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b/Number-Of-Pool/Two/picture/loss.png index 7ad35f0..450c908 100644 Binary files a/Number-Of-Pool/Two/picture/loss.png and b/Number-Of-Pool/Two/picture/loss.png differ diff --git a/Number-Of-Pool/_mian_tensorflow_Four_PoolCNN.py b/Number-Of-Pool/_mian_tensorflow_Four_PoolCNN.py index 829385a..fccbad5 100644 --- a/Number-Of-Pool/_mian_tensorflow_Four_PoolCNN.py +++ b/Number-Of-Pool/_mian_tensorflow_Four_PoolCNN.py @@ -6,7 +6,7 @@ import pywt import wfdb import tensorflow as tf from tensorflow import keras -from utils_two_of_PoolCNN import loadData, plot_history_tf, plot_heat_map +from utils_four_of_PoolCNN import loadData, plot_history_tf, plot_heat_map #from ECG_read_weiyuqing_without_wfdb import ECGDATAPATH #PATH of this test diff --git a/Number-Of-Pool/_mian_tensorflow_One_PoolCNN.py b/Number-Of-Pool/_mian_tensorflow_One_PoolCNN.py index ea27d02..5617ad6 100644 --- a/Number-Of-Pool/_mian_tensorflow_One_PoolCNN.py +++ b/Number-Of-Pool/_mian_tensorflow_One_PoolCNN.py @@ -6,7 +6,7 @@ import pywt import wfdb import tensorflow as tf from tensorflow import keras -from utils_two_of_PoolCNN import loadData, plot_history_tf, plot_heat_map +from utils_one_of_PoolCNN import loadData, plot_history_tf, plot_heat_map #from ECG_read_weiyuqing_without_wfdb import ECGDATAPATH #PATH of this test diff --git a/Number-Of-Pool/_mian_tensorflow_Three_PoolCNN.py b/Number-Of-Pool/_mian_tensorflow_Three_PoolCNN.py index 9674fee..acf652c 100644 --- a/Number-Of-Pool/_mian_tensorflow_Three_PoolCNN.py +++ b/Number-Of-Pool/_mian_tensorflow_Three_PoolCNN.py @@ -6,7 +6,7 @@ import pywt import wfdb import tensorflow as tf from tensorflow import keras -from utils_two_of_PoolCNN import loadData, plot_history_tf, plot_heat_map +from utils_three_of_PoolCNN import loadData, plot_history_tf, plot_heat_map #from ECG_read_weiyuqing_without_wfdb import ECGDATAPATH #PATH of this test diff --git a/Number_Of_Filters/SeventyOne/logs/20230327-204628/train/events.out.tfevents.1679921194.WEIYUQING.8076.0.v2 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b/Number_Of_Filters/TwentySeven/picture/loss.png index a968019..b5746b5 100644 Binary files a/Number_Of_Filters/TwentySeven/picture/loss.png and b/Number_Of_Filters/TwentySeven/picture/loss.png differ diff --git a/Number_Of_Filters/_mian_tensorflow_SeventyOne_Filters.py b/Number_Of_Filters/_mian_tensorflow_SeventyOne_Filters.py index 6129310..0ce5815 100644 --- a/Number_Of_Filters/_mian_tensorflow_SeventyOne_Filters.py +++ b/Number_Of_Filters/_mian_tensorflow_SeventyOne_Filters.py @@ -31,11 +31,11 @@ def CNN_model_level_one(): tf.keras.layers.InputLayer(input_shape=(300,)), tf.keras.layers.Reshape(target_shape=(300, 1)), - tf.keras.layers.Conv1D(filters=3, kernel_size=3, strides=2, padding='same', activation='relu'), + tf.keras.layers.Conv1D(filters=71, kernel_size=3, strides=2, padding='same', activation='relu'), tf.keras.layers.MaxPool1D(pool_size=1, strides=2, padding='same'), - tf.keras.layers.Conv1D(filters=3, kernel_size=3, strides=2, padding='same', activation='relu'), + tf.keras.layers.Conv1D(filters=71, kernel_size=3, strides=2, padding='same', activation='relu'), tf.keras.layers.MaxPool1D(pool_size=1, strides=2, padding='same'), - tf.keras.layers.Conv1D(filters=3, kernel_size=3, strides=2, padding='same', activation='relu'), + tf.keras.layers.Conv1D(filters=71, kernel_size=3, strides=2, padding='same', activation='relu'), tf.keras.layers.MaxPool1D(pool_size=1, strides=2, padding='same'), tf.keras.layers.Flatten(), diff --git a/The_last_model/utils_CNN_of_ECG.py b/The_last_model/utils_CNN_of_ECG.py index c946e22..964024b 100644 --- a/The_last_model/utils_CNN_of_ECG.py +++ b/The_last_model/utils_CNN_of_ECG.py @@ -33,7 +33,6 @@ def denoise(date): 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 diff --git a/some-test/activation.py b/some-test/activation.py new file mode 100644 index 0000000..9d8b396 --- /dev/null +++ b/some-test/activation.py @@ -0,0 +1,44 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Mar 11 20:41:57 2018 + +@author: brucelau +""" + +import matplotlib.pyplot as plt +import numpy as np + +x = np.linspace(-10,10) +y_sigmoid = 1/(1+np.exp(-x)) +y_tanh = (np.exp(x)-np.exp(-x))/(np.exp(x)+np.exp(-x)) + +fig = plt.figure() +# plot sigmoid +ax = fig.add_subplot(221) +ax.plot(x,y_sigmoid) +ax.grid() +ax.set_title('(a) Sigmoid') + +# plot tanh +ax = fig.add_subplot(222) +ax.plot(x,y_tanh) +ax.grid() +ax.set_title('(b) Tanh') + +# plot relu +ax = fig.add_subplot(223) +y_relu = np.array([0*item if item<0 else item for item in x ]) +ax.plot(x,y_relu) +ax.grid() +ax.set_title('(c) ReLu') + +#plot leaky relu +ax = fig.add_subplot(224) +y_relu = np.array([0.2*item if item<0 else item for item in x ]) +ax.plot(x,y_relu) +ax.grid() +ax.set_title('(d) Leaky ReLu') + +plt.tight_layout() +plt.show() + diff --git a/some-test/db5.py b/some-test/db5.py new file mode 100644 index 0000000..00b6342 --- /dev/null +++ b/some-test/db5.py @@ -0,0 +1,29 @@ +import pywt +import matplotlib.pyplot as plt + +# 生成DB5小波 +wavelet = pywt.Wavelet('db5') + +# 获取尺度函数和小波函数 +phi, psi, x = wavelet.wavefun(level=5) + +# 绘制尺度函数和小波函数 +plt.figure() +plt.subplot(2, 1, 1) +plt.plot(x, phi) +plt.title('DB5 Scaling Function (Phi)') +plt.subplot(2, 1, 2) +plt.plot(x, psi) +plt.title('DB5 Wavelet Function (Psi)') +plt.tight_layout() +plt.show() + +h0=wavelet.dec_lo +h1=wavelet.dec_hi +g0=wavelet.rec_lo +g1=wavelet.rec_hi + +print('h0:', h0) +print('h1:', h1) +print('g0:', g0) +print('g1:', g1) \ No newline at end of file diff --git a/some-test/keras_use.py b/some-test/keras_use.py new file mode 100644 index 0000000..12ea85d --- /dev/null +++ b/some-test/keras_use.py @@ -0,0 +1,36 @@ +import numpy as np +import keras +from keras.models import Sequential +from keras.layers import Dense, Dropout, Flatten +from keras.layers import Conv2D, MaxPooling2D +from keras.optimizers import SGD + +# 生成虚拟数据 +x_train = np.random.random((100, 100, 100, 3)) +y_train = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10) +x_test = np.random.random((20, 100, 100, 3)) +y_test = keras.utils.to_categorical(np.random.randint(10, size=(20, 1)), num_classes=10) + +model = Sequential() +# 输入: 3 通道 100x100 像素图像 -> (100, 100, 3) 张量。 +# 使用 32 个大小为 3x3 的卷积滤波器。 +model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3))) +model.add(Conv2D(32, (3, 3), activation='relu')) +model.add(MaxPooling2D(pool_size=(2, 2))) +model.add(Dropout(0.25)) + +model.add(Conv2D(64, (3, 3), activation='relu')) +model.add(Conv2D(64, (3, 3), activation='relu')) +model.add(MaxPooling2D(pool_size=(2, 2))) +model.add(Dropout(0.25)) + +model.add(Flatten()) +model.add(Dense(256, activation='relu')) +model.add(Dropout(0.5)) +model.add(Dense(10, activation='softmax')) + +sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) +model.compile(loss='categorical_crossentropy', optimizer=sgd) + +model.fit(x_train, y_train, batch_size=32, epochs=10) +score = model.evaluate(x_test, y_test, batch_size=32) \ No newline at end of file diff --git a/some-test/softmax.py b/some-test/softmax.py new file mode 100644 index 0000000..98b82be --- /dev/null +++ b/some-test/softmax.py @@ -0,0 +1,14 @@ +import numpy as np +import matplotlib.pyplot as plt + +def softmax(x): + return np.exp(x) / np.sum(np.exp(x)) + +x = np.arange(-10, 10, 0.1) +y = softmax(x) + +plt.plot(x, y) +plt.title('Softmax Function') +plt.xlabel('Input') +plt.ylabel('Output') +plt.show()