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有没有人想出一种使用 pr_curve_streaming_op 进行多类分类的方法？

我正在尝试关注这个演示和这个演示。但到目前为止，我在数据收集阶段本身就遇到了麻烦。似乎我需要能够在运行时收集数据，并且我面临着几个问题，例如无法在运行时修改布尔张量等。任何帮助都将非常有用。谢谢

这是我的代码片段和我得到的错误：

self.input_y = tf.placeholder(tf.float32, [None, num_classes], name='input_y')

with tf.device('/cpu:0'), tf.name_scope('embedding'):
    self.emb_var = tf.Variable(embedding_mat, name='emb_var')
    # if not non_static:
    #   self.emb_var = tf.constant(embedding_mat, name='emb_var')
    # else:
    #   self.emb_var = tf.Variable(embedding_mat, name='emb_var', trainable=True)
    self.embedded_chars = tf.nn.embedding_lookup(self.emb_var, self.input_x)
    self.emb = tf.expand_dims(self.embedded_chars, -1)
pooled_concat = []
reduced = np.int32(np.ceil((sequence_length) * 1.0 / max_pool_size))

###
.
.
 Removed some code for simplicity...
.
.
###
with tf.name_scope('probabilities'):
    self.probabilities = tf.nn.softmax(self.scores)

with tf.name_scope('confidence'):
    self.conf = tf.reduce_max(self.probabilities, reduction_indices=[1])

with tf.name_scope('Low_confidence'):
    self.conf_low = tf.reduce_min(self.conf, name='low_conf')

with tf.name_scope('Avg_confidence'):
    self.Avg_conf = tf.reduce_mean(self.conf, name='confidence')

with tf.name_scope('high_confidence'):
    self.conf_high = tf.reduce_max(self.conf, name='high_conf')

with tf.name_scope('loss'):
    losses = tf.nn.softmax_cross_entropy_with_logits(labels=self.input_y, logits=self.scores) #  only named arguments accepted
    self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss

with tf.name_scope('accuracy'):
    correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
    self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name='accuracy')

with tf.name_scope('num_correct'):
    correct = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
    self.num_correct = tf.reduce_sum(tf.cast(correct, 'float'))

''' start '''
with tf.name_scope('pr_curve'):
    self.pr_curve, update_op = summary_lib.pr_curve_streaming_op('pr_curve', predictions=self.probabilities, labels=tf.cast(self.input_y,tf.bool), num_thresholds=11)

''' end '''

