我在Windows 10中使用Tensorflow与Python 3.7.4(64位)。
我已经建立了一个卷积神经网络模型,它在Jupyter中运行良好。现在我想用Tensorboard来可视化它的性能。但在尝试设置时,我得到了一条错误信息。
# Setting up Tensorboard to view model performance
NAME = "Trains_vs_Cars_16by2_CNN_{}".format(int(time.time()))
tensorboard = TensorBoard(log_dir="logs/{}".format(NAME))
model.fit(X, y,
batch_size=25,
epochs=5,
validation_split=0.2,
callbacks=[tensorboard])
# ERROR MESSAGE
NotFoundError Traceback (most recent call last)
<ipython-input-6-c627053c0717> in <module>
67 epochs=5,
68 validation_split=0.2,
---> 69 callbacks=[tensorboard])
这个页面上的一个海报(https:/github.comtensorflowtensorboardissues2023#。)提到有一个windows特有的Tensorflow bug。我也遇到了这样的问题吗?我是Tensorflow (和Python)的新手。
谢谢!我在使用Tensorflow和Python 3.4。
你的不是windows特有的Tensorflow bug。我已经使用了你的代码,并做了一些小的修改,现在我能够使用Tensorboard来可视化模型的性能。
请参考下面的完整工作代码
# Load the TensorBoard notebook extension
%load_ext tensorboard
import tensorflow as tf
print(tf.__version__)
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'])
#NAME = "Trains_vs_Cars_16by2_CNN_{}".format(int(time.time()))
NAME = "Trains_vs_Cars_16by2_{}".format(str(datetime.datetime.now()))
tensorboard = tf.keras.callbacks.TensorBoard(log_dir="logs/{}".format(NAME))
model.fit(x=x_train,
y=y_train,
batch_size=25,
epochs=5,
# validation_split=0.2,
validation_data=(x_test, y_test),
callbacks=[tensorboard])
train_model()
%tensorboard --logdir logs
输出。
2.2.0
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
2400/2400 [==============================] - 6s 3ms/step - loss: 0.4953 - accuracy: 0.8207 - val_loss: 0.4255 - val_accuracy: 0.8428
Epoch 2/5
2400/2400 [==============================] - 6s 3ms/step - loss: 0.3851 - accuracy: 0.8589 - val_loss: 0.3715 - val_accuracy: 0.8649
Epoch 3/5
2400/2400 [==============================] - 6s 3ms/step - loss: 0.3515 - accuracy: 0.8708 - val_loss: 0.3718 - val_accuracy: 0.8639
Epoch 4/5
2400/2400 [==============================] - 6s 3ms/step - loss: 0.3315 - accuracy: 0.8771 - val_loss: 0.3649 - val_accuracy: 0.8686
Epoch 5/5
2400/2400 [==============================] - 6s 3ms/step - loss: 0.3160 - accuracy: 0.8827 - val_loss: 0.3435 - val_accuracy: 0.8736
更多详情请参考 此处
如果你遇到任何问题,请告诉我,我很乐意帮助你。