diff --git a/Number-Of-CNN-Layers/Two/logs/20230318-162330/train/events.out.tfevents.1679127816.WEIYUQING.46432.0.v2 b/Number-Of-CNN-Layers/Two/logs/20230318-162330/train/events.out.tfevents.1679127816.WEIYUQING.46432.0.v2 new file mode 100644 index 0000000..d3f29c6 Binary files /dev/null and b/Number-Of-CNN-Layers/Two/logs/20230318-162330/train/events.out.tfevents.1679127816.WEIYUQING.46432.0.v2 differ diff --git a/_mian_tensorflow_keras_.py b/_mian_tensorflow_keras_.py deleted file mode 100644 index 6ac1ebc..0000000 --- a/_mian_tensorflow_keras_.py +++ /dev/null @@ -1,92 +0,0 @@ -import numpy as np -import pywt -import wfdb -import tensorflow as tf - -ECGDATAPATH= "mit-bih-arrhythmia-database-1.0.0/" - - -def denoise(date): - # Wavelet db5 level9 - coeffs = pywt.wavedec(data=date, wavelet='db5', level=9) - cA9, cD9, cD8, cD7, cD6, cD5, cD4, cD3, cD2, cD1 = coeffs - # noice del - threshold = (np.median(np.abs(cD1)) / 0.6745) * (np.sqrt(2 * np.log(len(cD1)))) - # take cD 1,2 turn 0 to denoise - cD1.fill(0) - cD2.fill(0) - for i in range(1, len(coeffs) - 2): - coeffs[i] = pywt.threshold(coeffs[i], threshold) - # get signal that before wavelet - rdata = pywt.waverec(coeffs=coeffs, wavelet='db5') - return rdata - - -# get ECG data,date type and denoise -# this function without return -# use append take return data to pass data -def getDataSet(number, X_data, Y_data): - ecgClassSet = ['N', 'A', 'V', 'l', 'R'] - print('get' + number + ' ECG data') - record = wfdb.rdrecord(ECGDATAPATH + number, channel_names=['MLII']) - data = record.p_signal.flatten() - # use def denoise - rdata = denoise(date=data) - - # get type of ECG data - annotation = wfdb.rdann(ECGDATAPATH + number, 'atr') - Rlocation = annotation.sample # annotation.sample:get R - Rclass = annotation.symbol - - # delete stable date near R - start = 10 - end = 5 - i = start - j = len(annotation.symbol) - end - - # get NAVLR type data - while i < j: - try: - lable = ecgClassSet.index(Rclass[i]) - x_train = rdata[Rlocation[i] - 99:Rlocation[i] + 201] - X_data.append(x_train) - Y_data.append(lable) - i += 1 - except ValueError: - i += 1 - return - - -# RATIO=The ratio of the training set to the test set，usually is 0.3 -def loadData(RATIO): - numberSet = [ - '100', '101', '103', '105', '106', '107', '108', '109', '111', '112', '113', '114', '115', '116', '117', '119', - '121', '122', '123', '124', '200', '201', '202', '203', '205', '208', '210', '212', '213', '214', '215', '217', - '219', '220', '221', '222', '223', '228', '230', '231', '232', '233', '234' - ] - dataSet = [] - labelSet = [] - for n in numberSet: - getDataSet(n, dataSet, labelSet) - - dataSet = np.array(dataSet).reshape(-1, 300) - labelSet = np.array(labelSet).reshape(-1, 1) - train_ds = np.hstack((dataSet, labelSet)) - np.random.shuffle(train_ds) - - X = train_ds[:, :300].reshape(-1, 300, 1) - Y = train_ds[:, 300] - # cut test and train data from ECG data - shuffle_index = np.random.permutation(len(X)) - test_length = int(RATIO * len(shuffle_index)) - test_index = shuffle_index[:test_length] - train_index = shuffle_index[test_index:] - X_test, Y_test = X[test_index:], Y[test_index] - X_train, Y_train = X[train_index], Y[train_index] - return X_train, Y_test, X_test, Y_train - - -class CNN_model_level_one(): - def __init__(self): - super().__init__() - self.conv1 = tf.layers.Conv1d(filter=4) diff --git a/_mian_tensorflow_twoLayerCNN.py b/_mian_tensorflow_twoLayerCNN.py new file mode 100644 index 0000000..7daaec7 --- /dev/null +++ b/_mian_tensorflow_twoLayerCNN.py @@ -0,0 +1,113 @@ +import datetime +import os + +import numpy as np +import pywt +import wfdb +import tensorflow as tf +from tensorflow import keras +from utils import loadData, plot_history_tf, plot_heat_map + +#from ECG_read_weiyuqing_without_wfdb import ECGDATAPATH +#PATH of this test +Project_PATH = "./Number-Of-CNN-Layers/Two/" +#PICTUREPATH= Project_PATH + "picture/" +log_dir = Project_PATH + "logs/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S") +model_Path_one = Project_PATH + "model/ecg_model_one_layer.h5" +#model_Path_one = Project_PATH + "ecg_model_one_layer.h5" + +RATIO = 0.3 +RANDOM_SEED = 42 +BATCH_SIZE = 128 +NUM_EPOCHS = 30 + + +def CNN_model_level_one(): + leavlOneModel = tf.keras.models.Sequential([ + + # #four CNN layers test + # tf.keras.layers.InputLayer(input_shape=(300,)), + # # reshape the tensor with shape (batch_size, 300) to (batch_size, 300, 1) + # tf.keras.layers.Reshape(target_shape=(300, 1)), + # # the first convolution layer, 4 21x1 convolution kernels, output shape (batch_size, 300, 4) + # tf.keras.layers.Conv1D(filters=4, kernel_size=21, strides=1, padding='SAME', activation='relu'), + # # the first pooling layer, max pooling, pooling size=3 , stride=2, output shape (batch_size, 150, 4) + # tf.keras.layers.MaxPool1D(pool_size=3, strides=2, padding='SAME'), + # # the second convolution layer, 16 23x1 convolution kernels, output shape (batch_size, 150, 16) + # tf.keras.layers.Conv1D(filters=16, kernel_size=23, strides=1, padding='SAME', activation='relu'), + # # the second pooling layer, max pooling, pooling size=3, stride=2, output shape (batch_size, 75, 16) + # tf.keras.layers.MaxPool1D(pool_size=3, strides=2, padding='SAME'), + # # the third convolution layer, 32 25x1 convolution kernels, output shape (batch_size, 75, 32) + # tf.keras.layers.Conv1D(filters=32, kernel_size=25, strides=1, padding='SAME', activation='relu'), + # # the third pooling layer, average pooling, pooling size=3, stride=2, output shape (batch_size, 38, 32) + # tf.keras.layers.AvgPool1D(pool_size=3, strides=2, padding='SAME'), + # # the fourth convolution layer, 64 27x1 convolution kernels, output shape (batch_size, 38, 64) + # tf.keras.layers.Conv1D(filters=64, kernel_size=27, strides=1, padding='SAME', activation='relu'), + # # flatten layer, for the next fully connected layer, output shape (batch_size, 38*64) + # tf.keras.layers.Flatten(), + # # fully connected layer, 128 nodes, output shape (batch_size, 128) + # tf.keras.layers.Dense(128, activation='relu'), + # # Dropout layer, dropout rate = 0.2 + # tf.keras.layers.Dropout(rate=0.2), + # # fully connected layer, 5 nodes (number of classes), output shape (batch_size, 5) + # tf.keras.layers.Dense(5, activation='softmax') + + #take test for one CNN layers model + + tf.keras.layers.InputLayer(input_shape=(300, 1)), + + # ECG data cant add 0 in edge, will broken data ,take false answer. So we need padding same data in edge. + + # take four 21*1 conv excitation function used RElu + tf.keras.layers.Conv1D(filters=4, kernel_size=21, strides=1, padding='same', activation='relu'), + # take four 3*1 pool,take strids 2. + tf.keras.layers.MaxPool1D(pool_size=3, strides=2, padding='same'), + + tf.keras.layers.Conv1D(filters=4, kernel_size=21, strides=1, padding='same', activation='relu'), + tf.keras.layers.MaxPool1D(pool_size=3, strides=2, padding='same'), + # tf.keras.layers.Conv1D(filters=4, kernel_size=21, strides=1, padding='same', activation='relu'), + # tf.keras.layers.MaxPool1D(pool_size=3, strides=2, padding='same'), + + tf.keras.layers.Flatten(), + tf.keras.layers.Dense(128, activation='relu'), + tf.keras.layers.Dropout(rate=0.2), + tf.keras.layers.Dense(5, activation='softmax') + ]) + return leavlOneModel + + +def main(): + # get traxin test + X_train, X_test, y_train, y_test = loadData(RATIO, RANDOM_SEED) + + # get model or create model + if os.path.exists(model_Path_one): + print(' get model in h5 file') + model = tf.keras.models.load_model(filepath=model_Path_one) + else: + # create new model(if model unexists) + model = CNN_model_level_one() + model.compile(optimizer='adam', + loss='sparse_categorical_crossentropy', + metrics=['accuracy']) + model.summary() + # TB make + tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, + histogram_freq=1, + ) + # Training and validation + history = model.fit(X_train, y_train, epochs=NUM_EPOCHS, + batch_size=BATCH_SIZE, + validation_data=(X_test, y_test), + callbacks=[tensorboard_callback]) + # validation_split=RATIO, + model.save(filepath=model_Path_one) + plot_history_tf(history) + + y_pred = np.argmax(model.predict(X_test), axis=-1) + plot_heat_map(y_test, y_pred) + + +if __name__ == '__main__': + os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + main() diff --git a/noise_take_ecg.py b/noise_take_ecg.py deleted file mode 100644 index bd4f428..0000000 --- a/noise_take_ecg.py +++ /dev/null @@ -1,31 +0,0 @@ -import numpy as np -import pywt -import wfdb -from matplotlib import pyplot as plt - -#file error - -#use db5 ,level9 do deviose -from ECG_read_weiyuqing_without_wfdb import ECGDATAPATH - -#from ECG_read_weiyuqing_without_wfdb import PATH - -record=wfdb.rdrecord(ECGDATAPATH + 100, channel_names=['MLII']) -data=record.p_signal.flatten() - -coeffs=pywt.wavedec(data=data,wavelet='db5',level=9) -cA9,cD9, cD8, cD7, cD6, cD5, cD4, cD3, cD2, cD1 = coeffs - -threshold=(np.median(np.abs(cD1))/0.6745)*(np.sqrt(2*np.log(len(cD1)))) -cD1.fill(0) -cD2.fill(0) -for i in range(1,len(coeffs)-2): - coeffs[i]=pywt.threshold(coeffs[i],threshold) - -rdata=pywt.waverec(coeffs=coeffs,wavelet='db5') - -plt.figure(figsize=(20,4)) -plt.plot(data) -plt.show() -plt.plot(rdata) -plt.show() \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..094b32d --- /dev/null +++ b/requirements.txt @@ -0,0 +1,11 @@ +matplotlib==3.7.0 +numpy==1.23.5 +opencv_python==4.7.0.72 +Pillow==9.4.0 +PyWavelets==1.4.1 +scikit_learn==1.2.1 +seaborn==0.12.2 +tensorflow==2.10.0 +tensorflow_intel==2.11.0 +torch==2.0.0+cu117 +wfdb==4.1.0 diff --git a/some-test/noise_take_ecg.py b/some-test/noise_take_ecg.py new file mode 100644 index 0000000..a30bdf5 --- /dev/null +++ b/some-test/noise_take_ecg.py @@ -0,0 +1,31 @@ +import numpy as np +import pywt +import wfdb +from matplotlib import pyplot as plt + +# file error + +# use db5 ,level9 do deviose +ECGDATAPATH = './MIT-BIT/' + +# from ECG_read_weiyuqing_without_wfdb import PATH +# record = wfdb.rdrecord(ECGDATAPATH + number, channel_names=['MLII']) +record = wfdb.rdrecord(ECGDATAPATH + '100', channel_names=['MLII']) +data = record.p_signal.flatten() + +coeffs = pywt.wavedec(data=data, wavelet='db5', level=9) +cA9, cD9, cD8, cD7, cD6, cD5, cD4, cD3, cD2, cD1 = coeffs + +threshold = (np.median(np.abs(cD1)) / 0.6745) * (np.sqrt(2 * np.log(len(cD1)))) +cD1.fill(0) +cD2.fill(0) +for i in range(1, len(coeffs) - 2): + coeffs[i] = pywt.threshold(coeffs[i], threshold) + +rdata = pywt.waverec(coeffs=coeffs, wavelet='db5') + +plt.figure(figsize=(20, 4)) +plt.plot(data) +plt.show() +plt.plot(rdata) +plt.show() diff --git a/pytorch_gpu_test.py b/some-test/pytorch_gpu_test.py similarity index 100% rename from pytorch_gpu_test.py rename to some-test/pytorch_gpu_test.py diff --git a/test_plt_show.py b/some-test/test_plt_show.py similarity index 100% rename from test_plt_show.py rename to some-test/test_plt_show.py diff --git a/tf_run_in_gpu.py b/some-test/tf_run_in_gpu.py similarity index 100% rename from tf_run_in_gpu.py rename to some-test/tf_run_in_gpu.py diff --git a/wfdb_use.py b/some-test/wfdb_use.py similarity index 90% rename from wfdb_use.py rename to some-test/wfdb_use.py index 9da84a0..4a68aa6 100644 --- a/wfdb_use.py +++ b/some-test/wfdb_use.py @@ -4,14 +4,14 @@ import numpy as np import wfdb import matplotlib.pyplot -PATH = './mit-bih-arrhythmia-database-1.0.0/' +PATH = '../MIT-BIT/' # file error with read_ecg_data(data) parameter data false def read_ecg_data(data): # get the data from the ecg data file and set begin and end points - record = wfdb.rdrecord(PATH + data, sampfrom=0, sampto=1500) + record = wfdb.rdrecord(PATH + 'data', sampfrom=0, sampto=1500) # return type record print(type(record)) print(dir(record)) diff --git a/utils.py b/utils.py index 814fd91..12eac22 100644 --- a/utils.py +++ b/utils.py @@ -10,7 +10,7 @@ from sklearn.metrics import confusion_matrix from sklearn.model_selection import train_test_split # from _mian_tensorflow import Project_PATH -Project_PATH = "./Number-Of-CNN-Layers/One/" +Project_PATH = "./Number-Of-CNN-Layers/Two/" PICTUREPATH= Project_PATH +'picture/'