python类argmax()的实例源码

def testTrainEvalWithReuse(self):
    train_batch_size = 2
    eval_batch_size = 1
    train_height, train_width = 224, 224
    eval_height, eval_width = 256, 256
    num_classes = 1000
    with self.test_session():
      train_inputs = tf.random_uniform(
          (train_batch_size, train_height, train_width, 3))
      logits, _ = vgg.vgg_16(train_inputs)
      self.assertListEqual(logits.get_shape().as_list(),
                           [train_batch_size, num_classes])
      tf.get_variable_scope().reuse_variables()
      eval_inputs = tf.random_uniform(
          (eval_batch_size, eval_height, eval_width, 3))
      logits, _ = vgg.vgg_16(eval_inputs, is_training=False,
                             spatial_squeeze=False)
      self.assertListEqual(logits.get_shape().as_list(),
                           [eval_batch_size, 2, 2, num_classes])
      logits = tf.reduce_mean(logits, [1, 2])
      predictions = tf.argmax(logits, 1)
      self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])

def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 150, 150
    num_classes = 1000

    train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
    inception.inception_v2(train_inputs, num_classes)
    eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
    logits, _ = inception.inception_v2(eval_inputs, num_classes, reuse=True)
    predictions = tf.argmax(logits, 1)

    with self.test_session() as sess:
      sess.run(tf.initialize_all_variables())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,))

def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 150, 150
    num_classes = 1000
    with self.test_session() as sess:
      train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
      inception.inception_resnet_v2(train_inputs, num_classes)
      eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
      logits, _ = inception.inception_resnet_v2(eval_inputs,
                                                num_classes,
                                                is_training=False,
                                                reuse=True)
      predictions = tf.argmax(logits, 1)
      sess.run(tf.initialize_all_variables())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,))

def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 150, 150
    num_classes = 1000
    with self.test_session() as sess:
      train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
      inception.inception_v4(train_inputs, num_classes)
      eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
      logits, _ = inception.inception_v4(eval_inputs,
                                         num_classes,
                                         is_training=False,
                                         reuse=True)
      predictions = tf.argmax(logits, 1)
      sess.run(tf.initialize_all_variables())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,))

def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 224, 224
    num_classes = 1000

    train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
    inception.inception_v1(train_inputs, num_classes)
    eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
    logits, _ = inception.inception_v1(eval_inputs, num_classes, reuse=True)
    predictions = tf.argmax(logits, 1)

    with self.test_session() as sess:
      sess.run(tf.initialize_all_variables())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,))

def sample_dtype(self):
        return tf.int32

# WRONG SECOND DERIVATIVES
# class CategoricalPd(Pd):
#     def __init__(self, logits):
#         self.logits = logits
#         self.ps = tf.nn.softmax(logits)
#     @classmethod
#     def fromflat(cls, flat):
#         return cls(flat)
#     def flatparam(self):
#         return self.logits
#     def mode(self):
#         return U.argmax(self.logits, axis=-1)
#     def logp(self, x):
#         return -tf.nn.sparse_softmax_cross_entropy_with_logits(self.logits, x)
#     def kl(self, other):
#         return tf.nn.softmax_cross_entropy_with_logits(other.logits, self.ps) \
#                 - tf.nn.softmax_cross_entropy_with_logits(self.logits, self.ps)
#     def entropy(self):
#         return tf.nn.softmax_cross_entropy_with_logits(self.logits, self.ps)
#     def sample(self):
#         u = tf.random_uniform(tf.shape(self.logits))
#         return U.argmax(self.logits - tf.log(-tf.log(u)), axis=-1)

def add_evaluation_step(result_tensor, ground_truth_tensor):
  """Inserts the operations we need to evaluate the accuracy of our results.

  Args:
    result_tensor: The new final node that produces results.
    ground_truth_tensor: The node we feed ground truth data
    into.

  Returns:
    Nothing.
  """
  with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
      correct_prediction = tf.equal(tf.argmax(result_tensor, 1), \
        tf.argmax(ground_truth_tensor, 1))
    with tf.name_scope('accuracy'):
      evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    tf.summary.scalar('accuracy', evaluation_step)
  return evaluation_step

def add_evaluation_step(result_tensor, ground_truth_tensor):
  """Inserts the operations we need to evaluate the accuracy of our results.

  Args:
    result_tensor: The new final node that produces results.
    ground_truth_tensor: The node we feed ground truth data
    into.

