make import better.

doc/tutorial/loop_solution_1.py

 #!/usr/bin/env python
 # Theano tutorial
 # Solution to Exercise in section 'Loop'
-
-# 1. First example
+import numpy
 
 import theano
-import theano.tensor as tensor
+import theano.tensor as tt
+
+# 1. First example
 
 theano.config.warn.subtensor_merge_bug = False
 
-k = tensor.iscalar("k")
-A = tensor.vector("A")
+k = tt.iscalar("k")
+A = tt.vector("A")
 
 
 def inner_fct(prior_result, A):
@@ -18,7 +19,7 @@ def inner_fct(prior_result, A):
 
 # Symbolic description of the result
 result, updates = theano.scan(fn=inner_fct,
-                              outputs_info=tensor.ones_like(A),
+                              outputs_info=tt.ones_like(A),
                               non_sequences=A, n_steps=k)
 
 # Scan has provided us with A ** 1 through A ** k.  Keep only the last
@@ -34,16 +35,12 @@ print power(range(10), 2)
 
 # 2. Second example
 
-import numpy
-import theano
-import theano.tensor as tensor
-
-coefficients = theano.tensor.vector("coefficients")
-x = tensor.scalar("x")
+coefficients = tt.vector("coefficients")
+x = tt.scalar("x")
 max_coefficients_supported = 10000
 
 # Generate the components of the polynomial
-full_range = theano.tensor.arange(max_coefficients_supported)
+full_range = tt.arange(max_coefficients_supported)
 components, updates = theano.scan(fn=lambda coeff, power, free_var:
                                   coeff * (free_var ** power),
                                   sequences=[coefficients, full_range],
@@ -59,21 +56,17 @@ print calculate_polynomial1(test_coeff, 3)
 
 # 3. Reduction performed inside scan
 
-import numpy
-import theano
-import theano.tensor as tensor
-
 theano.config.warn.subtensor_merge_bug = False
 
-coefficients = theano.tensor.vector("coefficients")
-x = tensor.scalar("x")
+coefficients = tt.vector("coefficients")
+x = tt.scalar("x")
 max_coefficients_supported = 10000
 
 # Generate the components of the polynomial
-full_range = theano.tensor.arange(max_coefficients_supported)
+full_range = tt.arange(max_coefficients_supported)
 
 
-outputs_info = tensor.as_tensor_variable(numpy.asarray(0, 'float64'))
+outputs_info = tt.as_tensor_variable(numpy.asarray(0, 'float64'))
 
 components, updates = theano.scan(fn=lambda coeff, power, prior_value, free_var:
                                   prior_value + (coeff * (free_var ** power)),

doc/tutorial/modes_solution_1.py

@@ -4,7 +4,7 @@
 
 import numpy
 import theano
-import theano.tensor as T
+import theano.tensor as tt
 
 theano.config.floatX = 'float32'
 
@@ -17,8 +17,8 @@ rng.randint(size=N, low=0, high=2).astype(theano.config.floatX))
 training_steps = 10000
 
 # Declare Theano symbolic variables
-x = T.matrix("x")
-y = T.vector("y")
+x = tt.matrix("x")
+y = tt.vector("y")
 w = theano.shared(rng.randn(feats).astype(theano.config.floatX), name="w")
 b = theano.shared(numpy.asarray(0., dtype=theano.config.floatX), name="b")
 x.tag.test_value = D[0]
@@ -27,12 +27,12 @@ y.tag.test_value = D[1]
 #print w.get_value(), b.get_value()
 
 # Construct Theano expression graph
-p_1 = 1 / (1 + T.exp(-T.dot(x, w) - b))  # Probabily of having a one
+p_1 = 1 / (1 + tt.exp(-tt.dot(x, w) - b))  # Probabily 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 = T.cast(xent.mean(), 'float32') + \
+xent = -y * tt.log(p_1) - (1 - y) * tt.log(1 - p_1)  # Cross-entropy
+cost = tt.cast(xent.mean(), 'float32') + \
        0.01 * (w ** 2).sum()  # The cost to optimize
-gw, gb = T.grad(cost, [w, b])
+gw, gb = tt.grad(cost, [w, b])
 
 # Compile expressions to functions
 train = theano.function(

doc/tutorial/using_gpu_solution_1.py

@@ -13,7 +13,7 @@
 
 import numpy
 import theano
-import theano.tensor as T
+import theano.tensor as tt
 
 from theano import sandbox, Out
 
@@ -28,8 +28,8 @@ rng.randint(size=N, low=0, high=2).astype(theano.config.floatX))
 training_steps = 10000
 
 # Declare Theano symbolic variables
-x = T.matrix("x")
-y = T.vector("y")
+x = tt.matrix("x")
+y = tt.vector("y")
 w = theano.shared(rng.randn(feats).astype(theano.config.floatX), name="w")
 b = theano.shared(numpy.asarray(0., dtype=theano.config.floatX), name="b")
 x.tag.test_value = D[0]
@@ -38,21 +38,21 @@ y.tag.test_value = D[1]
 #print w.get_value(), b.get_value()
 
 # Construct Theano expression graph
-p_1 = 1 / (1 + T.exp(-T.dot(x, w) - b))  # Probabily of having a one
+p_1 = 1 / (1 + tt.exp(-tt.dot(x, w) - b))  # Probabily 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 = T.cast(xent.mean(), 'float32') + \
+xent = -y * tt.log(p_1) - (1 - y) * tt.log(1 - p_1)  # Cross-entropy
+cost = tt.cast(xent.mean(), 'float32') + \
        0.01 * (w ** 2).sum()  # The cost to optimize
-gw, gb = T.grad(cost, [w, b])
+gw, gb = tt.grad(cost, [w, b])
 
 """
 # Compile expressions to functions
 train = theano.function(
             inputs=[x, y],
-            outputs=[Out(theano.sandbox.cuda.basic_ops.gpu_from_host(T.cast(prediction, 'float32')),borrow=True), Out(theano.sandbox.cuda.basic_ops.gpu_from_host(T.cast(xent, 'float32')), borrow=True)],
+            outputs=[Out(theano.sandbox.cuda.basic_ops.gpu_from_host(tt.cast(prediction, 'float32')),borrow=True), Out(theano.sandbox.cuda.basic_ops.gpu_from_host(tt.cast(xent, 'float32')), borrow=True)],
             updates={w: w - 0.01 * gw, b: b - 0.01 * gb},
             name="train")
-predict = theano.function(inputs=[x], outputs=Out(theano.sandbox.cuda.basic_ops.gpu_from_host(T.cast(prediction, 'float32')), borrow=True),
+predict = theano.function(inputs=[x], outputs=Out(theano.sandbox.cuda.basic_ops.gpu_from_host(tt.cast(prediction, 'float32')), borrow=True),
             name="predict")
 """