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Models:
Renee Dark
ReneeD/
ECG-Arrhythmia-classes
0
Downloads: 1
Diff of /model.py [000000] .. [9e019c]

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a b/model.py 

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model = Sequential()





  
  

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model.add(Conv2D(64, (3,3),strides = (1,1), input_shape = IMAGE_SIZE + [3],kernel_initializer='glorot_uniform'))





  
  

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model.add(keras.layers.ELU())





  
  

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model.add(BatchNormalization())





  
  

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model.add(Conv2D(64, (3,3),strides = (1,1),kernel_initializer='glorot_uniform'))





  
  

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model.add(keras.layers.ELU())





  
  

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model.add(BatchNormalization())





  
  

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model.add(MaxPool2D(pool_size=(2, 2), strides= (2,2)))





  
  

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model.add(Conv2D(128, (3,3),strides = (1,1),kernel_initializer='glorot_uniform'))





  
  

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model.add(keras.layers.ELU())





  
  

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model.add(BatchNormalization())





  
  

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model.add(Conv2D(128, (3,3),strides = (1,1),kernel_initializer='glorot_uniform'))





  
  

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model.add(keras.layers.ELU())





  
  

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model.add(BatchNormalization())





  
  

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model.add(MaxPool2D(pool_size=(2, 2), strides= (2,2)))





  
  

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model.add(Conv2D(256, (3,3),strides = (1,1),kernel_initializer='glorot_uniform'))





  
  

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model.add(keras.layers.ELU())





  
  

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model.add(BatchNormalization())





  
  

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model.add(Conv2D(256, (3,3),strides = (1,1),kernel_initializer='glorot_uniform'))





  
  

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model.add(keras.layers.ELU())





  
  

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model.add(BatchNormalization())





  
  

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model.add(MaxPool2D(pool_size=(2, 2), strides= (2,2)))





  
  

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model.add(Flatten())





  
  

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model.add(Dense(2048))





  
  

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model.add(keras.layers.ELU())





  
  

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model.add(BatchNormalization())





  
  

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model.add(Dropout(0.5))





  
  

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model.add(Dense(7, activation='softmax'))





  
  

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model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])