Return a float representing the number of errors in the minibatch over the total number of examples of the minibatch ; zero one loss over the size of the minibatch :type y: theano.tensor.TensorType :param y: corresponds to a vector that gives for each example the
(self, y)
| 148 | # end-snippet-2 |
| 149 | |
| 150 | def errors(self, y): |
| 151 | """Return a float representing the number of errors in the minibatch |
| 152 | over the total number of examples of the minibatch ; zero one |
| 153 | loss over the size of the minibatch |
| 154 | |
| 155 | :type y: theano.tensor.TensorType |
| 156 | :param y: corresponds to a vector that gives for each example the |
| 157 | correct label |
| 158 | """ |
| 159 | |
| 160 | # check if y has same dimension of y_pred |
| 161 | if y.ndim != self.y_pred.ndim: |
| 162 | raise TypeError( |
| 163 | 'y should have the same shape as self.y_pred', |
| 164 | ('y', y.type, 'y_pred', self.y_pred.type) |
| 165 | ) |
| 166 | # check if y is of the correct datatype |
| 167 | if y.dtype.startswith('int'): |
| 168 | # the T.neq operator returns a vector of 0s and 1s, where 1 |
| 169 | # represents a mistake in prediction |
| 170 | return T.mean(T.neq(self.y_pred, y)) |
| 171 | else: |
| 172 | raise NotImplementedError() |
| 173 | |
| 174 | |
| 175 | def load_data(dataset): |
no outgoing calls
no test coverage detected