2023.3.22.18.50

Number-Of-CNN-Layers/_mian_tensorflow_oneLayerCNN.py

@@ -58,17 +58,17 @@ def CNN_model_level_one():
 
         # 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'),
+        # take four  conv excitation function used RElu
+        tf.keras.layers.Conv1D(filters=1, kernel_size=3, strides=1, padding='same', activation='relu'),
+        # take four  pool,take strids 2.
+        tf.keras.layers.MaxPool1D(pool_size=1, 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'),
+        # add dropout  layer, dropout rate = 0.2
         tf.keras.layers.Dropout(rate=0.2),
         tf.keras.layers.Dense(5, activation='softmax')
     ])

Number-Of-CNN-Layers/_mian_tensorflow_threeLayerCNN.py

@@ -0,0 +1,82 @@
+import datetime
+import os
+
+import numpy as np
+import pywt
+import wfdb
+import tensorflow as tf
+from tensorflow import keras
+from utils_threeLayerCNN 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/Three/"
+#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([
+
+
+        #take test for two CNN layers model
+
+        tf.keras.layers.InputLayer(input_shape=(300, 1)),
+
+        tf.keras.layers.Conv1D(filters=1, kernel_size=3, strides=1, padding='same', activation='relu'),
+        tf.keras.layers.MaxPool1D(pool_size=1, strides=2, padding='same'),
+        tf.keras.layers.Conv1D(filters=1, kernel_size=3, strides=1, padding='same', activation='relu'),
+        tf.keras.layers.MaxPool1D(pool_size=1, strides=2, padding='same'),
+        tf.keras.layers.Conv1D(filters=1, kernel_size=3, strides=1, padding='same', activation='relu'),
+        tf.keras.layers.MaxPool1D(pool_size=1, 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()

Number-Of-CNN-Layers/_mian_tensorflow_twoLayerCNN.py

@@ -25,47 +25,15 @@ 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
+        #take test for two 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=1, kernel_size=3, strides=1, padding='same', activation='relu'),
+        tf.keras.layers.MaxPool1D(pool_size=1, strides=2, padding='same'),
+        tf.keras.layers.Conv1D(filters=1, kernel_size=3, strides=1, padding='same', activation='relu'),
+        tf.keras.layers.MaxPool1D(pool_size=1, strides=2, padding='same'),
 
         tf.keras.layers.Flatten(),
         tf.keras.layers.Dense(128, activation='relu'),

Number-Of-CNN-Layers/utils_threeLayerCNN.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/Two/'
+Project_PATH = '../Number-Of-CNN-Layers/Three/'
 
 PICTUREPATH = Project_PATH + 'picture/'