Пример #1
0
    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)
Пример #2
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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]
Пример #3
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),