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diff --git a/mit-bih-arrhythmia-database-1.0.0/x_mitdb/x_233.atr b/MIT-BIT/x_mitdb/x_233.atr
similarity index 100%
rename from mit-bih-arrhythmia-database-1.0.0/x_mitdb/x_233.atr
rename to MIT-BIT/x_mitdb/x_233.atr
diff --git a/mit-bih-arrhythmia-database-1.0.0/x_mitdb/x_233.dat b/MIT-BIT/x_mitdb/x_233.dat
similarity index 100%
rename from mit-bih-arrhythmia-database-1.0.0/x_mitdb/x_233.dat
rename to MIT-BIT/x_mitdb/x_233.dat
diff --git a/mit-bih-arrhythmia-database-1.0.0/x_mitdb/x_233.hea b/MIT-BIT/x_mitdb/x_233.hea
similarity index 100%
rename from mit-bih-arrhythmia-database-1.0.0/x_mitdb/x_233.hea
rename to MIT-BIT/x_mitdb/x_233.hea
diff --git a/mit-bih-arrhythmia-database-1.0.0/x_mitdb/x_234.atr b/MIT-BIT/x_mitdb/x_234.atr
similarity index 100%
rename from mit-bih-arrhythmia-database-1.0.0/x_mitdb/x_234.atr
rename to MIT-BIT/x_mitdb/x_234.atr
diff --git a/mit-bih-arrhythmia-database-1.0.0/x_mitdb/x_234.dat b/MIT-BIT/x_mitdb/x_234.dat
similarity index 100%
rename from mit-bih-arrhythmia-database-1.0.0/x_mitdb/x_234.dat
rename to MIT-BIT/x_mitdb/x_234.dat
diff --git a/mit-bih-arrhythmia-database-1.0.0/x_mitdb/x_234.hea b/MIT-BIT/x_mitdb/x_234.hea
similarity index 100%
rename from mit-bih-arrhythmia-database-1.0.0/x_mitdb/x_234.hea
rename to MIT-BIT/x_mitdb/x_234.hea
diff --git a/logs/20230316-201043/train/events.out.tfevents.1678968646.WEIYUQING.70736.0.v2 b/Number-Of-CNN-Layers/One/logs/20230316-201043/train/events.out.tfevents.1678968646.WEIYUQING.70736.0.v2
similarity index 100%
rename from logs/20230316-201043/train/events.out.tfevents.1678968646.WEIYUQING.70736.0.v2
rename to Number-Of-CNN-Layers/One/logs/20230316-201043/train/events.out.tfevents.1678968646.WEIYUQING.70736.0.v2
diff --git a/logs/20230316-201316/train/events.out.tfevents.1678968799.WEIYUQING.69432.0.v2 b/Number-Of-CNN-Layers/One/logs/20230316-201316/train/events.out.tfevents.1678968799.WEIYUQING.69432.0.v2
similarity index 100%
rename from logs/20230316-201316/train/events.out.tfevents.1678968799.WEIYUQING.69432.0.v2
rename to Number-Of-CNN-Layers/One/logs/20230316-201316/train/events.out.tfevents.1678968799.WEIYUQING.69432.0.v2
diff --git a/logs/20230316-201450/train/events.out.tfevents.1678968893.WEIYUQING.65212.0.v2 b/Number-Of-CNN-Layers/One/logs/20230316-201450/train/events.out.tfevents.1678968893.WEIYUQING.65212.0.v2
similarity index 100%
rename from logs/20230316-201450/train/events.out.tfevents.1678968893.WEIYUQING.65212.0.v2
rename to Number-Of-CNN-Layers/One/logs/20230316-201450/train/events.out.tfevents.1678968893.WEIYUQING.65212.0.v2
diff --git a/logs/20230316-203620/train/events.out.tfevents.1678970182.WEIYUQING.35808.0.v2 b/Number-Of-CNN-Layers/One/logs/20230316-203620/train/events.out.tfevents.1678970182.WEIYUQING.35808.0.v2
similarity index 100%
rename from logs/20230316-203620/train/events.out.tfevents.1678970182.WEIYUQING.35808.0.v2
rename to Number-Of-CNN-Layers/One/logs/20230316-203620/train/events.out.tfevents.1678970182.WEIYUQING.35808.0.v2
diff --git a/logs/20230316-203806/train/events.out.tfevents.1678970289.WEIYUQING.72584.0.v2 b/Number-Of-CNN-Layers/One/logs/20230316-203806/train/events.out.tfevents.1678970289.WEIYUQING.72584.0.v2
similarity index 100%
rename from logs/20230316-203806/train/events.out.tfevents.1678970289.WEIYUQING.72584.0.v2
rename to Number-Of-CNN-Layers/One/logs/20230316-203806/train/events.out.tfevents.1678970289.WEIYUQING.72584.0.v2
diff --git a/logs/20230316-203916/train/events.out.tfevents.1678970359.WEIYUQING.71944.0.v2 b/Number-Of-CNN-Layers/One/logs/20230316-203916/train/events.out.tfevents.1678970359.WEIYUQING.71944.0.v2
similarity index 100%
rename from logs/20230316-203916/train/events.out.tfevents.1678970359.WEIYUQING.71944.0.v2
rename to Number-Of-CNN-Layers/One/logs/20230316-203916/train/events.out.tfevents.1678970359.WEIYUQING.71944.0.v2
diff --git a/logs/20230316-203916/validation/events.out.tfevents.1678970360.WEIYUQING.71944.1.v2 b/Number-Of-CNN-Layers/One/logs/20230316-203916/validation/events.out.tfevents.1678970360.WEIYUQING.71944.1.v2
similarity index 100%
rename from logs/20230316-203916/validation/events.out.tfevents.1678970360.WEIYUQING.71944.1.v2
