导入错误：张量板

Question

我在 Anaconda 环境中使用 Python（jupyter notebook）

操作系统：Ubuntu 张量流版本：1.14.0 Python 版本：3.6

https://github.com/tensorflow/tensorboard/issues/1425 这是同一个问题，但答案没有帮助。 除了尝试重新安装 Tensorflow 之外，是否有解决此问题的方法？还是我应该升级我的 tensorflow 版本？

    from keras.callbacks import  TensorBoard

    tensorboard = TensorBoard(log_dir='graph_1', histogram_freq=0, 
    batch_size=512, write_graph=True, write_grads=False, 
    write_images=False, embeddings_freq=0, embeddings_layer_names=None, 
    embeddings_metadata=None, embeddings_data=None, update_freq='epoch')

抛出如下错误：

ImportError                               Traceback (most recent call last)
~/anaconda3/lib/python3.6/site-packages/keras/callbacks.py in __init__(self, log_dir, histogram_freq, batch_size, write_graph, write_grads, write_images, embeddings_freq, embeddings_layer_names, embeddings_metadata, embeddings_data, update_freq)
    744             import tensorflow as tf
--> 745             from tensorflow.contrib.tensorboard.plugins import projector
    746         except ImportError:

~/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/__init__.py in <module>
     40 from tensorflow.contrib import distribute
---> 41 from tensorflow.contrib import distributions
     42 from tensorflow.contrib import estimator

~/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/distributions/__init__.py in <module>
     43   from tensorflow.contrib.distributions.python.ops.distribution_util import tridiag
---> 44   from tensorflow.contrib.distributions.python.ops.estimator import *
     45   from tensorflow.contrib.distributions.python.ops.geometric import *

~/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/distributions/python/ops/estimator.py in <module>
     20 
---> 21 from tensorflow.contrib.learn.python.learn.estimators.head import _compute_weighted_loss
     22 from tensorflow.contrib.learn.python.learn.estimators.head import _RegressionHead

~/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/__init__.py in <module>
     92 # pylint: disable=wildcard-import
---> 93 from tensorflow.contrib.learn.python.learn import *
     94 # pylint: enable=wildcard-import

~/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/__init__.py in <module>
     27 # pylint: disable=wildcard-import
---> 28 from tensorflow.contrib.learn.python.learn import *
     29 # pylint: enable=wildcard-import

~/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/__init__.py in <module>
     29 from tensorflow.contrib.learn.python.learn import datasets
---> 30 from tensorflow.contrib.learn.python.learn import estimators
     31 from tensorflow.contrib.learn.python.learn import graph_actions

~/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/__init__.py in <module>
    301 from tensorflow.contrib.learn.python.learn.estimators.constants import ProblemType
--> 302 from tensorflow.contrib.learn.python.learn.estimators.dnn import DNNClassifier
    303 from tensorflow.contrib.learn.python.learn.estimators.dnn import DNNEstimator

~/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn.py in <module>
     33 from tensorflow.contrib.learn.python.learn import metric_spec
---> 34 from tensorflow.contrib.learn.python.learn.estimators import dnn_linear_combined
     35 from tensorflow.contrib.learn.python.learn.estimators import estimator

~/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py in <module>
     35 from tensorflow.contrib.learn.python.learn import metric_spec
---> 36 from tensorflow.contrib.learn.python.learn.estimators import estimator
     37 from tensorflow.contrib.learn.python.learn.estimators import head as head_lib

~/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py in <module>
     51 from tensorflow.contrib.learn.python.learn.estimators._sklearn import NotFittedError
---> 52 from tensorflow.contrib.learn.python.learn.learn_io import data_feeder
     53 from tensorflow.contrib.learn.python.learn.utils import export

~/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/__init__.py in <module>
     36 from tensorflow.contrib.learn.python.learn.learn_io.graph_io import read_keyed_batch_features_shared_queue
---> 37 from tensorflow.contrib.learn.python.learn.learn_io.numpy_io import numpy_input_fn
     38 from tensorflow.contrib.learn.python.learn.learn_io.pandas_io import extract_pandas_data

~/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/learn_io/numpy_io.py in <module>
     25 
---> 26 from tensorflow.python.estimator.inputs.numpy_io import numpy_input_fn as core_numpy_input_fn
     27 from tensorflow.python.util.deprecation import deprecated

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/estimator/inputs/numpy_io.py in <module>
     25 
---> 26 from tensorflow_estimator.python.estimator.inputs import numpy_io
     27 

~/anaconda3/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/inputs/numpy_io.py in <module>
     25 
---> 26 from tensorflow_estimator.python.estimator.inputs.queues import feeding_functions
     27 from tensorflow.python.util.tf_export import estimator_export

~/anaconda3/lib/python3.6/site-packages/tensorflow_estimator/__init__.py in <module>
      9 
---> 10 from tensorflow_estimator._api.v2 import estimator
     11 _names_with_underscore = []

