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),
