Introducing the Keras Sequential API on Vertex AI Platform Reviews

# Build a keras DNN model using Sequential API # TODO 2a model = tf.keras.models.Sequential([ tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(10, activation='softmax') ]) # TODO -- Your code here. 2023-08-09 08:14:13.388506: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/usr/local/nccl2/lib:/usr/local/cuda/extras/CUPTI/lib64 2023-08-09 08:14:13.388596: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303) 2023-08-09 08:14:13.388628: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (instance-20230809-084045): /proc/driver/nvidia/version does not exist 2023-08-09 08:14:13.390192: 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: AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

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Michael S. · Reviewed حوالي سنتان ago

Great lab. But the time allotted for it is too short. Unless one is experienced in Keras already, it is very challenging

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