LSTM_Keras.py

def djmodel(Tx, n_a, n_values):
    
    # Define the input of your model with a shape 
    X = Input(shape=(Tx, n_values))
    
    # Define s0, initial hidden state for the decoder LSTM
    a0 = Input(shape=(n_a,), name='a0')
    c0 = Input(shape=(n_a,), name='c0')
    a = a0
    c = c0
    
    # Step 1: Create empty list to append the outputs while you iterate
    outputs = []
    
    # Step 2: Loop
    for t in range(Tx):
        
        # Step 2.A: select the "t"th time step vector from X. 
        x = Lambda(lambda x: X[:,t,:])(X)
        
        # Step 2.B: Use reshapor to reshape x to be (1, n_values)
        x = reshapor(x)
        
        # Step 2.C: Perform one step of the LSTM_cell
        a, _, c = LSTM_cell(x, initial_state=[a, c])
        
        # Step 2.D: Apply densor to the hidden state output of LSTM_Cell
        out = densor(a)
        
        # Step 2.E: add the output to "outputs"
        outputs.append(out)
        
    # Step 3: Create model instance
    model = Model(inputs=[X, a0, c0], outputs=outputs)
    
    return model