  Returns:
    Tuple of (evaluation step, prediction).
  """
  with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
      prediction = tf.argmax(result_tensor, 1)
      correct_prediction = tf.equal(
          prediction, tf.argmax(ground_truth_tensor, 1))
    with tf.name_scope('accuracy'):
      evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  tf.summary.scalar('accuracy', evaluation_step)
  return evaluation_step, prediction

def cal_loss(self):
        one_hot_labels = tf.one_hot(
            self.labels, depth=self.conf.class_num,
            axis=self.channel_axis, name='labels/one_hot')
        losses = tf.losses.softmax_cross_entropy(
            one_hot_labels, self.predictions, scope='loss/losses')
        self.loss_op = tf.reduce_mean(losses, name='loss/loss_op')
        self.decoded_preds = tf.argmax(
            self.predictions, self.channel_axis, name='accuracy/decode_pred')
        correct_prediction = tf.equal(
            self.labels, self.decoded_preds,
            name='accuracy/correct_pred')
        self.accuracy_op = tf.reduce_mean(
            tf.cast(correct_prediction, tf.float32, name='accuracy/cast'),
            name='accuracy/accuracy_op')
        # weights = tf.cast(
        #     tf.greater(self.decoded_preds, 0, name='m_iou/greater'),
        #     tf.int32, name='m_iou/weights')
        weights = tf.cast(
            tf.less(self.labels, self.conf.channel, name='m_iou/greater'),
            tf.int64, name='m_iou/weights')
        labels = tf.multiply(self.labels, weights, name='m_iou/mul')
        self.m_iou, self.miou_op = tf.metrics.mean_iou(
            self.labels, self.decoded_preds, self.conf.class_num,
            weights, name='m_iou/m_ious')

def _joint_positions(self):
        highest_activation = tf.reduce_max(self.sigm_network, [1, 2])
        x = tf.argmax(tf.reduce_max(self.smoothed_sigm_network, 1), 1)
        y = tf.argmax(tf.reduce_max(self.smoothed_sigm_network, 2), 1)

        x = tf.cast(x, tf.float32)
        y = tf.cast(y, tf.float32)
        a = tf.cast(highest_activation, tf.float32)

        scale_coef = (self.image_size / self.heatmap_size)
        x *= scale_coef
        y *= scale_coef

        out = tf.stack([y, x, a])

        return out

def euclidean_distance(self):
        x = tf.argmax(tf.reduce_max(self.smoothed_sigm_network, 1), 1)
        y = tf.argmax(tf.reduce_max(self.smoothed_sigm_network, 2), 1)

        x = tf.cast(x, tf.float32)
        y = tf.cast(y, tf.float32)

        dy = tf.squeeze(self.desired_points[:, 0, :])
        dx = tf.squeeze(self.desired_points[:, 1, :])

        sx = tf.squared_difference(x, dx)
        sy = tf.squared_difference(y, dy)

        l2_dist = tf.sqrt(sx + sy)

        return l2_dist

def create(config):
    batch_size = config["batch_size"]
    x = tf.placeholder(tf.float32, [batch_size, X_DIMS[0], X_DIMS[1], 1], name="x")
    y = tf.placeholder(tf.float32, [batch_size, Y_DIMS], name="y")

    hidden = hidden_layers(config, x)
    output = output_layer(config, hidden)

    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(output, y), name="loss")

    output = tf.nn.softmax(output)
    correct_prediction = tf.equal(tf.argmax(output,1), tf.argmax(y,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    variables = tf.trainable_variables()

    optimizer = tf.train.GradientDescentOptimizer(config['learning_rate']).minimize(loss)


    set_tensor("x", x)
    set_tensor("y", y)
    set_tensor("loss", loss)
    set_tensor("optimizer", optimizer)
    set_tensor("accuracy", accuracy)

def build(self, inputData, ss, keepProb=1):
        self.conv1_1 = self._conv_layer(inputData, params=self._params["depth/conv1_1"])
        self.conv1_2 = self._conv_layer(self.conv1_1, params=self._params["depth/conv1_2"])
        self.pool1 = self._average_pool(self.conv1_2, 'depth/pool')

        self.conv2_1 = self._conv_layer(self.pool1, params=self._params["depth/conv2_1"])
        self.conv2_2 = self._conv_layer(self.conv2_1, params=self._params["depth/conv2_2"])
        self.conv2_3 = self._conv_layer(self.conv2_2, params=self._params["depth/conv2_3"])
        self.conv2_4 = self._conv_layer(self.conv2_3, params=self._params["depth/conv2_4"])
        self.pool2 = self._average_pool(self.conv2_4, 'depth/pool')

