Why for loop adding only one Conv1D layer in ds2_gru_model

[menon92]

Hello @robmsmt,

I'm working with your repo. In your model.py file bellow code should three layer but this add just only one Conv1D Layer

conv = ZeroPadding1D(padding=(0, 2048))(x)
for l in range(conv_layers):
  x = Conv1D(filters=fc_size, name='conv_{}'.format(l+1), kernel_size=11, padding='valid', activation='relu', strides=2)(conv)

This is the model summary I get,

Model: "model_1"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
the_input (InputLayer)          (None, None, 161)    0                                            
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, None, 161)    644         the_input[0][0]                  
__________________________________________________________________________________________________
zero_padding1d_1 (ZeroPadding1D (None, None, 161)    0           batch_normalization_1[0][0]      
__________________________________________________________________________________________________
conv_3 (Conv1D)                 (None, None, 512)    907264      zero_padding1d_1[0][0]           
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, None, 512)    2048        conv_3[0][0]                     
__________________________________________________________________________________________________
bidirectional_1 (Bidirectional) (None, None, 1024)   9443328     batch_normalization_2[0][0]      
__________________________________________________________________________________________________
bidirectional_2 (Bidirectional) (None, None, 1024)   12589056    bidirectional_1[0][0]            
__________________________________________________________________________________________________
bidirectional_3 (Bidirectional) (None, None, 1024)   12589056    bidirectional_2[0][0]            
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, None, 1024)   4096        bidirectional_3[0][0]            
__________________________________________________________________________________________________
time_distributed_1 (TimeDistrib (None, None, 512)    524800      batch_normalization_3[0][0]      
__________________________________________________________________________________________________
time_distributed_2 (TimeDistrib (None, None, 1102)   565326      time_distributed_1[0][0]         
__________________________________________________________________________________________________
the_labels (InputLayer)         (None, None)         0                                            
__________________________________________________________________________________________________
input_length (InputLayer)       (None, 1)            0                                            
__________________________________________________________________________________________________
label_length (InputLayer)       (None, 1)            0                                            
__________________________________________________________________________________________________
ctc (Lambda)                    (None, 1)            0           time_distributed_2[0][0]         
                                                                 the_labels[0][0]                 
                                                                 input_length[0][0]               
                                                                 label_length[0][0]               
==================================================================================================
Total params: 36,625,618
Trainable params: 36,622,224
Non-trainable params: 3,394
__________________________________________________________________________________________________

What could be the possible reason.
Thanks in advance