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I have a model build which is shown below:

def og_build_model_5layer(n_rows,n_cols):
  input = Input(shape=(n_cols,n_rows),NAME='INP')
  print('model_input shape:' , input.shape)

  c1 = Conv1D(50, 3,name = 'conv_1',padding='same',kernel_initializer="glorot_uniform")(INP)
  b1 = BatchNormalization(name = 'BN_1')(c1)       
  a1 = Activation('relu')(b1)

  c2 = Conv1D(50,3,name = 'conv_2',padding='same',kernel_initializer="glorot_uniform")(a1)
  b2 = BatchNormalization(name = 'BN_2')(c2)
  a2 = Activation('relu')(b2)

  c3 = Conv1D(50, 3,name = 'conv_3',padding='same',kernel_initializer="glorot_uniform")(a2)
  b3 = BatchNormalization(name = 'BN_3')(c3)
  a3 = Activation('relu')(b3)

  c4 = Conv1D(50, 3,name = 'conv_4',padding='same',kernel_initializer="glorot_uniform")(a3)
  b4 = BatchNormalization(name = 'BN_4')(c4)
  a4 = Activation('relu')(b4)

  c5 = Conv1D(50, 3,name = 'conv_5',padding='same',kernel_initializer="glorot_uniform")(a4)
  b5 = BatchNormalization(name = 'BN_5')(c5)
  a5 = Activation('relu')(b5)
  ######## ADD one LSTM layer HERE ##################
  
  fl = Flatten(name='fl')(LSTM_OUTPUT)
  den = Dense(30,name='dense_1')(fl)
  drp = Dropout(0.5)(den)
  output = Dense(1, activation='sigmoid')(drp)    
  opt = Adam(learning_rate=1e-4)
  model = Model(inputs=INP, outputs=output, name='model')
  extractor = Model(inputs=ecg_input,outputs = model.get_layer('fl').output)
  model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])
  print(model.summary)
  return model,extractor

Here I have 5 Conv1D layers (each accepting one image) and I want to add one LSTM layer that would take a sequence of 200 images together, and I want to train this CNN+LSTM model end to end. I am confused about how I will add the LSTM layer as that needs a sequence (of 200 processed inputs) where as the previous 5 layers will accept one input at a time. Any help here is appreciated. I know the concept of timedistributed conv1D however I do not want to use it. can this end-to-end training be done ?

Kathan Vyas
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  • So, first, take 200 images as a sequence and give them to `LSTM` and then send the output of this `LSTM` layer as an input to `CONV1D` Layers. The other way around is to take the batch of 200 images at once and then pass it through the `CONV1D` Layer and then the output of `a5` will become the input of the `LSTM` Layer. – Mohammad Ahmed Jan 23 '23 at 10:20

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