tensorflow / tensorflow/tensorboard
Malfomed Graphdef error.
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Description
I create a MLP model as follows but get error.
import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.callbacks import TensorBoard
import datetime
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = Sequential([
Flatten(input_shape=(28, 28)),
Dense(128, activation='relu'),
Dense(64, activation='relu'),
Dense(10, activation='softmax')
])
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
)
log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = TensorBoard(
log_dir=log_dir,
histogram_freq=1,
write_graph=True,
write_images=True,
update_freq='epoch'
)
try:
model.fit(
x_train, y_train,
epochs=5,
validation_data=(x_test, y_test),
callbacks=[tensorboard_callback],
verbose=1
)
except Exception as e:
print(f"error when train: {str(e)}")
raise
try:
model.save('mnist_mlp_model.keras')
except Exception as e:
print(f"error when save: {str(e)}")
raise
print(model.summary())
My environment info is as follows.
macos 15.5 (24F74)
python 3.9.6
tensorboard 2.19.0
tensorboard-data-server 0.7.2
tensorflow 2.19.0
tensorflow-io-gcs-filesystem 0.37.1
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by running the supplied TensorFlow MNIST script with the listed macOS, Python, TensorBoard, and TensorFlow versions, then inspect the full traceback behind the “Malformed GraphDef” error rather than relying on the screenshot. Trace the failure from the TensorBoard callback used during model.fit; done means the cause and affected behavior are confirmed and the issue has a reproducible resolution.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- devtools, machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100