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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.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__': - main() diff --git a/_mian_tensorflow_keras_.py b/_mian_tensorflow_keras_.py index 832b64e..6ac1ebc 100644 --- a/_mian_tensorflow_keras_.py +++ b/_mian_tensorflow_keras_.py @@ -3,7 +3,7 @@ import pywt import wfdb import tensorflow as tf -from ECG_read_weiyuqing_without_wfdb import ECGDATAPATH +ECGDATAPATH= "mit-bih-arrhythmia-database-1.0.0/" def denoise(date): diff --git a/_mian_tensorflow_oneLayerCNN.py b/_mian_tensorflow_oneLayerCNN.py new file mode 100644 index 0000000..1d817c3 --- /dev/null +++ b/_mian_tensorflow_oneLayerCNN.py @@ -0,0 +1,112 @@ +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/One/" +#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.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/pytorch_gpu_test.py b/pytorch_gpu_test.py index 3d43daa..66dd6d1 100644 --- a/pytorch_gpu_test.py +++ b/pytorch_gpu_test.py @@ -1,2 +1,10 @@ import torch -torch.cuda.is_available() \ No newline at end of file +torch.cuda.is_available() +print(torch.cuda.is_available()) +print(torch.__version__) + + +print(torch.cuda.is_available() )# cuda是否可用 +print(torch.cuda.device_count() )# gpu数量 +print(torch.cuda.current_device())# 当前设备索引, 从0开始 +print(torch.cuda.get_device_name(0))# 返回gpu名字 diff --git a/tf_run_in_gpu.py b/tf_run_in_gpu.py index ba34a47..662f7f3 100644 --- a/tf_run_in_gpu.py +++ b/tf_run_in_gpu.py @@ -2,51 +2,51 @@ import tensorflow as tf tf.test.is_gpu_available() -# # Tensorflow 2.10是最后一个在本地windows上支持GPU的版本。从2.11版本开始，需要在windows WLS2（适用于 Linux 的 Windows 子系统）上安装才能使用GPU。所以要在native-windows上使用GPU，就只能安装2.10.0版本及以下的版本，或者安装老版的tensorflow-gpu. -# # 指定在cpu上运行 -# with tf.device('/cpu:0'): -# cpu_a = tf.random.normal([10000, 1000]) -# cpu_b = tf.random.normal([1000, 2000]) -# cpu_c = tf.matmul(cpu_a, cpu_b) -# print("cpu_a:", cpu_a.device) -# print("cpu_b:", cpu_b.device) -# print("cpu_c:", cpu_c.device) -# # 查看gpu是否可用 -# print(tf.config.list_physical_devices('GPU')) -# # 指定在gpu上运行 -# with tf.device('/gpu:0'): -# gpu_a = tf.random.normal([10000, 1000]) -# gpu_b = tf.random.normal([1000, 2000]) -# gpu_c = tf.matmul(gpu_a, gpu_b) -# print("gpu_a:", gpu_a.device) -# print("gpu_b:", gpu_b.device) -# print("gpu_c:", gpu_c.device) -# -# import tensorflow as tf -# import timeit -# -# -# def cpu_run(): -# with tf.device('/cpu:0'): -# cpu_a = tf.random.normal([10000, 1000]) -# cpu_b = tf.random.normal([1000, 2000]) -# c = tf.matmul(cpu_a, cpu_b) -# return c -# -# -# def gpu_run(): -# with tf.device('/gpu:0'): -# gpu_a = tf.random.normal([10000, 1000]) -# gpu_b = tf.random.normal([1000, 2000]) -# c = tf.matmul(gpu_a, gpu_b) -# return c -# -# -# cpu_time = timeit.timeit(cpu_run, number=10) -# gpu_time = timeit.timeit(gpu_run, number=10) -# print("cpu:", cpu_time, " gpu:", gpu_time) -# # 检测 tensorflow 能使用的设备情况 -# from tensorflow.python.client import device_lib -# -# # os.environ["CUDA_VISIBLE_DEVICES"] = "1" # 这个可以指定使用哪个设备 -# print(device_lib.list_local_devices()) +# Tensorflow 2.10是最后一个在本地windows上支持GPU的版本。从2.11版本开始，需要在windows WLS2（适用于 Linux 的 Windows 子系统）上安装才能使用GPU。