我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用tensorflow.confusion_matrix()。
def confusion_matrix_op(logits, labels, num_classes): """Creates the operation to build the confusion matrix between the predictions and the labels. The number of classes are required to build the matrix correctly. Args: logits: a [batch_size, 1,1, num_classes] tensor or a [batch_size, num_classes] tensor labels: a [batch_size] tensor Returns: confusion_matrix_op: the confusion matrix tf op """ with tf.variable_scope('confusion_matrix'): # handle fully convolutional classifiers logits_shape = logits.shape if len(logits_shape) == 4 and logits_shape[1:3] == [1, 1]: top_k_logits = tf.squeeze(logits, [1, 2]) else: top_k_logits = logits # Extract the predicted label (top-1) _, top_predicted_label = tf.nn.top_k(top_k_logits, k=1, sorted=False) # (batch_size, k) -> k = 1 -> (batch_size) top_predicted_label = tf.squeeze(top_predicted_label, axis=1) return tf.confusion_matrix( labels, top_predicted_label, num_classes=num_classes)
def single_label(predictions_batch, one_hot_labels_batch, moving_average=True): with tf.variable_scope('metrics'): shape = predictions_batch.get_shape().as_list() batch_size, num_outputs = shape[0], shape[1] # get the most probable label predicted_batch = tf.argmax(predictions_batch, axis=1) real_label_batch = tf.argmax(one_hot_labels_batch, axis=1) # tp, tn, fp, fn predicted_bool = tf.cast(tf.one_hot(predicted_batch, depth=num_outputs), dtype=tf.bool) real_bool = tf.cast(tf.one_hot(real_label_batch, depth=num_outputs), dtype=tf.bool) d = _metrics(predicted_bool, real_bool, moving_average) # confusion matrix confusion_batch = tf.confusion_matrix(labels=real_label_batch, predictions=predicted_batch, num_classes=num_outputs) if moving_average: # calculate moving averages confusion_batch = tf.cast(confusion_batch, dtype=tf.float32) ema = tf.train.ExponentialMovingAverage(decay=0.9) update_op = ema.apply([confusion_batch]) confusion_matrix = ema.average(confusion_batch) d['update_op'] = [d['update_op'], update_op] else: # accumulative confusion_matrix = tf.Variable(tf.zeros([num_outputs, num_outputs], dtype=tf.int32), name='confusion_matrix', trainable=False) confusion_matrix = tf.assign_add(confusion_matrix, confusion_batch) d['confusion_matrix'] = confusion_matrix return d
def _compute_specific(self, predicted, targets): targets = tf.cast(targets, tf.int64) return tf.confusion_matrix(labels=targets, predictions=predicted, num_classes=self._num_classes)
def _calculate_miou(self,logits,label_batch): with tf.variable_scope('MIOU_CAL'): confusion_matrix = tf.confusion_matrix(labels=tf.reshape(label_batch,[-1]),predictions=tf.reshape(logits,[-1]),num_classes=N_CLASSES,dtype=tf.float32) def cal_miou(matrix): sum_col = np.zeros(shape = [N_CLASSES],dtype=np.float32) sum_row = np.zeros(shape = [N_CLASSES],dtype=np.float32) miou = np.zeros(shape = [],dtype=np.float32) for i in range(N_CLASSES): for j in range(N_CLASSES): sum_row[i] += matrix[i][j] sum_col[j] += matrix[i][j] for i in range(N_CLASSES): miou += matrix[i][i]/(sum_col[i]+sum_row[i]-matrix[i][i]) miou = (miou/N_CLASSES).astype(np.float32) return miou miou = tf.py_func(cal_miou, [confusion_matrix], tf.float32) # def miou_fun(eval,label): # # miou1 = 0.0 # for i in range(BATCH_SIZE): # img = eval[i] # gt = label[i] # miou1 += py_img_seg_eval.mean_IU(img, gt) # miou1 = float(miou1/BATCH_SIZE) # print miou1 # return miou1 # # # miou1 = tf.py_func(miou_fun,[logits,label_batch],tf.double) return miou #,miou1
def confusion_matrix(self): return self._confusion_matrix
