Keras LSTM from for loop, using functional API with custom number of layers

Question

I am trying to build a network through the keras functional API feeding two lists containing the number of units of the LSTM layers and of the FC (Dense) layers. I want to analyse 20 consecutive segments (batches) which contain fs time steps each and 2 values (2 features per time step). This is my code:

Rec = [4,4,4]  
FC = [8,4,2,1]    
def keras_LSTM(Rec,FC,fs, n_witness, lr=0.04, optimizer='Adam'):
    model_LSTM = Input(batch_shape=(20,fs,n_witness))
    return_state_bool=True
    for i in range(shape(Rec)[0]):
        nRec = Rec[i]
        if i == shape(Rec)[0]-1:
            return_state_bool=False
        model_LSTM = LSTM(nRec, return_sequences=True,return_state=return_state_bool,
                     stateful=True, input_shape=(None,n_witness),            
                     name='LSTM'+str(i))(model_LSTM)
    for j in range(shape(FC)[0]):
        nFC = FC[j]
        model_LSTM = Dense(nFC)(model_LSTM)
        model_LSTM = LeakyReLU(alpha=0.01)(model_LSTM)
    nFC_final = 1
    model_LSTM = Dense(nFC_final)(model_LSTM)
    predictions = LeakyReLU(alpha=0.01)(model_LSTM)

    full_model_LSTM = Model(inputs=model_LSTM, outputs=predictions)
    model_LSTM.compile(optimizer=keras.optimizers.Adam(lr=lr, beta_1=0.9, beta_2=0.999,
                    epsilon=1e-8, decay=0.066667, amsgrad=False), loss='mean_squared_error')
    return full_model_LSTM

model_new = keras_LSTM(Rec, FC, fs=fs, n_witness=n_wit)
model_new.summary()

When compiling I get the following error:

ValueError: Graph disconnected: cannot obtain value for tensor Tensor("input_1:0", shape=(20, 2048, 2), dtype=float32) at layer "input_1". The following previous layers were accessed without issue: []

Which I actually don't quite understand, but suspect it may have something to do with inputs?

GWSurfer GWSurfer · Accepted Answer · 2019-01-17T18:18:06

I solved the issue by modifying line 4 of the code as in the following:

x = model_LSTM = Input(batch_shape=(20,fs,n_witness))

along with line 21, as in the following:

full_model_LSTM = Model(inputs=x, outputs=predictions)

Keras LSTM from for loop, using functional API with custom number of layers

1 Answers