mirror of
https://github.com/This-weiyuqing/ECG_by_Diffenert_CNN_Test.git
synced 2026-10-17 19:05:30 +08:00
114 lines
5.2 KiB
Python
114 lines
5.2 KiB
Python
import datetime
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import os
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import numpy as np
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import pywt
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import wfdb
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import tensorflow as tf
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from tensorflow import keras
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from utils import loadData, plot_history_tf, plot_heat_map
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#from ECG_read_weiyuqing_without_wfdb import ECGDATAPATH
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#PATH of this test
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Project_PATH = "./Number-Of-CNN-Layers/Two/"
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#PICTUREPATH= Project_PATH + "picture/"
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log_dir = Project_PATH + "logs/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
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model_Path_one = Project_PATH + "model/ecg_model_one_layer.h5"
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#model_Path_one = Project_PATH + "ecg_model_one_layer.h5"
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RATIO = 0.3
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RANDOM_SEED = 42
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BATCH_SIZE = 128
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NUM_EPOCHS = 30
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def CNN_model_level_one():
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leavlOneModel = tf.keras.models.Sequential([
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# #four CNN layers test
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# tf.keras.layers.InputLayer(input_shape=(300,)),
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# # reshape the tensor with shape (batch_size, 300) to (batch_size, 300, 1)
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# tf.keras.layers.Reshape(target_shape=(300, 1)),
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# # the first convolution layer, 4 21x1 convolution kernels, output shape (batch_size, 300, 4)
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# tf.keras.layers.Conv1D(filters=4, kernel_size=21, strides=1, padding='SAME', activation='relu'),
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# # the first pooling layer, max pooling, pooling size=3 , stride=2, output shape (batch_size, 150, 4)
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# tf.keras.layers.MaxPool1D(pool_size=3, strides=2, padding='SAME'),
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# # the second convolution layer, 16 23x1 convolution kernels, output shape (batch_size, 150, 16)
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# tf.keras.layers.Conv1D(filters=16, kernel_size=23, strides=1, padding='SAME', activation='relu'),
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# # the second pooling layer, max pooling, pooling size=3, stride=2, output shape (batch_size, 75, 16)
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# tf.keras.layers.MaxPool1D(pool_size=3, strides=2, padding='SAME'),
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# # the third convolution layer, 32 25x1 convolution kernels, output shape (batch_size, 75, 32)
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# tf.keras.layers.Conv1D(filters=32, kernel_size=25, strides=1, padding='SAME', activation='relu'),
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# # the third pooling layer, average pooling, pooling size=3, stride=2, output shape (batch_size, 38, 32)
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# tf.keras.layers.AvgPool1D(pool_size=3, strides=2, padding='SAME'),
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# # the fourth convolution layer, 64 27x1 convolution kernels, output shape (batch_size, 38, 64)
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# tf.keras.layers.Conv1D(filters=64, kernel_size=27, strides=1, padding='SAME', activation='relu'),
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# # flatten layer, for the next fully connected layer, output shape (batch_size, 38*64)
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# tf.keras.layers.Flatten(),
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# # fully connected layer, 128 nodes, output shape (batch_size, 128)
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# tf.keras.layers.Dense(128, activation='relu'),
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# # Dropout layer, dropout rate = 0.2
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# tf.keras.layers.Dropout(rate=0.2),
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# # fully connected layer, 5 nodes (number of classes), output shape (batch_size, 5)
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# tf.keras.layers.Dense(5, activation='softmax')
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#take test for one CNN layers model
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tf.keras.layers.InputLayer(input_shape=(300, 1)),
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# ECG data cant add 0 in edge, will broken data ,take false answer. So we need padding same data in edge.
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# take four 21*1 conv excitation function used RElu
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tf.keras.layers.Conv1D(filters=4, kernel_size=21, strides=1, padding='same', activation='relu'),
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# take four 3*1 pool,take strids 2.
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tf.keras.layers.MaxPool1D(pool_size=3, strides=2, padding='same'),
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tf.keras.layers.Conv1D(filters=4, kernel_size=21, strides=1, padding='same', activation='relu'),
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tf.keras.layers.MaxPool1D(pool_size=3, strides=2, padding='same'),
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# tf.keras.layers.Conv1D(filters=4, kernel_size=21, strides=1, padding='same', activation='relu'),
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# tf.keras.layers.MaxPool1D(pool_size=3, strides=2, padding='same'),
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tf.keras.layers.Flatten(),
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tf.keras.layers.Dense(128, activation='relu'),
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tf.keras.layers.Dropout(rate=0.2),
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tf.keras.layers.Dense(5, activation='softmax')
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])
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return leavlOneModel
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def main():
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# get traxin test
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X_train, X_test, y_train, y_test = loadData(RATIO, RANDOM_SEED)
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# get model or create model
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if os.path.exists(model_Path_one):
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print(' get model in h5 file')
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model = tf.keras.models.load_model(filepath=model_Path_one)
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else:
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# create new model(if model unexists)
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model = CNN_model_level_one()
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model.compile(optimizer='adam',
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loss='sparse_categorical_crossentropy',
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metrics=['accuracy'])
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model.summary()
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# TB make
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tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir,
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histogram_freq=1,
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)
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# Training and validation
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history = model.fit(X_train, y_train, epochs=NUM_EPOCHS,
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batch_size=BATCH_SIZE,
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validation_data=(X_test, y_test),
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callbacks=[tensorboard_callback])
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# validation_split=RATIO,
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model.save(filepath=model_Path_one)
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plot_history_tf(history)
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y_pred = np.argmax(model.predict(X_test), axis=-1)
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plot_heat_map(y_test, y_pred)
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if __name__ == '__main__':
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os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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main()
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