decoder.py 文件源码

def getTrainedRNN():
    ''' Read from file and set the params (To Do: Refactor 
        so as to do this only once) '''
    input_size = 39
    hidden_size = 50
    num_output_classes = 29
    learning_rate = 0.001
    output_size = num_output_classes+1
    batch_size = None
    input_seq_length = None
    gradient_clipping = 5

    l_in = InputLayer(shape=(batch_size, input_seq_length, input_size))
    n_batch, n_time_steps, n_features = l_in.input_var.shape #Unnecessary in this version. Just collecting the info so that we can reshape the output back to the original shape
    # h_1 = DenseLayer(l_in, num_units=hidden_size, nonlinearity=clipped_relu)
    l_rec_forward = RecurrentLayer(l_in, num_units=hidden_size, grad_clipping=gradient_clipping, nonlinearity=clipped_relu)
    l_rec_backward = RecurrentLayer(l_in, num_units=hidden_size, grad_clipping=gradient_clipping, nonlinearity=clipped_relu, backwards=True)
    l_rec_accumulation = ElemwiseSumLayer([l_rec_forward,l_rec_backward])
    l_rec_reshaped = ReshapeLayer(l_rec_accumulation, (-1,hidden_size))
    l_h2 = DenseLayer(l_rec_reshaped, num_units=hidden_size, nonlinearity=clipped_relu)
    l_out = DenseLayer(l_h2, num_units=output_size, nonlinearity=lasagne.nonlinearities.linear)
    l_out_reshaped = ReshapeLayer(l_out, (n_batch, n_time_steps, output_size))#Reshaping back
    l_out_softmax = NonlinearityLayer(l_out, nonlinearity=lasagne.nonlinearities.softmax)
    l_out_softmax_reshaped = ReshapeLayer(l_out_softmax, (n_batch, n_time_steps, output_size))


    with np.load('CTC_model.npz') as f:
        param_values = [f['arr_%d' % i] for i in range(len(f.files))]
    lasagne.layers.set_all_param_values(l_out_softmax_reshaped, param_values, trainable = True)
    output = lasagne.layers.get_output( l_out_softmax_reshaped )
    return l_in, output