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Can NOT fit LSTM layer with default parameters?

I have a problem using LSTM from keras. When I try to train the model, the training stops at “Epoch 1/50” and never progresses. The program just stops with a Process finished code and shows no error messages regarding the missing training.

The problem only occurs when I try to use LSTM’s default parameters. So if I for example give a new activation argument with such as “relu” then it works fine?

It seems to be on my local computer the problem pertains as the code can run on Colab with and without default parameters, but for some reason I can not be allowed to train the model at all. This is especially frustrating as there are no error messages displayed.

I really hope there is a skilled person who can help or guide me in the right direction with this problem.

Thanks 🙂

import tensorflow as tf

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense, Dropout, LSTM, InputLayer

mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train / 255

y_train = y_train / 255

model = Sequential()

model.add(InputLayer((x_train.shape[1:])))

model.add(LSTM(32)) # <— the problem occurs here

model.add(Dense(10, activation=’softmax’))

opt = tf.keras.optimizers.Adam(learning_rate=1e-3, decay=1e-5)

model.compile( loss=’sparse_categorical_crossentropy’, optimizer=opt, metrics=[‘accuracy’] )

model.fit(x_train, y_train, epochs=50, validation_data=(x_test, y_test), verbose=’auto’)

print(“Model is trained.”)

The output from my console:

2021-08-27 10:20:22.799484: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudart64_110.dll 2021-08-27 10:20:26.443509: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library nvcuda.dll 2021-08-27 10:20:26.488972: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: pciBusID: 0000:01:00.0 name: NVIDIA GeForce RTX 2060 computeCapability: 7.5 coreClock: 1.2GHz coreCount: 30 deviceMemorySize: 6.00GiB deviceMemoryBandwidth: 245.91GiB/s 2021-08-27 10:20:26.489580: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudart64_110.dll 2021-08-27 10:20:26.532650: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cublas64_11.dll 2021-08-27 10:20:26.533109: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cublasLt64_11.dll 2021-08-27 10:20:26.559639: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cufft64_10.dll 2021-08-27 10:20:26.565224: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library curand64_10.dll 2021-08-27 10:20:26.637251: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cusolver64_11.dll 2021-08-27 10:20:26.660612: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cusparse64_11.dll 2021-08-27 10:20:26.661815: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudnn64_8.dll 2021-08-27 10:20:26.662201: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0 2021-08-27 10:20:26.662770: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2021-08-27 10:20:26.664656: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: pciBusID: 0000:01:00.0 name: NVIDIA GeForce RTX 2060 computeCapability: 7.5 coreClock: 1.2GHz coreCount: 30 deviceMemorySize: 6.00GiB deviceMemoryBandwidth: 245.91GiB/s 2021-08-27 10:20:26.665660: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0 2021-08-27 10:20:27.284225: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix: 2021-08-27 10:20:27.284551: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0 2021-08-27 10:20:27.284738: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N 2021-08-27 10:20:27.285152: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3961 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2060, pci bus id: 0000:01:00.0, compute capability: 7.5) 2021-08-27 10:20:28.198186: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2) Epoch 1/50 2021-08-27 10:20:29.513381: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudnn64_8.dll Process finished with exit code -1073740791 (0xC0000409) 

  • TensorFlow version: 2.5.0
  • Python version: 3.9.6

submitted by /u/j_whoooo
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