encounter a problem

[zc317414]

InvalidArgumentError: Graph execution error:

2 root error(s) found.
(0) INVALID_ARGUMENT: No OpKernel was registered to support Op 'CudnnRNN' used by {{node CudnnRNN}} with these attrs: [seed=0, dropout=0, T=DT_FLOAT, input_mode="linear_input", direction="unidirectional", rnn_mode="lstm", seed2=0, is_training=true]
Registered devices: [CPU, GPU]
Registered kernels:

 [[CudnnRNN]]
 [[model/bidirectional/forward_lstm_3/PartitionedCall]]
 [[binary_crossentropy/logistic_loss/_12]]

(1) INVALID_ARGUMENT: No OpKernel was registered to support Op 'CudnnRNN' used by {{node CudnnRNN}} with these attrs: [seed=0, dropout=0, T=DT_FLOAT, input_mode="linear_input", direction="unidirectional", rnn_mode="lstm", seed2=0, is_training=true]
Registered devices: [CPU, GPU]
Registered kernels:

 [[CudnnRNN]]
 [[model/bidirectional/forward_lstm_3/PartitionedCall]]

0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_53639]

[zc317414]

import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
max_features = 20000 # Only consider the top 20k words
maxlen = 200 # Only consider the first 200 words of each movie review

Input for variable-length sequences of integers

inputs = keras.Input(shape=(None,), dtype="int32")

Embed each integer in a 128-dimensional vector

x = layers.Embedding(max_features, 128)(inputs)

Add 2 bidirectional LSTMs

x = layers.Bidirectional(layers.LSTM(64, return_sequences=True))(x)
x = layers.Bidirectional(layers.LSTM(64))(x)

Add a classifier

outputs = layers.Dense(1, activation="sigmoid")(x)
model = keras.Model(inputs, outputs)

(x_train, y_train), (x_val, y_val) = keras.datasets.imdb.load_data(
num_words=max_features
)
print(len(x_train), "Training sequences")
print(len(x_val), "Validation sequences")
x_train = keras.preprocessing.sequence.pad_sequences(x_train, maxlen=maxlen)
x_val = keras.preprocessing.sequence.pad_sequences(x_val, maxlen=maxlen)

model.compile("adam", "binary_crossentropy", metrics=["accuracy"])
model.fit(x_train, y_train, batch_size=32, epochs=2, validation_data=(x_val, y_val))

[PatriceVignola]

Hi @zc317414 ,

This is similar to this issue. We're currently working on it and will update this issue once we have more updates.