CTC.py

y_pred = TimeDistributed(Dense(output_dim, activation = 'softmax'))(X) 

# ctc 
y_true = Input(name='the_labels', shape=[None,], dtype='int32') 
input_length = Input(name='input_length', shape=[1], dtype='int32') 
label_length = Input(name='label_length', shape=[1], dtype='int32') 

# Keras doesn't currently support loss funcs with extra parameters 
# so CTC loss is implemented in a lambda layer 
loss_out = Lambda(ctc_lambda_func, output_shape=(1,), 
                  name='ctc')([y_pred, 
                               y_true, 
                               input_length, 
                               label_length]) 
test_model = Model(inputs = X_input, outputs = y_pred) 
model = Model(inputs = [X_input, 
                        y_true, 
                        input_length, 
                        label_length], 
              outputs = loss_out)