import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Input, LSTM, Conv2D, Flatten, Reshape,Convolution1D,MaxPooling1D
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
# from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
from tensorflow.keras import backend as K
# from keras.layers import Activation
# from keras.utils.generic_utils import get_custom_objects
from tensorflow import keras
# from tensorflow_core.python.keras.utils import np_utils
#
# k=26
# dataset = pd.read_csv('train-data16.csv')
# X = np.array(dataset.values[:, :-1]) #导入数据，维度为1024
# pca = PCA(n_components=k ) #降到k维
# pca.fit(X) #训练
# newX=pca.fit_transform(X) #降维后的数据
# PCA(copy=True, n_components=2, whiten=False)
# print(pca.explained_variance_ratio_) #输出贡献率
# print(newX)
# labels1 = dataset.values[:, -1]
# # images = dataset.values[:, 2:1026]
# labels2 = pd.get_dummies(labels1) # 转化one-hot格式
# labels = labels2.values # DataFrame转化为array
# X = np.expand_dims(newX, axis=2)#增加一维轴
# X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2) # 划分训练集和测试集
#新添加的
dataset = pd.read_csv('ENB_nor.csv',header=None)
X = np.array(dataset.values[2:, :-1]) #导入数据
X= (X-X.min(axis=0)) / (X.max(axis=0)-X.min(axis=0)) #数据归一化处理
X = X.reshape(X.shape[0], -1, 1)
print(X.shape)
labels1 = dataset.values[2:, -1]
labels2 = pd.get_dummies(labels1) # 转化one-hot格式
labels = labels2.values # DataFrame转化为array
X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2) # 划分训练集和测试集
X_train = K.cast_to_floatx(X_train)
y_train = K.cast_to_floatx(y_train)
X_test= K.cast_to_floatx(X_test)
y_test = K.cast_to_floatx(y_test)
def create_model(learning_rate, num_dense_layers,num_dense_nodes):
# tf.nn.swish(x)
model = Sequential()
# model.add(Dense(k,input_shape=(k,)))
model.add(Convolution1D(filters=64, kernel_size=3, padding='same',
activation='relu',input_shape=(52,1)))
# model.add(MaxPooling1D(pool_size=2))
model.add(Convolution1D(filters=64, kernel_size=3, padding='same', activation='relu',kernel_regularizer=keras.regularizers.l2(0.001)))
# model.add(MaxPooling1D(pool_size=2))
model.add(Convolution1D(filters=64, kernel_size=3, padding='same', activation='relu'))
model.add(Convolution1D(filters=64, kernel_size=3, padding='same', activation='relu'))
model.add(Convolution1D(filters=64, kernel_size=3, padding='same', activation='relu'))
model.add(MaxPooling1D(pool_size=2))
# for i in range(num_dense_layers):
# name = 'layer_dense_{0}'.format(i + 1)
# # add dense layer
# model.add(Dense(num_dense_nodes,activation='relu',name=name))
# # use softmax-activation for classification.
model.add(Flatten())
model.add(Dense(6, activation='softmax', name='output'))
# Use the Adam method for training the network.
#optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
# optimizer = Adam(lr=learning_rate)
# compile the model so it can be trained.
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate),loss='categorical_crossentropy',metrics=['accuracy'])
model.summary() #打印出模型
return model
def fitness(learning_rate, num_dense_layers, num_dense_nodes):
""" Hyper-parameters: learning_rate: Learning-rate for the optimizer. num_dense_layers: Number of dense layers. num_dense_nodes: Number of nodes in each dense layer. activation: Activation function for all layers. """
# Print the hyper-parameters.
print('learning rate: {0:.1e}'.format(learning_rate))
print('num_dense_layers:', num_dense_layers)
print('num_dense_nodes:', num_dense_nodes)
# Create the neural network with these hyper-parameters.
model = create_model(learning_rate=learning_rate,
num_dense_layers=num_dense_layers,
num_dense_nodes=num_dense_nodes,
)
# Use Keras to train the model.
history = model.fit(x=X_train,
y=y_train,
epochs=200,
batch_size=64,
validation_data=(X_test, y_test),
callbacks=None,verbose=2)
accuracy = history.history['val_accuracy'][-1]
print("Accuracy: {0:.2%}".format(accuracy))
return history
history = fitness(0.0005, 0, 0) #设置学习率，隐藏层数及其节点
# model.save('rnn_weight.h5')
# plt.plot(epoches,accurcy)
# plt.show()
accuracy = history.history['val_accuracy']
accura = history.history['accuracy']
# plt.plot(history.history['loss'], label='train')
plt.plot(accuracy, label='test')
plt.plot(accura, label='train')
# plt.plot(history.history('loss'),label='train')
# plt.plot(history.history('val_loss'),label='test')
plt.title('1DCNN', fontsize='12')
plt.ylabel('acc', fontsize='10')
plt.xlabel('epoch', fontsize='10')
plt.legend()
plt.show()
