model = Sequential() model.name = 'model' model.add( Dense(200, activation='relu', kernel_initializer='random_uniform', input_shape=(x_clean.shape[1], ))) model.add(Dropout(0.3)) model.add(Dense(3, activation='softmax')) optim = keras.optimizers.Adadelta() model.compile(optimizer=optim, loss='categorical_crossentropy', metrics=['accuracy']) # model.fit(x_ktrain, y_ktrain, batch_size=100, epochs=100, verbose=1) # y_kpred = np.argmax(model.predict(x_ktest), axis=1) # score = balanced_accuracy_score(y_ktest, y_kpred) # print(score) ############### model training y_clean = keras.utils.to_categorical(y_clean, 3) model.fit(x_clean, y_clean, batch_size=50, epochs=100, verbose=1) y_pred_mat[:, k] = np.argmax(model.predict(x_test_selected), axis=1)
test_data = final_array[int(0.8 * len(final_array)):len(final_array), 0:len(final_array[0])] test_target = main_target.reshape( -1, 1)[int(0.8 * len(final_array)):len(final_array), 0] model = Sequential() model.add( keras.layers.core.Dense(len(train_data[0]), input_dim=len(train_data[0]), init='uniform', activation='relu', bias=True)) model.add( keras.layers.core.Dense(8, init='uniform', activation='relu', bias=True)) model.add(keras.layers.core.Dense(1, init='uniform', bias=True)) model.compile(loss='mean_squared_error', optimizer='adam') keras.layers.core.Dropout(0.1) model.fit(train_data, train_target, nb_epoch=150, batch_size=10) model.evaluate(train_data, train_target, batch_size=10) #training the 2nd Neural network #For category II LOS>7 #array_2 = scipy.delete(array_2,0,1); train_data_2 = array_2[0:int(0.9 * len(array_2)), 0:len(array_2[0])] train_target_2 = main_target_2.reshape(-1, 1)[0:int(0.9 * len(array_2)), 0] test_data_2 = array_2[int(0.9 * len(array_2)):len(array_2), :] test_target_2 = main_target_2.reshape(-1, 1)[int(0.9 * len(array_2)):len(array_2), 0]
from keras.layers import Dense from keras.models import Sequential from keras.layers import Dense model = Sequential() #Swish model.add(Dense(8, activation='swish', input_shape=(8, ))) model.add(Dense(8, activation='swish')) model.add(Dense(8, activation='swish')) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy']) model.fit(X_train, y_train, epochs=5, batch_size=1, verbose=1) y_pred = model.predict_classes(X_test) lrcm = confusion_matrix(y_test, y_pred) AlSumm = AlSumm.append( { 'Model': 'DNN-Swish', 'ModelParameter': 0, 'TN': lrcm[0][0], 'FP': lrcm[0][1], 'FN': lrcm[1][0], 'TP': lrcm[1][1], 'Accuracy': accuracy_score(y_test, y_pred),