所以要在native-windows上使用GPU，就只能安装2.10.0版本及以下的版本，或者安装老版的tensorflow-gpu. +# 指定在cpu上运行 +with tf.device('/cpu:0'): + cpu_a = tf.random.normal([10000, 1000]) + cpu_b = tf.random.normal([1000, 2000]) + cpu_c = tf.matmul(cpu_a, cpu_b) +print("cpu_a:", cpu_a.device) +print("cpu_b:", cpu_b.device) +print("cpu_c:", cpu_c.device) +# 查看gpu是否可用 +print(tf.config.list_physical_devices('GPU')) +# 指定在gpu上运行 +with tf.device('/gpu:0'): + gpu_a = tf.random.normal([10000, 1000]) + gpu_b = tf.random.normal([1000, 2000]) + gpu_c = tf.matmul(gpu_a, gpu_b) +print("gpu_a:", gpu_a.device) +print("gpu_b:", gpu_b.device) +print("gpu_c:", gpu_c.device) + +import tensorflow as tf +import timeit + + +def cpu_run(): + with tf.device('/cpu:0'): + cpu_a = tf.random.normal([10000, 1000]) + cpu_b = tf.random.normal([1000, 2000]) + c = tf.matmul(cpu_a, cpu_b) + return c + + +def gpu_run(): + with tf.device('/gpu:0'): + gpu_a = tf.random.normal([10000, 1000]) + gpu_b = tf.random.normal([1000, 2000]) + c = tf.matmul(gpu_a, gpu_b) + return c + + +cpu_time = timeit.timeit(cpu_run, number=10) +gpu_time = timeit.timeit(gpu_run, number=10) +print("cpu:", cpu_time, " gpu:", gpu_time) +# 检测 tensorflow 能使用的设备情况 +from tensorflow.python.client import device_lib + +# os.environ["CUDA_VISIBLE_DEVICES"] = "1" # 这个可以指定使用哪个设备 +print(device_lib.list_local_devices()) diff --git a/utils.py b/utils.py index 48a84a1..814fd91 100644 --- a/utils.py +++ b/utils.py @@ -1,3 +1,6 @@ + +#this file give + import wfdb import pywt import seaborn @@ -5,8 +8,15 @@ import numpy as np import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix from sklearn.model_selection import train_test_split -from ECG_read_weiyuqing_without_wfdb import ECGDATAPATH +# from _mian_tensorflow import Project_PATH +Project_PATH = "./Number-Of-CNN-Layers/One/" + +PICTUREPATH= Project_PATH +'picture/' + + +# from ECG_read_weiyuqing_without_wfdb import ECGDATAPATH +MITBITECGDATAPATH='./MIT-BIT/' # wavelet denoise preprocess using mallat algorithm @@ -30,15 +40,15 @@ def denoise(date): # 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'] + ecgClassSet = ['N', 'A', 'V', 'L', 'R'] print('get' + number + ' ECG data') - record = wfdb.rdrecord(ECGDATAPATH + number, channel_names=['MLII']) + record = wfdb.rdrecord(MITBITECGDATAPATH+ 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') + annotation = wfdb.rdann(MITBITECGDATAPATH + number, 'atr') Rlocation = annotation.sample # annotation.sample:get R Rclass = annotation.symbol @@ -58,7 +68,7 @@ def getDataSet(number, X_data, Y_data): i += 1 except ValueError: i += 1 - return + return # RATIO=The ratio of the training set to the test set，usually is 0.3 @@ -79,8 +89,10 @@ def loadData(ratio, random_seed): dataSet = np.array(dataSet).reshape(-1, 300) lableSet = np.array(labelSet).reshape(-1) X_train, X_test, y_train, y_test = train_test_split(dataSet, lableSet, test_size=ratio, random_state=random_seed) + # print(X_train, X_test, y_train, y_test) return X_train, X_test, y_train, y_test + # train_ds = np.hstack((dataSet, labelSet)) # np.random.shuffle(train_ds) # @@ -110,7 +122,7 @@ def plot_heat_map(y_test, y_pred): plt.xlabel('Predicted labels') plt.ylabel('True labels') plt.title('Confusion Matrix') - plt.savefig('confusion_matrix.png') + plt.savefig(PICTUREPATH + 'confusion_matrix.png') plt.show() @@ -122,7 +134,7 @@ def plot_history_tf(history): plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc='upper left') - plt.savefig('accuracy.png') + plt.savefig(PICTUREPATH + 'accuracy.png') plt.show() plt.figure(figsize=(8, 8)) @@ -132,7 +144,7 @@ def plot_history_tf(history): plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc='upper left') - plt.savefig('loss.png') + plt.savefig(PICTUREPATH + 'loss.png') plt.show() @@ -144,7 +156,7 @@ def plot_history_torch(history): plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc='upper left') - plt.savefig('accuracy.png') + plt.savefig(PICTUREPATH + 'accuracy.png') plt.show() plt.figure(figsize=(8, 8)) @@ -154,5 +166,5 @@ def plot_history_torch(history): plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc='upper left') - plt.savefig('loss.png') + plt.savefig(PICTUREPATH +'loss.png') plt.show()

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