以下是我得到的错误：

    2017-12-25 18:37:30.433250: W tensorflow/core/framework/op_kernel.cc:1192] Failed precondition: Attempting to use uninitialized value pr_curve/pr_curve/true_negatives/true_negatives
         [[Node: pr_curve/pr_curve/true_negatives/true_negatives/read = Identity[T=DT_FLOAT, _class=["loc:@pr_curve/pr_curve/true_negatives/true_negatives"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](pr_curve/pr_curve/true_negatives/true_negatives)]]
2017-12-25 18:37:30.433565: W tensorflow/core/framework/op_kernel.cc:1192] Failed precondition: Attempting to use uninitialized value pr_curve/pr_curve/true_negatives/true_negatives
         [[Node: pr_curve/pr_curve/true_negatives/true_negatives/read = Identity[T=DT_FLOAT, _class=["loc:@pr_curve/pr_curve/true_negatives/true_negatives"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](pr_curve/pr_curve/true_negatives/true_negatives)]]
2017-12-25 18:37:30.433613: W tensorflow/core/framework/op_kernel.cc:1192] Failed precondition: Attempting to use uninitialized value pr_curve/pr_curve/true_negatives/true_negatives
         [[Node: pr_curve/pr_curve/true_negatives/true_negatives/read = Identity[T=DT_FLOAT, _class=["loc:@pr_curve/pr_curve/true_negatives/true_negatives"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](pr_curve/pr_curve/true_negatives/true_negatives)]]
2017-12-25 18:37:30.433630: W tensorflow/core/framework/op_kernel.cc:1192] Failed precondition: Attempting to use uninitialized value pr_curve/pr_curve/true_negatives/true_negatives
         [[Node: pr_curve/pr_curve/true_negatives/true_negatives/read = Identity[T=DT_FLOAT, _class=["loc:@pr_curve/pr_curve/true_negatives/true_negatives"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](pr_curve/pr_curve/true_negatives/true_negatives)]]
2017-12-25 18:37:30.433652: W tensorflow/core/framework/op_kernel.cc:1192] Failed precondition: Attempting to use uninitialized value pr_curve/pr_curve/true_negatives/true_negatives
         [[Node: pr_curve/pr_curve/true_negatives/true_negatives/read = Identity[T=DT_FLOAT, _class=["loc:@pr_curve/pr_curve/true_negatives/true_negatives"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](pr_curve/pr_curve/true_negatives/true_negatives)]]
2017-12-25 18:37:30.433699: W tensorflow/core/framework/op_kernel.cc:1192] Failed precondition: Attempting to use uninitialized value pr_curve/pr_curve/true_negatives/true_negatives
         [[Node: pr_curve/pr_curve/true_negatives/true_negatives/read = Identity[T=DT_FLOAT, _class=["loc:@pr_curve/pr_curve/true_negatives/true_negatives"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](pr_curve/pr_curve/true_negatives/true_negatives)]]
2017-12-25 18:37:30.433729: W tensorflow/core/framework/op_kernel.cc:1192] Failed precondition: Attempting to use uninitialized value pr_curve/pr_curve/true_negatives/true_negatives
         [[Node: pr_curve/pr_curve/true_negatives/true_negatives/read = Identity[T=DT_FLOAT, _class=["loc:@pr_curve/pr_curve/true_negatives/true_negatives"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](pr_curve/pr_curve/true_negatives/true_negatives)]]
2017-12-25 18:37:30.433767: W tensorflow/core/framework/op_kernel.cc:1192] Failed precondition: Attempting to use uninitialized value pr_curve/pr_curve/true_negatives/true_negatives
         [[Node: pr_curve/pr_curve/true_negatives/true_negatives/read = Identity[T=DT_FLOAT, _class=["loc:@pr_curve/pr_curve/true_negatives/true_negatives"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](pr_curve/pr_curve/true_negatives/true_negatives)]]
2017-12-25 18:37:30.433805: W tensorflow/core/framework/op_kernel.cc:1192] Failed precondition: Attempting to use uninitialized value pr_curve/pr_curve/true_negatives/true_negatives
         [[Node: pr_curve/pr_curve/true_negatives/true_negatives/read = Identity[T=DT_FLOAT, _class=["loc:@pr_curve/pr_curve/true_negatives/true_negatives"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](pr_curve/pr_curve/true_negatives/true_negatives)]]
Traceback (most recent call last):
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1323, in _do_call
    return fn(*args)
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1302, in _run_fn
    status, run_metadata)
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 473, in __exit__
    c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value pr_curve/pr_curve/true_negatives/true_negatives
         [[Node: pr_curve/pr_curve/true_negatives/true_negatives/read = Identity[T=DT_FLOAT, _class=["loc:@pr_curve/pr_curve/true_negatives/true_negatives"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](pr_curve/pr_curve/true_negatives/true_negatives)]]
         [[Node: rnn/LessEqual_4/_37 = _HostRecv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_105_rnn/LessEqual_4", tensor_type=DT_BOOL, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "./code/train.py", line 357, in <module>
    train_cnn_rnn()
  File "./code/train.py", line 276, in train_cnn_rnn
    train_step(x_train_batch, y_train_batch)
  File "./code/train.py", line 223, in train_step
    _, step, summaries = sess.run([train_op, global_step, train_summary_op], feed_dict)
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorflow/python/debug/wrappers/framework.py", line 534, in run
    run_metadata=run_metadata)
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 889, in run
    run_metadata_ptr)
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1120, in _run
    feed_dict_tensor, options, run_metadata)
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1317, in _do_run
    options, run_metadata)
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1336, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value pr_curve/pr_curve/true_negatives/true_negatives
         [[Node: pr_curve/pr_curve/true_negatives/true_negatives/read = Identity[T=DT_FLOAT, _class=["loc:@pr_curve/pr_curve/true_negatives/true_negatives"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](pr_curve/pr_curve/true_negatives/true_negatives)]]
         [[Node: rnn/LessEqual_4/_37 = _HostRecv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_105_rnn/LessEqual_4", tensor_type=DT_BOOL, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

