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feat: add func to reduce data sets

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This commit is contained in:
Skudalen 2021-07-21 13:46:28 +02:00
parent 978b1a2ccd
commit 6452e158b4
1 changed files with 18 additions and 8 deletions
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26
Neural_Network_Analysis.py
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@ -337,6 +337,17 @@ def get_session_info(session_lengths_soft, session_lengths_hard):
print('Avg session:', soft_avg_sess, hard_avg_sess)
print('Avg sub:', soft_avg_sub, hard_avg_sub)
# Reduces the size of the train and test set with values [0.0, 1.0]
# Input: Data sets, how much to reduce train set, how much to reduce test set with
# Output: Reduced data sets
def reduce_data_set_sizes(X_train, X_test, y_train, y_test, train_reduction=0.5, test_reduction=0):
train_keep = X_train.shape[0] * (1 - train_reduction)
test_keep = X_test.shape[0] * (1 - test_reduction)
X_train = X_train[:train_keep]
y_train = y_train[:train_keep]
X_test = X_test[:test_keep]
y_test = y_test[:test_keep]
return X_train, X_test, y_train, y_test
# ----- PLOTS ------
@ -850,11 +861,10 @@ if __name__ == "__main__":
X_train, X_test, y_train, y_test = prepare_datasets_sessions(X_soft, y_soft, session_lengths_soft, TEST_SESSION_NR)
'''
# ----- Make model ------
#model_GRU = GRU(input_shape=(1, 208)) # (timestep, 13*16 MFCC coefficients)
#model_LSTM = LSTM(input_shape=(1, 208)) # (timestep, 13*16 MFCC coefficients)
model_CNN_1D = CNN(input_shape=(208, 1)) # (timestep, 13*16 MFCC coefficients)
model_CNN_1D = CNN_1D(input_shape=(208, 1)) # (timestep, 13*16 MFCC coefficients)
model_CNN_1D.summary()
#model_GRU.summary()
@ -869,7 +879,7 @@ if __name__ == "__main__":
# ----- Plot train accuracy/error -----
#plot_train_history(history)
plot_train_history(history_CNN_1D)
# ----- Evaluate model on test set ------
@ -878,13 +888,13 @@ if __name__ == "__main__":
#print('\nTest accuracy GRU:', test_acc, '\n')
#test_loss, test_acc = model_LSTM.evaluate(X_test, y_test, verbose=VERBOSE)
#print('\nTest accuracy LSTM:', test_acc, '\n')
test_loss, test_acc = model_CNN_1D.evaluate(np.reshape(X_test, (X_test.shape[0], 208, 1)), y_test, verbose=0)
print('\nTest accuracy CNN_1D:', test_acc, '\n')
#test_loss, test_acc = model_CNN_1D.evaluate(np.reshape(X_test, (X_test.shape[0], 208, 1)), y_test, verbose=0)
#print('\nTest accuracy CNN_1D:', test_acc, '\n')
# ----- Store test predictions in CSV ------
prediction_csv_logger(np.reshape(X_test, (X_test.shape[0], 208, 1)), y_test, MODEL_NAME, model_CNN_1D, TEST_SESSION_NR)
'''
#prediction_csv_logger(np.reshape(X_test, (X_test.shape[0], 208, 1)), y_test, MODEL_NAME, model_CNN_1D, TEST_SESSION_NR)
'''
@ -949,7 +959,7 @@ if __name__ == "__main__":
#plot_comp_accuracy_single(X_soft, y_soft, session_lengths_soft, NR_SESSIONS, epochs=30)
#plot_comp_SoftHard_single(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, NR_SESSIONS, epochs=30)
plot_comp_SoftHard_3(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, NR_SESSIONS, epochs=30)
#plot_comp_SoftHard_3(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, NR_SESSIONS, epochs=30)