在同一图表中构建多个模型

Question

我正在尝试构建两个预测不同输出类型的相似模型。一个预测两个类别，另一个有六个输出类别。他们的输入是相同的，他们都是LSTM RNN。

我已将培训和预测分解为各自文件model1.py，model2.py中的单独函数。

我已在每个模型变量命名同样的事情的错误，这样，当我打电话分别与predict1从predict2和MODEL1 MODEL2我碰到下面的命名空间的错误： ValueError异常：变量W已经存在，不允许。你是否想在VarScope中设置reuse = True？最初定义在：

其中W是权重矩阵的名称。

有没有从同一地点运行这些预测的好方法？我试图重命名所涉及的变量，但仍然出现以下错误。似乎没有可能在它的创建上命名lstm_cell，是吗？

ValueError: Variable RNN/BasicLSTMCell/Linear/Matrix already exists 

编辑：作用域各地model1pred和model2pred的预测文件，我得到以下错误调用model1pred时后（），然后model2pred（）

tensorflow.python.framework.errors.NotFoundError: Tensor name model1/model1/BasicLSTMCell/Linear/Matrix" not found in checkpoint files './variables/model1.chk 

编辑：该代码包括在这里。 model2.py中的代码缺失，但相当于model1.py，除了n_classes = 2，并且在dynamicRNN函数和pred内部，范围设置为'model2'。

解决方案：问题是保存程序尝试从第一个pred（）执行中恢复包含变量的图形。我能够将pred函数的调用包装在不同的图表中来解决问题，从而消除了对变量范围的需求。

在收集的预测文件：

def model1pred(test_x, test_seqlen): 
    from model1 import pred 
    with tf.Graph().as_default(): 
     return pred(test_x, test_seqlen) 

def model2pred(test_x, test_seqlen): 
    from model2 import pred 
    with tf.Graph().as_default(): 
     return pred(test_x, test_seqlen) 

##Import test_x, test_seqlen 

probs1, preds1 = model1pred(test_x, test_seq) 
probs2, cpreds2 = model2Pred(test_x, test_seq) 

在model1.py

def dynamicRNN(x, seqlen, weights, biases): 
    n_steps = 10 
    n_input = 14 
    n_classes = 6 
    n_hidden = 100 

    # Prepare data shape to match `rnn` function requirements 
    # Current data input shape: (batch_size, n_steps, n_input) 
    # Required shape: 'n_steps' tensors list of shape (batch_size, n_input) 

    # Permuting batch_size and n_steps 
    x = tf.transpose(x, [1, 0, 2]) 
    # Reshaping to (n_steps*batch_size, n_input) 
    x = tf.reshape(x, [-1,n_input]) 
    # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input) 
    x = tf.split(0, n_steps, x) 

    # Define a lstm cell with tensorflow 
    lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0) 

    # Get lstm cell output, providing 'sequence_length' will perform dynamic calculation. 
    outputs, states = tf.nn.rnn(lstm_cell, x, dtype=tf.float32, sequence_length=seqlen) 

    # When performing dynamic calculation, we must retrieve the last 
    # dynamically computed output, i.e, if a sequence length is 10, we need 
    # to retrieve the 10th output. 
    # However TensorFlow doesn't support advanced indexing yet, so we build 
    # a custom op that for each sample in batch size, get its length and 
    # get the corresponding relevant output. 

    # 'outputs' is a list of output at every timestep, we pack them in a Tensor 
    # and change back dimension to [batch_size, n_step, n_input] 
    outputs = tf.pack(outputs) 
    outputs = tf.transpose(outputs, [1, 0, 2]) 

    # Hack to build the indexing and retrieve the right output. 
    batch_size = tf.shape(outputs)[0] 
    # Start indices for each sample 
    index = tf.range(0, batch_size) * n_steps + (seqlen - 1) 
    # Indexing 
    outputs = tf.gather(tf.reshape(outputs, [-1, n_hidden]), index) 

    # Linear activation, using outputs computed above 
    return tf.matmul(outputs, weights['out']) + biases['out'] 

def pred(test_x, test_seqlen): 
    with tf.Session() as sess: 
     n_steps = 10 
     n_input = 14 
     n_classes = 6 
     n_hidden = 100 
     weights = {'out': tf.Variable(tf.random_normal([n_hidden, n_classes]), name='W1')} 
     biases = {'out': tf.Variable(tf.random_normal([n_classes]), name='b1')} 
     x = tf.placeholder("float", [None, n_steps, n_input]) 
     y = tf.placeholder("float", [None, n_classes]) 
     seqlen = tf.placeholder(tf.int32, [None]) 

     pred = dynamicRNN(x, seqlen, weights, biases) 
     saver = tf.train.Saver(tf.all_variables()) 
     y_p =tf.argmax(pred,1) 

     init = tf.initialize_all_variables() 
     sess.run(init) 

     saver.restore(sess,'./variables/model1.chk') 
     y_prob, y_pred= sess.run([pred, y_p], feed_dict={x: test_x, seqlen: test_seqlen}) 
     y_prob = np.array([softmax(x) for x in y_prob]) 
     return y_prob, y_pred 

'

Answer 1

为此，您可以通过添加各地的模型构建两段代码with tf.variable_scope():块。这具有在变量的名称前加上不同前缀的作用，避免了冲突。

例如（使用在你的问题中定义的model1pred()和model2pred()功能）：

with tf.variable_scope('model1'): 
    # Variables created in here will be named 'model1/W', etc. 
    probs1, preds1 = model1pred(test_x, test_seq) 

with tf.variable_scope('model2'): 
    # Variables created in here will be named 'model2/W', etc. 
    probs2, cpreds2 = model2Pred(test_x, test_seq) 

有关详细信息，请参阅深入HOWTO on variable sharing in TensorFlow。