From 7406aa8a86ec203d82c3ab3f1582a94ed557dc87 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=BD=95=E9=9B=A8=E6=99=B4?= <10205501458@stu.ecnu.edu.cn> Date: Thu, 2 Jun 2022 11:05:48 +0800 Subject: [PATCH] Upload files to 'code/CNN' --- code/CNN/3.ipynb | 498 +++++++++++++++++++++++++++++++++++++++++++++ code/CNN/CNN_classifier.py | 188 +++++++++++++++++ 2 files changed, 686 insertions(+) create mode 100644 code/CNN/3.ipynb create mode 100644 code/CNN/CNN_classifier.py diff --git a/code/CNN/3.ipynb b/code/CNN/3.ipynb new file mode 100644 index 0000000..4da95a0 --- /dev/null +++ b/code/CNN/3.ipynb @@ -0,0 +1,498 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-0.73833905 -0.14152196 -0.35304517 -0.5694006 -0.41008208 -0.11232905]\n", + " [-0.73833905 -0.14164066 -0.35324653 -0.56959656 -0.41008208 -0.11232905]\n", + " [-0.73833905 -0.14176103 -0.35345072 -0.56979527 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6], [3, 6, 5, 4, 4, 5], [2, 7, 3, 6, 4, 5], [7, 2, 4, 5, 7, 2], [2, 7, 6, 3, 2, 7], [3, 6, 5, 4, 1, 8], [5, 4, 6, 3, 7, 2], [9, 0, 7, 2, 1, 8], [6, 3, 2, 7, 5, 4], [7, 2, 5, 4, 6, 3], [3, 6, 5, 4, 5, 4], [3, 6, 6, 3, 2, 7], [4, 5, 8, 1, 5, 4], [5, 4, 3, 6, 5, 4], [4, 5, 3, 6, 5, 4], [5, 4, 3, 6, 6, 3]]\n" + ] + } + ], + "source": [ + "import scipy.io as sio\n", + "import numpy as np\n", + "\n", + "'''a=sio.loadmat('s_2_e_1.mat')\n", + "b=sio.loadmat('s_2_e_2.mat')\n", + "c=sio.loadmat('s_2_e_3.mat')\n", + "print(a.keys())\n", + "for i in range(0,len(a[\"y\"])):\n", + " print(a['y'][i],b['y'][i],c['y'][i],)\n", + "'''\n", + "X=[]\n", + "O=[]\n", + "for c in range(1,23):\n", + " if c==17:\n", + " continue\n", + " a1=sio.loadmat('s_%d_e_1.mat'%(c))\n", + " a2=sio.loadmat('s_%d_e_2.mat'%(c))\n", + " a3=sio.loadmat('s_%d_e_3.mat'%(c))\n", + " \n", + " lenth=len(a1[\"y\"])\n", + " x=a1[\"x\"][lenth-1]\n", + " #print(a1[\"y\"][lenth-1],a2[\"y\"][lenth-1],a3[\"y\"][lenth-1])\n", + " o1=a1[\"y\"][lenth-1][0]\n", + " o2=a1[\"y\"][lenth-1][1]\n", + " o3=a2[\"y\"][lenth-1][0]\n", + " o4=a2[\"y\"][lenth-1][1]\n", + " o5=a3[\"y\"][lenth-1][0]\n", + " o6=a3[\"y\"][lenth-1][1]\n", + " o1=int(o1*10)\n", + " o2=int(o2*10)\n", + " o3=int(o3*10)\n", + " o4=int(o4*10)\n", + " o5=int(o5*10)\n", + " o6=int(o6*10)\n", + " o=[o1,o2,o3,o4,o5,o6]\n", + " X.append(x)\n", + " O.append(o)\n", + " print(x,o)\n", + "print(X,O)\n", + "sio.savemat('result.mat', {'X':X,\"Y\":O,}) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "60d0a6506e690051ad38d772e690e6e667f43736fc28c7db55a8b02aefc08bd8" + }, + "kernelspec": { + "display_name": "Python 3.8.5 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/code/CNN/CNN_classifier.py b/code/CNN/CNN_classifier.py new file mode 100644 index 0000000..8f2e095 --- /dev/null +++ b/code/CNN/CNN_classifier.py @@ -0,0 +1,188 @@ +# Load the libraries +from keras.callbacks import ModelCheckpoint, EarlyStopping +import time +from sklearn.metrics import f1_score, precision_score, recall_score +import gc +from keras import backend as K +import tensorflow as tf +from sklearn.utils import class_weight +import os +import scipy.io as sio +from tensorflow.keras.utils import to_categorical +from keras.layers import Dense, Dropout, Input, Conv2D, BatchNormalization, Activation, MaxPooling2D, GlobalAveragePooling2D +from keras.models import Model +import numpy as np +#from keras.optimizers import Adam +from keras.optimizers import adam_v2 +adam = adam_v2.