import numpy as np
from keras.callbacks import EarlyStopping
from keras.models import Sequential
from keras.layers import Dense, Conv1D, MaxPooling1D, Flatten
from sklearn.metrics import confusion_matrix


def build_cnn_network():
    model = Sequential()
    model.add(Conv1D(32, kernel_size=5, strides=1,
                     activation='relu',
                     input_shape=(122, 1)))
    model.add(MaxPooling1D(pool_size=2, strides=2))
    model.add(Conv1D(64, 5, activation='relu'))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Flatten())
    model.add(Dense(1000, activation='relu'))
    model.add(Dense(5, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model


def run_cnn_network(train, target, epochs):
    cnn = build_cnn_network()
    train = np.expand_dims(train, axis=2)
    early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=3, verbose=0, mode='auto')
    cnn.fit(x=train, y=target, epochs=epochs, validation_split=0.1, batch_size=128, callbacks=[early_stopping])
    return cnn


def evaluate_cnn_network(cnn, test, test_target, flag):
    loss, accuracy = cnn.evaluate(np.expand_dims(test, axis=2), test_target, batch_size=None, verbose=1)
    # print('[-]( 测试集: ' + flag + ' )  测试损失为：' + str(loss) + '  准确度为：' + str(accuracy))
    return loss, accuracy