.tanh(T.dot(v,A))
-          results, updates = theano.scan(lambda i:T.grad(y[i], v), \
-                    sequences = [T.arange(y.shape[0])])
-          compute_jac_t = theano.function([A,v], [results], \
-                allow_input_downcast = True) # shape (d_out, d_in)
-  
-          # test values
-          x = numpy.eye(5)[0]
-          w = numpy.eye(5,3)
-          w[2] = numpy.ones((3))
-          print "Scan results:", compute_jac_t(w,x)[0]
-  
-          # compare with numpy
-          print "Numpy results:", ((1 - numpy.tanh(x.dot(w))**2)*w).T
+        # define tensor variables
+        v = T.vector()
+        A = T.matrix()
+        y = T.tanh(T.dot(v,A))
+        results, updates = theano.scan(lambda i:T.grad(y[i], v), \
+                                       sequences = [T.arange(y.shape[0])])
+        compute_jac_t = theano.function([A,v], [results], \
+                                        allow_input_downcast = True) # shape (d_out, d_in)
+
+        # test values
+        x = numpy.eye(5)[0]
+        w = numpy.eye(5,3)
+        w[2] = numpy.ones((3))
+        print "Scan results:", compute_jac_t(w,x)[0]
+
+        # compare with numpy
+        print "Numpy results:", ((1 - numpy.tanh(x.dot(w))**2)*w).T
 
     def test_accumulator(self):
-          # define shared variables
-          k = theano.shared(0)
-          n_sym = T.iscalar("n_sym")
-  
-          results, updates = theano.scan(lambda:{k:(k+1)}, n_steps=n_sym)
-          accumulator = theano.function([n_sym], [], updates=updates, \
-                allow_input_downcast = True)
-  
-          print "Before 5 steps:", k.get_value()
-          accumulator(5)
-          print "After 5 steps:", k.get_value()
-    
+        # define shared variables
+        k = theano.shared(0)
+        n_sym = T.iscalar("n_sym")
+
+        results, updates = theano.scan(lambda:{k:(k+1)}, n_steps=n_sym)
+        accumulator = theano.function([n_sym], [], updates=updates,
+                                      allow_input_downcast = True)
+
+        print "Before 5 steps:", k.get_value()
+        accumulator(5)
+        print "After 5 steps:", k.get_value()
+
     def test_random(self):
-          # define tensor variables
-          X = T.matrix("X")
-          W = T.matrix("W")
-          b_sym = T.vector("b_sym")
-  
-          # define shared random stream
-          trng = T.shared_randomstreams.RandomStreams(1234)
-          d=trng.binomial(size=W[1].shape)
-  
-          results, updates = theano.scan(lambda v:T.tanh(T.dot(v,W) \
+        # define tensor variables
+        X = T.matrix("X")
+        W = T.matrix("W")
+        b_sym = T.vector("b_sym")
+
+        # define shared random stream
+        trng = T.shared_randomstreams.RandomStreams(1234)
+        d=trng.binomial(size=W[1].shape)
+
+        results, updates = theano.scan(lambda v:T.tanh(T.dot(v,W) \
                         + b_sym)*d, sequences=X)
-          compute_with_bnoise = theano.function(inputs = [X, W, b_sym], \
+        compute_with_bnoise = theano.function(inputs = [X, W, b_sym], \
                         outputs=[results], \
                         updates=updates, \
                         allow_input_downcast = True)
-          x = numpy.eye(10,2)
-          w = numpy.ones((2,2))
-          b = numpy.ones((2))
-  
-          print compute_with_bnoise(x, w, b)
+        x = numpy.eye(10,2)
+        w = numpy.ones((2,2))
+        b = numpy.ones((2))
 
+        print compute_with_bnoise(x, w, b)