pep8

doc/tutorial/loop.txt

@@ -37,7 +37,7 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
   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])
+  compute_elementwise = theano.function(inputs=[X, W, b_sym], outputs=[results])
 
   # test values
   x = np.eye(2)
@@ -70,7 +70,7 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
   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])
+  compute_seq = theano.function(inputs=[X, W, Y, U, P, V], outputs=[results])
 
   # test values
   x = np.zeros((2))
@@ -101,7 +101,7 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
   # 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])
+  compute_norm_lines = theano.function(inputs=[X], outputs=[results])
 
   # test value
   x = np.diag(np.arange(1, 6), 1)
@@ -120,7 +120,7 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
   # define tensor variable
   X = T.matrix("X")
   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])
+  compute_norm_cols = theano.function(inputs=[X], outputs=[results])
 
   # test value
   x = np.diag(np.arange(1, 6), 1)
@@ -143,7 +143,7 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
                     sequences=[T.arange(X.shape[0]), T.arange(X.shape[1])],
                     outputs_info=np.asarray(0., dtype=floatX))
   result = results[-1]
-  compute_trace = theano.function(inputs = [X], outputs=[result])
+  compute_trace = theano.function(inputs=[X], outputs=[result])
 
   # test value
   x = np.eye(5)
@@ -169,8 +169,8 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
   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])
+                      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 = np.zeros((2, 2)) # the initial value must be able to return x[-2]
@@ -203,8 +203,8 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
   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)
+  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 = np.eye(5)[0]
@@ -229,7 +229,7 @@ Note that we need to iterate over the indices of ``y`` and not over the elements
   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)
+  accumulator = theano.function([n_sym], [], updates=updates, allow_input_downcast=True)
 
   k.get_value()
   accumulator(5)
@@ -252,8 +252,8 @@ Note that we need to iterate over the indices of ``y`` and not over the elements
   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], outputs=[results],
-                            updates=updates, allow_input_downcast = True)
+  compute_with_bnoise = theano.function(inputs=[X, W, b_sym], outputs=[results],
+                            updates=updates, allow_input_downcast=True)
   x = np.eye(10, 2)
   w = np.ones((2, 2))
   b = np.ones((2))