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08) +#from keras.utils import to_categorical + +t1 = time.time() +np.random.seed(50) # 200 +HIST_VLoss, HIST_TLoss, HIST_VAcc, HIST_TAcc, pred, SUB, P_score, F_score, R_score, K_val, Len = [ +], [], [], [], [], [], [], [], [], [], [] +'''这些是结果''' +for iterate in range(5): # 5 + # set the seed constant for each fold 1,200 + seed = np.random.randint(1, 20) + + for j in range(3): # each class 3 + for i in range(22): # for each subjects 22 + training = sio.loadmat( + 'Data_Class_%d_Subject_%d.mat' % ((j+1), (i+1))) + X = training['Feature'] + Y = np.ravel(training['labels']) + + identifier_val = training['identifier_val'] + '''读取数据''' + X = np.reshape(X, (X.shape[0], X.shape[1], X.shape[2], 1)) + loc = np.where(np.isin(identifier_val[:, 1], i+1))[0] + '''划分训练集验证集，下面都是数据预处理''' + # Shuffle unique trials to divide into training and validation set + loc0 = loc[np.where(Y[loc] == 0)[0]] + loc1 = loc[np.where(Y[loc] == 1)[0]] + classes = identifier_val[:, 0] + class0 = np.unique(classes[loc0]) + class1 = np.unique(classes[loc1]) + + order = np.arange(len(class0)) + np.random.seed(seed) + np.random.shuffle(order) + class0 = class0[order] + + order = np.arange(len(class1)) + np.random.seed(seed) + np.random.shuffle(order) + class1 = class1[order] + + # Divide 20% trials into validation set and rest into training set + classes = 5 + Test = np.append(class0[:classes], class1[:classes]) + Train = np.append(class0[classes:], class1[classes:]) + + test_loc = np.where(np.logical_and( + np.isin(identifier_val[:, 0], Test), np.isin(identifier_val[:, 1], i+1)))[0] + train_loc = np.where(np.logical_and( + np.isin(identifier_val[:, 0], Train), np.isin(identifier_val[:, 1], i+1)))[0] + + # Shuffle the data + order = np.arange(len(test_loc)) + np.random.seed(seed) + np.random.shuffle(order) + test_loc = test_loc[order] + + order = np.arange(len(train_loc)) + np.random.seed(seed) + np.random.shuffle(order) + train_loc2 = train_loc[order] + + # To account for class imbalance + class_weights = class_weight.compute_class_weight('balanced', + np.unique( + Y[train_loc2]), + Y[train_loc2]) + print(class_weights) + if len(class_weights) == 1: + #sio.savemat('temp_%d.mat' %(iterate),{'iterate': iterate,'Y':Y,'train_loc':train_loc,'train_loc2':train_loc2,'npunique': Y[train_loc2]}) + continue + + class_weights = {i: class_weights[i] + for i in range(len(class_weights))} + # class_weights = {i: class_weights[i] for i in range(2)} + # Divide into training and validation set + X_train = X[train_loc] + X_valid = X[test_loc] + y_train = Y[train_loc] + y_valid = Y[test_loc] + Y_train = to_categorical(y_train) + Y_valid = to_categorical(y_valid) + '''开始初始化模型''' + # %% Define model architecture + ch = 6 # Number of features + act = 'tanh' # Activation function + dense_num_units, cnn_units = 16, 8 # Model architecture hyper parameters + bn_axis = 3 # batch normalization dimension + + input_shape = (320, ch, 1) + Allinput_img = Input(shape=(input_shape)) + x_c = Conv2D(cnn_units, kernel_size=(2, 1), strides=( + 2, 1), name='convch_0', use_bias=True)(Allinput_img) # 卷积 + x_c = BatchNormalization(axis=3)(x_c) # 分批 + x_c = Activation(act)(x_c) # 激活 + x_c = MaxPooling2D(pool_size=(2, 1))(x_c) # 最大池化 + '''x_c一层层往下传递，就是神经网络那种''' + x_c = Conv2D(cnn_units, kernel_size=(2, 1), + strides=(2, 1), use_bias=True)(x_c) + x_c = BatchNormalization(axis=3)(x_c) + x_c = Activation(act)(x_c) + x_c = MaxPooling2D(pool_size=(2, 1))(x_c) + + x_c = Conv2D(cnn_units, kernel_size=(2, ch), + strides=(2, 1), use_bias=True)(x_c) + x_c = BatchNormalization(axis=3)(x_c) + x_c = Activation(act)(x_c) + x_c = MaxPooling2D(pool_size=(2, 1))(x_c) + '''多整几层''' + x_c = GlobalAveragePooling2D()(x_c) + x = (Dropout(rate=0.5, name='Drop_D2'))(x_c) + Out = (Dense(2, activation='softmax', name='Allenc_18'))(x) + model = Model(Allinput_img, Out) + model.compile(optimizer=adam_v2.Adam(lr=0.000001), + loss='categorical_crossentropy', metrics=['accuracy']) + model.summary() + + # File to save the model weights to + Checkpoint_filename = './save_weights_CNN_RR.hdf5' + '''保存临时文件''' + # Callbacks to perfrorm early stopping if model does not improve in successive 5 epochs and to save + # model weights only if there is improvement + callback_array = [EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=1, mode='auto'), + ModelCheckpoint(Checkpoint_filename, monitor='val_loss', verbose=1, save_best_only=True, mode='auto')] + '''epoch越大，训练次数越多，越慢''' + # Train the model + history = model.fit(X_train, Y_train, + epochs=10, validation_data=(X_valid, Y_valid), + batch_size=32, verbose=1, callbacks=callback_array, shuffle=True, class_weight=class_weights) + model.load_weights(Checkpoint_filename) + print('Iteration number: %d; Condition: %d; Subject: %d' % + (iterate, j+1, i+1)) + + # Compute the performance metrics + y_pred = np.argmax(model.predict(X_valid), axis=1) + y_pred2 = model.predict(X_valid) + F_score.append([f1_score(y_valid, y_pred, average="macro"), f1_score( + y_valid, y_pred, average="micro"), f1_score(y_valid, y_pred, average="weighted")]) + P_score.append([precision_score(y_valid, y_pred, average="macro"), precision_score( + y_valid, y_pred, average="micro"), precision_score(y_valid, y_pred, average="weighted")]) + R_score.append([recall_score(y_valid, y_pred, average="macro"), recall_score( + y_valid, y_pred, average="micro"), recall_score(y_valid, y_pred, average="weighted")]) + print(y_pred) + print(y_pred2) + hist_dict = history.history + print('====histore_dict----') + print(hist_dict.keys()) + + HIST_VLoss.append(history.history['val_loss']) + HIST_TLoss.append(history.history['loss']) + # HIST_VAcc.append(history.history['val_acc']) #报错就改成后面这个 val_accuracy + HIST_VAcc.append(history.history['val_accuracy']) + # HIST_TAcc.append(history.history['acc']) #报错就改成后面这个 accuracy + HIST_TAcc.append(history.history['accuracy']) + pred.append(model.evaluate(X_valid, Y_valid)) + SUB.append(i) + Len.append(len(history.history['val_loss'])) + + # Reset the variables and delete the model + os.remove(Checkpoint_filename) + K.clear_session() + gc.collect() + del model, training, X, Y, identifier_val, loc, loc0, loc1, order, class0, class1, Test, Train, test_loc, train_loc, class_weights, X_train, X_valid, Y_valid, Y_train + '''prf三个，是那三个参数，由预测得出，另外的，是训练过程中产生的''' + '''保存文件''' + # Save the final results + sio.savemat('Prediction_V%d.mat' % (iterate), {'HIST_VLoss': HIST_VLoss, + 'HIST_VAcc': HIST_VAcc, 'HIST_TLoss': HIST_TLoss, + 'HIST_TAcc': HIST_TAcc, 'pred': pred, 'sub': SUB, + 'F_score': F_score, 'P_score': P_score, 'R_score': R_score, 'Length': Len, }) +t2 = time.time() +print(t2-t1)