Fixing inpt bug

Author: mnielsen

src/network3.py

@@ -111,10 +111,10 @@ def __init__(self, layers, mini_batch_size):
  self.x = T.matrix("x") 
  self.y = T.ivector("y")
  init_layer = self.layers[0]
- init_layer.set_inpt(self.x, mini_batch_size)
+ init_layer.set_inpt(self.x, self.mini_batch_size)
  for j in xrange(1, len(self.layers)):
  prev_layer, layer = self.layers[j-1], self.layers[j]
- layer.set_inpt(prev_layer.output, mini_batch_size)
+ layer.set_inpt(prev_layer.output, self.mini_batch_size)
  self.output = self.layers[-1].output
 
  def SGD(self, training_data, epochs, mini_batch_size, eta, 
@@ -261,8 +261,7 @@ def __init__(self, n_in, n_out, activation_fn=sigmoid):
  # Initialize weights and biases
  self.w = theano.shared(
  np.asarray(
- np.random.normal(
- loc=0.0, scale=np.sqrt(1.0/n_out), size=(n_in, n_out)),
+ np.random.normal(loc=0.0, scale=np.sqrt(1.0/n_out), size=(n_in, n_out)),
  dtype=theano.config.floatX),
  name='w', borrow=True)
  self.b = theano.shared(
@@ -272,9 +271,8 @@ def __init__(self, n_in, n_out, activation_fn=sigmoid):
  self.params = [self.w, self.b]
 
  def set_inpt(self, inpt, mini_batch_size):
- self.mini_batch_size = mini_batch_size
- self.inpt = inpt.reshape((self.mini_batch_size, self.n_in))
- self.output = self.activation_fn(T.dot(inpt, self.w)+self.b)
+ self.inpt = inpt.reshape((mini_batch_size, self.n_in))
+ self.output = self.activation_fn(T.dot(self.inpt, self.w) + self.b)
 
 class SoftmaxLayer():
 
@@ -293,9 +291,8 @@ def __init__(self, n_in, n_out):
  self.params = [self.w, self.b]
 
  def set_inpt(self, inpt, mini_batch_size):
- self.mini_batch_size = mini_batch_size
- self.inpt = inpt.reshape((self.mini_batch_size, self.n_in))
- self.output = softmax(T.dot(self.inpt, self.w)+self.b)
+ self.inpt = inpt.reshape((mini_batch_size, self.n_in))
+ self.output = softmax(T.dot(self.inpt, self.w) + self.b)
  self.y_out = T.argmax(self.output, axis=1)
 
  def accuracy(self, y):
@@ -307,3 +304,4 @@ def accuracy(self, y):
 def size(data):
  "Return the size of the dataset `data`."
  return data[0].get_value(borrow=True).shape[0]
+