        self.fcn1 = self._conv_layer_dropout(self.pool2, params=self._params["depth/fcn1"], keepProb=keepProb)
        self.fcn2 = self._conv_layer_dropout(self.fcn1, params=self._params["depth/fcn2"], keepProb=keepProb)

        self.outputData = self._upscore_layer(self.fcn2, params=self._params["depth/upscore"],
                                       shape=tf.shape(inputData))

        self.outputDataArgMax = tf.argmax(input=self.outputData, dimension=3)

def add_evaluation_step(result_tensor, ground_truth_tensor):
  """Inserts the operations we need to evaluate the accuracy of our results.

  Args:
    result_tensor: The new final node that produces results.
    ground_truth_tensor: The node we feed ground truth data
    into.

  Returns:
    Nothing.
  """
  with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
      correct_prediction = tf.equal(tf.argmax(result_tensor, 1), \
        tf.argmax(ground_truth_tensor, 1))
    with tf.name_scope('accuracy'):
      evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    tf.scalar_summary('accuracy', evaluation_step)
  return evaluation_step

def categorical_max(logits, d):
    value = tf.argmax(logits - tf.reduce_max(logits, [1], keep_dims=True), axis=1)
    return tf.one_hot(value, d)

def categorical_max(logits, d):
    value = tf.argmax(logits - tf.reduce_max(logits, [1], keep_dims=True), axis=1)
    return tf.one_hot(value, d)

def argmax(x, axis=None):
    return tf.argmax(x, axis=axis)

def categorical_sample_logits(X):
    # https://github.com/tensorflow/tensorflow/issues/456
    U = tf.random_uniform(tf.shape(X))
    return argmax(X - tf.log(-tf.log(U)), axis=1)

def sample(self):
        u = tf.random_uniform(tf.shape(self.logits))
        return tf.argmax(self.logits - tf.log(-tf.log(u)), axis=1)

def _region_proposal(self, net_conv, is_training, initializer):
    rpn = slim.conv2d(net_conv, cfg.RPN_CHANNELS, [3, 3], trainable=is_training, weights_initializer=initializer,
                        scope="rpn_conv/3x3")
    self._act_summaries.append(rpn)
    rpn_cls_score = slim.conv2d(rpn, self._num_anchors * 2, [1, 1], trainable=is_training,
                                weights_initializer=initializer,
                                padding='VALID', activation_fn=None, scope='rpn_cls_score')
    # change it so that the score has 2 as its channel size
    rpn_cls_score_reshape = self._reshape_layer(rpn_cls_score, 2, 'rpn_cls_score_reshape')
    rpn_cls_prob_reshape = self._softmax_layer(rpn_cls_score_reshape, "rpn_cls_prob_reshape")
    rpn_cls_pred = tf.argmax(tf.reshape(rpn_cls_score_reshape, [-1, 2]), axis=1, name="rpn_cls_pred")
    rpn_cls_prob = self._reshape_layer(rpn_cls_prob_reshape, self._num_anchors * 2, "rpn_cls_prob")
    rpn_bbox_pred = slim.conv2d(rpn, self._num_anchors * 4, [1, 1], trainable=is_training,
                                weights_initializer=initializer,
                                padding='VALID', activation_fn=None, scope='rpn_bbox_pred')
    if is_training:
      rois, roi_scores = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois")
      rpn_labels = self._anchor_target_layer(rpn_cls_score, "anchor")
      # Try to have a deterministic order for the computing graph, for reproducibility
      with tf.control_dependencies([rpn_labels]):
        rois, _ = self._proposal_target_layer(rois, roi_scores, "rpn_rois")
    else:
      if cfg.TEST.MODE == 'nms':
        rois, _ = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois")
      elif cfg.TEST.MODE == 'top':
        rois, _ = self._proposal_top_layer(rpn_cls_prob, rpn_bbox_pred, "rois")
      else:
        raise NotImplementedError

    self._predictions["rpn_cls_score"] = rpn_cls_score
    self._predictions["rpn_cls_score_reshape"] = rpn_cls_score_reshape
    self._predictions["rpn_cls_prob"] = rpn_cls_prob
    self._predictions["rpn_cls_pred"] = rpn_cls_pred
    self._predictions["rpn_bbox_pred"] = rpn_bbox_pred
    self._predictions["rois"] = rois

    return rois