Caused by op 'pr_curve/pr_curve/true_negatives/true_negatives/read', defined at:
  File "./code/train.py", line 357, in <module>
    train_cnn_rnn()
  File "./code/train.py", line 170, in train_cnn_rnn
    l2_reg_lambda=params['l2_reg_lambda'])
  File "/media/hemant/MVV/MyValueVest-local/main/code/text_cnn_rnn.py", line 127, in __init__
    self.pr_curve, update_op = summary_lib.pr_curve_streaming_op('pr_curve', predictions=self.probabilities, labels=tf.cast(self.input_y,tf.bool), num_thresholds=11)
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorboard/plugins/pr_curve/summary.py", line 234, in streaming_op
    weights=weights)
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorflow/python/ops/metrics_impl.py", line 1482, in true_negatives_at_thresholds
    labels, predictions, thresholds, weights=weights, includes=('tn',))
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorflow/python/ops/metrics_impl.py", line 543, in _confusion_matrix_at_thresholds
    true_n = _create_local('true_negatives', shape=[num_thresholds])
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorflow/python/ops/metrics_impl.py", line 196, in _create_local
    validate_shape=validate_shape)
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 1927, in variable
    caching_device=caching_device, name=name, dtype=dtype)
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorflow/python/ops/variables.py", line 213, in __init__
    constraint=constraint)
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorflow/python/ops/variables.py", line 356, in _init_from_args
    self._snapshot = array_ops.identity(self._variable, name="read")
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py", line 125, in identity
    return gen_array_ops.identity(input, name=name)
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py", line 2071, in identity
    "Identity", input=input, name=name)
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2956, in create_op
    op_def=op_def)
  File "/home/hemant/anaconda3/envs/tf14/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1470, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

FailedPreconditionError (see above for traceback): Attempting to use uninitialized value pr_curve/pr_curve/true_negatives/true_negatives
         [[Node: pr_curve/pr_curve/true_negatives/true_negatives/read = Identity[T=DT_FLOAT, _class=["loc:@pr_curve/pr_curve/true_negatives/true_negatives"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](pr_curve/pr_curve/true_negatives/true_negatives)]]
         [[Node: rnn/LessEqual_4/_37 = _HostRecv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_105_rnn/LessEqual_4", tensor_type=DT_BOOL, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
4

1 回答 1

2

想通了... - 创建一个单独的会话，因为我需要所有预测的值。- 循环播放我所有的课程。

pr_graph = tf.Graph()
with pr_graph.as_default():
    session_conf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
    pr_sess = tf.Session(config=session_conf)
    with pr_sess.as_default():
        for cat in range(y_train.shape[1]):
            with tf.name_scope('%s' % labels[cat]):
                _, update_op = summary_lib.pr_curve_streaming_op('pr_curve', predictions=probs[:, cat], labels=tf.cast(y_test[:, cat], tf.bool), num_thresholds=500, metrics_collections='pr')
        pr_summary_op = tf.summary.merge_all()
        pr_summary_dir = os.path.join(checkpoint_dir, "s", "pr")
        pr_summary_writer = tf.summary.FileWriter(pr_summary_dir, pr_sess.graph)
        pr_sess.run(tf.local_variables_initializer())
        pr_sess.run([update_op])
        pr_summary_writer.add_summary(pr_sess.run(pr_summary_op))
        pr_summary_writer.close()
于 2018-01-07T23:33:41.837 回答