保存和恢复Keras BLSTM CTC模型 [英] Saving and restoring Keras BLSTM CTC model
问题描述
我一直在研究语音情感识别深度神经网络.我使用了具有CTC损失的keras双向LSTM.我训练了模型并保存了
I have been working on speech emotion recognition deep neural network. I have used keras Bidirectional LSTM with CTC loss. i trained the model and saved it
model_json = model.to_json()
with open("ctc_model.json", "w") as json_file:
json_file.write(model_json)
model.save_weights("ctc_weights.h5")
model_json = model.to_json()
with open("ctc_model.json", "w") as json_file:
json_file.write(model_json)
model.save_weights("ctc_weights.h5")
问题是我无法使用该模型对看不见的数据进行测试，因为该模型接受4个参数作为输入并计算ctc损失.所以我该如何保存模型而只需要一个输入.而不是标签和长度.基本上，我如何将模型另存为此函数test_func = K.function([net_input], [output])
The problem is i can not use this model to test on on unseen data because the model accepts 4 argument as input and calculates the ctc loss..just build the model and train. so how can i save a model in such away that in require only one input. not the labels, and length. Basically how can i save a model as this function test_func = K.function([net_input], [output])
def ctc_lambda_func(args):
y_pred, labels, input_length, label_length = args
shift = 2
y_pred = y_pred[:, shift:, :]
input_length -= shift
return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
def build_model(nb_feat, nb_class, optimizer='Adadelta'):
net_input = Input(name="the_input", shape=(200, nb_feat))
forward_lstm1 = LSTM(output_dim=64,
return_sequences=True,
activation="tanh"
)(net_input)
backward_lstm1 = LSTM(output_dim=64,
return_sequences=True,
activation="tanh",
go_backwards=True
)(net_input)
blstm_output1 = Merge(mode='concat')([forward_lstm1, backward_lstm1])
forward_lstm2 = LSTM(output_dim=64,
return_sequences=True,
activation="tanh"
)(blstm_output1)
backward_lstm2 = LSTM(output_dim=64,
return_sequences=True,
activation="tanh",
go_backwards=True
)(blstm_output1)
blstm_output2 = Merge(mode='concat')([forward_lstm2, backward_lstm2])
hidden = TimeDistributed(Dense(512, activation='tanh'))(blstm_output2)
output = TimeDistributed(Dense(nb_class + 1, activation='softmax')) (hidden)
labels = Input(name='the_labels', shape=[1], dtype='float32')
input_length = Input(name='input_length', shape=[1], dtype='int64')
label_length = Input(name='label_length', shape=[1], dtype='int64')
loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name="ctc")([output, labels, input_length, label_length])
model = Model(input=[net_input, labels, input_length, label_length], output=[loss_out])
model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=optimizer, metrics=[])
test_func = K.function([net_input], [output])
return model, test_func
model, test_func = build_model(nb_feat=nb_feat, nb_class=nb_class, optimizer=optimizer)
for epoch in range(number_epoches):
inputs_train = {'the_input': X_train[i:i+batch_size],
'the_labels': y_train[i:i+batch_size],
'input_length': np.sum(X_train_mask[i:i+batch_size], axis=1, dtype=np.int32),
'label_length': np.squeeze(y_train_mask[i:i+batch_size]),
}
outputs_train = {'ctc': np.zeros([inputs_train["the_labels"].shape[0]])}
ctcloss = model.train_on_batch(x=inputs_train, y=outputs_train)
total_ctcloss += ctcloss * inputs_train["the_input"].shape[0] * 1.
loss_train[epoch] = total_ctcloss / X_train.shape[0]
Here is the my model summary
推荐答案
尝试以下解决方案:
import keras.backend as K
def get_prediction_function(model):
input_tensor = model.layers[0].input
output_tensor = model.layers[-5].output
net_function = K.function([input_tensor, K.learning_phase()], [output_tensor])
def _result_function(x):
return net_function([x, 0])[0]
return _result_function
现在您的网络功能可以通过以下方式获得:
Now your network function might be obtained by:
test_function = get_prediction_function(model)
这篇关于保存和恢复Keras BLSTM CTC模型的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
