Comments (4)
@mohammad0081,
Could you please, share colab link or simple standalone code with supporting files to reproduce the issue in our environment. It helps us in localizing the issue faster. Thank you!
from tensorflow.
This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.
from tensorflow.
yes , this issue will accur even with mnist dataset using tpu strategy, accuracy will be random !
about 10%
i will provide the code soon
from tensorflow.
import tensorflow as tf
(x_train, y_train), (_, __) = tf.keras.datasets.mnist.load_data()
resolver = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
print("All devices: ", tf.config.list_logical_devices('TPU'))
def create_model():
model = tf.keras.models.Sequential()
import tensorflow.keras.layers as l
model.add(l.Input(shape=(28, 28, 1)))
model.add(l.Conv2D(64, (3, 3), activation = 'relu'))
model.add(l.Conv2D(32, (3, 3), activation = 'relu'))
model.add(l.Flatten())
model.add(l.Dense(512, activation = 'relu'))
model.add(l.Dense(64, activation = 'relu'))
model.add(l.Dense(10, activation = 'softmax'))
model.compile(optimizer = 'sgd', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])
return model
with strategy.scope() :
model = create_model()
model.fit(x_train, y_train, epochs= 20)
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