rename to Number-Of-CNN-Layers/One/logs/20230316-203916/validation/events.out.tfevents.1678970360.WEIYUQING.71944.1.v2
diff --git a/logs/20230316-204324/train/events.out.tfevents.1678970606.WEIYUQING.72540.0.v2 b/Number-Of-CNN-Layers/One/logs/20230316-204324/train/events.out.tfevents.1678970606.WEIYUQING.72540.0.v2
similarity index 100%
rename from logs/20230316-204324/train/events.out.tfevents.1678970606.WEIYUQING.72540.0.v2
rename to Number-Of-CNN-Layers/One/logs/20230316-204324/train/events.out.tfevents.1678970606.WEIYUQING.72540.0.v2
diff --git a/logs/20230316-204324/validation/events.out.tfevents.1678970607.WEIYUQING.72540.1.v2 b/Number-Of-CNN-Layers/One/logs/20230316-204324/validation/events.out.tfevents.1678970607.WEIYUQING.72540.1.v2
similarity index 100%
rename from logs/20230316-204324/validation/events.out.tfevents.1678970607.WEIYUQING.72540.1.v2
rename to Number-Of-CNN-Layers/One/logs/20230316-204324/validation/events.out.tfevents.1678970607.WEIYUQING.72540.1.v2
diff --git a/logs/20230316-204654/train/events.out.tfevents.1678970817.WEIYUQING.59208.0.v2 b/Number-Of-CNN-Layers/One/logs/20230316-204654/train/events.out.tfevents.1678970817.WEIYUQING.59208.0.v2
similarity index 100%
rename from logs/20230316-204654/train/events.out.tfevents.1678970817.WEIYUQING.59208.0.v2
rename to Number-Of-CNN-Layers/One/logs/20230316-204654/train/events.out.tfevents.1678970817.WEIYUQING.59208.0.v2
diff --git a/logs/20230316-204654/validation/events.out.tfevents.1678970818.WEIYUQING.59208.1.v2 b/Number-Of-CNN-Layers/One/logs/20230316-204654/validation/events.out.tfevents.1678970818.WEIYUQING.59208.1.v2
similarity index 100%
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diff --git a/logs/20230316-204755/train/events.out.tfevents.1678970878.WEIYUQING.70276.0.v2 b/Number-Of-CNN-Layers/One/logs/20230316-204755/train/events.out.tfevents.1678970878.WEIYUQING.70276.0.v2
similarity index 100%
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diff --git a/logs/20230316-204755/validation/events.out.tfevents.1678970879.WEIYUQING.70276.1.v2 b/Number-Of-CNN-Layers/One/logs/20230316-204755/validation/events.out.tfevents.1678970879.WEIYUQING.70276.1.v2
similarity index 100%
rename from logs/20230316-204755/validation/events.out.tfevents.1678970879.WEIYUQING.70276.1.v2
rename to Number-Of-CNN-Layers/One/logs/20230316-204755/validation/events.out.tfevents.1678970879.WEIYUQING.70276.1.v2
diff --git a/logs/20230316-205441/train/events.out.tfevents.1678971284.WEIYUQING.74296.0.v2 b/Number-Of-CNN-Layers/One/logs/20230316-205441/train/events.out.tfevents.1678971284.WEIYUQING.74296.0.v2
similarity index 100%
rename from logs/20230316-205441/train/events.out.tfevents.1678971284.WEIYUQING.74296.0.v2
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diff --git a/logs/20230316-205441/validation/events.out.tfevents.1678971285.WEIYUQING.74296.1.v2 b/Number-Of-CNN-Layers/One/logs/20230316-205441/validation/events.out.tfevents.1678971285.WEIYUQING.74296.1.v2
similarity index 100%
rename from logs/20230316-205441/validation/events.out.tfevents.1678971285.WEIYUQING.74296.1.v2
rename to Number-Of-CNN-Layers/One/logs/20230316-205441/validation/events.out.tfevents.1678971285.WEIYUQING.74296.1.v2
diff --git a/logs/20230317-131055/train/events.out.tfevents.1679029869.WEIYUQING.58064.0.v2 b/Number-Of-CNN-Layers/One/logs/20230317-131055/train/events.out.tfevents.1679029869.WEIYUQING.58064.0.v2
similarity index 100%
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diff --git a/_mian_tensorflow.py b/_mian_tensorflow.py
deleted file mode 100644
index 2609590..0000000
--- a/_mian_tensorflow.py
+++ /dev/null
@@ -1,105 +0,0 @@
-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
-
-Project_PATH = './'
-log_dir = Project_PATH + "logs/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
-model_Path_one = Project_PATH + "Number-Of-CNN-Layers/" + "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([
-        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')
-        # 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__':
-    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()