~/anaconda3/lib/python3.6/site-packages/tensorflow_estimator/_api/v2/estimator/__init__.py in <module>
      9 
---> 10 from tensorflow_estimator._api.v2.estimator import experimental
     11 from tensorflow_estimator._api.v2.estimator import export

~/anaconda3/lib/python3.6/site-packages/tensorflow_estimator/_api/v2/estimator/experimental/__init__.py in <module>
     10 from tensorflow_estimator.python.estimator.canned.linear import LinearSDCA
---> 11 from tensorflow_estimator.python.estimator.canned.rnn import RNNClassifier
     12 from tensorflow_estimator.python.estimator.canned.rnn import RNNEstimator

~/anaconda3/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/canned/rnn.py in <module>
     22 
---> 23 from tensorflow.python.feature_column import dense_features
     24 from tensorflow.python.feature_column import feature_column_lib as fc

ImportError: cannot import name 'dense_features'

During handling of the above exception, another exception occurred:

ImportError                               Traceback (most recent call last)
<ipython-input-83-0d8f7236c1d1> in <module>
     16 reduce_lr_1 = ReduceLROnPlateau(monitor = 'val_loss', factor = 0.2, patience = 1, verbose = 1, min_delta = 0.0001)
     17 
---> 18 tensorboard_1 = TensorBoard(log_dir='graph_1', histogram_freq=0, batch_size=512, write_graph=True, write_grads=False, write_images=False, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None, embeddings_data=None, update_freq='epoch')
     19 
     20 callbacks_1 = [checkpoint_1,earlystop_1,tensorboard_1,reduce_lr_1]

~/anaconda3/lib/python3.6/site-packages/keras/callbacks.py in __init__(self, log_dir, histogram_freq, batch_size, write_graph, write_grads, write_images, embeddings_freq, embeddings_layer_names, embeddings_metadata, embeddings_data, update_freq)
    745             from tensorflow.contrib.tensorboard.plugins import projector
    746         except ImportError:
--> 747             raise ImportError('You need the TensorFlow module installed to '
    748                               'use TensorBoard.')
    749 

ImportError: You need the TensorFlow module installed to use TensorBoard.

Answer 1

正如错误清楚地表明ImportError: You need the TensorFlow module installed to use TensorBoard。

您当前的 Tensorflow (TF=1.14.0) 安装似乎存在问题。

请求您升级最新版本的 Tensorflow (TF 2.2.0) 如下

pip install tensorflow==2.2.0
#OR
pip install --upgrade tensorflow

我在 Jupyter Notebook 中使用 TF 2.2.0 为 TensorBoard 启用了可视化功能，如下所示

tf.keras.callbacks.TensorBoard

这是可视化模型准确性和损失的完整工作代码，如下所示

%load_ext tensorboard

import tensorflow as tf
import datetime, os

fashion_mnist = tf.keras.datasets.fashion_mnist

(x_train, y_train),(x_test, y_test) = fashion_mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

def create_model():
  return tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation='softmax')
  ])

def train_model():

  model = create_model()
  model.compile(optimizer='adam',
                loss='sparse_categorical_crossentropy',
                metrics=['accuracy'])

  logdir = os.path.join("logs", datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
  tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)

  model.fit(x=x_train, 
            y=y_train, 
            epochs=5, 
            validation_data=(x_test, y_test), 
            callbacks=[tensorboard_callback])

train_model()

 %tensorboard --logdir logs

输出：

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz
32768/29515 [=================================] - 0s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz
26427392/26421880 [==============================] - 0s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz
8192/5148 [===============================================] - 0s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz
4423680/4422102 [==============================] - 0s 0us/step
Epoch 1/5
1875/1875 [==============================] - 8s 4ms/step - loss: 0.4957 - accuracy: 0.8237 - val_loss: 0.4407 - val_accuracy: 0.8385
Epoch 2/5
1875/1875 [==============================] - 8s 4ms/step - loss: 0.3816 - accuracy: 0.8608 - val_loss: 0.3885 - val_accuracy: 0.8613
Epoch 3/5
1875/1875 [==============================] - 8s 4ms/step - loss: 0.3503 - accuracy: 0.8717 - val_loss: 0.3683 - val_accuracy: 0.8661
Epoch 4/5
1875/1875 [==============================] - 8s 4ms/step - loss: 0.3255 - accuracy: 0.8804 - val_loss: 0.3495 - val_accuracy: 0.8719
Epoch 5/5
1875/1875 [==============================] - 8s 4ms/step - loss: 0.3137 - accuracy: 0.8850 - val_loss: 0.3532 - val_accuracy: 0.8716

对于 Jupyter 用户：如果您已将 Jupyter 和 TensorBoard 安装到同一个 virtualenv 中，那么您应该可以开始使用了。如果您使用更复杂的设置，例如全局 Jupyter 安装和用于不同 Conda/virtualenv 环境的内核，那么您必须确保 tensorboard 二进制文件位于 Jupyter notebook 上下文中的 PATH 上。一种方法是修改 kernel_spec 以将环境的 bin 目录添加到 PATH，如 described here。

更多详情请参考here