How to choose best fit parameters for your ANN or SVM model using scikit learn grid search ? (This topic refers to classification problem). As a beginner, after creating a couple of simple neural networks either via tensorflow or Matlab I was thinking about one question: How do I decide which network architecture (number of layers, neurons) and parameters (epoch number, batch size, optimization algorithms and initialization types) to choose? And actually, this question was answered via Grid Search scikit python library that automatizes process of the best parameters. In example below I tried cover all typical tuning parameters: # Since running GridSearch may be quite time/calculation consuming # I used GPU based tensorflow in order to speed-up calculation import tensorflow as tf from keras.backend.tensorflow_backend import set_session config = tf.ConfigProto() # Defining GPU usage limit to 75% config.gpu_options.per_process_gpu_memory_fraction = 0.75 ...
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