我在 Windows 10 中使用帶有 Python 3.7.4（64 位）的 Tensorflow。我已經建立了一個卷積神經網絡模型，它在 Jupyter 中運行良好?，F在我想用 Tensorboard 可視化它的性能。但是嘗試設置它時，我收到一條錯誤消息。# 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])此頁面上的海報 ( https://github.com/tensorflow/tensorboard/issues/2023# ) 提到有一個特定于 Windows 的 Tensorflow 錯誤。這就是我遇到的嗎？我是 TensorFlow（和 Python）的新手。謝謝！
1 回答
有只小跳蛙
TA貢獻1824條經驗 獲得超8個贊
您的不是特定于 Windows 的 Tensorflow 錯誤。我已經使用您的代碼進行了少量修改，現在我可以使用 Tensorboard 可視化模型性能。
請參考下面的完整工作代碼
# 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
輸出：
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
有關更多詳細信息，請參閱此處
如果您遇到任何問題，請告訴我，我很樂意為您提供幫助。
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