Open Willian-Zhang opened 3 years ago
@Willian-Zhang Thank you for reporting this failure and providing a reproducible test case. We will investigate.
@Willian-Zhang, could you try the updated wheel and let us know if you still see the crash? Thank you!
I can use tf.keras.layers.Dropout now, but I found a Weird bug: "dropout can not work on CPU mode".
use CPU, can only get loss > 1000, and accuracy around 0.82 on test set and 0.90 on validation set after 3 epochs,
469/469 [==============================] - 18s 35ms/step - loss: 337.7770 - sparse_categorical_accuracy: 0.6640 - val_loss: 956.9423 - val_sparse_categorical_accuracy: 0.9067
Epoch 2/60
469/469 [==============================] - 16s 35ms/step - loss: 2547.7716 - sparse_categorical_accuracy: 0.7927 - val_loss: 581.1567 - val_sparse_categorical_accuracy: 0.9197
Epoch 3/60
469/469 [==============================] - 16s 35ms/step - loss: 1371.1409 - sparse_categorical_accuracy: 0.8203 - val_loss: 489.0378 - val_sparse_categorical_accuracy: 0.8921
but same code use GPU can get loss less than 0.2 and accuracy around 0.95 on test set and 0.99 on validation set after 3 epochs.
469/469 [==============================] - 26s 52ms/step - loss: 1.5316 - sparse_categorical_accuracy: 0.6025 - val_loss: 0.2223 - val_sparse_categorical_accuracy: 0.9504
Epoch 2/60
469/469 [==============================] - 24s 51ms/step - loss: 0.2544 - sparse_categorical_accuracy: 0.9356 - val_loss: 0.1293 - val_sparse_categorical_accuracy: 0.9691
Epoch 3/60
469/469 [==============================] - 24s 51ms/step - loss: 0.1716 - sparse_categorical_accuracy: 0.9553 - val_loss: 0.0920 - val_sparse_categorical_accuracy: 0.9748
and if we remove dropout layer, everything works fine.
the code is here:
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.python.compiler.mlcompute import mlcompute
#mlcompute.set_mlc_device(device_name='gpu')
mlcompute.set_mlc_device(device_name='cpu')
(ds_train, ds_test), ds_info = tfds.load(
'mnist',
split=['train', 'test'],
shuffle_files=True,
as_supervised=True,
with_info=True,
)
def normalize_img(image, label):
"""Normalizes images: `uint8` -> `float32`."""
return tf.cast(image, tf.float32) / tf.constant(255., dtype=tf.float32), label
ds_train = ds_train.map(
normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)
ds_train = ds_train.cache()
ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples)
ds_train = ds_train.batch(128)
ds_train = ds_train.prefetch(tf.data.experimental.AUTOTUNE)
ds_test = ds_test.map(
normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)
ds_test = ds_test.batch(128)
ds_test = ds_test.cache()
ds_test = ds_test.prefetch(tf.data.experimental.AUTOTUNE)
#model = tf.keras.models.Sequential([
# tf.keras.layers.Flatten(input_shape=(28, 28)),
# tf.keras.layers.Dense(128,activation='relu'),
# tf.keras.layers.Dense(10)
#])
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(32, kernel_size=(3, 3),
activation='relu'),
tf.keras.layers.Conv2D(64, kernel_size=(3, 3),
activation='relu'),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
tf.keras.layers.Dropout(0.25),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(
optimizer=tf.keras.optimizers.Adam(0.001),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],
)
model.fit(
ds_train,
epochs=60,
validation_data=ds_test,
)
Adding
tf.keras.layers.Dropout
to model results the following error:Expected behavior: Running
.fit
on model successExample model configuration: