From 7f915f939d90b6f10ed7806d092230934b145098 Mon Sep 17 00:00:00 2001 From: Matthew Scholefield Date: Thu, 12 Jul 2018 17:20:09 -0500 Subject: [PATCH] Remove unused variables --- precise/scripts/train_optimize.py | 18 +++--------------- 1 file changed, 3 insertions(+), 15 deletions(-) diff --git a/precise/scripts/train_optimize.py b/precise/scripts/train_optimize.py index 696fa9c..a1ae189 100644 --- a/precise/scripts/train_optimize.py +++ b/precise/scripts/train_optimize.py @@ -17,7 +17,6 @@ import h5py import numpy # Optimizer blackhat from bbopt import BlackBoxOptimizer -from decimal import * from keras.layers.core import Dense from keras.layers.recurrent import GRU from keras.models import Sequential @@ -74,25 +73,14 @@ def main(): model.compile('rmsprop', weighted_log_loss, metrics=['accuracy']) - # goodness metric for optimization - def goodness(y_true, y_pred) -> Any: - from math import exp - try: - param_score = 1.0 / (1.0 + exp((model.count_params() - 11000) / 2000)) - except OverflowError: - param_score = 1.0 / (1.0 + Decimal(exp((model.count_params() - 11000)) / 2000)) - fitness = param_score * ( - ((1.0 - (0.05 * false_neg(y_true, y_pred))) - (0.95 * false_pos(y_true, y_pred)))) - return fitness - from keras.callbacks import ModelCheckpoint checkpoint = ModelCheckpoint('tested_models.hdf5', monitor='val_loss', save_best_only=True) - train_history = model.fit(train_inputs, train_outputs, batch_size=batch_size, epochs=100, - validation_data=(test_inputs, test_outputs), - callbacks=[checkpoint]) + model.fit(train_inputs, train_outputs, batch_size=batch_size, epochs=100, + validation_data=(test_inputs, test_outputs), + callbacks=[checkpoint]) test_loss, test_acc = model.evaluate(test_inputs, test_outputs) predictions = model.predict(test_inputs)