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Tuner image6/29/2023 In addition to these features, we also have a HParams tab, in which there are For example, youĬan view the loss and metrics curves and visualize the computational graph of You have access to all the common features of the TensorBoard. If running in Colab, the following two commands will show you the TensorBoard Initialize the RandomSearch tuner with 10 trials and using validationĪccuracy as the metric for selecting models.īest val_accuracy So Far: 0.984749972820282 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 04:13:39.860436: I tensorflow/core/platform/cpu_feature_:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA 04:13:39.860073: I tensorflow/stream_executor/cuda/cuda_:156] kernel driver does not appear to be running on this host (haifengj.c.): /proc/driver/nvidia/version does not exist 04:13:39.860040: W tensorflow/stream_executor/cuda/cuda_:269] failed call to cuInit: UNKNOWN ERROR (303) 04:13:39.859939: W tensorflow/stream_executor/platform/default/dso_:64] Could not load dynamic library 'libcuda.so.1' dlerror: libcuda.so.1: cannot open shared object file: No such file or directory We can do a quick test of the models to check if it build successfully for both compile ( loss = "sparse_categorical_crossentropy", metrics =, optimizer = "adam", ) return model Model ( inputs = inputs, outputs = outputs ) # Compile the model. Dense ( units = 10, activation = "softmax" )( x ) model = keras. Dropout ( 0.5 )( x ) # The last layer contains 10 units, # which is the same as the number of classes. Flatten ()( x ) # A hyperparamter for whether to use dropout layer. MaxPooling2D ( pool_size = ( 2, 2 ))( x ) x = layers. Int ( "mlp_layers", 1, 3 )): # Number of units of each layer are # different hyperparameters with different names. Flatten ()( x ) # Number of layers of the MLP is a hyperparameter. Choice ( "model_type", ) x = inputs if model_type = "mlp" : x = layers. Input ( shape = ( 28, 28, 1 )) # Model type can be MLP or CNN.
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