take CNN test by keras or nokeras use withtf、pytorch

_mian_pytorch.py

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+import numpy as np
+import pywt
+import wfdb
+import torch
+
+from ECG_read_weiyuqing_without_wfdb import PATH
+
+
+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(PATH + number, channel_names=['MLII'])
+    data = record.p_signal.flatten()
+    # use def denoise
+    rdata = denoise(date=data)
+
+    # get type of ECG data
+    annotation = wfdb.rdann(PATH + 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(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = torch.nn.Conv1d(in_channels=1,out_channels=1,)